NHTS Compendium of uses

National Household Travel Survey
Compendium of Uses
January 2019 –May 2019

Foreword
This compendium contains various uses and applications of the National Household
Travel Survey (NHTS) data used in transportation planning and research from January
to May 2019. Published journal articles and reports that cite the use of NHTS data were
selected using the Transportation Research Board’s (TRB’s) Annual Meeting Online
Portal, Google Scholar, and Google Alerts. Notification emails were sent by Google
when new search results matched predetermined search terms pertaining to NHTS
data. The keyword and search engine terms used in both online sources were “National
Household Travel Survey” and “NHTS”.
The articles and reports in this compendium cover a diverse range of topics in the areas
of transportation, health, safety, environment, and engineering and were published in
various journals including, but not limited to, the American Journal of Public Health, the
International Journal of Behavioral Nutrition and Physical Activity, and the National Center
for Transit Research. Several papers were also submitted by researchers and graduate
students for presentation and publication to TRB’s 98th Annual Meeting and can be
found in the 2019 TRB Annual Meeting Compendium of Papers.
These selected articles and reports were grouped into 11 categories that were created
based on the subject areas and index terms identified in each abstract as well as category
titles used in previous NHTS compendium databases. The categories are as follows:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.

Bicycle and pedestrian studies.
Energy consumption.
Environment.
Health.
Policy and mobility.
Special population groups.
Survey, data synthesis, and other applications.
Traffic safety.
Transit planning.
Travel behavior.
Trend analysis and market segmentation.

This compendium includes a short description of each article and report along with the
title, author(s), abstract, subject areas, and availability.
Please note that this 2019 (interim) compendium consists of 137 research articles and
reports. It is updated on an ongoing basis, with newly published papers that cite NHTS
data. For information about adding a research paper to the NHTS compendium, please
contact Daniel Jenkins at [email protected].
Search and documentation support was provided by Apara Banerjee (MacroSys), who
also categorized the paper abstracts.

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TABLE OF CONTENTS

Contents
Chapter 1. Bicycle and Pedestrian Studies
1.1
Title: Examining Urban and Rural Bicycling in the United
States: Early Findings from the 2017 National Household Travel
Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2
Title: Pedestrian and Bicyclist: Scalable Risk Assessment Methods . . .
1.3
Title: Transportation Safety Planning Approach for Pedestrians:
An Integrated Framework of Modeling Walking Duration and
Pedestrian Fatalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.4
Title: Walking and Biking are Hurt by Lack of National Leadership:
Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.5
Title: Can this Woman save Biking in Washington State? . . . . . . . . .
1.6
Title: Uber Concerned with Lime’s Issues ahead of Jump e-scooters
and e-bikes Launch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.7
Title: The Economic Value of Actually Following Through on a
Bike Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.8
Title: Why Traveling on Electric Bike is a Better Choice . . . . . . . . . .
1.9
Title: Where should New Schools be Built to Encourage Walking
and Wheeling? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.10
Title: Avid Bicyclists celebrate National Bike Month . . . . . . . . . . . .
1.11
Title: Walkability in Tucson: An Overview of Current Trends
and Growth Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.12
Title: Examining the Influence of Network, Land Use, and
Demographic Characteristics to Estimate the Number of Bicycle-Vehicle
Crashes on Urban Roads . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.13
Title: How Have Travelers Changed Mode Choices for First/Last
Mile Trips after the Introduction of Bicycle-Sharing Systems:
An Empirical Study in Beijing, China . . . . . . . . . . . . . . . . . . . .
1.14
Title: Disaster Relief Trials: Perceptions of a Disaster-Themed
Bicycling Event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.15
Title: Utilizing Multi-Stage Behavior Change Theory to Model
the Process of Bike Share Adoption . . . . . . . . . . . . . . . . . . . . .
1.16
Title: Bicycle Safety Analysis at Intersections from Crowdsourced
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 2. Energy Consumption
2.1
Title: Fleet Right-sizing: The Corporate Average Fuel Economy
Effect of a Transition to a Shared Autonomous Fleet . . . . . . . . .
2.2
Title: Energy Efficiency Standards Are More Regressive Than
Energy Taxes: Theory and Evidence . . . . . . . . . . . . . . . . . . .
2.3
Title: Ownership and Usage Analysis of Alternative Fuel Vehicles
in the United States with the 2017 National Household Travel
Survey Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4
Title: Forecasting the Impact of Connected and Automated
Vehicles on Energy Use: A Microeconomic Study of Induced
Travel and Energy Rebound . . . . . . . . . . . . . . . . . . . . . . .
2.5
Title: Charging Demand of Plug-in Electric Vehicles: Forecasting
Travel Behavior Based on a Novel Rough Artificial Neural
Network Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6
Title: Fuel Cell Electric Vehicle Driving and Fueling Behavior . . . .
2.7
Title: Comparison of Marginal and Average Emission Factors
for Passenger Transportation Modes . . . . . . . . . . . . . . . . . .
2.8
Title: Charging Load Forecasting of Electric Vehicle Based on
Charging Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.9
Title: Modeling Charging Behavior of Battery Electric Vehicle
Drivers: A Cumulative Prospect Theory Based Approach . . . . . .
2.10
Title: Potential Energy Implications of Connected and Automated
Vehicles: Exploring Key Leverage Points through Scenario
Screening and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .
2.11
Title: Assessment of Light-Duty Plug-In Electric Vehicles in the
United States, 2010–2018 . . . . . . . . . . . . . . . . . . . . . . . . .
2.12
Title: Modeling Electric Vehicle Adoption Considering A Latent
Travel Pattern Construct And Charging Infrastructure . . . . . . . .
2.13
Title: Should Electric Vehicle Drivers Pay a Mileage Tax? . . . . . . .
2.14
Title: Full-scale Electric Vehicles Penetration in the Danish
Island of Bornholm — Optimal Scheduling and Battery Degradation
under Driving Constraints . . . . . . . . . . . . . . . . . . . . . . . .
2.15
Title: Meeting 2025 Zero Emission Vehicle Goals: An Assessment
of Electric Vehicle Charging Infrastructure in Maryland . . . . . . .
2.16
Title: Modeling the GHG Emissions Intensity of Plug-in Electric
Vehicles using Short-Term and Long-Term Perspectives . . . . . . .
2.17
Title: Acceptability, Energy Consumption, and Costs of Electric
Vehicle for Ride-hailing Drivers in Beijing . . . . . . . . . . . . . . .
2.18
Title: Optimization of Charging Method for Scaled EVs . . . . . . .
2.19
Title: An Intelligent Hybrid Energy Management System for
a Smart House Considering Bidirectional Power Flow and
Various EV Charging Techniques . . . . . . . . . . . . . . . . . . . .
2.20
Title: Joint Optimization Scheme for the Planning and Operations
of Shared Autonomous Electric Vehicle Fleets Serving Mobility
on Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Title: Are Consumers Poorly Informed about Fuel Economy?
Evidence from Two Experiments . . . . . . . . . . . . . . . . . . . . . . .
Title: Distribution System Planning Considering Stochastic EV
Penetration and V2G Behavior . . . . . . . . . . . . . . . . . . . . . . . .
Title: Fleet Performance and Cost Evaluation of a Shared Autonomous
Electric Vehicle (SAEV) Fleet: A case study for Austin, Texas . . . . . .
Title: Machine Learning Estimates of Plug-in Hybrid Electric
Vehicle Utility Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Title: An EV Charging Demand Model for the Distribution
System Using Traffic Property . . . . . . . . . . . . . . . . . . . . . . . .
Title: CalAmp and Swiftmile Partner to Deliver First-Ever
Solar-Powered Parking and Charging Station Providing “Power
Nap” for Micro-Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . .
Title: Risk Evaluation of Distribution Networks Considering
Residential Load Forecasting with Stochastic Modelling of
Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Title: Consumer Valuation of Fuel Economy: Findings from
Recent Panel Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Title: Subsidy and Pricing Model of Electric Vehicle Sharing
Based on Two-Stage Stackelberg Game — A Case Study in China . . . .
Title: Electric Car Subsidies Hurt Middle Class Americans . . . . . . . .
Title: How well do Electric Vehicles perform in our Extreme Weather? .
Title: An Electric Vehicle in Every Driveway? . . . . . . . . . . . . . . .
Title: Fueling Up for Your Summer Travel Plans . . . . . . . . . . . . . .
Title: A Comparison Study on Stochastic Modeling Methods
for Home Energy Management System . . . . . . . . . . . . . . . . . . .
Title: Optimal Energy-Emission Management in Hybrid AC-DC
Microgrids with Vehicle-2-Grid Technology . . . . . . . . . . . . . . . .
Title: Reliability-based Metrics to Quantify the Maximum
Permissible Load Demand of Electric Vehicles . . . . . . . . . . . . . . .
Title: Electric Vehicle Charging Station Placement Method for
Urban Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Title: Online EV Charging Scheduling with On-Arrival Commitment .
Title: Modelling of Distributed Energy Components and Optimization
of Energy Vector Dispatch within Smart Energy Systems . . . . . . . . .

Chapter 3. Environment
3.1
Title: Decoupling the Value of Leisure Time from Labor Market
Returns in Travel Cost Models . . . . . . . . . . . . . . . . . . . . . .
3.2
Title: Developing Commute Optimization System to Minimize
Negative Environmental Impacts and Time of Business Commuters
3.3
Title: Evaluating the Potential Environmental Impacts of Connected
and Automated Vehicles . . . . . . . . . . . . . . . . . . . . . . . . .
3.4
Title: Material Efficiency Strategies to Reducing Greenhouse
Gas Emissions Associated with Buildings, Vehicles, and Electronics
— A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.5

Title: Winter is Finally Gone: How We’re Getting Our Cars
Shining Again . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 4. Health
4.1
Title: Integrating Multiple Transportation Modes into Measures
of Spatial Food Accessibility . . . . . . . . . . . . . . . . . . . . . . . . .
4.2
Title: Active Transport, Not Device Use, Associates with Self-Reported
School Week Physical Activity in Adolescents . . . . . . . . . . . . . . .
4.3
Title: A Time-Based Objective Measure of Exposure to the
Food Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4
Title: Human Behavior Modeling and Calibration in Epidemic
Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 5. Policy and Mobility
5.1
Title: Smart Cities and Mobility: Does the Smartness of Australian
Cities Lead to Sustainable Commuting Patterns? . . . . . . . . . . .
5.2
Title: Evaluation of The Effects of Trends on Vehicle Concepts
based on a Forecast of Travel Demand . . . . . . . . . . . . . . . . .
5.3
Title: Estimating the Social Cost of Congestion Using the Bottleneck
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4
Title: Improving Structural Models of Congestion . . . . . . . . . . .
5.5
Title: Role of Flying Cars in Sustainable Mobility . . . . . . . . . . .
5.6
Title: Would Uber Help to Reduce Traffic Congestion? . . . . . . . .
5.7
Title: Dynamic Shared Autonomous Taxi System Considering
On-Time Arrival Reliability . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 6. Special Population Groups
6.1
Title: Comparing Immigrant Travel Assimilation among Racial/Ethnic
Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2
Title: Aging in Activity Spaces: How Does Individual Accessibility
Compare across Age Cohorts? . . . . . . . . . . . . . . . . . . . . . . . .
6.3
Title: The Effects of Driver Licensing Laws on Immigrant Travel . . . .
6.4
Title: Use of Ride-Hailing Services among Older Adults in the
United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.5
Title: Exploring Patterns of Heterogeneity in Activity-Travel
Behaviors of Older People . . . . . . . . . . . . . . . . . . . . . . . . . .
6.6
Title: A Hierarchical Game Approach on Real-Time Navigation
Scheduling of Agricultural Harvesters . . . . . . . . . . . . . . . . . . .
6.7
Title: Spatio-temporal Travel Patterns Of Elderly People –A
Comparative Study Based On Buses Usage in Qingdao, China . . . . .
6.8
Title: The Association of Commuting Time and Wages for
American Workers with Disabilities . . . . . . . . . . . . . . . . . . . . .
6.9
Title: The Poverty of the Carless: Toward Universal Auto Access . . . .
6.10
Title: Synchronization of Home Departure and Arrival Times
in Dual Earner Households with Children: Panel Regression
Model of Time Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6.15
6.16
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Title: Five Innovative Ways Cities Are Improving Life for Seniors
Title: What kinds of Vehicles do Americans drive? . . . . . . . .
Title: A Resurgence in Urban Living? Trends in Residential
Location Patterns of Young and Older Adults since 2000 . . . . .
Title: University Students’ Transportation Patterns, and the
Role of Neighbourhood Types and Attitudes . . . . . . . . . . . .
Title: Car Brands with the Youngest Drivers . . . . . . . . . . . .
Title: African-American Millennials Prefer Cadillac . . . . . . . .
Title: Despite ‘Car-Free’ Hype, Millennials Drive a Lot . . . . . .
Title: The Rural Telecommuter Surplus in Southwestern Ontario,
Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 7. Survey, Data Synthesis, and Other Applications
7.1
Title: State of the Practice of Long Distance and Intercity Travel
Modeling in US Metropolitan Planning Organizations and
State Departments of Transportation . . . . . . . . . . . . . . . . . . .
7.2
Title: Recommended Mounting Heights for Freestanding On-Premise
Signs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3
Title: Filling in the Gaps of Connected Car Data Helps Transportation
Planners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.4
Title: 2045 Long-range Transportation Plan . . . . . . . . . . . . . . . .
7.5
Title: Overestimation of Self-reported Driving Exposure: Results
from the SHRP2 Naturalistic Driving Study . . . . . . . . . . . . . . .
7.6
Title: Trail Users in the Cincinnati Metropolitan Region: Purposes,
Patterns, and Preferences . . . . . . . . . . . . . . . . . . . . . . . . . .
7.7
Title: Are Estimates of Early Education Programs Too Pessimistic?
Evidence from a Large-Scale Field Experiment that Causally
Measures Neighbor Effects . . . . . . . . . . . . . . . . . . . . . . . . .
7.8
Title: Genesis: Trip Generation Model using ACS, CTPP, and
NHTS data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 8. Traffic Safety
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8.1
Title: Self-reported Handheld Device Use while Driving . . . . . . . . . 98
8.2
Title: Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run
in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating
Data Imbalance Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . 99
8.3
Title: Evaluation of Not-At-Fault Assumption in Quasi-Induced
Exposure Method using Traffic Crash Data at Varied Geographical
Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
8.4
Title: Safety Evaluation of Statewide Off-Highway Vehicle Use
in Alaska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Chapter 9. Transit Planning
102
9.1
Title: Socioeconomic and Usage Characteristics of Public Transit
Riders in the United States . . . . . . . . . . . . . . . . . . . . . . . . . . 102
9.2
Title: Charting Public Transit’s Decline . . . . . . . . . . . . . . . . . . . 103

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9.4
9.5

Title: A Comparison of the Personal and Neighborhood Characteristics
associated with Ridesourcing, Transit Use, and Driving with
NHTS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
Title: A Direct Demand Model for Commuter Rail Ridership in
the San Francisco Bay Area . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Title: Transit Access Equity in Richmond, VA . . . . . . . . . . . . . . . 106

Chapter 10.Travel Behavior
10.1
Title: Exploring the Relationship between Vehicle Type Choice
and Distance Traveled: A Latent Segmentation Approach . . . . . . . .
10.2
Title: A Machine-Learning Decision-Support Tool for Travel-Demand
Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.3
Title: Not Parking Lots but Parks: A Joint Association of Parks
and Transit Stations with Travel Behavior . . . . . . . . . . . . . . . . .
10.4
Title: Exploring the Relationships Among Travel Multimodality,
Driving Behavior, Use of Ridehailing and Energy Consumption . . . . .
10.5
Title: Socioeconomic and Usage Characteristics of Transportation
Network Company (TNC) Riders . . . . . . . . . . . . . . . . . . . . . .
10.6
Title: Location Choice, Life Cycle and Amenities . . . . . . . . . . . . .
10.7
Title: The Rise of Long-Distance Trips, in a World of Self-Driving
Cars: Anticipating Trip Counts and Evolving Travel Patterns
Across the Texas Triangle Megaregion . . . . . . . . . . . . . . . . . . . .
10.8
Title: Factors Associated with Round-trip Carsharing Frequency
and Driving-Mileage Impacts in London . . . . . . . . . . . . . . . . . .
10.9
Title: The Impact of Ride Hailing on Parking (and Vice Versa) . . . . . .
10.10 Title: Understand the Multi-Level Effects of the Built Environment
on Trip-Chaining Behavior . . . . . . . . . . . . . . . . . . . . . . . . . .
10.11 Title: Measuring Mobilities of Care, a Challenge for Transport
Agendas: From One to Many Tracks . . . . . . . . . . . . . . . . . . . . .
10.12 Title: Eliciting Preferences of Ridehailing Users and Drivers:
Evidence from the United States . . . . . . . . . . . . . . . . . . . . . . .
10.13 Title: Rider-to-rider Discriminatory Attitudes and Ridesharing Behavior
10.14 Title: Predicting the Ownership, Use, and Environmental Impacts
of New Vehicle Technologies with a Focus on the Relationship
between Travel Behavior and the Built Environment . . . . . . . . . . .
10.15 Title: What Drives the use of Ridehailing In California? Ordered
Probit Models of the Usage Frequency of Uber and Lyft . . . . . . . . .
10.16 Title: Nudging People towards More Sustainable Residential
Choice Decisions: An Intervention Based on Focalism and Visualization
10.17 Title: Are Americans Driving Older Cars Or Just Leaving Them
In The Driveway? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.18 Title: Why do we Trust Ridesharing Apps so much? . . . . . . . . . . .
10.19 Title: Modeling Individuals’ Willingness to Share Trips with
Strangers in an Autonomous Vehicle Future . . . . . . . . . . . . . . . .

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Title: How do Activities Conducted while Commuting influence
Mode Choice? Using Revealed Preference Models to Inform
Public Transportation Advantage and Autonomous Vehicle Scenarios . 128

Chapter 11.Trend Analysis and Market Segmentation
11.1
Title: Trends of Home Deliveries in the U.S.: Changes from
2009 to 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.2
Title: Modeling the Willingness to Work as Crowd-Shippers
and Travel Time Tolerance in Emerging Logistics Services . . . .
11.3
Title: Generational Trends in Vehicle Ownership and Use: Are
Millennials Any Different? . . . . . . . . . . . . . . . . . . . . . .
11.4
Title: Estimating the Cost and Utility of Statewide Travel Models
using Scenario-Based Interviews . . . . . . . . . . . . . . . . . . .
11.5
Title: Forecast Households at the County Level: An Application
of the ProFamy Extended Cohort-Component Method in Six
Counties of Southern California, 2010 to 2040 . . . . . . . . . . .
11.6
Title: Transportation Network Companies and Taxis: The Case
of Seattle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.7
Title: Estimating Preference Heterogeneity in Discrete Choice
Models of Product Differentiation . . . . . . . . . . . . . . . . . .
11.8
Title: Consumer Myopia in Vehicle Purchases: Evidence from
a Natural Experiment . . . . . . . . . . . . . . . . . . . . . . . . .
11.9
Title: Selection of Optimal Target Reliability in RBDO through
Reliability-Based Design for Market Systems (RBDMS) and
Application to Electric Vehicle Design . . . . . . . . . . . . . . . .
11.10 Title: Game Theory Approach on Modeling of Residential
Electricity Market by Considering the Uncertainty due to the
Battery Electric Vehicles (BEVs) . . . . . . . . . . . . . . . . . . .
11.11 Title: Quantifying the Electric Vehicle Charging Infrastructure
Gap across US Markets . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 1. Bicycle and Pedestrian Studies
1.1. Title: Examining Urban and Rural Bicycling in the United States: Early Findings from
the 2017 National Household Travel Survey
Author(s): Tribby, C.P. and Tharp, D.S.
Abstract: Introduction: Bicycling has personal and population health benefits. While
bicycle research has focused primarily on the urban context, the rural context is equally
important. There are documented disparities in health behaviors and health outcomes
in rural areas compared to urban areas. It is unknown whether bicycling is one of these
health behavior disparities.
Method: This study addresses two questions: 1) what is the prevalence of bicycling
behaviors by urbanicity (urban/rural and population density category), and 2) what
are the characteristics that best categorize individuals as cyclists versus non-cyclists,
overall and for rural populations. We used the 2017 National Household Travel Survey,
a nationally representative sample of the US non-institutionalized population (age ≥5
years). Bicycling was defined as any bicycling, bicycling for exercise, bicycle commuting,
and bike share program use. Analyses used complex survey procedures to estimate
unadjusted and adjusted prevalence; and, random forest to rank characteristics that
best categorize respondents as bicyclists.
Results: The unadjusted prevalence of any reported bicycling was higher in high-density
urban areas (≥10,000 persons per square mile (ppsm)), 14.7% (95% CI: 13.4%–16.0%),
than very low-density rural areas (<500 ppsm), 11.8% (10.4%–13.2%). However, when
adjusting for covariates, the prevalence was comparable: high-density urban: 14.4%
(12.9%–16.0%); very low-density rural: 12.2% (10.8%–13.7%). Unadjusted prevalence
of bicycling for exercise were also similar (high-density urban: 8.2% (7.0%–9.3%); very
low-density rural: 7.0% (6.1%–7.9%)). We also found that characteristics that best categorize
bicyclists from the overall sample were different from the rural only sample.
Conclusions: This research suggests that bicycling prevalence overall and for exercise
are similar between urban and rural areas across population densities. It also suggests
that characteristics that were important for rural bicyclists were different from urban
bicyclists. Urban-focused bicycle research may need modification to fit the rural context
to promote bicycling and physical activity.
Subject Areas: Bicycling; Urban; Rural; Commuting; Bike share program
Availability: Tribby, C.P. and Tharp, D.S., 2019. Examining Urban and Rural Bicycling in
the United States: Early Findings from the 2017 National Household Travel Survey. Journal of
Transport & Health, 13, pp.143-149.
https://www.sciencedirect.com/science/article/pii/S2214140518305826

1

1.2. Title: Pedestrian and Bicyclist: Scalable Risk Assessment Methods
Author(s): Turner, S., Hampshire, R., Redmon, T. and Fitzpatrick,K.
Abstract: Many transportation agencies are placing more emphasis on improving pedestrian
and bicyclist safety and reducing the risk of a fatality or serious injury to pedestrians
and bicyclists. Practitioners need a methodical approach to assess pedestrian and bicyclist
risk for the purposes of identifying high-priority areas and transportation facilities for
safety improvement, evaluating specific countermeasures and locations before and
after improvements are made, and tracking safety performance measures over time to
gauge progress toward established goals.
Subject Areas: Pedestrian; Bicyclist; Risk
Availability: Turner, S., Hampshire, R., Redmon, T. and Fitzpatrick,K., 2019. Pedestrian
and Bicyclist: Scalable Risk Assessment Methods. Institute of Transportation Engineers. ITE
Journal; Washington Vol. 89, Iss. 4, pp. 45-49.
https://search.proquest.com/openview/23ae937e8ed56d54f0eddcb3892982e3/1?
pq-origsite=gscholar&cbl=42116

2

1.3. Title: Transportation Safety Planning Approach for Pedestrians: An Integrated Framework
of Modeling Walking Duration and Pedestrian Fatalities
Author(s): Lee, J., Abdel-Aty, M., Huang, H. and Cai, Q.
Abstract: Multiple approaches have been proposed to take traffic safety into consideration
in long-term transportation plans, referred to as transportation safety planning. Some
early studies used trip generation data as the explanatory variables for their macro-level
crash safety performance functions, or crash prediction models. However, no study to
date has attempted to integrate walking exposure and pedestrian safety at the modeling
stage. Thus, a novel methodological framework for integrating the analyses of walking
exposure and pedestrian crashes is proposed toward better transportation safety planning
for pedestrians. In comparison with walking trips and walking miles, walking hours
was identified as the best walking exposure variable by a preliminary analysis. Thus,
an integrated modeling structure with walking hours as its exposure variable was developed.
The modeling results indicate that climate conditions, population, and car usage patterns
affect walking hours, and predicted walking hours, climate conditions, percentage of
mid-elderly (64–75years), proportions of minority race/ethnicity, and percent of tertiary
industry occupations have significant effects on pedestrian fatalities. In addition, the
integrated modeling framework was compared with non-integrated ones, and the results
indicate that the integrated framework outperforms its counterparts in relation to deviance
information criterion. The proposed approach and the findings from this study are expected
to provide useful insights not only to researchers but also to policy makers and practitioners
in the fields of transportation planning and traffic safety.
Subject Areas: Pedestrian; Crash; Transportation Safety Planning
Availability: Lee, J., Abdel-Aty, M., Huang, H. and Cai, Q., 2019. Transportation Safety
Planning Approach for Pedestrians: An Integrated Framework of Modeling Walking Duration
and Pedestrian Fatalities. Transportation Research Record, p.0361198119837962.
https://journals.sagepub.com/doi/abs/10.1177/0361198119837962

3

1.4. Title: Walking and Biking are Hurt by Lack of National Leadership: Report
Author(s): Schmitt, A.
Abstract: Blog
Subject Areas: Biking; Walking; Pedestrian fatalities
Availability: Schmitt, A., 2019. Walking and Biking are Hurt by Lack of National Leadership:
Report. StreetsBlogUSA.
https://usa.streetsblog.org/2019/02/11/walking-and-biking-are-hurt-by-lack-of-nationalleadership-report/

4

1.5. Title: Can this Woman save Biking in Washington State?
Author(s): Weinberger, H.
Abstract: Blog
Subject Areas: Bikes; Features; Urban design; Washington State
Availability: Weinberger, H., 2019. Can this woman save biking in Washington state?
StreetsBlogUSA.
https://crosscut.com/2019/02/can-woman-save-biking-washington-state

5

1.6. Title: Uber Concerned with Lime’s Issues ahead of Jump e-scooters and e-bikes Launch
Author(s): Nadkarni, A.
Abstract: Blog
Subject Areas: E-Scooters; E-Bikes; Uber
Availability: Nadkarni, A., 2019. Uber Concerned with Lime’s Issues ahead of Jump e-scooters
and e-bikes Launch. stuff.co.nz.
https://www.stuff.co.nz/business/110916170/uber-concerned-with-limes-issuesahead-of-jump-escooters-and-ebikes-launch

6

1.7. Title: The Economic Value of Actually Following Through on a Bike Plan
Author(s): Schmitt, A.
Abstract: Blog
Subject Areas: Bike plan; Bike Infrastructure; Safety; Local business
Availability: Schmitt, A., 2019. The Economic Value of Actually Following Through on a
Bike Plan. StreetsBlogUSA.
https://usa.streetsblog.org/2019/04/12/the-economic-value-of-actuallyfollowing-through-on-a-bike-plan/

7

1.8. Title: Why Traveling on Electric Bike is a Better Choice
Author(s): Frometa, RJ.
Abstract: Blog
Subject Areas: Electric Bike; Easy Commuting; Healthy Choice;
Availability: Frometa, RJ., 2019. Why Traveling on Electric Bike is a Better Choice. Vents
Magazine.
https://ventsmagazine.com/2019/04/26/why-travelling-on-electric-bike-is-a-better-choice/

8

1.9. Title: Where should New Schools be Built to Encourage Walking and Wheeling?
Author(s): Nicholas, N.
Abstract: Blog
Subject Areas: Walking; Biking; Schools; Students
Availability: Nicholas, N., 2019. Where should New Schools be Built to Encourage Walking
and Wheeling? StreetsBlogUSA..
https://denver.streetsblog.org/2019/05/01/guest-post-where-should-new-schoolsbe-built-to-encourage-walking-and-wheeling/

9

1.10. Title: Avid Bicyclists celebrate National Bike Month
Author(s): Chodnicki, A.
Abstract: Blog
Subject Areas: Bike; Cyclists; National Bike Month; NHTS; Benefits
Availability: Chodnicki, A., 2019. Avid Bicyclists celebrate National Bike Month. 25news.com.
https://nbc25news.com/news/local/avid-bicyclists-celebrate-national-bike-month

10

1.11. Title: Walkability in Tucson: An Overview of Current Trends and Growth Potential
Author(s): Abou-Zeid, G.
Abstract: In the United States, the transportation sector was responsible for 28% of 2016
GHG emissions–the largest contribution of any industry (U.S. EPA, 2018). To reduce
dependence on fossil fuels and mitigate their effects, active modes of transportation,
like walking, need be planned for. This study provides an overview of walking in Tucson,
AZ and subsequent guidance for future development through a) an assessment of walk-mode
splits, b) a survey on residential preferences for walking, and c) a built environment
case study analysis. It found that walking constituted 11% of all trips, compared to
motorized vehicles, which accounted for more than 80% of all trips. Percentage of respondent
walk and car trips varied significantly by income and trip purpose. Both Tucson residents
and existing literature identified destination proximity as the most important built environment
factor considered in deciding to walk. A complete streets project that incorporated many
built environment features found to improve walkability (e.g., street connectivity, accessibility,
walking infrastructure) but failed to account for destination proximity had little impact
of walking behavior. To better promote walkability in Tucson, emphasis on coordination
between transportation and land use planning and connection of walkability to social
and cultural values is necessary.
Subject Areas: GHG emissions; Walkability; Transportation; Land use planning
Availability: Abou-Zeid, G., 2019. Walkability in Tucson: An Overview of Current Trends
and Growth Potential. Doctoral dissertation, The University of Arizona.
https://repository.arizona.edu/handle/10150/632229

11

1.12. Title: Examining the Influence of Network, Land Use, and Demographic Characteristics
to Estimate the Number of Bicycle-Vehicle Crashes on Urban Roads
Author(s): Mukoko, K.K. and Pulugurtha, S.S.
Abstract: The focus of this paper is to examine the influence of network, land use, and
demographic characteristics on the number of bicycle-vehicle crashes, and to develop
area-level bicycle-vehicle crash estimation models (safety performance functions) for
urban roads. Mecklenburg County in the State of North Carolina was considered as the
study area. The reported bicycle-vehicle crash data, from 2010 to 2015, along with the
network, land use, and demographic characteristics data were obtained from the local
agencies. Data within a one-mile buffer of 119 selected locations was then captured.
Data for 99 selected locations were used for the modeling purpose, while data for the
remaining 20 selected locations were used for validating the models. Six alternate models
were developed, considering various combinations of explanatory variables that are
not correlated with each other. As the bicycle-vehicle crash dataset used in this research
was observed to be over-dispersed (variance greater than the mean), Negative Binomial
log-link distribution-based models were developed. The validation dataset was used
to compare the estimated number of bicycle-vehicle crashes from each model with the
actual number of bicycle-vehicle crashes. The results obtained from the analysis and
modeling suggest that bicyclists are more often involved in crashes while traveling
on segments with no bicycle lane, the traffic light, 45 mph as the speed limit, and in
commercial activity, research activity, institutional, multi-family residential (densely
populated), and heavy industrial areas. The computed Moran’s Index values indicate
weak to no spatial correlation between the residuals of each model. However, the residuals
seem to depend on the area type and the number of bicycle-vehicle crashes.
Subject Areas: Bicycle; Crash; Network; Land use; Demographics; SPF
Availability: Mukoko, K.K. and Pulugurtha, S.S., 2019. Examining the Influence of Network,
Land Use, and Demographic Characteristics to Estimate the Number of Bicycle-Vehicle Crashes
on Urban Roads. IATSS Research.
https://www.sciencedirect.com/science/article/pii/S0386111218300256

12

1.13. Title: How Have Travelers Changed Mode Choices for First/Last Mile Trips after
the Introduction of Bicycle-Sharing Systems: An Empirical Study in Beijing, China
Author(s): Fan, A., Chen, X. and Wan, T.
Abstract: In recent years, there has been rapid development in bicycle-sharing systems
(BSS) in China. Moreover, such schemes are considered promising solutions to the first/last
mile problem. This study investigates the mode choice behaviors of travelers for first/last
mile trips before and after the introduction of bicycle-sharing systems. Travel choice
models for first/last mile trips are determined using a multinomial logit model. It also
analyzes the differences in choice behavior between the young and other age groups.
The findings show that shared bicycles become the preferred mode, while travelers
preferred walking before bicycle-sharing systems were implemented. Gender, bicycle
availability, and travel frequency were the most significant factors before the implementation
of bicycle-sharing systems. However, after implementation, access distance dramatically
affects mode choices for first/last mile trips. When shared bicycles are available, the
mode choices of middle-aged group depend mainly on gender and access distance.
All factors are not significant for the young and aged groups. More than 80% of public
transport travelers take walking and shared bicycles as feeder modes. The proposed
models and findings contribute to a better understanding of travelers’ choice behaviors
and to the development of solutions for the first/last mile problem.
Subject Areas: Bicycle-Sharing Systems (BSS); First/Last Mile Trips; Mode Choices
Availability: Fan, A., Chen, X. and Wan, T., 2019. How Have Travelers Changed Mode
Choices for First/Last Mile Trips after the Introduction of Bicycle-Sharing Systems: An Empirical
Study in Beijing, China. Journal of Advanced Transportation.
https://www.hindawi.com/journals/jat/2019/5426080/abs/

13

1.14. Title: Disaster Relief Trials: Perceptions of a Disaster-Themed Bicycling Event
Author(s): Kirkpatrick, S.B.
Abstract: Purpose: Bicycling enthusiasts have been organizing community events in
US cities to demonstrate how bicycles may be of use in the aftermath of a disaster event.
The purpose of this paper is to examine the perceived value of these events and levels
of engagement in the same amongst emergency managers, community organizers and
bicycling advocates.
Design/methodology/approach: Data were collected through 21 in-depth, telephone
interviews with emergency management officials and bicycling advocates in bicycle-friendly
jurisdictions in the USA and analyzed using initial and focused coding, analytic memos
and theoretical sorting.
Findings: The study found that event organizers and other bicycle advocates widely
embraced the concept as a means to change societal perceptions of bicycles as viable
modes of tra nsportation, indicating at least some level of interest in taking an active
role in its pursuit. Emergency managers were generally receptive to the idea, but they
largely saw the value as restricted to raising public awareness about hazards and individual
preparedness measures; and they mostly envisioned for themselves a minimal role in
event planning and execution.
Practical implications: The findings suggest that when operating in a resource-poor
environment with limited public and political support, there are innovative partnerships
and ideas that can be successfully leveraged to advance multiple purposes.
Originality/value: Almost no empirical research has looked at the disaster relief trial
concept, given the relative newness and novelty of the idea. An examination of perceived
value of disaster-oriented community bicycling events seems warranted as such events
continue to grow in existing locations and emerge in new locales each year.
Subject Areas: Disaster; Bicyclists; Preparedness; Emergency management; Community
engagement; Bicycle; Disaster relief trial
Availability: Kirkpatrick, S.B., 2019. Disaster Relief Trials: Perceptions of a Disaster-Themed
Bicycling Event. An International Journal, Vol. 28 Issue: 3, pp.386-400.
https://www.emeraldinsight.com/doi/abs/10.1108/DPM-10-2018-0334

14

1.15. Title: Utilizing Multi-Stage Behavior Change Theory to Model the Process of Bike
Share Adoption
Author(s): Biehl, A., Ermagun, A. and Stathopoulos, A.
Abstract: This paper studies bike share adoption decisions as a dynamic change process
from early contemplation to consolidated user status. This runs counter to the typical
representation of mode adoption decisions as an instantaneous shift from pre to post
usage. A two-level nested logit model that draws from the stage-of-change framework
posited by the Transtheoretical Model is developed to study the adoption process. Using
survey data collected from an online U.S. sample (n=910), the model illustrates how
personal, psychosocial, and community-oriented factors influence the probability of
transitioning between different levels of readiness to participate in a bike share scheme.
The findings suggest that encouraging forward movement in the contemplation-use
ladder requires tailored, stage-specific interventions that are likely be overlooked if
instead a one-size-fits-all psychological theory is applied to investigate travel behavior.
In particular, the intermediate stages encapsulate more flexible (i.e. less habitual) orientation
among respondents. Among the explanatory variables, the pronounced elasticities for
active travel identity formation and norm integration are especially significant for crafting
policies that influence bike share membership decisions. This paper adds to the nascent
literature on the behavioral foundations of shared mobility adoption. The findings are
translated to practical interventions, from operations to design and community-initiatives
to guide practitioners seeking to promote bike share. The stage-based adoption representation
helps to align interventions across the spectrum of user readiness to translate intention
into behavior.
Subject Areas: Bike share; Stages of change; Factor analysis; Discrete choice model;
Segmentation
Availability: Biehl, A., Ermagun, A. and Stathopoulos, A., 2019. Utilizing Multi-Stage
Behavior Change Theory to Model the Process of Bike Share Adoption. Transport Policy, 77,
pp.30-45.
https://www.sciencedirect.com/science/article/abs/pii/S0967070X18301033

15

1.16. Title: Bicycle Safety Analysis at Intersections from Crowdsourced Data
Author(s): Saad, M., Abdel-Aty, M., Lee, J. and Cai, Q.
Abstract: Cycling is encouraged in countries around the world as an economic, energy
efficient, and sustainable mode of transportation. Although there are many studies
focusing on analyzing bicycle safety, they have limitations because of the shortage of
bicycle exposure data. This study represents a major step forward in estimating safety
performance functions for bicycle crashes at intersections by using crowdsourced data
from STRAVA. Several adjustments in respect of the population distribution and field
observations were made to overcome the disproportionate representation of the STRAVA
data. The adjusted STRAVA data which include bicycle exposure information were
used as input to develop safety performance functions. The functions are negative binomial
models aimed at predicting frequencies of bicycle crashes at intersections. The developed
model was compared with three counterparts: the model using the unadjusted STRAVA
data, the model using the STRAVA data with field observation data adjustments only,
and the model using the STRAVA data with adjusted population. The results revealed
that the case of STRAVA data with both population and field observation data adjustments
had the best performance in bicycle crash modeling. The results also addressed several
key factors (e.g., signal control system, intersection size, bike lanes) which are associated
with bicycle safety at intersections. Additionally, the safety-in-numbers effect was acknowledged
when bicycle crash rates decreased as bicycle activities increased. The study concluded
that crowdsourced data are a reliable source for exploring bicycle safety after the appropriate
adjustments.
Subject Areas: Bicycle; Bicycle Safety; Crowdsourced Data; STRAVA
Availability: Saad, M., Abdel-Aty, M., Lee, J. and Cai, Q., 2019. Bicycle Safety Analysis at
Intersections from Crowdsourced Data. Transportation Research Record.
https://journals.sagepub.com/doi/abs/10.1177/0361198119836764

16

Chapter 2. Energy Consumption
2.1. Title: Fleet Right-sizing: The Corporate Average Fuel Economy Effect of a Transition
to a Shared Autonomous Fleet
Author(s): Barber, E., Chernicoff, W. and Mackenzie, D.
Abstract: Prior research suggests that one of the largest opportunities for highly automated
vehicles to save energy comes through enabling on-demand mobility services. Matching
vehicle sizes to trip needs can reduce vehicle size compared with private consumers’
preference for one vehicle that can meet all (or nearly all) their trip needs. This paper
develops a framework to assess how shared mobility fleet right-sizing will affect Corporate
Average Fuel Economy (CAFE) standards, while considering spatial and temporal variation
in demand for different Travel party sizes. The authors develop an example implementation
using National Household Travel Survey (NHTS) data and model year 2016 CAFE standard
target curves, which suggest that a right-sized shared mobility fleet would face a Corporate
Average Fuel Economy standard about 7 mpg (20%) higher than the actual fleet sold in
MY 2016.
Subject Areas: Energy; Environment; Highways; Planning and Forecasting; Policy;
Vehicles and Equipment
Availability: Barber, E., Chernicoff, W. and Mackenzie, D., 2019. Fleet Right-sizing: The
Corporate Average Fuel Economy Effect of a Transition to a Shared Autonomous Fleet. Transportation
Research Board 98th Annual Meeting.
https://trid.trb.org/view/1573054

17

2.2. Title: Energy Efficiency Standards Are More Regressive Than Energy Taxes: Theory
and Evidence
Author(s): Levinson, A.
Abstract: Economists endorse taxes as a cost-effective means of reducing pollution.
But policy makers raise concerns about their regressivity, or disproportional burden
on poorer families, preferring instead to regulate energy efficiency. I first show that in
theory, energy efficiency standards are more regressive than energy taxes, not less. I
then provide an example using data on automobiles in the United States. Taxing gas
would be less regressive than regulating the fuel economy of cars if the two policies are
compared on a revenue-equivalent basis.
Subject Areas: Externalities; Redistributive effects; Environmental taxes; Subsidies
Availability: Levinson, A., 2019. Energy Efficiency Standards Are More Regressive Than
Energy Taxes: Theory and Evidence. Journal of the Association of Environmental and Resource
Economists, 6(S1), pp.S7-S36.
https://www.journals.uchicago.edu/doi/full/10.1086/701186

18

2.3. Title: Ownership and Usage Analysis of Alternative Fuel Vehicles in the United States
with the 2017 National Household Travel Survey Data
Author(s): Li, X., Liu, C. and Jia, J.
Abstract: By using the 2017 National Household Travel Survey (NHTS) data, this study
explores the status quo of ownership and usage of conventional vehicles (CVs) and
alternative fuel vehicles (AFVs), i.e., Hybrid Electric Vehicles (HEVs), Plug-in Hybrid
Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs), in the United States. The
young ages of HEVs (6.0 years), PHEVs (3.2 years) and BEVs (3.1 years) demonstrate
the significance of the 2017 NHTS data. The results show that after two decades of development,
AFVs only occupy about 5% of annual vehicle sales, and their share does not show big
increases in recent years. Meanwhile, although HEVs still dominate the AFV market,
the share of PHEVs & BEVs has risen to nearly 50% in 2017. In terms of ownership,
income still seems to be a major factor influencing AFV adoption, with the median annual
household incomes of CVs, HEVs, PHEVs and BEVs being $75,000, $100,000, $150,000
and $200,000, respectively. Besides, AFV households are more likely to live in urban
areas, especially large metropolitan areas. Additionally, for AFVs, the proportions of
old drivers are much smaller than CVs, indicating this age group might still have concerns
regarding adopting AFVs. In terms of travel patterns, the mean and 85th percentile
daily trip distances of PHEVs and HEVs are significantly larger than CVs, followed by
BEVs. BEVs might still be able to replace CVs for meeting most travel demands after
a single charge, considering most observed daily trip distances are fewer than 93.5 km
for CVs. However, the observed max daily trip distances of AFVs are still much smaller
than CVs, implying increasing the endurance to meet extremely long-distance travel
demands is pivotal for encouraging consumers to adopt AFVs instead of CVs in the
future.
Subject Areas: Alternative Fuel Vehicle; Hybrid Electric Vehicle; Plug-In Hybrid Electric
Vehicle; Battery Electric Vehicle; 2017 National Household Travel Survey; Ownership;
Travel Patterns
Availability: Li, X., Liu, C. and Jia, J., 2019. Ownership and Usage Analysis of Alternative
Fuel Vehicles in the United States with the 2017 National Household Travel Survey Data. Sustainability,
11(8), p.2262.
https://www.mdpi.com/2071-1050/11/8/2262

19

2.4. Title: Forecasting the Impact of Connected and Automated Vehicles on Energy Use:
A Microeconomic Study of Induced Travel and Energy Rebound
Author(s): Taiebat, M., Stolper, S. and Xu, M.
Abstract: Connected and automated vehicles (CAVs) are expected to yield significant
improvements in safety, energy efficiency, and time utilization. However, their net effect
on energy and environmental outcomes is unclear. Higher fuel economy reduces the
energy required per mile of travel, but it also reduces the fuel cost of travel, incentivizing
more travel and causing an energy “rebound effect.” Moreover, CAVs are predicted to
vastly reduce the time cost of travel, inducing further increases in travel and energy
use. In this paper, we forecast the induced travel and rebound from CAVs using data
on existing travel behavior. We develop a microeconomic model of vehicle miles traveled
(VMT) choice under income and time constraints; then we use it to estimate elasticities
of VMT demand with respect to fuel and time costs, with fuel cost data from the 2017
United States National Household Travel Survey (NHTS) and wage-derived predictions
of travel time cost. Our central estimate of the combined price elasticity of VMT demand
is –0.4, which differs substantially from previous estimates. We also find evidence that
wealthier households have more elastic demand, and that households at all income
levels are more sensitive to time costs than to fuel costs. We use our estimated elasticities
to simulate VMT and energy use impacts of full, private CAV adoption under a range
of possible changes to the fuel and time costs of travel. We forecast a 2–47% increase in
travel demand for an average household. Our results indicate that backfire –i.e., a net
rise in energy use –is a possibility, especially in higher income groups. This presents
a stiff challenge to policy goals for reductions in not only energy use but also traffic
congestion and local and global air pollution, as CAV use increases.
Subject Areas: Automated vehicles; Rebound effect; Fuel economy; Energy demand;
Induced travel; Travel time cost
Availability: Taiebat, M., Stolper, S. and Xu, M., 2019. Forecasting the Impact of Connected
and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy
Rebound. Journal of Applied Energy.
https://www.sciencedirect.com/science/article/pii/S0306261919305823?via%3Dihub

20

2.5. Title: Charging Demand of Plug-in Electric Vehicles: Forecasting Travel Behavior
Based on a Novel Rough Artificial Neural Network Approach
Author(s): Jahangir, H., Tayarani, H., Ahmadian, A., Golkar, M.A., Miret, J., Tayarani,
M. and Gao, H.O.
Abstract: The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due
to their energy saving and environmental benefits. In order to address PEVs impact on
the electric networks, the aggregators need to accurately predict the PEV Travel Behavior
(PEV-TB) since the addition of a great number of PEVs to the current distribution network
poses serious challenges to the power system. Forecasting PEV-TB is critical because of
the high degree of uncertainties in drivers’ behavior. Existing studies mostly simplified
the PEV-TB by mapping travel behavior from conventional vehicles. This could cause
bias in power estimation considering the differences in PEV-TB because of charging
pattern which consequently could bungle economic analysis of aggregators. In this
study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent
Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method
based on Rough structure - is developed. The method is based on historical data including
arrival time, departure time and trip length. In this study, the correlation among arrival
time, departure time and trip length is also considered. The forecasted PEV-TB is then
compared with Monte Carlo Simulation (MCS) which is the main benchmarking method
in this field. The results comparison depicted the robustness of the proposed methodology.
The proposed method reduces the aggregators’ financial loss approximately by 16$/PEV
per year compared to the conventional methods. The findings underline the importance
of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle
electrification in the years to come.
Subject Areas: Plug-in electric vehicle; Travel behavior; Artificial neural network; Rough
neuron; Smart charging
Availability: Jahangir, H., Tayarani, H., Ahmadian, A., Golkar, M.A., Miret, J., Tayarani,
M. and Gao, H.O., 2019. Charging Demand of Plug-in Electric Vehicles: Forecasting Travel
Behavior Based on a Novel Rough Artificial Neural Network Approach. Journal of Cleaner
Production, 229, pp.1029-1044.
https://www.sciencedirect.com/science/article/pii/S0959652619314428

21

2.6. Title: Fuel Cell Electric Vehicle Driving and Fueling Behavior
Author(s): Kurtz, J.M., Sprik, S., Saur, G. and Onorato, S.
Abstract: The objectives of this project are to validate hydrogen fuel cell electric vehicles
in real-world settings and to identify the current status and evolution of the technology.
The analysis objectively assesses progress toward targets and market needs defined
by the U.S. Department of Energy and stakeholders, provides feedback to hydrogen
research and development, and publishes results for key stakeholder use and investment
decisions. Fiscal year 2018 objectives focused on analysis and reporting of fuel cell electric
vehicle driving range, fuel economy, drive and fill behaviors, durability, fill performance,
and fuel cell performance. This report specifically addresses the topics of driving range,
fuel economy, drive and fill behaviors, and fill performance.
Subject Areas: 33 ADVANCED PROPULSION SYSTEMS; Fuel Cell; Hydrogen; FCEV;
Driving Range, Fuel Economy; Fill Performance; Drive And Fill Behaviors; NFCTEC;
National Fuel Cell Technology Evaluation Center
Availability: Kurtz, J.M., Sprik, S., Saur, G. and Onorato, S., 2019. Fuel Cell Electric Vehicle
Driving and Fueling Behavior. (No. NREL/TP-5400-73010). National Renewable Energy
Lab.(NREL), Golden, CO (United States).
https://www.osti.gov/biblio/1501674

22

2.7. Title: Comparison of Marginal and Average Emission Factors for Passenger Transportation
Modes
Author(s): Bigazzi, A.
Abstract: Comparisons of the energy and emission intensity of transportation modes
are standard features of sustainable transportation research, policy, and advocacy. These
comparisons are typically based on average energy and emission factors per passenger
trip or per passenger-kilometer traveled. However, as acknowledged in the energy
production sector, comparing average emission factors can misinform policy and other
decisions because it fails to represent the marginal impact of changing demand. The
objective of this paper is to quantify the difference between average and marginal energy
and emission factors for passenger transportation modes. Transportation system operations
data are used to estimate energy and emission factors per passenger-kilometer traveled
for U.S. urban and intercity travel. Marginal emission factors range from 30% (intercity
rail) to 90% (private vehicles) of average factors. For urban travel, private vehicles and
public transit have similar average emission factors, but marginal factors are 50% lower
for transit. The average emission factor for intercity rail is 10% lower than air travel
and 30% lower than private vehicles, but the marginal factor is 60% and 80% lower,
respectively. Using average energy and emission factors to represent the impacts of
travel by different modes is biased against public transit and discounts the benefits of
shifting travel away from private passenger vehicles.
Subject Areas: Emission factors; Marginal effects; Transportation systems; Transportation
modes; Motor vehicles
Availability: Bigazzi, A., 2019. Comparison of Marginal and Average Emission Factors for
Passenger Transportation Modes. Applied Energy, 242, pp.1460-1466.
https://www.sciencedirect.com/science/article/pii/S030626191930580X

23

2.8. Title: Charging Load Forecasting of Electric Vehicle Based on Charging Frequency
Author(s): Wang, H.J., Wang, B., Fang, C., Li, W. and Huang, H.W.
Abstract: The rapid development of electric vehicles (EVs) will gradually increase the
operating pressure of the power grid. The charging load of EVs need to be predicred to
release this pressure. In this paper, the influence factors of EV charging load are analyzed
and the load curve of different charging modes is obtained. According to the 2009 American
families travel survey, EVs are divided into three types: private cars, buses and taxis.
Then, different EVs’ charging frequency, together with the starting point for state of
charge (SOC) and daily mileage can be calculated. Correspondingly, the probability
density function model can be established. Finally, the daily charging load is calculated
by Monte Carlo algorithm, which provides the basis for the research of orderly EV charging.
Subject Areas: Electric vehicles (EVs); Power Grid; Monte Carlo algorithm
Availability: Wang, H.J., Wang, B., Fang, C., Li, W. and Huang, H.W., 2019. Charging
Load Forecasting of Electric Vehicle Based on Charging Frequency. In IOP Conference Series:
Earth and Environmental Science (Vol. 237, No. 6, p. 062008). IOP Publishing.
https://iopscience.iop.org/article/10.1088/1755-1315/237/6/062008/pdf

24

2.9. Title: Modeling Charging Behavior of Battery Electric Vehicle Drivers: A Cumulative
Prospect Theory Based Approach
Author(s): Hu, L., Dong, J. and Lin, Z.
Abstract: The behavior of drivers in charging a battery electric vehicle (BEV) can be
influenced by psychological factors such as personality and risk preference. This paper
proposes a cumulative prospect theory (CPT) based modeling framework to describe
the charging behavior of BEV drivers. CPT captures an individual’s attitude and preference
toward risk in the decision-making process. A BEV mass-market scenario is constructed
using the 2017 National Household Travel Survey (NHTS) data. This paper applies the
CPT-based charging behavior model to study the battery state-of-charge (SOC) when
drivers decide to charge their vehicles, charging timing and location choices, and charging
power demand profile under the mass-market scenario. In addition, sensitivity analyses
are used to examine the drivers’ risk attitudes and public charger network coverage.
BEV drivers who display a higher degree of risk-seeking tend to charge vehicles at a
lower SOC. Some home charging shifts to workplace and public charging as the public
charger network expands, but home charging still plays the most significant role in
BEV use. The power demand from public chargers increases significantly with BEV
expansion and has a larger impact on the power grid. The time-of-use (TOU) electricity
rate can shift peak power demand to off-peak periods from midnight to early morning.
Subject Areas: Battery electric vehicle; Charging behavior; Cumulative prospect theory;
2017 National Household Travel Survey (NHTS); Power grid
Availability: Hu, L., Dong, J. and Lin, Z., 2019. Modeling Charging Behavior of Battery
Electric Vehicle Drivers: A Cumulative Prospect Theory Based Approach. Transportation
Research Part C: Emerging Technologies, 102, pp.474-489.
https://www.sciencedirect.com/science/article/pii/S0968090X18312087

25

2.10. Title: Potential Energy Implications of Connected and Automated Vehicles: Exploring
Key Leverage Points through Scenario Screening and Analysis
Author(s): Bush, B., Vimmerstedt, L. and Gonder, J.
Abstract: Connected and automated vehicle (CAV) technologies could transform the
transportation system over the coming decades, but face vehicle and systems engineering
challenges, as well as technological, economic, demographic, and regulatory issues.
The authors have developed a system dynamics model for generating, analyzing, and
screening self-consistent CAV adoption scenarios. Results can support selection of scenarios
for subsequent computationally intensive study using higher-resolution models. The
potential for and barriers to large-scale adoption of CAVs have been analyzed using
preliminary quantitative data and qualitative understandings of system relationships
among stakeholders across the breadth of these issues. Although they are based on
preliminary data, the results map possibilities for achieving different levels of CAV
adoption and system-wide fuel use and demonstrate the interplay of behavioral parameters
such as how consumers value their time versus financial parameters such as operating
cost. By identifying the range of possibilities, estimating the associated energy and transportation
service outcomes, and facilitating screening of scenarios for more detailed analysis, this
work could inform transportation planners, researchers, and regulators.
Subject Areas: Connected and automated vehicle (CAV); Fuel use; Operating cost
Availability: Bush, B., Vimmerstedt, L. and Gonder, J., 2019. Potential Energy Implications
of Connected and Automated Vehicles: Exploring Key Leverage Points through Scenario Screening
and Analysis. Transportation Research Record.
https://journals.sagepub.com/doi/abs/10.1177/0361198119838840

26

2.11. Title: Assessment of Light-Duty Plug-In Electric Vehicles in the United States, 2010–2018
Author(s): Gohlke, D. and Zhou, Y.
Abstract: This report examines properties of plug-in electric vehicles (PEVs) sold in
the United States from 2010 to 2018, exploring vehicle sales, miles driven, electricity
consumption, petroleum reduction, vehicle manufacturing, and battery production,
among other factors. Over one million PEVs have been sold, driving over 25 billion
miles on electricity since 2010, thereby reducing national gasoline consumption by 0.23%
in 2018 and 950 million gallons cumulatively through 2018. In 2018, PEVs used 2.8 terawatt-hours
of electricity to drive 8.6 billion miles, offsetting 320 million gallons of gasoline. The
majority of these vehicles were assembled in the United States, and over 42 gigawatt-hours
of lithium-ion batteries have been installed in vehicles.
Subject Areas: Plug-in electric vehicles (PEVs); Fuel consumption; Gasoline
Availability: Gohlke, D. and Zhou, Y., 2019. Assessment of Light-Duty Plug-In Electric
Vehicles in the United States, 2010–2018. No. ANL/ESD-19/2). Argonne National Lab.(ANL),
Argonne, IL (United States).
https://www.osti.gov/biblio/1506474-assessment-light-duty-plug-electric-vehiclesunited-states

27

2.12. Title: Modeling Electric Vehicle Adoption Considering A Latent Travel Pattern Construct
And Charging Infrastructure
Author(s): Nazari, F., Mohammadian, A.K. and Stephens, T.
Abstract: This paper presents a behavioral model of public, revealed preferences (RP)
for various types of electric vehicles (EVs) while accounting for a latent (green) travel
pattern construct and charging infrastructure characteristics. Specifically, a two-level
nested logit (NL) model is estimated to explain households’ fuel type choice among
five alternatives and three nests: (1) battery electric vehicles (BEVs); (2) hybrid vehicles
including hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs); and (3) conventional
vehicles including gasoline and diesel vehicles. Further, a latent travel pattern construct
which captures a week-long number of trips by non-vehicle travel modes as well as
daily vehicle and tollway use is estimated in a structural equation setting and subsequently
fed into the NL model. Using a recent RP dataset from the California Household Travel
Survey, we identify market segments for alternative fuel types based on households’
socio-economic characteristics, built environment factors concerning public plug-in
EV (PEV) charging infrastructure characteristics, latent and observable travel behavior
factors of a household vehicle’s principal driver, and vehicle attributes. The results highlight
that the number of public PEV charging stations is only significant for households choosing
PHEVs and interestingly insignificant in the BEV utility. Furthermore, the sensitivity
analysis of the findings reveals that PHEV users are elastic with respect to household
vehicle ownership ratio and the latent green travel pattern construct, while BEV users
are inelastic to any explanatory variable.
Subject Areas: Battery electric vehicle; Plug-in hybrid electric vehicle; Latent travel
pattern; Charging infrastructure; Revealed preferences
Availability: Nazari, F., Mohammadian, A.K. and Stephens, T., 2019. Modeling Electric
Vehicle Adoption Considering A Latent Travel Pattern Construct And Charging Infrastructure.
Transportation Research Part D: Transport and Environment, 72, pp.65-82.
https://www.sciencedirect.com/science/article/pii/S1361920918311866

28

2.13. Title: Should Electric Vehicle Drivers Pay a Mileage Tax?
Author(s): Davis, L. and Sallee, J.
Abstract: In many countries the revenue from gasoline taxes is used to fund highways
and other transportation infrastructure. As the number of electric vehicles on the road
increases, this raises questions about the effectiveness and equity of this financing mechanism.
In this paper, we ask whether electric vehicle drivers should pay a mileage tax. Though
the gasoline tax has been traditionally viewed as a benefits tax, we take instead the
perspective of economic efficiency. We derive a condition for the optimal electric vehicle
mileage tax that highlights a key trade-off. On the one hand, there are externalities from
driving including traffic congestion and accidents that imply a mileage tax is efficient.
On the other hand, gasoline tends to be underpriced, so a low (or even negative) mileage
tax might be justified to encourage substitution away from gasoline-powered vehicles.
We then turn to an empirical analysis aimed at better understanding the current policy
landscape for electric vehicles in the United States. Using newly-available nationally
representative microdata we calculate that electric vehicles have reduced gasoline tax
revenues by $250 million annually. We show that the foregone tax revenue is highly
concentrated in a handful of states and is highly regressive, as most electric vehicles
are driven by high-income households, and we discuss how this motivates and informs
optimal policy.
Subject Areas: Electric Vehicles; Gasoline Tax, U.S. Highway Trust Fund, Distributional
Impacts
Availability: Davis, L. and Sallee, J., 2019. Should Electric Vehicle Drivers Pay a Mileage
Tax? Energy Institute WP301.
https://ei.haas.berkeley.edu/research/papers/WP301.pdf

29

2.14. Title: Full-scale Electric Vehicles Penetration in the Danish Island of Bornholm —
Optimal Scheduling and Battery Degradation under Driving Constraints
`
Author(s): Gonz`alez-Garrido, A., Thingvad, A., Gaztanaga,
H. and Marinelli, M.
Abstract: The paper proposes an analysis of a 100% electric vehicle (EV) scenario on
the energy system of the island of Bornholm in Denmark. The paper intends to present
challenges and opportunities that a realistic system would face when completely shifting
to electric transportation. The EVs are subject to different charging strategies in order to
assess the impact on the grid, the potential savings on the charging cost and the effects
on battery degradation. In contrast to uncontrolled charging, smart charging strategies
are designed not only to satisfy the same charging requirements at the EV departure
time, but also maximize the savings on the charging cost and avoid interconnection
congestions. Smart strategies bring a reduction in annual charging cost around 12%,
on top of a reduction in the degradation because of lower average SOC and number of
cycles. Moreover, results show a limited benefit in bidirectional charging because of a
marginal increase in savings: this more demanding operation, which allows discharges,
leads to higher battery degradation, due to the increase in the number of cycles.
Subject Areas: Battery degradation; Electric vehicles; Optimization; Smart charging;
User behavior; Vehicle-to-grid
`
Availability: Gonz`alez-Garrido, A., Thingvad, A., Gaztanaga,
H. and Marinelli, M.,
2019. Full-scale Electric Vehicles Penetration in the Danish Island of Bornholm — Optimal
Scheduling and Battery Degradation under Driving Constraints Journal of Energy Storage,
23, pp.381-391.
https://www.sciencedirect.com/science/article/pii/S2352152X18308405

30

2.15. Title: Meeting 2025 Zero Emission Vehicle Goals: An Assessment of Electric Vehicle
Charging Infrastructure in Maryland
Author(s): Moniot, M., Rames, C.L. and Wood, E.W.
Abstract: The National Renewable Energy Laboratory (NREL) has been enlisted to
conduct a statewide assessment of the electric vehicle charging infrastructure requirements
for Maryland to meet its goal of supporting 300,000 zero emission vehicles by 2025.
NREL’s Electric Vehicle Infrastructure Projection Tool (EVI-Pro) was used to generate
scenarios of statewide charging infrastructure to support consumer plug-in electric
vehicle (PEV) adoption based on travel patterns provided by INRIX (a commercial mapping/traffic
company) that are used to characterize regional travel in Maryland and to anticipate
future demand for PEV charging. Results indicate that significant expansion of Maryland’s
electric vehicle charging infrastructure will be required to support the state’s PEV goal
for 2025. Analysis shows that a fleet of 300,000 PEVs will require 17,400 workplace Level
2 plugs, 9,300 public Level 2 plugs, and 1,000 fast charge plugs. These estimates assume
that future PEVs will be driven in a manner consistent with present day gasoline vehicles
and that most charging will happen at residential locations. A sensitivity study explores
edge cases pertaining to several assumptions, highlighting factors that heavily influence
the projected infrastructure requirements. Variations in the makeup of the PEV fleet,
evolving consumer charging preferences, and availability of residential charging are all
shown to influence 2025 infrastructure requirements.
Subject Areas: 33 ADVANCED PROPULSION SYSTEMS; Zero Emission Vehicles; Electric
Vehicles; Charging Infrastructure; PEVs; Electric Vehicle Supply Equipment
Availability: Moniot, M., Rames, C.L. and Wood, E.W., 2019. Meeting 2025 Zero Emission
Vehicle Goals: An Assessment of Electric Vehicle Charging Infrastructure in Maryland. (No.
NREL/TP-5400-71198). National Renewable Energy Lab.(NREL), Golden, CO (United
States).
https://www.osti.gov/biblio/1496855

31

2.16. Title: Modeling the GHG Emissions Intensity of Plug-in Electric Vehicles using Short-Term
and Long-Term Perspectives
Author(s): Kamiya, G., Axsen, J. and Crawford, C.
Abstract: Plug-in electric vehicles (PEVs) can contribute to deep greenhouse gas (GHG)
reduction targets but their efficacy depends on the sources of electricity. PEV GHG intensity
can vary over time (and regionally), making it unclear how policymakers should regulate
PEVs in the short and long-term. To explore this uncertainty, we model the short-term
(Study 1) and long-term (Study 2) well-to-wheels GHG intensity of PEVs in three regions
with very different electricity grid profiles: the Canadian provinces of British Columbia,
Alberta, and Ontario. Study 1 uses empirical data on vehicle preferences, driving patterns,
and recharge access from a representative survey of new vehicle buyers in Canada (n=1754)
to construct a temporally-explicit model of PEV usage in 2015. Fleet-wide emissions
intensity of PEVs varies substantially between regions, with the greatest reduction potential
relative to conventional gasoline vehicles seen in British Columbia (78–98%), followed
by Ontario (58–92%) and Alberta (34–41%). Study 2 simulates the potential long-term
dynamics of technology, behavior, and emissions with the CIMS energy-economy model.
With the emissions intensity of electricity decreasing by at least one-third by 2050 and
vehicle energy efficiency improving over time, simulation results find that, compared
to 2015, 2050 fleet average PEV emissions are 40–52% lower in British Columbia, 57–74%
lower in Alberta, and 36–46% lower in Ontario. Overall, we find that PEVs offer substantial
GHG emissions benefits compared to conventional vehicles in all scenarios explored.
Policy makers seeking deep GHG cuts may want to support PEV adoption, even in
jurisdictions that presently use relatively carbon-intensive electricity.
Subject Areas: Greenhouse gas emissions; Passenger vehicles; Plug-in electric vehicles;
Well-to-wheel; Consumer behavior; Climate change
Availability: Kamiya, G., Axsen, J. and Crawford, C., 2019. Modeling the GHG Emissions
Intensity of Plug-in Electric Vehicles using Short-Term and Long-Term Perspectives. Transportation
Research Part D: Transport and Environment, 69, pp.209-223.
https://www.sciencedirect.com/science/article/pii/S1361920917307113

32

2.17. Title: Acceptability, Energy Consumption, and Costs of Electric Vehicle for Ride-hailing
Drivers in Beijing
Author(s): Tu, W., Santi, P., Zhao, T., He, X., Li, Q., Dong, L., Wallington, T.J. and Ratti,
C.
Abstract: The acceptability, energy consumption, and environmental benefits of electric
vehicles are highly dependent on travel patterns. With increasing ride-hailing popularity
in mega-cities, urban mobility patterns are greatly changing; therefore, an investigation
of the extent to which electric vehicles would satisfy the needs of ride-hailing drivers
becomes important to support sustainable urban growth. A first step in this direction
is reported here. GPS-trajectories of 144,867 drivers over 104 million km in Beijing were
used to quantify the potential acceptability, energy consumption, and costs of ride-hailing
electric vehicle fleets. Average daily travel distance and travel time for ride-hailing
drivers was determined to be 129.4km and 5.7h; these values are substantially larger
than those for household drivers (40.0km and 1.5h). Assuming slow level-1 (1.8 KW) or
moderate level-2 (7.2 KW) charging is available at all home parking locations, battery
electric vehicles with 200km all electric range (BEV200) could be used by up to 47% or
78% of ride-hailing drivers and electrify up to 20% or 55% of total distance driven by
the ride-hailing fleet. With level-2 charging available at home, work, and public parking,
the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study
suggests that long range BEVs and widespread level-2 charging infrastructure are needed
for large-scale electrification of ride-hailing mobility in Beijing. The marginal benefits of
increased all electric range, effects on charging infrastructure distribution, and payback
times are also presented and discussed. Given the observed heterogeneity of ride-hailing
vehicle travel, our study outlines the importance of individual-level analysis to understand
the electrification potential and future benefits of electric vehicles in the era of shared
smart transportation.
Subject Areas: Ride-hailing; Urban mobility; GPS trajectories; Electrification; Machine
learning; Big data
Availability: Tu, W., Santi, P., Zhao, T., He, X., Li, Q., Dong, L., Wallington, T.J. and
Ratti, C., 2019. Acceptability, Energy Consumption, and Costs of Electric Vehicle for Ride-hailing
Drivers in Beijing. Applied Energy, 250, pp.147-160.
https://www.sciencedirect.com/science/article/pii/S0306261919308177

33

2.18. Title: Optimization of Charging Method for Scaled EVs
Author(s): Zhang, X., Liu, Z., Cao, Y., Duan, L., Tang, G. and Liu, W.
Abstract: At present, EVs(electrical vehicles) charging strategies are mainly focusing
on reasonable optimization of EVs’ charging. However, previous researches have rarely
studied optimizing the charging power of each EV, and the algorithms are always too
complicated to implement. Based on this, this paper examines the lowest total load
charging method and proposes the automatic breaking charging method which do not
rely on complicated algorithms. Through Monte Carlo simulation, the simulation results
of the two charging methods are compared respectively, and it is verified that the automatic
breaking charging method has a better effect on peak load shifting and it can contribute
to the reduction of energy loss.
Subject Areas: EVs(electrical vehicles); Monte Carlo simulation; Charging Method
Availability: Zhang, X., Liu, Z., Cao, Y., Duan, L., Tang, G. and Liu, W., 2019. Optimization
of Charging Method for Scaled EVs. In Journal of Physics: Conference Series (Vol. 1187,
No. 2, p. 022040). IOP Publishing.
https://iopscience.iop.org/article/10.1088/1742-6596/1187/2/022040/meta

34

2.19. Title: An Intelligent Hybrid Energy Management System for a Smart House Considering
Bidirectional Power Flow and Various EV Charging Techniques
Author(s): Rafique, M.K., Khan, S.U., Saeed Uz Zaman, M., Mehmood, K.K., Haider,
Z.M., Bukhari, S.B.A. and Kim, C.H.
Abstract: Compelled by environmental and economic reasons and facilitated by modern
technological advancements, the share of hybrid energy systems (HES) is increasing
at modern smart house (SH) level. This work proposes an intelligent hybrid energy
management system (IHEMS) for an SH connected to a power network that allows a
bidirectional power flow. The SH has electrical and thermal power loops, and its main
components include renewable energy from wind and photovoltaics, electric vehicle
(EV), battery energy storage system, a fuel cell which serves as a micro-combined heat
and power system, and a boiler. The proposed IHEMS models the components of the
SH, defines their constraints, and develops an optimization model based on the real
coded genetic algorithm. The key features of the developed IHEMS are highlighted
under six simulation cases considering different configurations of the SH components.
Moreover, the standard EV charging techniques are compared, and it is observed that
the charging method which is flexible in timing and power injection to the EV is best
suited for the economic operation of the SH. The simulation results reveal that the proposed
IHEMS minimizes the 24-hour operational cost of the SH by optimally scheduling the
energy resources and loads.
Subject Areas: Micro-combined heat and power (Micro-CHP) system; Real Coded Genetic
Algorithm (RCGA); Smart Home (SH); Electric Vehicle Supply Equipment (EVSE); Photovoltaics
(PV)
Availability: Rafique, M.K., Khan, S.U., Saeed Uz Zaman, M., Mehmood, K.K., Haider,
Z.M., Bukhari, S.B.A. and Kim, C.H., 2019. An Intelligent Hybrid Energy Management
System for a Smart House Considering Bidirectional Power Flow and Various EV Charging
Techniques. Applied Sciences, 9(8), p.1658.
https://www.mdpi.com/2076-3417/9/8/1658

35

2.20. Title: Joint Optimization Scheme for the Planning and Operations of Shared Autonomous
Electric Vehicle Fleets Serving Mobility on Demand
Author(s): Sheppard, C.J., Bauer, G.S., Gerke, B.F., Greenblatt, J.B., Jenn, A.T. and Gopal,
A.R.
Abstract: As the transportation sector undergoes three major transformations–electrification,
shared/on-demand mobility, and automation–there are new challenges to analyzing
the impacts of these trends on both the transportation system and the power sector.
Most models that analyze the requirements of fleets of shared autonomous electric vehicles
(SAEVs) operate at the scale of an urban region, or smaller. A quadratically constrained,
quadratic programming problem is formulated, designed to model the requirements of
SAEVs at a national scale. The size of the SAEV fleet, the necessary charging infrastructure,
the fleet charging schedule, and the dispatch required to serve demand for trips in a
region are treated as decision variables. By minimizing both the amortized cost of the
fleet and chargers as well as the operational costs of charging, it is possible to explore
the coupled interactions between system design and operation. To apply the model at
a national scale, key complications about fleet operations are simplified; but a detailed
agent-based regional simulation model to parameterize those simplifications is leveraged.
Preliminary results are presented, finding that all mobility in the United States (U.S.)
currently served by 276 million personally owned vehicles could be served by 12.5 million
SAEVs at a cost of $0.27/vehicle-mile or $0.18/passenger-mile. The energy requirements
for this fleet would be 1142 GWh/day (8.5% of 2017 U.S. electricity demand) and the
peak charging load 76.7 GW (11% of U.S. power peak). Several model sensitivities are
explored, and it is found that sharing is a key factor in the analysis.
Subject Areas: Shared Autonomous Electric Vehicles (SAEVs); Vehicle Fleets; Mobility
Availability: Sheppard, C.J., Bauer, G.S., Gerke, B.F., Greenblatt, J.B., Jenn, A.T. and
Gopal, A.R., 2019. Joint Optimization Scheme for the Planning and Operations of Shared Autonomous
Electric Vehicle Fleets Serving Mobility on Demand. Transportation Research Record and
Lawrence Berkeley National Laboratory.
https://escholarship.org/uc/item/5zs7f6hg

36

2.21. Title: Are Consumers Poorly Informed about Fuel Economy? Evidence from Two
Experiments
Author(s): Allcott, H. and Knittel, C.
Abstract: It is often asserted that consumers are poorly informed about and inattentive
to fuel economy, causing them to buy low-fuel economy vehicles despite their own
best interest. This paper presents evidence on this assertion through two experiments
providing fuel economy information to new vehicle shoppers. Results show zero statistical
or economic effect on average fuel economy of vehicles purchased. In the context of a
simple optimal policy model, the estimates suggest that current and proposed US fuel
economy standards are significantly more stringent than needed to address the classes
of imperfect information and inattention addressed by our interventions.
Subject Areas: Fuel Economy; Consumers
Availability: Allcott, H. and Knittel, C., 2019. Are Consumers Poorly Informed about Fuel
Economy? Evidence from Two Experiments. American Economic Journal: Economic Policy,
11(1), pp.1-37.
https://www.aeaweb.org/articles?id=10.1257/pol.20170019

37

2.22. Title: Distribution System Planning Considering Stochastic EV Penetration and
V2G Behavior
Author(s): Wang, X., Nie, Y. and Cheng, K.W.E.
Abstract: The increasing integration of electric vehicles (EVs) is adding higher future
potentials for the smart grid because the residual energy stored in EV batteries can be
discharged to support the grid when needed. However, the stochasticity of EV user
behaviors pose challenges to the regulators of distribution systems. How the regulators
decide upon a control strategy for the vehicle to grid and how EV users respond to
the strategy will significantly influence the variation of load profiles in the planning
horizon. In this paper, a comprehensive cost analysis is performed to obtain the optimal
planning scheme, considering the variation in EV penetration, charging preference, and
customer damage cost. The economics and stability of the planned distribution system
are assessed with real-world travel records and cost statistics to quantitatively show
the effectiveness of the optimization algorithm and the importance of user behavior
concern.
Subject Areas: Planning; Reliability; Vehicle-to-grid; Substations; Electric vehicle charging;
Batteries; Regulators
Availability: Wang, X., Nie, Y. and Cheng, K.W.E., 2019. Distribution System Planning
Considering Stochastic EV Penetration and V2G Behavior. IEEE Transactions on Intelligent
Transportation Systems.
https://ieeexplore.ieee.org/abstract/document/8621612

38

2.23. Title: Fleet Performance and Cost Evaluation of a Shared Autonomous Electric Vehicle
(SAEV) Fleet: A case study for Austin, Texas
Author(s): Loeb, B. and Kockelman, K.M.
Abstract: Shared Autonomous Vehicles (SAVs) have gained significant public interest
as a possible less expensive, safer and more efficient version of today’s transportation
networking companies (TNCs) and taxis. One way to expand on the possible benefits
of an SAV fleet is through electric vehicles (EVs), which tend to be more energy efficient,
more reliable, quicker, and may reduce system-wide emissions when coupled with
renewable power. EVs are quickly becoming more financially viable as the price of these
vehicles drops and charging infrastructure is appearing in more and more locations
across the world. EVs are disadvantaged by their relatively short range and long recharge
times, so it is important to understand how these factors will affect an electrified SAV
(SAEV) fleet in terms of vehicle miles traveled (VMT), vehicle productivity, and response
times.
Perhaps the most important factor to consider before implementation is cost, since it
is quite unlikely that a fleet operator will elect to use an EV fleet when a gasoline fleet
is more profitable. This study makes in-depth estimates of the cost of this SAEV fleet
based on vehicle purchasing costs, vehicle maintenance, batteries, electricity, charger
construction (including land acquisition and paving), charger maintenance, insurance,
registration and general administrative costs. These costs are estimated at low-, highand mid-cost scenarios, where mid-cost is the most expected.
This study performed a simulation of SAEVs across the Austin, Texas 6-county region
under 6 different fleet scenarios to assess what factors make the fleet the most profitable
and provide the best customer experience. The simulation process features thoughtful
charging strategies, dynamic ridesharing, mode choice, and a multi-step search algorithm.
Results showed that for all metrics studied, the gasoline hybrid-electric (HEV) fleet
performed better than EV fleets, while remaining more profitable, providing response
times of 4.5min compared to 5.5min. The HEV fleet is the more profitable option until
the cost of gasoline exceeds $10 per gallon or the cost of a long-range EV falls below
$16,000 through subsidies. Of all the EVs studied, the long-range fast-charging scenario
not only provides the best service in terms of all metrics studied, but is by far the most
profitable. Even though EVs may not be financially advantageous in the near term, the
environmental benefits could be substantial; EVs have the potential to provide zero-carbon
transportation when coupled with a renewable power grid. Gasoline vehicles have no
such potential. Environmentalism tends to have little effect on financial decisions, but a
carbon tax could change that perspective.
Subject Areas: Shared Autonomous Vehicles (SAVs); Hybrid Electric Vehicles; Investments;
Benefits
Availability: Loeb, B. and Kockelman, K.M., 2019. Fleet Performance and Cost Evaluation
of a Shared Autonomous Electric Vehicle (SAEV) Fleet: A case study for Austin, Texas. Transportation
Research Part A: Policy and Practice, 121, pp.374-385.
https://www.sciencedirect.com/science/article/pii/S096585641730112X
39

2.24. Title: Machine Learning Estimates of Plug-in Hybrid Electric Vehicle Utility Factors
¨ P.
Author(s): Goebel, D. and Plotz,
Abstract: Plug-in hybrid electric vehicles (PHEV) combine an electric drive train with
a conventional one and are able to drive on gasoline when the battery is fully depleted.
They can thus electrify many vehicle miles travelled (VMT) without fundamental range
limits. The most important variable for the electrification potential is the ratio of electric
VMT to total VMT, the so-called utility factor (UF). However, the empirical assessment
of UFs is difficult since important factors such as daily driving, re-charging behaviour
and frequency of long-distance travel vary noteworthy between drivers and large data
collections are required. Here, we apply machine learning techniques (regression tree,
random forest, support vector machine, and neural nets) to estimate real-world UF and
compare the estimates to actual long-term average UF of 1768 individual Chevrolet Volt
PHEV. Our results show that UFs can be predicted with high accuracy from individual
summary statistics to noteworthy accuracy with a mean absolute error of five percentage
points. The accuracy of these methods is higher than a simple simulation with electric
driving until the battery is discharged and one full daily recharge. The most important
variables in estimating UF according to a linear regression model are the variance and
skewness of the daily VMT distributions as well as the frequency of long-distance driving.
Thus, our findings make UF predictions from existing data sets for driving of conventional
vehicles more accurate.
Subject Areas: Electric vehicles; Plug-in hybrid electric vehicle; Utility factor; Machine
learning
¨ P., 2019. Machine Learning Estimates of Plug-in Hybrid
Availability: Goebel, D. and Plotz,
Electric Vehicle Utility Factors. Transportation Research Part D: Transport and Environment,
72, pp.36-46.
https://www.sciencedirect.com/science/article/pii/S1361920918301561

40

2.25. Title: An EV Charging Demand Model for the Distribution System Using Traffic
Property
Author(s): Xia, Y., Hu, B., Xie, K., Tang, J. and Tai, H.M.
Abstract: This paper proposes a mathematical model for the spatial-temporal charging
demand for electric vehicle (EV). The determination of spatial-temporal charging demand
is a key step for the planning of distribution systems with a scalable application of EV.
The spatial-temporal allocation of EV is conventionally obtained through a simulation
procedure using traffic topology data, which is not suitable for the regions lacking such
information. This model converts the problem of travel distance to travel duration so
that the requirement of network geographic information can be avoided. Static EV parameters,
EV spatial-temporal moving parameters and system charging model parameters are
treated as the deterministic factors for the charging demand allocation. A stochastic
travel route simulation procedure, which relies purely on statistical data of traffic flow,
is also developed to obtain the EV moving parameters by adopting the traffic property
information. The designed procedure derives travel time parameters from the vehicle
dynamic-location-property (DLP) model and the travel time probability distribution.
The DLP model is established using the traffic property matrix and the regional origin-destination
matrix. A simple case is presented to illustrate the result of stochastic travel route simulation.
Then, a modified eastern China system is used as an example to analyze the EV charging
demand under multiple scenarios. The feasibility and versatility of the proposed model
in the large complex system are verified by the test results.
Subject Areas: Electric vehicle charging; Load modeling; Data models; Batteries; Mathematical
model; Resource management; Roads
Availability: Xia, Y., Hu, B., Xie, K., Tang, J. and Tai, H.M., 2019. An EV Charging Demand
Model for the Distribution System Using Traffic Property. IEEE Access.
https://ieeexplore.ieee.org/abstract/document/8654186

41

2.26. Title: CalAmp and Swiftmile Partner to Deliver First-Ever Solar-Powered Parking
and Charging Station Providing “Power Nap” for Micro-Mobility
Author(s): NA
Abstract: Blog
Subject Areas: Light Electric Vehicle (LEV) Charging Systems; Solar-Powered Parking
and Charging Station
Availability: 2019. CalAmp and Swiftmile Partner to Deliver First-Ever Solar-Powered Parking
and Charging Station Providing “Power Nap” for Micro-Mobility. PRNewswire.
https://www.barchart.com/story/news/344317/calamp-and-swiftmilepartner-to-deliver-first-ever-solar-powered-parking-and-charging-station-providingpower-nap-for-micro-mobility

42

2.27. Title: Risk Evaluation of Distribution Networks Considering Residential Load Forecasting
with Stochastic Modelling of Electric Vehicles
Author(s): Habib, S., Khan, M.M., Abbas, F., Ali, A., Hashmi, K., Shahid, M.U., Bo, Q.
and Tang, H.
Abstract: Large-scale integration of electric vehicles (EVs) into residential distribution
networks (RDNs) is an evolving issue of paramount significance for utility operators.
Similarly, electric load forecasting is an operational process permitting the utilities to
manage demand issues for optimal energy utilization. Unbalanced voltages prevent the
effective and reliable operation of RDNs. This study implements a novel framework to
examine risks associated with RDNs by applying a residential forecasting model with
a stochastic model of EVs charging pattern. Diversified EV loads require a stochastic
approach to predict EVs charging demand; consequently, a probabilistic model is developed
to account for several realistic aspects comprising charging time, battery capacity, driving
mileage, state-of-charge, travelling frequency, charging power, and time-of-use mechanism
under peak and off-peak charging strategies. Peak-day forecast of various households
is obtained in summer and winter by implementing an optimum nonlinear auto-regressive
neural-network (NN) with time-varying external input vectors (NARX). Outputs of
the EV stochastic model and residential forecasting model obtained from Monte-Carlo
simulations and the NARX-NN model, respectively, are utilized to evaluate power quality
parameters of RDNs. Performance specifications of RDNs including voltage unbalance
factor (VUF) and voltage behavior are assessed in context to EV charging scenarios
with various charging power levels under different penetration levels.
Subject Areas: Distribution Networks; Stochastic Modelling; Electric Vehicles
Availability: Habib, S., Khan, M.M., Abbas, F., Ali, A., Hashmi, K., Shahid, M.U., Bo, Q.
and Tang, H., 2019. Risk Evaluation of Distribution Networks Considering Residential Load
Forecasting with Stochastic Modelling of Electric Vehicles. Energy Technology.
https://onlinelibrary.wiley.com/doi/abs/10.1002/ente.201900191

43

2.28. Title: Consumer Valuation of Fuel Economy: Findings from Recent Panel Studies
Author(s): Klemick, H., Elizabeth, K. and Wolverton, A.
Abstract: Engineering-based studies of energy efficiency often find that firms and consumers
fail to adopt technologies that appear to provide net private benefits absent regulation.
We examine the recent empirical literature on the extent to which expected future fuel
costs are reflected in vehicle prices and therefore valued by consumers when making
purchase decisions. These studies improve upon the prior literature due to their use
of highly disaggregated panel data that allows for defensible identification strategies.
These studies found that vehicle purchase prices reflect about 50 to 100 percent of future
fuel expenses, assuming static consumer expectations about future gasoline prices and
a discount rate of five to six percent. Recent regulatory analyses have estimated the
benefits of more stringent vehicle standards implicitly assuming that no improvements
in fuel economy will occur in the baseline, absent regulation. This assumption is consistent
with consumers placing no value on future fuel costs when making vehicle purchase
decision. The recent empirical evidence supports using a range of consumer valuation
assumptions and applying this range consistently in the baseline and regulatory scenarios
when modeling consumer purchase and firm investment decisions.
Subject Areas: Consumer valuation; Fuel economy; Vehicle purchase decisions; Benefit-cost
analysis
Availability: Klemick, H., Elizabeth, K. and Wolverton, A., 2019. Consumer Valuation
of Fuel Economy: Findings from Recent Panel Studies. Working Paper, National Center for
Environmental Economics.
https://ageconsearch.umn.edu/record/283626/

44

2.29. Title: Subsidy and Pricing Model of Electric Vehicle Sharing Based on Two-Stage
Stackelberg Game — A Case Study in China
Author(s): Yang, J., Lin, Y., Wu, F. and Chen, L.
Abstract: Electric vehicle sharing provides an effective way to improve the traffic situation
and relieve environmental pressure. The government subsidy policy and the car-sharing
operator’s pricing strategy are the key factors that affect the large-scale application of
electric vehicle sharing. To address this issue, a subsidy and pricing model for electric
vehicle sharing based on the two-stage Stackelberg game is proposed in this paper according
to the current situation in China. First, an electric vehicle sharing operation mode under
government participation is constructed. Then, a two-stage Stackelberg game model
involving the government, the car-sharing operator and the consumers is proposed
to determine the subsidy rates and pricing strategies. The improved particle swarm
optimization algorithm is used to obtain the Nash equilibrium of the model. Also, the
influence of private car cost and shared travel comfort on subsidy rates and pricing
strategies is analyzed. Finally, the simulation of electric vehicle sharing in a town of
China is carried out to investigate the performance of the proposed subsidy and price
model. The simulation results show that the model rationally formulates subsidy policies
and pricing strategies of the electric vehicle sharing to balance the interests of the three
participants, mobilizing users’ enthusiasm while guaranteeing the benefits of the government
and operator, making the overall benefit optimal.
Subject Areas: Two-stage Stackelberg game; Electric vehicle sharing; Subsidy policy;
Pricing strategy
Availability: Yang, J., Lin, Y., Wu, F. and Chen, L., 2019. Subsidy and Pricing Model of
Electric Vehicle Sharing Based on Two-Stage Stackelberg Game — A Case Study in China.
Applied Sciences, 9(8), p.1631.
https://www.mdpi.com/2076-3417/9/8/1631

45

2.30. Title: Electric Car Subsidies Hurt Middle Class Americans
Author(s): Landrith, G.
Abstract: Blog
Subject Areas: Electric Vehicles; American taxpayers; AAA study
Availability: Landrith, G., 2019. Electric Car Subsidies Hurt Middle Class Americans.
RealclearEnergy.Org.
https://www.realclearenergy.org/articles/

46

2.31. Title: How well do Electric Vehicles perform in our Extreme Weather?
Author(s): Harlow, T.
Abstract: Blog
Subject Areas: Electric Vehicles; Environment friendly; Temperature
Availability: Harlow, T., 2019. How well do Electric Vehicles perform in our Extreme Weather?
StarTribune.
http://www.startribune.com.

47

2.32. Title: An Electric Vehicle in Every Driveway?
Author(s): Davis, L.
Abstract: Blog
Subject Areas: Electric Vehicles; Households; Household income category; Tesla
Availability: Davis, L., 2019. An Electric Vehicle in Every Driveway? Energy Institute.
https://energyathaas.wordpress.com/2019/05/13/an-electric-vehicle-in-every-driveway/

48

2.33. Title: Fueling Up for Your Summer Travel Plans
Author(s): NA
Abstract: Blog
Subject Areas: Gas Prices; NHTS; Long-distance trip
Availability: 2019. Fueling Up for Your Summer Travel Plans Fox40.com
https://fox40.com/2019/05/24/fueling-up-for-you-summer-travel-plans/

49

2.34. Title: A Comparison Study on Stochastic Modeling Methods for Home Energy Management
System
Author(s): Yousefi, M., Hajizadeh, A. and Soltani, M.N.
Abstract: Obtaining an appropriate model is very crucial to develop an efficient energy
management system (EMS) for the smart home including, Photovoltaic array (PV), Plug-in
Electric Vehicle (PEV), home loads and Heat Pump (HP). Stochastic modeling methods
of smart home explain random parameters and uncertainties of the above components.
In this paper, a concise yet comprehensive analysis and comparison are presented for
these techniques. First, modeling methods are implemented to find appropriate and
precise forecasting models for PV, PEV, HP and home load demand. Then, the accuracy
of each model is validated by the real measured data. Finally, the pros and cons of each
method are discussed and reviewed. The obtained results show the conditions, under
which the methods can provide a reliable and accurate description of smart home dynamics.
Subject Areas: Comparison; Stochastic modeling; Uncertainties; Smart home; Energy
management system; Modeling techniques
Availability: Yousefi, M., Hajizadeh, A. and Soltani, M.N., 2019. A Comparison Study on
Stochastic Modeling Methods for Home Energy Management System. IEEE Transactions on
Industrial Informatics.
https://ieeexplore.ieee.org/abstract/document/8678456

50

2.35. Title: Optimal Energy-Emission Management in Hybrid AC-DC Microgrids with
Vehicle-2-Grid Technology
Author(s): Papari, B., Edrington, C.S. and Gonsoulin, D.
Abstract: This article focuses on the optimal operation management challenges in hybrid
AC-DC microgrids considering optimal feeder switching, renewable energy sources,
and plug-in electric vehicles. In comparison with the traditional hybrid AC-DC microgrid
concept, the reconfigurable hybrid AC-DC microgrids provide more flexibility for better
supporting consumers and reducing the operation costs through the remotely controlled
switches. In addition, the reconfigurable structure of the hybrid microgrid along with
the vehicle-to-grid technology supports the high penetration of plug-in electric vehicles
by changing their role from only consuming loads into mobile storages. The proposed
problem is prepared as a constrained multi-objective problem optimizing both cost and
emission objectives. Due to the high complication and nonlinearity of the problem, an
effective optimization algorithm called the theta-crow search algorithm is developed to
solve the problem optimally. Also, a stochastic framework based on the point estimate
method is used to model the high uncertainties of the problem. The high reliable and
satisfying performance of the new method is shown on a test AC-DC microgrid.
Subject Areas: Renewable energy; Vehicle-2-Grid; AC-DC microgrids; plug-in electric
vehicles
Availability: Papari, B., Edrington, C.S. and Gonsoulin, D., 2019. Optimal Energy-Emission
Management in Hybrid AC-DC Microgrids with Vehicle-2-Grid Technology. Journal of Renewable
and Sustainable Energy, 11(1), p.015902.
https://aip.scitation.org/doi/abs/10.1063/1.5041492

51

2.36. Title: Reliability-based Metrics to Quantify the Maximum Permissible Load Demand
of Electric Vehicles
Author(s): Kamruzzaman, M. and Benidris, M.
Abstract: The continuous increase of electric vehicles (EVs) is expected to introduce
several challenges to power systems among which is deteriorating the reliability of
power supply. This paper proposes two adequacy metrics to quantify the maximum
permissible EV loads for a power system without deteriorating its reliability and the
required improvements to power systems to accommodate high penetrations of EVs
which are defined as follows: (1) extra effective available energy for EVs’ charging (EEAE-EVs)
and (2) extra effective required generation to accommodate EVs (EERG-EVs). The EEAE-EVs
provides a measure to the maximum amount of EV loads that a power system can accommodate
without adding new generation while maintaining its reliability. The EERG-EVs provides
a measure to the minimum amount of new generation that is needed to restore the reliability
level of a power system if the load of EVs exceeds the maximum permissible load of the
system. These metrics provide a decision aid to power system planners and operators
on when and how to perform generation expansion. The importance of the proposed
metrics is demonstrated on the IEEE Reliability Test System (IEEE RTS) with real EV
charging data. The sequential Monte Carlo simulation method is used in evaluating the
well-known power system reliability indices, the EEAE-EVs, and the EERG-EVs. The
results show that a power system can accommodate EV loads without deteriorating its
reliability as long as EV loads do not exceed the EEAE-EVs of the system.
Subject Areas: Power system reliability; Reliability; Power measurement; Maintenance
engineering; Electric vehicles
Availability: Kamruzzaman, M. and Benidris, M., 2019. Reliability-based Metrics to Quantify
the Maximum Permissible Load Demand of Electric Vehicles. IEEE Transactions on Industry
Applications.
https://ieeexplore.ieee.org/abstract/document/8705332

52

2.37. Title: Electric Vehicle Charging Station Placement Method for Urban Areas
Author(s): Cui, Q., Weng, Y. and Tan, C.W.
Abstract: For accommodating more electric vehicles (EVs) to battle against fossil fuel
emission, the problem of charging station placement is inevitable and could be costly
if done improperly. Some researches consider a general setup, using conditions such
as driving ranges for planning. However, most of the EV growths in the next decades
will happen in the urban area, where driving ranges is not the biggest concern. For
such a need, we consider several practical aspects of urban systems, such as voltage
regulation cost and protection device upgrade resulting from the large integration of
EVs. Notably, our diversified objective can reveal the trade-off between different factors
in different cities worldwide. To understand the global optimum of large-scale analysis,
we studied each feature to preserve the problem convexity. Our sensitivity analysis
before and after convexification shows that our approach is not only universally applicable
but also has a small approximation error for prioritizing the most urgent constraint in
a specific setup. Finally, numerical results demonstrate the trade-off, the relationship
between different factors and the global objective, and the small approximation error. A
unique observation in this study shows the importance of incorporating the protection
device upgrade in urban system planning on charging stations.
Subject Areas: Electric vehicle charging station; Distribution grid; Convexification;
Protective devices upgrade.
Availability: Cui, Q., Weng, Y. and Tan, C.W., 2019. Electric Vehicle Charging Station Placement
Method for Urban Areas. IEEE Transactions on Smart Grid.
https://ieeexplore.ieee.org/abstract/document/8673613

53

2.38. Title: Online EV Charging Scheduling with On-Arrival Commitment
Author(s): Alinia, B., Hajiesmaili, M.H. and Crespi, N.
Abstract: The rapid proliferation of electric vehicles has resulted in a drastic increase in
the total energy demand of EVs. Given the limited charging rate capacity of charging
stations and uncertainty of EV arrivals, the aggregate demand might go beyond the
charging station capacity, even with proper scheduling. This paper formulates a social
welfare maximization problem for EV charging scheduling with charging capacity constraint.
Even though the underlying problem is linear, it is difficult to tackle since the input
to the problem, i.e., the charging profile of EVs, reveals in online fashion. We devise
charging scheduling algorithms that not only work in the online scenario, but also provide
the following two key features: 1) on-arrival commitment; respecting the capacity constraint
may hinder fulfilling charging requirement of the deadline-constrained EVs entirely.
Therefore, committing a guaranteed charging amount upon arrival of each EV is highly
essential; 2) (group)-strategy-proofness as a salient feature to promote EVs to reveal
their true type and do not collude with other EVs. Extensive simulations using real
traces demonstrate the effectiveness of our online scheduling algorithms as compared
to the optimal non-committed offline solution.
Subject Areas: Electric vehicle charging; Charging stations; Aggregates; Scheduling
algorithms; Batteries; Scheduling
Availability: Alinia, B., Hajiesmaili, M.H. and Crespi, N., 2019. Online EV Charging
Scheduling with On-Arrival Commitment. IEEE Transactions on Intelligent Transportation
Systems.
https://ieeexplore.ieee.org/document/8610387

54

2.39. Title: Modelling of Distributed Energy Components and Optimization of Energy
Vector Dispatch within Smart Energy Systems
Author(s): Kong, Q.
Abstract: The smart energy system concept provides an integrated framework for the
adoption of renewable energy resources and novel energy technologies, such as distributed
battery energy storage systems and electric vehicles. In this effort, large-scale transition
towards smart energy systems can significantly reduce the environmental emissions of
energy production, while leveraging the more operation of numerous distributed grid
components to improve upon the energy utility, reliability, and flexibility of existing
power grids. Most importantly, transitioning from fossil fuels to renewable energy resources
provides environmental benefits within both the building and transportation sectors,
which must adapt to address both increasing pressure from international climate change-related
policy-making, as well as to meet the increasing power demands of future generations.
Subject Areas: Energy Components; Renewable Energy Resources; Smart Energy Systems;
Power Grids
Availability: Kong, Q., 2019. Modelling of Distributed Energy Components and Optimization
of Energy Vector Dispatch within Smart Energy Systems. Master’s thesis, University of
Waterloo.
https://uwspace.uwaterloo.ca/handle/10012/14491

55

Chapter 3. Environment
3.1. Title: Decoupling the Value of Leisure Time from Labor Market Returns in Travel
Cost Models
Author(s): Lloyd-Smith, P., Abbott, J.K., Adamowicz, W. and Willard, D.
Abstract: Understanding the extent to which people substitute activities across time
is important for evaluating behavior and welfare impacts in many contexts, including
assessing the damages caused by oil spills and climate change impacts. We implement
a flexible, individualized approach to measuring how people value their leisure time.
We incorporate these heterogeneous values into a structural demand model that explicitly
focuses on intertemporal substitution and incorporates time constraints on behavior.
The model is estimated using data on recreation demand for for-hire offshore fishing
trips in the US Gulf of Mexico. We find that respondents value their leisure time heterogeneously
and substantially differently from their implied wage rate. We also find that accounting
for this heterogeneity has significant impacts on estimated welfare measures for policies
with large intertemporal substitution effects. These findings raise concerns with the
common practice of solely relying on labor market information to value people’s leisure
time.
Subject Areas: Intertemporal Substitution; Value of Time; Demand System
Availability: Lloyd-Smith, P., Abbott, J.K., Adamowicz, W. and Willard, D., 2019. Decoupling
the Value of Leisure Time from Labor Market Returns in Travel Cost Models. Journal of the
Association of Environmental and Resource Economists, 6(2), pp.215-242.
https://www.journals.uchicago.edu/doi/abs/10.1086/701760

56

3.2. Title: Developing Commute Optimization System to Minimize Negative Environmental
Impacts and Time of Business Commuters
Author(s): Abdallah, M., Tawfik, A.M., Monghasemi, S., Clevenger, C.M. and Adame,
B.A.
Abstract: The objective of this research is to develop a novel and innovative system,
called Business+ Commute Optimization System (B+COS) that is capable of identifying
the optimal selection of individualized commute alternatives of employees in a business
to minimize their greenhouse gas (GHG) emissions, air pollution, and commute time.
B+COS is designed to identify the optimal travel behavior for each commuter (e.g.,
drive car, carpool, use public transit, bike or walk) while maintaining convenience and
incentivizing commuters using monetary incentives. The system consists of a geographical
information system (GIS) and a multi-objective optimization model. The GIS is designed
to measure and quantify business commute attributes such as emissions, commute cost,
and time of each commute option. The multi-objective optimization model is designed
to generate optimal trade-offs among two optimization objectives (1) minimizing equivalent
social cost of GHG emissions and air pollution, and (2) minimizing total commute time
of business commuters. Performance of the system is evaluated and verified using a
case study of 21 commuters. Results show the capabilities of the new system in identifying
Pareto-optimal solutions of the two optimization objectives for various tolerances of
commute time increase ranging from 5 to 25min. The promising results highlight the
effectiveness of such an innovative system to minimize transportation-related emissions
and commute time for businesses.
Subject Areas: Incentivized emission reduction; Selection of commute alternatives;
Multi-objective optimization; Minimizing commute time
Availability: Abdallah, M., Tawfik, A.M., Monghasemi, S., Clevenger, C.M. and Adame,
B.A., 2019. Developing Commute Optimization System to Minimize Negative Environmental
Impacts and Time of Business Commuters. International Journal of Sustainable Transportation,
pp.1-19.
https://www.tandfonline.com/doi/abs/10.1080/15568318.2018.1531184

57

3.3. Title: Evaluating the Potential Environmental Impacts of Connected and Automated
Vehicles
Author(s): Gawron, J.
Abstract: Although recent studies of connected and automated vehicles (CAVs) have
begun to explore the potential energy and greenhouse gas (GHG) emission impacts
from an operational perspective, little is known about how the full life cycle of the vehicle
will be impacted. We report the results of a life cycle assessment (LCA) of Level 4 CAV
sensing and computing subsystems integrated into internal combustion engine vehicle
(ICEV) and battery electric vehicle (BEV) platforms. The results indicate that CAV subsystems
could increase vehicle primary energy use and GHG emissions by 3–20% due to increases
in power consumption, weight, drag, and data transmission. However, when potential
operational effects of CAVs are included (e.g., eco-driving, platooning, and intersection
connectivity), the net result is up to a 9% reduction in energy and GHG emissions in
the base case. Overall, this study highlights opportunities where CAVs can improve net
energy and environmental performance.
Subject Areas: Autonomous Vehicle; Life Cycle Assessment; Greenhouse Gas Emissions;
Primary Energy
Availability: Gawron, J., 2019. Evaluating the Potential Environmental Impacts of Connected
and Automated Vehicles. Doctoral dissertation, University of Michigan.
https://deepblue.lib.umich.edu/handle/2027.42/148652

58

3.4. Title: Material Efficiency Strategies to Reducing Greenhouse Gas Emissions Associated
with Buildings, Vehicles, and Electronics — A Review
Author(s): Hertwich, E.G., Ali, S., Ciacci, L., Fishman, T., Heeren, N., Masanet, E., Asghari,
F.N., Olivetti, E., Pauliuk, S., Tu, Q. and Wolfram, P.
Abstract: As one quarter of global energy use serves the production of materials, the
more efficient use of these materials presents a significant opportunity for the mitigation
of greenhouse gas (GHG) emissions. With the renewed interest of policy makers in
the circular economy, material efficiency (ME) strategies such as light-weighting and
downsizing of and lifetime extension for products, reuse and recycling of materials,
and appropriate material choice are being promoted. Yet, the emissions savings from
ME remain poorly understood, owing in part to the multitude of material uses and
diversity of circumstances and in part to a lack of analytical effort. We have reviewed
emissions reductions from ME strategies applied to buildings, cars, and electronics. We
find that there can be a systematic trade-off between material use in the production
of buildings, vehicles, and appliances and energy use in their operation, requiring a
careful life cycle assessment of ME strategies. We find that the largest potential emission
reductions quantified in the literature result from more intensive use of and lifetime
extension for buildings and the light-weighting and reduced size of vehicles. Replacing
metals and concrete with timber in construction can result in significant GHG benefits,
but trade-offs and limitations to the potential supply of timber need to be recognized.
Repair and remanufacturing of products can also result in emission reductions, which
have been quantified only on a case-by-case basis and are difficult to generalize. The
recovery of steel, aluminum, and copper from building demolition waste and the end-of-life
vehicles and appliances already results in the recycling of base metals, which achieves
significant emission reductions. Higher collection rates, sorting efficiencies, and the
alloy-specific sorting of metals to preserve the function of alloying elements while avoiding
the contamination of base metals are important steps to further reduce emissions.
Subject Areas: Greenhouse Gas Emissions; Global Energy; Material Efficiency Strategies;
Life Cycle Assessment
Availability: Hertwich, E.G., Ali, S., Ciacci, L., Fishman, T., Heeren, N., Masanet, E.,
Asghari, F.N., Olivetti, E., Pauliuk, S., Tu, Q. and Wolfram, P., 2019. Material Efficiency
Strategies to Reducing Greenhouse Gas Emissions Associated with Buildings, Vehicles, and
Electronics — A Review. Environmental Research Letters, 14(4), p.043004.
https://iopscience.iop.org/article/10.1088/1748-9326/ab0fe3/meta

59

3.5. Title: Winter is Finally Gone: How We’re Getting Our Cars Shining Again
Author(s): Anthony, C.
Abstract: Blog
Subject Areas: Environment; Average car age; Temperature
Availability: Anthony, C., 2019. Winter is Finally Gone: How We’re Getting Our Cars Shining
Again. Automoblog.net
https://www.automoblog.net/2019/05/28/turtle-wax-review/

60

Chapter 4. Health
4.1. Title: Integrating Multiple Transportation Modes into Measures of Spatial Food Accessibility
Author(s): Zhang, J. and Mao, L.
Abstract: Introduction: People can access to healthy food via different modes of transportation,
such as traveling by car, transit, bicycle and foot. We categorize current measures of
food accessibility under an origin-destination-mode framework and find that few of
them integrate multiple travel modes. As a result, these measures can bias the identification
of truly low-access areas.
Method: To fill this gap, we propose two new measures that integrate sub-populations
of various travel modes, and estimate the overall food accessibility of a whole population.
Taking Florida, USA, as a study area, we illustrate our measures with actual multiple
mode commuting data from the U.S census transportation planning products (CTPP).
We then compare the results to those from conventional single-modal measures.
Results The proposed multiple-mode measures tend to estimate a larger population
with low accessibility and fewer accessible supermarkets for a census tract, as compared
to single-mode measures. The incorporation of multiple travel modes into food accessibility
measures also narrows the disparities between urban and rural areas, which are indicated
by conventional measures.
Conclusions: By considering modal-split subpopulations, our measures offer a more
realistic representation of local people’s travel for grocery shopping, and thus a better
identification of populations with low food access. The finer modeling scale at a subpopulation
level provides health and urban planners more flexibility in policy design, in that interventions
can be tailored to not only a neighborhood but also a specific subpopulation within it.
Such knowledge could improve the cost-effectiveness of food intervention programs.
Subject Areas: Food access; Spatial measure; Travel modes; Commuting pattern; Health
geography
Availability: Zhang, J. and Mao, L., 2019. Integrating Multiple Transportation Modes into
Measures of Spatial Food Accessibility. Journal of Transport & Health, 13, pp.1-11.
https://www.sciencedirect.com/science/article/pii/S2214140518305413

61

4.2. Title: Active Transport, Not Device Use, Associates with Self-Reported School Week
Physical Activity in Adolescents
Author(s): Burns, R.D., Pfledderer, C.D. and Brusseau, T.A.
Abstract: The purpose of this study was to examine the relationships among active
transport, electronic device-use, and self-reported school week moderate-to-vigorous
physical activity (MVPA) in a sample of adolescents. The sample consisted of 1445 adolescents
enrolled in the Family Life, Activity, Sun, Health, and Eating study. A panel research
organization invited panel members balanced to the US population on sex, census division,
household income and size, and race/ethnicity. Web-based surveys were administered
to each selected adolescent. Adolescents answered questions pertaining to out-of-school
electronic device-use and active transport to and from school. Predicted weekly minutes
of MVPA were calculated from the Youth Activity Profile. The outcome variable was
predicted school week MVPA (in minutes). The predictive utility of device-use and
active transport variables on self-reported school week MVPA were examined using
weighted multiple linear regression models. After adjusting for age, sex, and BMI, active
transport to school (b=12.32, 95% CI [9.72–14.93], p<0.001) and from school (b=7.18,
95% CI [4.79–5.57], p<0.001) were significantly associated with self-reported school
week MVPA. No device-use variables were significantly associated with school week
MVPA. Active transport to and from school may have an impact on school week MVPA
in adolescents.
Subject Areas: Adolescent health; Behavioral science; Epidemiology; Physical activity;
School health
Availability: Burns, R.D., Pfledderer, C.D. and Brusseau, T.A., 2019. Active Transport,
Not Device Use, Associates with Self-Reported School Week Physical Activity in Adolescents.
Behavioral Sciences, 9(3), p.32.
https://www.mdpi.com/2076-328X/9/3/32

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4.3. Title: A Time-Based Objective Measure of Exposure to the Food Environment
Author(s): Scully, J.Y., Moudon, A.V., Hurvitz, P.M., Aggarwal, A. and Drewnowski, A.
Abstract: Exposure to food environments has mainly been limited to counting food
outlets near participants’ homes. This study considers food environment exposures in
time and space using global positioning systems (GPS) records and fast food restaurants
(FFRs) as the environment of interest. Data came from 412 participants (median participant
age of 45) in the Seattle Obesity Study II who completed a survey, wore GPS receivers,
and filled out travel logs for seven days. FFR locations were obtained from Public Health
Seattle King County and geocoded. Exposure was conceptualized as contact between
stressors (FFRs) and receptors (participants’ mobility records from GPS data) using four
proximities: 21 m, 100 m, 500 m, and 1/2 mile. Measures included count of proximal
FFRs, time duration in proximity to ≥1 FFR, and time duration in proximity to FFRs
weighted by FFR counts. Self-reported exposures (FFR visits) were excluded from these
measures. Logistic regressions tested associations between one or more reported FFR
visits and the three exposure measures at the four proximities. Time spent in proximity
to an FFR was associated with significantly higher odds of FFR visits at all proximities.
Weighted duration also showed positive associations with FFR visits at 21-m and 100-m
proximities. FFR counts were not associated with FFR visits. Duration of exposure helps
measure the relationship between the food environment, mobility patterns, and health
behaviors. The stronger associations between exposure and outcome found at closer
proximities <100 m) need further research.
Subject Areas: Fast food; Spatio-temporal exposure; Mobility patterns; GPS; Selective
mobility bias
Availability: Scully, J.Y., Moudon, A.V., Hurvitz, P.M., Aggarwal, A. and Drewnowski,
A., 2019. A Time-Based Objective Measure of Exposure to the Food Environment. International
journal of environmental research and public health, 16(7), p.1180.
https://www.mdpi.com/1660-4601/16/7/1180

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4.4. Title: Human Behavior Modeling and Calibration in Epidemic Simulations
Author(s): Singh, M.
Abstract: Human behavior plays an important role in infectious disease epidemics. The
choice of preventive actions taken by individuals can completely change the epidemic
outcome. Computational epidemiologists usually employ large-scale agent-based simulations
of human populations to study disease outbreaks and assess intervention strategies.
Such simulations rarely take into account the decision-making process of human beings
when it comes to preventive behaviors. Absence of realistic agent behavior can undermine
the reliability of insights generated by such simulations and might make them ill-suited
for informing public health policies. In this thesis, we address this problem by developing
a methodology to create and calibrate an agent decision-making model for a large multi-agent
simulation, in a data driven way. Our method optimizes a cost vector associated with
the various behaviors to match the behavior distributions observed in a detailed survey
of human behaviors during influenza outbreaks. Our approach is a data-driven way of
incorporating decision making for agents in large-scale epidemic simulations.
Subject Areas: Health policies; Epidemic Simulations; Human Behavior
Availability: Singh, M., 2019. Human Behavior Modeling and Calibration in Epidemic Simulations.
M.S. Thesis, Virginia Tech.
https://vtechworks.lib.vt.edu/handle/10919/87050

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Chapter 5. Policy and Mobility
5.1. Title: Smart Cities and Mobility: Does the Smartness of Australian Cities Lead to
Sustainable Commuting Patterns?
Author(s): Yigitcanlar, T. and Kamruzzaman, M.
Abstract: Smart cities have become a popular concept because they have the potential
to create a sustainable and livable urban future. Smart mobility forms an integral part
of the smart city agenda. This paper investigates “smart mobility” from the angle of
sustainable commuting practices in the context of smart cities. This paper studies a
multivariate multiple regression model within a panel data framework and examines
whether increasing access to broadband Internet connections leads to the choice of a
sustainable commuting mode in Australian local government areas. In this case, access
to the Internet is used as a proxy for determining urban smartness, and the use of different
modes of transport including working at home is used to investigate sustainability in
commuting behavior. The findings show that an increasing access to broadband Internet
reduces the level of working from home, public transport use, and active transport use,
but increases the use of private vehicles, perhaps to overcome the fragmentation of
work activities the Internet creates. How to overcome the need for car-based travel
for fragmented work activities while increasing smartness through the provisioning
of broadband access should be a key smart city agenda for Australia to make its cities
more sustainable.
Subject Areas: Smart Cities; Smart Mobility; Sustainable Commuting; Sustainable Urban
Development; Australian Cities
Availability: Yigitcanlar, T. and Kamruzzaman, M., 2019. Smart Cities and Mobility: Does
the Smartness of Australian Cities Lead to Sustainable Commuting Patterns? Journal of Urban
Technology, 26(2), pp.21-46.
https://www.tandfonline.com/doi/abs/10.1080/10630732.2018.1476794

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5.2. Title: Evaluation of The Effects of Trends on Vehicle Concepts based on a Forecast
of Travel Demand
Author(s): Peters, P.L., Demuth, R. and Schramm, D.
Abstract: Today, vehicle concepts are developed on the basis of technical design. For
the respective positioning of the concept, the customer requirements of the relevant
target group determined by market research and the competitive comparison are decisive.
Technological trends (FAD) and business model innovations (ODM) have the potential
to change the mobility behavior of users and thus the characteristics of vehicle concepts.
Subject Areas: Mobility behavior; Vehicle concepts; Fully automated driving (FAD);
Dema
Availability: Peters, P.L., Demuth, R. and Schramm, D., 2019. Evaluation of The Effects
of Trends on Vehicle Concepts based on a Forecast of Travel Demand. In 19. Internationales
Stuttgarter Symposium (pp. 540-556). Springer Vieweg, Wiesbaden.
https://doi.org/10.1007/978-3-658-25939-6 46

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5.3. Title: Estimating the Social Cost of Congestion Using the Bottleneck Model
Author(s): Kim, J.
Abstract: This paper uses the bottleneck model of Vickrey (1969) to empirically measure
the social cost of traffic congestion in the US. Using a detailed trip-level data, we estimate
extra travel time over and above hypothetical free-flow travel time, which we call “queuing
time”, for each average commute trip. The estimated individual queuing time implies
that the annual cost of congestion borne by all US commuters is about 29 billion dollars.
We find that a higher level of congestion in a city may be attributed to a smaller per
capita road stock in the city. This paper also empirically quantifies a toll that depends
both on the commuter’s arrival time and trip distance.
Subject Areas: Mobility; Traffic congestion; Bottleneck; Economic inefficiency; Causal
effect
Availability: Kim, J., 2019. Estimating the Social Cost of Congestion Using the Bottleneck
Model. California State University, Long Beach.
https://www.sciencedirect.com/science/article/abs/pii/S2212012218301151

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5.4. Title: Improving Structural Models of Congestion
Author(s): Hall, J.D.
Abstract: We need structural models of traffic congestion to answer a wide variety of
questions, but the standard models fail to match the data on travel times across the
day. I establish the nature and magnitude of the problem, and show its source lies in
how we model traveler preferences, not in the specifics of the congestion technology.
The poor fit of the models suggests that we are abstracting away from features with a
first-order impact on model predictions, which limits our ability to use these models
to evaluate counterfactuals quantitatively and – when travelers are heterogeneous –
qualitatively as well. I explore several ways of improving the fit of these models, concluding
with recommendations for tractable and intuitive ways of doing so.
Subject Areas: Structural model; Congestion; Model fit; Calibration; Dynamic; Bottleneck
model; Traffic
Availability: Hall, J.D., 2019. Improving Structural Models of Congestion. University of
Toronto.
https://ideas.repec.org/p/tor/tecipa/tecipa-590.html

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5.5. Title: Role of Flying Cars in Sustainable Mobility
Author(s): Kasliwal, A., Furbush, N.J., Gawron, J.H., McBride, J.R., Wallington, T.J., De
Kleine, R.D., Kim, H.C. and Keoleian, G.A.
Abstract: Interest and investment in electric vertical takeoff and landing aircraft (VTOLs),
commonly known as flying cars, have grown significantly. However, their sustainability
implications are unclear. We report a physics-based analysis of primary energy and
greenhouse gas (GHG) emissions of VTOLs vs. ground-based cars. Tilt-rotor/duct/wing
VTOLs are efficient when cruising but consume substantial energy for takeoff and climb;
hence, their burdens depend critically on trip distance. For our base case, traveling
100km (point-to-point) with one pilot in a VTOL results in well-to-wing/wheel GHG
emissions that are 35% lower but 28% higher than a one-occupant internal combustion
engine vehicle (ICEV) and battery electric vehicle (BEV), respectively. Comparing fully
loaded VTOLs (three passengers) with ground-based cars with an average occupancy
of 1.54, VTOL GHG emissions per passenger-kilometer are 52% lower than ICEVs and
6% lower than BEVs. VTOLs offer fast, predictable transportation and could have a
niche role in sustainable mobility.
Subject Areas: Mobility; Vertical takeoff and landing aircraft (VTOLs); Greenhouse Gas
(GHG) emissions
Availability: Kasliwal, A., Furbush, N.J., Gawron, J.H., McBride, J.R., Wallington, T.J.,
De Kleine, R.D., Kim, H.C. and Keoleian, G.A., 2019. Role of Flying Cars in Sustainable
Mobility. Nature communications, 10(1), p.1555.
https://www.nature.com/articles/s41467-019-09426-0

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5.6. Title: Would Uber Help to Reduce Traffic Congestion?
Author(s): Zheng, Q.
Abstract: This research explores the effects of Uber entry on New York City’s traffic.
The two major questions I am trying to answer that might be of vital importance to
transportation authorities are 1) does Uber substitute public transits? 2) does an introduce
of Uber slow down average travel speed? After Uber was first introduced in year 2009,
there are continuous debates on distinguishing its impact on traffic (Rayle et al., 2014;
Li et al., 2016; Schaller, 2018; Castiglione et al., 2018). Considering that Uber is relatively
new, relevant traffic data such as congestion indices are in general unavailable, which
appears as a common limitation in previous analysis. In this research, I use monthly
number of public transit trips in NYC to estimate a substitution effect of Uber on public
transit ridership. To measure its direct impact on road traffic, I use Average Travel Speed
generated from NYC yellow cab trips as a proxy for the citywide Average Travel Speed.
A further application of monthly number of vehicles crossing nine major bridges and
tunnels is used to capture a trend of traffic volume in NYC. The final dataset comprises
133 observations range from January 2008 to January 2018. Perceiving that Uber was
introduced to NYC on May 2011 and was suspended on issuance of new vehicle licenses
starting from August 2018, I use a regression discontinuity (RD) design and set the two
events as cutoff points in the model. Additional use of Google Trend helps to more
precisely determine the cutoff point. The regression results suggest that after Uber was
introduced to NYC, 1) number of public transit trips has increased by about 3%; 2) average
travel speed has decreased by 127 mph; and 3) traffic volume was not affected.
Subject Areas: Uber; Public transit; Traffic Congestion; Average travel speed
Availability: Zheng, Q., 2019. Would Uber Help to Reduce Traffic Congestion? B.S. Thesis,
Skidmore College.
https://creativematter.skidmore.edu/econ studt schol/129/

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5.7. Title: Dynamic Shared Autonomous Taxi System Considering On-Time Arrival Reliability
Author(s): Liu, Z., Miwa, T., Zeng, W., Bell, M.G. and Morikawa, T.
Abstract: Dynamic shared autonomous taxi (SAT) systems are regarded as a promising
means of improving travel flexibility. With no human drivers, SATs urgently require
precise traffic information in order to plan accurate paths independently; in addition,
on-time arrival is an essential service quality in SAT systems. In this study, taxis are
assumed to be replaced with ride-sharing autonomous vehicles. To improve the probability
of on-time arrival, the reliable path concept and collected travel time information are
used to facilitate path finding for SATs, and the potential benefits are examined. Two
simulation scenarios – one based on historical traffic information and the other based
on real-time traffic information – are executed to evaluate the information’s usefulness
in reliable path finding. In simulation results, reliable path scenarios showed a higher
on-time arrival ratio than shortest path scenarios, in which the shortest path algorithm
is used in path finding for SATs, and the historical information-based scenarios showed
a higher on-time arrival ratio than the real-time information-based scenarios. A system-beneficial
path finding method is proposed and is verified to be effective for mitigating road network
congestion.
Subject Areas: Shared autonomous taxi system; On-time arrival reliability; Historical
travel time information; System-beneficial path; Travel time
Availability: Liu, Z., Miwa, T., Zeng, W., Bell, M.G. and Morikawa, T., 2019. Dynamic
Shared Autonomous Taxi System Considering On-Time Arrival Reliability. Transportation
Research Part C: Emerging Technologies, 103, pp.281-297.
https://www.sciencedirect.com/science/article/pii/S0968090X18306922

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Chapter 6. Special Population Groups
6.1. Title: Comparing Immigrant Travel Assimilation among Racial/Ethnic Groups
Author(s): Hu, L., Klein, N.J. and Smart, M.J.
Abstract: This research investigates differences in travel assimilation among immigrants
of different races/ethnicities in the United States. Using the 2017 National Household
Travel Survey (NHTS) data, the authors compare commute distance and commute mode
between immigrants and native-born Americans in three groups: whites, Hispanics,
and Asians. The results show that when the authors consider racial/ethnic groups separately,
the initial difference in commuting travel between immigrants and native-born Americans
in the same racial/ethnic group is smaller, and the time it takes to reduce the difference
is shorter, compared with the results when the authors consider all native-born Americans
together. Therefore, the authors suggest that transportation policymakers consider racial/ethnic
differences when providing services for immigrants.
Subject Areas: Commuting; Ethnic groups; Immigrants; Mode choice; Race; Trip length
Availability: Hu, L., Klein, N.J. and Smart, M.J., 2019. Comparing Immigrant Travel Assimilation
among Racial/Ethnic Groups. Transportation Research Board 98th Annual Meeting.
https://trid.trb.org/view/1572993

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6.2. Title: Aging in Activity Spaces: How Does Individual Accessibility Compare across
Age Cohorts?
Author(s): Wood, B.S. and Horner, M.W.
Abstract: The proportion of individuals age sixty-five and over is growing at an astronomical
rate in the United States, and some estimate that this demographic age group will double
by the year 2025. Older adults and adults nearing retirement age tend to reside in suburban
neighborhoods and rely heavily on personal vehicles. This study uses travel diary data
on automobile trips to construct activity spaces to explore whether or not travel patterns
across age groups result in differential access to particular goods and services in the
Orlando Metropolitan Statistical Area (MSA). Using an approach based on time geographic
density estimation, this research identifies activity spaces across different age cohorts to
identify differences in the automobility of different age groups. Results indicate that
the geographic dispersion of activities with the Orlando MSA currently favors younger
adults. Adults age fifty to sixty-four had the lowest accessibility scores compared to
other age cohorts. If this preretirement group has poor access now, holding other effects
constant, their access might only get worse as they get older and stop commuting. Transportation
is an important consideration in planning for aging populations, and analyzing differences
in how older adults travel compared to their younger counterparts can offer insight
into the diverse needs of this group.
Subject Areas: Accessibility; Aging Populations; Mobility; Time Geography; Transportation.
Availability: Wood, B.S. and Horner, M.W., 2019. Aging in Activity Spaces: How Does
Individual Accessibility Compare across Age Cohorts? The Professional Geographer, 71(1),
pp.1-14.
https://www.tandfonline.com/doi/abs/10.1080/00330124.2018.1518718

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6.3. Title: The Effects of Driver Licensing Laws on Immigrant Travel
Author(s): Barajas, J.M.
Abstract: Car use is critical to improving access to regional opportunities, especially
for low-wage immigrants. But many states have restricted the ability of undocumented
immigrants to obtain drivers licenses, making it potentially difficult for them to improve
their economic standing. The effects of these laws have been tested for their association
with traffic safety, but not on mode choice itself. Using the 2017 National Household
Travel Survey, the author fits a series of logistic regression models to test the influence
of permissive immigrant driver licensing on mode choice decisions. The author finds
that immigrants in states with permissive licensing laws are more likely to drive in
carpools but not necessarily to drive alone. The results suggest permissive licensing has
positive impacts for all immigrants, in addition to positive safety externalities documented
in the literature.
Subject Areas: Automobile travel; Driver licensing; Immigrants; Travel behavior
Availability: Barajas, J.M., 2019. The Effects of Driver Licensing Laws on Immigrant Travel.
Transportation Research Board 98th Annual Meeting.
https://trid.trb.org/view/1573175

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6.4. Title: Use of Ride-Hailing Services among Older Adults in the United States
Author(s): Mitra, S.K., Bae, Y. and Ritchie, S.G.
Abstract: This paper presents an analysis of data from the 2017 National Household
Travel Survey to examine the factors influencing the adoption and the frequency of use
of on-demand ride-hailing services such as Uber and Lyft among older adults. Using a
zero-inflated negative binomial model (ZINB), the results indicate that the determinants
of adoption of on-demand ride-hailing services (users versus non-users) are different
from the determinants of the frequency of use of these services among older adult users.
Seniors that are younger, living alone, urban dwelling, more highly educated, more
affluent, or male with a medical condition that results in asking others for rides, are
more likely to be adopters of ride-hailing services. However, seniors who are middle
elderly, less educated, or are carless older adults, are more likely to be frequent users
of on-demand ride-hailing services as long as they adopt these services. In addition,
smartphone possession plays an important role in the adoption behavior of on-demand
ride-hailing services among older adults. Results of bivariate analysis showed that older
adult ride-hailing users make more transit trips than their non-user counterparts, suggesting
that ride-hailing services have the potential to serve as a complementary form of public
transportation for older adults. The findings of this research will help ride-hailing operators
in identifying potential market segments of their services and in developing campaign
strategies for potential adopters.
Subject Areas: Older Adults; Seniors; Ride-Hailing; Zero-Inflated Negative Binomial
Model (ZINB)
Availability: Mitra, S.K., Bae, Y. and Ritchie, S.G., 2019. Use of Ride-Hailing Services among
Older Adults in the United States. Transportation Research Record, p.0361198119835511.
https://journals.sagepub.com/doi/abs/10.1177/0361198119835511

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6.5. Title: Exploring Patterns of Heterogeneity in Activity-Travel Behaviors of Older People
Author(s): Hutchinson, J., da Silva, D.C., Dias, F.F., Bhat, C.R., Khoeini, S., Pendyala,
R.M. and Lam, W.H.
Abstract: The travel behavior and mobility needs of older people have been topics of
much interest to transport planners and policy makers for a number of reasons. The
desire to provide mobility to older people even as their capabilities diminish, and the
need to recognize their vulnerability when they do attempt to navigate the transportation
network on their own, has motivated a rich stream of research dedicated to studying
their activity-travel behavior. Many studies in the past, and most travel models to date,
consider older people as a single market segment of 65 years of age or over. To better
understand differences among various subgroups of the older population, this paper
presents a detailed analysis and comparison of older population subgroups using data
derived from the 2017 National Household Travel Survey (NHTS) of the United States.
The paper includes a review of earlier studies on the activity-travel patterns of the older
segment of our population, and a detailed descriptive statistical analysis on technology
and time use patterns with a view to identify how these behaviors evolve as people
age. In addition, the paper presents three modeling efforts to understand the differential
effects of age on the action space, the use of transportation modes, and the activity participation
and time allocation behavior of older people. The analysis suggests that there is considerable
heterogeneity among older people, which calls for more targeted policy interventions
and a more disaggregate treatment of older population subgroups in travel models.
The analysis reveals that an individual’s medical condition and need for use of a medical
device are significant explanatory variables affecting all three of the choice dimensions
modeled in this study. This calls for the development of policies and mobility options
that serve the disabled regardless of age, while recognizing the inherent correlation
between age and disability status.
Subject Areas: Travel of Older People; Heterogeneity; Action Space; Time Use; Technology
Use; Activity travel Engagement
Availability: Hutchinson, J., da Silva, D.C., Dias, F.F., Bhat, C.R., Khoeini, S., Pendyala,
R.M. and Lam, W.H., 2019. Exploring Patterns of Heterogeneity in Activity-Travel Behaviors
of Older People. University of Texas.
http://www.caee.utexas.edu/prof/bhat/ABSTRACTS/OlderPeople.pdf

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6.6. Title: A Hierarchical Game Approach on Real-Time Navigation Scheduling of Agricultural
Harvesters
Author(s): Si, H., Li, Y., Sun, C., Qiao, H. and Hu, X.
Abstract: A navigation scheduling framework for agricultural harvesters (AHs) is proposed,
which takes the impacts from both farmer’s demands and dispatch system (DS) into
consideration. Farmer’s demands with AHs and the DS are linked in this framework.
It benefits farmers and AHs owners by attracting AHs to schedule at rush hours and
saving the time of AHs owners with real-time navigation. A hierarchical game approach
is proposed based on the formulated framework to effectively navigate AHs to farms
that need to harvest (FNHs). And a non-cooperative game is proposed to model the
competition between FNHS at the upper level of the hierarchical game. At the lower
level, multiple evolutionary games are formulated based on the pricing strategies obtained
from the non-cooperative game to evolve AHs’ strategies in choosing FNHs. The simulation
results show that the proposed navigation scheduling method is effective in improving
both the reliability of the DS and economic profits of AHs owners.
Subject Areas: Agricultural harvester; Navigation scheduling; Hierarchical game
Availability: Si, H., Li, Y., Sun, C., Qiao, H. and Hu, X., 2019. A Hierarchical Game Approach
on Real-Time Navigation Scheduling of Agricultural Harvesters. Computers and Electronics
in Agriculture, 162, pp.112-118.
https://www.sciencedirect.com/science/article/pii/S016816991831278X

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6.7. Title: Spatio-temporal Travel Patterns Of Elderly People –A Comparative Study Based
On Buses Usage in Qingdao, China
Author(s): Shao, F., Sui, Y., Yu, X. and Sun, R.
Abstract: With the increasing demographic shift towards a larger population of elderly,
it is essential for policy makers and planners to have an understanding of travel characteristics
of elderly and their difference with younger counterparts. Existing studies emphasize
elderly’s travel mode, travel frequency, travel distance, travel purpose and affecting
factors, however, very few aim at comparing the spatio-temporal characteristics difference
between weekday and weekend. In this paper, based on GPS data and Smart Card data
of buses in Qingdao, the two cohorts’ basic spatio-temporal travel patterns in aspects
of travel distance, travel frequency and travel start time in weekday and weekend are
compared. In addition, directed weighted networks of elderly’s trips and younger people’s
trips in weekday and weekend are constructed for analyzing spatial characteristics.
Results show that although the number of elderly experiences a reduction on weekend
their travel frequency and travel distance show growth on weekend. In contrast with
younger people, larger geospatial expansion of elderly’s travel on weekend is observed.
Elderly are found to prefer traveling in areas with high elderly people’s residential density.
Our study provides a deeper and nuanced understanding of elderly’s spatio-temporal
travel characteristics difference between weekday and weekend, so as to support better
traffic policy making and the promotion on age-friendly public transport service.
Subject Areas: Elderly; Spatio-temporal travel characteristics; Weekday and weekend;
Trip network
Availability: Shao, F., Sui, Y., Yu, X. and Sun, R., 2019. Spatio-temporal Travel Patterns Of
Elderly People –A Comparative Study Based On Buses Usage in Qingdao, China. Journal of
Transport Geography, 76, pp.178-190.
https://www.sciencedirect.com/science/article/abs/pii/S0966692318306963

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6.8. Title: The Association of Commuting Time and Wages for American Workers with
Disabilities
Author(s): Brucker, D.L. and Rollins, N.G.
Abstract: Background: Transportation research suggests that persons who travel further
to work earn higher hourly wages.
Objective:To explore whether workers with disabilities who have longer commute times
earn higher wages.
Methods: Data from the 2016 American Community Survey is used to examine commuting
time and wages for workers with and without disabilities, controlling for individual
characteristics.
Results: Travel time to work is quite similar between workers with and without disabilities,
but workers with disabilities who travel similar amounts of time as workers without
disabilities earn substantially less per hour, even when controlling for individual characteristics.
Conclusions: Commuting time does not contribute to the wage gap between workers
with and without disabilities.
Subject Areas: Transportation; Commuting; Wage; American community survey
Availability: Brucker, D.L. and Rollins, N.G., 2019. The Association of Commuting Time
and Wages for American Workers with Disabilities. Journal of Vocational Rehabilitation,
50(1), pp.13-21.
https://content.iospress.com/articles/journal-of-vocational-rehabilitation/jvr180984

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6.9. Title: The Poverty of the Carless: Toward Universal Auto Access
Author(s): King, D.A., Smart, M.J. and Manville, M.
Abstract: We document the falling socioeconomic status of American households without
private vehicles and the continuing financial burden that cars present for low-income
households that own them. We tie both these trends to the auto-orientation of America’s
built environment, which forces people to either spend heavily on cars or risk being
locked out of the economy. We first show that vehicle access remains difficult for low-income
households and vehicle operating costs remain high and volatile. Using data from the
Panel Study of Income Dynamics, Survey of Consumer Finances, and Census Public
Use Microdata, we then show that in the last fifty years households without vehicles
have lost income, both in absolute terms and relative to households with vehicles. We
link these trends to the built environment by examining the fortunes of carless households
in New York City, and particularly in Manhattan. Most of New York’s built environment
did not change to accommodate cars, and in New York the fortunes of the carless did
not fall. Our results suggest that planners should see vehicles, in most of the United
States, as essential infrastructure, and work to close gaps in vehicle access.
Subject Areas: Elderly; Urban Form; Urban History; Transportation Poverty; Income
Inequality
Availability: King, D.A., Smart, M.J. and Manville, M., 2019. The Poverty of the Carless:
Toward Universal Auto Access. Journal of Planning Education and Research, p.0739456X18823252.
https://journals.sagepub.com/doi/abs/10.1177/0739456X18823252

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6.10. Title: Synchronization of Home Departure and Arrival Times in Dual Earner Households
with Children: Panel Regression Model of Time Gaps
Author(s): Han, B. and Timmermans, H.
Abstract: Organizing schedules and allocating time to different activities is always a
challenge in dual-earner households, especially when they have children. Parents may
need to link their schedule to those of their children to allow them escorting their children
to school or to take care or be with their children at home. This paper reports the results
of an analysis of the degree of synchronization of home departure and arrival times in
dual earner households with children, where the degree of synchronization is defined
as the gap between departure and arrival times of a parent and child. Using activity-travel
diary data of different household members, a random parameters regression model
is estimated to examine differences in time gaps in home departure and arrival times
between parents and children as a function of gender, day of the week, age of the youngest
child, and other socio-demographic characteristics. The results of the analysis provide
insight into factors influencing the degree of synchronization and coordination of double
activity-travel scheduling decisions in households with children. Findings indicate that
gender, number of children in the household, age of the youngest child, travel within or
outside peak hours, day of the week, transport mode used for the work commute and
household income level significantly affect time gaps, especially arrival time gaps.
Subject Areas: Home departure and arrival times; Synchronization; Time gap; Dual-earner;
Households; Random parameters panel regression model
Availability: Han, B. and Timmermans, H., 2019. Synchronization of Home Departure
and Arrival Times in Dual Earner Households with Children: Panel Regression Model of Time
Gaps. Journal of Traffic and Transportation Engineering (English Edition).
https://www.sciencedirect.com/science/article/pii/S2095756419300443

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6.11. Title: Five Innovative Ways Cities Are Improving Life for Seniors
Author(s): Oliver, S.
Abstract: Blog
Subject Areas: Seniors; Cities
Availability: Oliver, S., 2019. Five Innovative Ways Cities Are Improving Life for Seniors.
The Wall Street Journal.
https://www.wsj.com/articles/five-innovative-ways-cities-areimproving-life-for-seniors-11558450968

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6.12. Title: What kinds of Vehicles do Americans drive?
Author(s): NA
Abstract: Blog
Subject Areas: Vehicle type; Demographic groups; NHTS
Availability: 2019. What kinds of Vehicles do Americans drive? Engaging-data.com
https://engaging-data.com/vehicles-state/

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6.13. Title: A Resurgence in Urban Living? Trends in Residential Location Patterns of
Young and Older Adults since 2000
Author(s): Blumenberg, E., Brown, A., Ralph, K., Taylor, B.D. and Turley Voulgaris, C.
Abstract: Some have heralded a resurgence of urban living in the U.S., particularly
among young adults. Are Americans abandoning suburbs in favor of more urban lifestyles?
What is the scope and scale of this urban resurgence? We develop a typology of neighborhoods
to analyze the residential location of young and older U.S. adults from 2000 to 2011–15.
Census and national travel survey data reveal that suburban population growth continues
to outpace that in urban neighborhoods. Although young adults are more likely than
older adults to live in urban neighborhoods, recent urban population growth is neither
associated with suburban decline, nor being led by young adults. Significant recent
population growth in the newest, suburban neighborhoods suggests that greenfield
development remains the primary means to increase American housing supply. Shifting
metropolitan growth from the suburban fringe would likely require expanding housing
supply in urban neighborhoods, and bringing urban amenities to established inner-ring
suburbs.
Subject Areas: Urban living; Young adults; Residential location
Availability: Blumenberg, E., Brown, A., Ralph, K., Taylor, B.D. and Turley Voulgaris,
C., 2019. A Resurgence in Urban Living? Trends in Residential Location Patterns of Young
and Older Adults since 2000. Urban Geography, pp.1-23.
https://www.tandfonline.com/doi/abs/10.1080/02723638.2019.1597594

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6.14. Title: University Students’ Transportation Patterns, and the Role of Neighbourhood
Types and Attitudes
Author(s): Nash, S. and Mitra, R.
Abstract: Research on the millennial generation’s travel behaviour is emerging, but
little is known about the socio-demographic, attitudinal and environmental factors that
influence day-to-day trips by these young adults. In this study, data collected from four
universities in Toronto, Canada, was analyzed to explore patterns in transportation
behaviour, or transportation life-styles, of post-secondary students. A latent class analysis
identified five distinct student groups based on a one-day travel diary data and self-reported
long-term travel behaviour, namely: Transit dependent (31%), Active and neighbourhood
oriented (23%), Multi-modal (11%), Occasional driver (12%) and Driver (23%). Two-thirds
of students (65%) predominantly relied on either walking/cycling or transit. Logistic
regression models indicated that a student’s socio-demographic characteristics and
life-course situations might explain their travel behaviour. Neighbourhood type of residence
was an important indicator of a student’s transportation life-style. Strong associations
between travel attitudes, residential location preferences and a student’s transportation
life-style was also observed. Post-secondary students are at an important stage in their
life-course where they begin to form habitual travel behaviour as young adults. Findings
from this study contribute an improved understanding of travel behaviour, which may
also inform planning, policy and service provision relating to transportation, land development
and affordable housing.
Subject Areas: Young adults; Life-course; Transportation life-style; Modality style; Neighbourhood
typology; Mobility biography
Availability: Nash, S. and Mitra, R., 2019. University Students’ Transportation Patterns,
and the Role of Neighbourhood Types and Attitudes. Journal of Transport Geography, 76,
pp.200-211.
https://www.sciencedirect.com/science/article/abs/pii/S0966692318304691

85

6.15. Title: Car Brands with the Youngest Drivers
Author(s): Kamenov, A.
Abstract: Blog
Subject Areas: Young drivers; Driver’s Age; Car Brand; CarMax; NHTS
Availability: Kamenov, A., 2019. Car Brands with the Youngest Drivers. City-Data.com
http://www.city-data.com/blog/6719-car-brands-with-the-youngest-drivers/

86

6.16. Title: African-American Millennials Prefer Cadillac
Author(s): Gazdik, T.
Abstract: Blog
Subject Areas: African-American; Millennials; Full size cars; Demographic group
Availability: Gazdik, T., 2019. African-American Millennials Prefer Cadillac. Media-post.com
https://www.mediapost.com/publications/article/331449/african-americanmillennials-prefer-cadillac.html

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6.17. Title: Despite ‘Car-Free’ Hype, Millennials Drive a Lot
Author(s): Bliss, L.
Abstract: Blog
Subject Areas: Millennials; Driving; Older generations
Availability: Bliss, L., 2019. Despite ‘Car-Free’ Hype, Millennials Drive a Lot. Citylab.
https://www.citylab.com/transportation

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6.18. Title: The Rural Telecommuter Surplus in Southwestern Ontario, Canada
Author(s): Hambly, H. and Lee, J.D.
Abstract: This paper asks the question: what kind of economic benefits do rural telecommuters
experience in Southwestern Ontario? This is a relevant question in Canada where, according
to Statistics Canada (2017) one in 14 people work from home. This paper presents an
overview of the current literature on telecommuting. We estimate the telecommuter
surplus in Southwestern Ontario where the region is currently deploying one of Canada’s
largest publicly-funded ultra-high-speed broadband initiatives known as SouthWest
Integrated Fibre Technology Inc. (SWIFT). The analysis is based on SWIFT residential
and farm surveys (n=3948) conducted in 2017. We find that an average telecommuter’s
surplus in terms of costs saved, including opportunity cost ranges from $8820 to $23964
per annum per telecommuter, depending on the number of days telecommuted per
week for home and primary residence dwelling type. The social net benefits of telecommuting
differ from its private net benefit (the focus of our paper) since the former includes both
positive and negative externalities associated with telecommuting such as reduced
traffic congestion, reduced probability of road accidents, as well as some workers shirking
their duties (a negative impact). We leave this for future work.
Subject Areas: Telecommuting surplus; Teleworking; Economics of telecommuting;
Opportunity cost of commuting
Availability: Hambly, H. and Lee, J.D., 2019. The Rural Telecommuter Surplus in Southwestern
Ontario, Canada. Telecommunications Policy, 43(3), pp.278-286.
https://www.sciencedirect.com/science/article/pii/S0308596118301046

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Chapter 7. Survey, Data Synthesis, and Other Applications
7.1. Title: State of the Practice of Long Distance and Intercity Travel Modeling in US Metropolitan
Planning Organizations and State Departments of Transportation
Author(s): Cordero, F.
Abstract: Long distance and intercity travel represent a small percentage of total trips
in the U.S., yet they represent a large percentage of total VMT. Long distance trips represent
an important travel market with over $317 billion in business-travel and $718 billion in
leisure travel profits in 2017. Metropolitan Planning Organizations (MPOs) and State
Department of Transportation (DOTs) are responsible for developing the Long-Range
Transportation Plan (LRTP) and Statewide Transportation Plan, respectively. Within
these plans, future infrastructure and funding investment is defined based on model
estimation from past, current, and future travel and socio-economic variables. Currently,
the lack of guidance in long distance travel modeling has derived concerns among practitioners
and scholars. Therefore, two national state-of-practice surveys on long distance travel
modeling were conducted among MPOs and State DOTs to gain insight in long distance
travel modeling among these agencies. The purpose of this thesis is to draw recommendation
for future guidance on long distance travel.
Subject Areas: Long distance travel; Intercity travel; Long-Range Transportation Plan
(LRTP); Statewide Transportation Plan; State-of-practice Surveys
Availability: Cordero, F., 2019. State of the Practice of Long Distance and Intercity Travel
Modeling in US Metropolitan Planning Organizations and State Departments of Transportation.
University of Auburn, M.S. Thesis.
https://etd.auburn.edu/handle/10415/6689

90

7.2. Title: Recommended Mounting Heights for Freestanding On-Premise Signs
Author(s): Garvey, P.M. and Klena, M.J.
Abstract: Freestanding on-premise signs are commercial signs that are not attached
to buildings or other structures and include ground-mounted, monument, pylon, and
pole signs. This report focuses on issues related to the appropriate mounting height of
freestanding signs. The objective of this report is to develop best practices for optimal
freestanding on-premise sign mounting height based on roadway factors, sign visibility,
and traffic safety, relying on existing research and practice and basic geometry, and
describing variations for different road types and sign lateral offsets. To achieve this,
the existing on-premise and traffic sign mounting height research was reviewed, and
the current state-of-the-practice was summarized. In addition, a technical analysis of
on-premise sign height and sign visibility based on roadway cross-section and driver-to-sign
sightlines was conducted.
Subject Areas: Mounting; Height; On-premise; Sign
Availability: Garvey, P.M. and Klena, M.J., 2019. Recommended Mounting Heights for
Freestanding On-Premise Signs. Interdisciplinary Journal of Signage and Wayfinding,
3(1), pp.3-15.
https://journals.shareok.org/ijsw/article/view/36

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7.3. Title: Filling in the Gaps of Connected Car Data Helps Transportation Planners
Author(s): Miller, J.A., 2019.
Abstract: Blog
Subject Areas: Vehicle Data; Connected Cars; Models
Availability: Miller, J.A., 2019. Filling in the Gaps of Connected Car Data Helps Transportation
Planners. Michigan Technological University Blog.
https://www.mtu.edu/news/stories/2019/april/filling-in-the-gaps-ofconnected-car-data-helps-transportation-planners.html

92

7.4. Title: 2045 Long-range Transportation Plan
Author(s): NA
Abstract: NA (Transportation Plan)
Subject Areas: Transportation Plan; MPO; Long-range; Policy
Availability: 2018. 2045 Long-range Transportation Plan. Black Hawk County, Metropolitan
Area Transportation Board, INRCOG.
http://www.inrcog.org/pub.htm

93

7.5. Title: Overestimation of Self-reported Driving Exposure: Results from the SHRP2
Naturalistic Driving Study
Author(s): Friedrich, T.E., Duerksen, K.N. and Elias, L.J.
Abstract: Objectives: The accuracy of self-reported driving exposure has questioned the
validity of using self-reported mileage to inform research questions. Studies examining
the accuracy of self-reported driving exposure compared to objective measures find
low validity, with drivers overestimating and underestimating driving distance. The
aims of the current study were to (1) examine the discrepancy between self-reported
annual mileage and driving exposure the following year and (2) investigate whether
these differences depended on age and annual mileage.
Methods: Two estimates of drivers’ self-reported annual mileage collected during vehicle
installation (obtained via prestudy questionnaires) and approximated annual mileage
driven (based upon Global Positioning System data) were acquired from 3,323 participants
who participated in the Strategic Highway Research Program 2 (SHRP2) Naturalistic
Driving Study.
Results: A Wilcoxon signed rank test showed that there was a significant difference
between self-reported and annual driving exposure during participation in SHRP 2,
with the majority of self-reported responses overestimating annual mileage the following
year, irrespective of whether an ordinal or ratio variable was examined. Over 15% of
participants provided self-reported responses with over 100% deviation, which were
exclusive to participants underestimating annual mileage. Further, deviations in reporting
differed between participants who had low, medium, and high exposure, as well as
between participants in different age groups.
Conclusions: These findings indicate that although self-reported annual mileage is heavily
relied on for research, such estimates of driving distance may be an overestimate of
current or future mileage and can influence the validity of prior research that has utilized
estimates of driving exposure.
Subject Areas: Driving exposure; Naturalistic driving; Measurement; Self-report; Age
Availability: Friedrich, T.E., Duerksen, K.N. and Elias, L.J., 2019. Overestimation of Self-reported
Driving Exposure: Results from the SHRP2 Naturalistic Driving Study. Traffic injury prevention,
pp.1-6.
https://www.tandfonline.com/doi/abs/10.1080/15389588.2018.1549731

94

7.6. Title: Trail Users in the Cincinnati Metropolitan Region: Purposes, Patterns, and Preferences
Author(s): Chen, N., Lindsey, G., Johnston, W., Adcock, K. and West, E.
Abstract: The benefits of using multi-use trails have been recognized from different
perspectives, such as improving public health, expanding active transportation options,
and enhancing environmental quality. Trail managers in Greater Cincinnati have developed
a 212-mile trail network, with plans to expand and connect the system. Given regional
priorities for trail development, trail managers and advocates need to understand more
about trail users and how they use the network. In response, two nonprofit organizations
in this region, Tri-State Trails and Interact for Health, along with the assistance from
researchers at the University of Minnesota, launched Greater Cincinnati’s first comprehensive
trail measurement program including both trail traffic monitoring and an intercept
survey of trail users. Monitoring results show the network is heavily used: in 2017,
monitoring results on 137 miles of the network showed users traveled an estimated 11
million miles on those segments annually (Lindsey et al. 2019).
This paper describes results of the survey which was designed with questions covering
trip characteristics, perceptions of the trails, socio-demographics, and locational information.
Between August 2017 and October 2017, 31 trail staff and volunteers administered the
survey at 20 locations. 734 responses were obtained. Three methods are used to analyze
the survey: descriptive summary, statistical association analysis, and geographical mapping.
The descriptive results show 89% of respondents are recreational users while only 8.8%
are utilitarian users. These utilitarian users cluster in areas close to Cincinnati downtown
and along a centrally located, long trail that connects several communities. Most recreational
users are female, white, between 35 and 64 years old, well-educated, and with relatively
high incomes. These users primarily bicycled and walked on trails, drove less than 25
minutes to trail, and traveled less than 5 miles. In contrast, most utilitarian users are
male, with income of less than $59,999, walked or biked to trail, and traveled no more
than 2 miles on trails. Recreational and utilitarian users’ preferences are consistent with
positive attitudes towards trail use and environment. Some differences between recreational
and utilitarian users are statistically supported using the statistical association analysis.
Geographically, the neighborhood context of trail users with different socio-demographics
is displayed to illustrate clustering phenomenon among trail users by race and income.
The differences between recreational and utilitarian users imply the importance of developing
policies to satisfy various needs of trail users. This analysis provides a valuable framework
for local governments to evaluate, manage, and improve the multi-use trail network.
Subject Areas: Survey; Trip characteristics and perceptions; Trail users
Availability: Chen, N., Lindsey, G., Johnston, W., Adcock, K. and West, E., 2019. Trail
Users in the Cincinnati Metropolitan Region: Purposes, Patterns, and Preferences. In Proceedings
of the F`abos Conference on Landscape and Greenway Planning (Vol. 6, No. 1, p. 59).
https://scholarworks.umass.edu/fabos/vol6/iss1/59/

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7.7. Title: Are Estimates of Early Education Programs Too Pessimistic? Evidence from
a Large-Scale Field Experiment that Causally Measures Neighbor Effects
Author(s): List, J.A., Momeni, F. and Zenou, Y.
Abstract: We estimate the direct and spillover effects of a large-scale early childhood
intervention on the educational attainment of over 2,000 disadvantaged children in the
United States. We show that failing to account for spillover effects results in a severe
underestimation of the impact. The intervention induced positive direct effects on test
scores of children assigned to the treatment groups. We document large spillover effects
on both treatment and control children who live near treated children. On average,
spillover effects increase a child’s non-cognitive (cognitive) scores by about 1.2 (0.6 to
0.7) standard deviations. The spillover effects are localized, decreasing with the spatial
distance to treated neighbors. Our evidence suggests the spillover effect on non-cognitive
scores are likely to operate through the child’s social network. Alternatively, parental
investment is an important channel through which cognitive spillover effects operate.
We view our results as speaking to several literatures, perhaps most importantly the
role of public programs and neighborhoods on human capital formation at an early
age.
Subject Areas: Education; Neighborhood; Field experiment; Spillover effects; Non-cognitive
skills
Availability: List, J.A., Momeni, F. and Zenou, Y., 2019. Are Estimates of Early Education
Programs Too Pessimistic? Evidence from a Large-Scale Field Experiment that Causally Measures
Neighbor Effects. Available at SSRN.
http://dx.doi.org/10.2139/ssrn.3385107

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7.8. Title: Genesis: Trip Generation Model using ACS, CTPP, and NHTS data
Author(s): Kim, K. and Chang, Y.
Abstract: Poster
Subject Areas: Trip Generation Model; NHTS data
Availability: Kim, K. and Chang, Y., 2019. Genesis: Trip Generation Model using ACS,
CTPP, and NHTS data. Poster at Transportation Research Board 98th Annual Meeting
http://www.trbcensus.com/TRB2019/

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Chapter 8. Traffic Safety
8.1. Title: Self-reported Handheld Device Use while Driving
Author(s): Kim, K., Ghimire, J., Pant, P. and Yamashita, E.
Abstract: In spite of research and awareness of the hazards associated with handheld
mobile device use while driving, many motorists continue to engage in this risky behavior.
The mobile device use while driving has a detrimental effect on the operation of the
vehicle. It contributes significantly to distraction which is a leading cause of accidents.
Especially, the use of text messaging and the dialing of a 10-digit number while driving
can be attributable to crash risks. Phone use bans have a positive role in reducing mobile
phone use for texting while operating vehicles. There are limited studies on whether
drivers admit to the use of handheld devices while driving. The aim of this study was
to identify the experiences, practices, and attitudes of handheld device use while driving.
A total of 337 respondents nationwide replied to the survey on the attitudes and self-reported
behaviors of handheld device use while driving. In the survey, the characteristics of
handheld device users, use of handheld devices, and the differences in self-reported
behaviors across states with and without device use restrictions were compared. The
perceptions and experiences of device users are also examined. Based on the background
of device users and their attitudes, a multivariate logistic regression is used to identify
the characteristics of those who use handheld devices while driving. The model is relevant
to this research because it allows the consideration and comparison of many variables
to identify the attitudes of people towards distracted driving. The affirmative self-reporting
of 59 percent of the respondents is a surprising result given that there are state bans on
texting and the use of handheld mobile phones while driving. Older drivers are least
likely to engage in these behaviors, compared to younger drivers and adult drivers.
Based on the findings, targeted educational and enforcement campaigns to reduce device
use during driving are suggested. Additional promising areas for further inquiry and
research are also proposed.
Subject Areas: Handheld device use; Distracted driving; Accident analysis; Prevention
Availability: Kim, K., Ghimire, J., Pant, P. and Yamashita, E., 2019. Self-reported Handheld
Device Use while Driving. Accident Analysis & Prevention, 125, pp.106-115.
https://www.sciencedirect.com/science/article/abs/pii/S0001457519301617

98

8.2. Title: Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle
Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment
Author(s): Zhou, B., Li, Z., Zhang, S., Zhang, X., Liu, X. and Ma, Q.
Abstract: Hit-and-run (HR) crashes refer to crashes involving drivers of the offending
vehicle fleeing incident scenes without aiding the possible victims or informing authorities
for emergency medical services. This paper aims at identifying significant predictors of
HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification
and regression tree (CART) method. An oversampling technique is applied to deal with
the data imbalance problem, where the number of minority instances (HR crash) is
much lower than that of the majority instances (NHR crash). The police-reported data
within City of Chicago from September 2017 to August 2018 is collected. The G-mean
(geometric mean) is used to evaluate the classification performance. Results indicate
that, compared with original CART model, the G-mean of CART model incorporating
data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals
that the following five variables play the most important roles in classifying HR and
NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action,
trafficway type, and gender of drivers. Several countermeasures are recommended
accordingly. The current study demonstrates that, by incorporating data imbalance
treatment, the CART method could provide much more robust classification results.
Subject Areas: Bicyclist; Hit-and-run; Traffic safety; Classification and regression tree;
Data imbalance
Availability: Zhou, B., Li, Z., Zhang, S., Zhang, X., Liu, X. and Ma, Q., 2019. Analysis of
Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric
Approach Incorporating Data Imbalance Treatment. Sustainability, 11(5), p.1327.
https://www.mdpi.com/2071-1050/11/5/1327

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8.3. Title: Evaluation of Not-At-Fault Assumption in Quasi-Induced Exposure Method
using Traffic Crash Data at Varied Geographical Levels
Author(s): Zhao, S., Wang, K. and Jackson, E.
Abstract: Acquiring real-world driver distribution data on roadways is a challenge.
The quasi-induced exposure (QIE) method is a promising alternative as it only requires
the available crash data. The question to be answered through this study is whether
the not-at-fault driver assumption of the QIE still holds when the population is broken
down to smaller geographical levels, such as counties, towns, or routes. This is important
because the result will provide statistical support to choose for or against the application
of QIE at disaggregate levels. In this study, the distributions of driver gender, age, and
vehicle type between four groups of drivers in the crash data were examined, using
data obtained from the state of Connecticut from 2015 to 2017. Namely, they are the
not-at-fault drivers and at-fault drivers in two-vehicle crashes (NF2 and AF2) and the
not-at-fault drivers and at-fault drivers in three-or-more vehicle crashes (NF3 and AF3).
Chi-square tests and Wilcoxon Mann-Whitney tests were used to provide statistical
evidence of whether the driver groups come from the same population. The evidence
shows that there are no statistical differences between the distributions of NF2 and
NF3. The QIE assumption of not-at-fault drivers is valid at all tested geographical levels.
Driver characteristic distribution in the NF2 (and NF3) groups in the crash data should
be a good representation of the driving population. The results also revealed the similarities
of distributions between AF2 and AF3 and the significant differences between the not-at-fault
drivers (NF2 and NF3) and at-fault-drivers (AF2 and AF3).
Subject Areas: Traffic Crash; Varied Geographical Levels; Traffic safety; Quasi-Induced
Exposure Method; Not-at-fault drivers and At-fault drivers
Availability: Zhao, S., Wang, K. and Jackson, E., 2019. Evaluation of Not-At-Fault Assumption
in Quasi-Induced Exposure Method using Traffic Crash Data at Varied Geographical Levels.
Transportation Research Record, p.0361198119841036.
https://journals.sagepub.com/doi/abs/10.1177/0361198119841036

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8.4. Title: Safety Evaluation of Statewide Off-Highway Vehicle Use in Alaska
Author(s): Belz, N.
Abstract: Standard measures of risk and conflict, design guidelines, and informed policies
and regulations for off-highway vehicle users (e.g., all-terrain vehicles and snow machines)
near and on the traveled way are not well established from a rural safety perspective.
The State of Alaska currently has a Department policy to not prohibit their travel within
the off-pavement area, but it does not currently design for or address crossings or other
conflicts when these users approach roads and other publicly traveled ways. There is a
need for statewide assessment of conflicts between these users and traditional roadway
users. A recent all terrain vehicle (ATV)-related fatality in Akiachak, discussions on
safety concerns surrounding ATV/off highway vehicle (OHV) use and policies in Wasilla,
Bethel, and Kotzebue, and requests for AKDOT&PF to address conflicts in rural Native
Alaska communities off the main road network make this very timely research. This
research presents a statewide review of the data and types of conflicts occurring on
highways, a compilation of borough and city/town OHV policies, and the results of
a discourse analysis of nationwide news articles on OHV issues. Findings build on
previous work related to mixed-use safety and provide greater insight on special user
groups and modes to address safety concerns and the transportation needs of rural and
small-urban areas of Alaska.
Subject Areas: Off-Highway Vehicle (OHV); Transportation Safety; Safety Analysis;
Trauma Registry; Field Data Collection
Availability: Belz, N., 2019. Safety Evaluation of Statewide Off-Highway Vehicle Use in
Alaska. Pacific Northwest Transportation Consortium University of Alaska Fairbanks,
Alaska Department of Transportation & Public Facilities.
https://digital.lib.washington.edu/researchworks/handle/1773/43585?show=full

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Chapter 9. Transit Planning
9.1. Title: Socioeconomic and Usage Characteristics of Public Transit Riders in the United
States
Author(s): Grahn, R., Hendrickson, C., Qian, Z.S. and Matthews, H.S.
Abstract: Urbanization trends and the emergence of alternative modes of transportation
have influenced the way individuals travel. The authors use the 2017 National Household
Travel Survey (NHTS) to explore socioeconomic and frequency of use characteristics
associated with public transit users. The results indicate that transit riders are younger
than the general population and reside in urban regions. Low and high income households
represent large proportions of the transit rider population. Low income users tend to
use bus services while high income users often use rail. Minority groups rely on public
transit more heavily than the white population. A higher proportion of African Americans
and Asians use bus and rail services respectively compared to the general NHTS population.
High frequency transit users own fewer household vehicles on average and tend to be
more frequent rideshare users. The private vehicle commuter mode share decreased
by 3% between 2009 and 2017 while increases were observed for walking, biking, and
public transit.
Subject Areas: Modal split; Socioeconomic factors; Transit riders; Travel surveys
Availability: Grahn, R., Hendrickson, C., Qian, Z.S. and Matthews, H.S., 2019. Socioeconomic
and Usage Characteristics of Public Transit Riders in the United States. Transportation Research
Board 98th Annual Meeting.
https://pubsindex.trb.org/view/2019/C/1572513

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9.2. Title: Charting Public Transit’s Decline
Author(s): O’Toole, R.
Abstract: Blog
Subject Areas: Transit; Ridership; NHTS
Availability: O’Toole, R., 2018. Charting Public Transit’s Decline. Cato Institute.
https://www.cato.org/publications/policy-analysis/charting-public-transits-decline#full

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9.3. Title: A Comparison of the Personal and Neighborhood Characteristics associated
with Ridesourcing, Transit Use, and Driving with NHTS Data
Author(s): Deka, D. and Fei, D.
Abstract: The opportunity to conduct an overarching national study to examine the
characteristics of ridesourcing users and their neighborhoods was absent until the 2017
National Household Travel Survey (NHTS) dataset was released in 2018. As the 2017
NHTS combines ridesourcing with taxi and limo in the trip file, ridesourcing trip characteristics
cannot be separately analyzed. Thus, the paper examines the characteristics of users
and neighborhoods associated with ridesourcing trip frequency from the person file,
defined as the number of rides taken in 30days. Because the public-use NHTS dataset
includes only limited information about the neighborhoods where the respondents live,
additional data from the NHTS DOT files were analyzed so that the characteristics of
the neighborhoods could be fully comprehended. In an effort to examine the proximity
of those neighborhoods to public transit stations and stops, GIS data on transit stations
and stops were analyzed from the Bureau of Transportation Statistics and the US Department
of Homeland Security. A zero-inflated negative binomial (ZINB) model was used to
identify the variables associated with ridesourcing trip frequency. Two similar models
were used to identify the variables associated with public transit trip frequency and
annual vehicle miles driven. The analysis showed that the direction of the effects of the
personal and neighborhood characteristics on ridesourcing and transit trip frequency is
mostly similar, but often dissimilar to their effects on miles driven. A significant finding
of this research is that people living near transit stations/stops use ridesourcing more
frequently.
Subject Areas: Ridesourcing; Transit Use; Neighborhood Characteristic; Zero-Inflated
Negative Binomial (ZINB) Model
Availability: Deka, D. and Fei, D., 2019. A Comparison of the Personal and Neighborhood
Characteristics associated with Ridesourcing, Transit Use, and Driving with NHTS Data. Journal
of Transport Geography, 76, pp.24-33.
https://www.sciencedirect.com/science/article/abs/pii/S0966692318304435

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9.4. Title: A Direct Demand Model for Commuter Rail Ridership in the San Francisco
Bay Area
Author(s): Kwong, J.
Abstract: This thesis documents the development of a direct travel demand model for
commuter rail in the San Francisco Bay Area. A direct demand model simultaneously
estimates trip generation and attraction, which for this thesis would be trips between
an origin-destination pair of stations. In the model, the number of trips assigned to
an origin-destination pair of stations is dependent on land use characteristics at the
origin and destination stations in combination with travel time on the network during
congested peak periods and via transit. The model uses a multiplicative direct demand
model to estimate ordinary least square regression parameters for the origin-destination
trips. From the model form, the resultant estimated regression parameters are elasticities,
and as such, can be used to postulate the effects of the selected land use characteristics
and network travel times upon the number of trips made.
At both the origin and destination, the location of the station within the central business
districts of the San Francisco Bay region had the largest effect on trip generation and
attraction. Higher employment density at the destination and a larger number of workers
per household at the origin had a positive effect on trips, while the total number of
industrial workers at the destination and an increased number of two car households
had a negative effect on trips. Longer travel times on transit appeared to have a positive
effect on trips, yet longer travel times in congested peak periods appeared to have a
negative effect on trips.
Subject Areas: Commuter Rail; Direct Demand Model; Origin-destination; Transit
Availability: Kwong, J., 2019. A Direct Demand Model for Commuter Rail Ridership in the
San Francisco Bay Area. Doctoral dissertation, UC Irvine.
https://escholarship.org/uc/item/7q20v7dz

105

9.5. Title: Transit Access Equity in Richmond, VA
Author(s): Jordan, R.
Abstract: The purpose of this thesis is to analyze the extent of public transit access equity
issues in Richmond, VA. The City of Richmond has an established public transportation
network system, and the thesis explores the level of access for urban residents to use
existing public transportation services. Technologies and programs have begun to emerge
across the United States to help solve transit accessibility challenges. The thesis assesses
the level of transit access equity that exists in Richmond and introduces technologies
and services that could help improve accessibility and equity. The thesis uses a mixed
methods approach that will consist of accessibility and equity measures, Geographic
Information System (GIS), and key informant interviews.
Subject Areas: Public Transit; Urban Residents; Geographic Information System (GIS)
Availability: Jordan, R., 2019. Transit Access Equity in Richmond, VA. Doctoral dissertation,
Virginia Commonwealth University.
https://scholarscompass.vcu.edu/etd/5772/

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Chapter 10. Travel Behavior
10.1. Title: Exploring the Relationship between Vehicle Type Choice and Distance Traveled:
A Latent Segmentation Approach
Author(s): Angueira, J., Konduri, K.C., Chakour, V. and Eluru, N.
Abstract: In the context of vehicle usage decisions, there are two important choice dimensions
namely, the choice of vehicle from household fleet that will be utilized for trips and
second, the distance traveled to pursue the planned activities. There are interrelationships
between these two choice dimensions with one dimension potentially influencing the
other. The direction of the interrelationship has important implications for transportation
planning and policy analyses. In an effort to explore the interrelationships, a latent segmentation-based
modeling approach is proposed in this paper. The approach allows for exploring alternative
interrelationship structures between choice dimensions in the same modeling framework.
The methodology is demonstrated using data from the latest wave of the National Household
Travel Survey (NHTS) in the United States. The results show the need for accommodating
alternative structures between choice dimensions to accurately describe the vehicle
usage decision processes exhibited by individuals.
Subject Areas: Latent Segmentation Models; Short-Term Vehicle Usage Decisions; Vehicle
Type Choice; Distance Traveled
Availability: Angueira, J., Konduri, K.C., Chakour, V. and Eluru, N., 2019. Exploring
the Relationship between Vehicle Type Choice and Distance Traveled: A Latent Segmentation
Approach. Transportation Letters, 11(3), pp.146-157.
https://www.tandfonline.com/doi/abs/10.1080/19427867.2017.1299346

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10.2. Title: A Machine-Learning Decision-Support Tool for Travel-Demand Modeling
Author(s): Brown, C.S., Garikapati, V. and Hou, Y.
Abstract: Utility maximization(UM) models are the lifeblood of virtually all travel demand
models (TDM) in practice. Be it the traditional travel demand models or more advanced
activity-based models, utility maximization models are used extensively to model and
predict myriad travel choices such as location choice, mode choice, route choice etc.
More recently machine learning (ML) models are being applied in a variety of contexts
to predict choice patterns (product suggestions on Amazon, restaurant suggestions
on Yelp etc.,). In the TDM arena, there has been interest in incorporating ML models
where they can enhance prediction accuracy. Though there have been sporadic efforts
at comparing specific utility maximization models to machine learning models, there
is a need for a standard comparison tool which can evaluate ML models against UM
models for a given choice context. Addressing this need, we present a tool for applying
an array of models including logit, nested logit, neural network, Naive Bayes and decision
tree classifiers. The tool is specifically tailored to aid in the deciding the best model
for a given choice context and can be used to choose an appropriate model family or
to construct a model ensemble to improve upon current modeling standards in travel
demand modeling. We test our proposed system on household vehicle count and work
schedule targets from the 2017 National Household Travel Survey. Results demonstrate
that for some variables, logit are not the most effective models, and the proposed system
can aid in selecting a better model.
Subject Areas: 33 ADVANCED PROPULSION SYSTEMS; Machine Learning; Decision
Support Tool; Travel Demand Modeling
Availability: Brown, C.S., Garikapati, V. and Hou, Y., 2019. A Machine-Learning Decision-Support
Tool for Travel-Demand Modeling. (No. NREL/PO-5400-72993). National Renewable Energy
Lab.(NREL), Golden, CO (United States).
https://www.osti.gov/biblio/1494741

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10.3. Title: Not Parking Lots but Parks: A Joint Association of Parks and Transit Stations
with Travel Behavior
Author(s): Park, K., Choi, D.A., Tian, G. and Ewing, R.
Abstract: Urban design literature says that public open space in a station area could
promote walking and other types of physical activity, enhance place attractiveness,
and increase property values. In the context of station areas, however, there is a lack
of empirical studies on the relationship between the presence of parks and sustainable
travel behavior, which is one of the primary goals of transit-oriented developments
(TODs). This study examined the impact of park provision on transit users’ mode choice
in three U.S. regions: Atlanta (GA), Boston (MA), and Portland (OR). This study utilized
multilevel multinomial logistic regression to account for hierarchical data structures —
trips nested within station areas — and multiple travel modes — automobiles, transit,
and walking. After controlling for the built environment and trip attributes, this study
showed that when there was a park, people were more likely to walk or take transit to
access or egress a transit station. A transit station having a park nearby may provide
a more pleasant first-mile/last-mile travel experience. This paper demonstrated that
station areas need to incorporate more public space, an overlooked element in current
TOD plans.
Subject Areas: Mode Choice; Transit-Oriented Development; Public Space; First-Mile
And Last-Mile Connection
Availability: Park, K., Choi, D.A., Tian, G. and Ewing, R., 2019. Not Parking Lots but
Parks: A Joint Association of Parks and Transit Stations with Travel Behavior. International
journal of environmental research and public health, 16(4), p.547.
https://www.mdpi.com/1660-4601/16/4/547

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10.4. Title: Exploring the Relationships Among Travel Multimodality, Driving Behavior,
Use of Ridehailing and Energy Consumption
Author(s): Circella, G., Lee, Y. and Alemi, F.
Abstract: In the last decade, advances in information and communication technologies
and the introduction of the shared economy engendered new forms of transportation
options and, in particular, shared mobility. Shared mobility services such as carsharing
(e.g., Zipcar and Car2go), dynamic ridesharing (e.g., Carma), ridehailing (e.g., Uber
and Lyft), and bike/scooter sharing (e.g., CitiBike, Jump Bike, Bird, and Lime) have
gained growing popularity especially among subgroups in the population including
college-educated or urban-oriented young adults (e.g., millennials). These emerging
transportation services have evolved at an unprecedented pace, and new business models
and smartphone applications are frequently introduced to the market. However, their
fast-changing nature and lack of relevant data have placed difficulties on research projects
that aim to gain a better understanding of the adoption/use patterns of such emerging
services, not to mention their impacts on various components of travel behavior and
transportation policy and planning, and their related environmental impacts.
This report builds on an on-going research effort that investigates emerging mobility
patterns and the adoption of new mobility services. In this report, the authors focus on
the environmental impacts of various modality styles and the frequency of ridehailing
use among a sample of millennials (i.e., born from 1981 to 1997) and members of the
preceding Generation X (i.e., born from 1965 to 1980). The total sample for the analysis
included in this report includes 1,785 individuals who participated in a survey administered
in Fall 2015 in California. In this study, the researchers focus on the vehicle miles traveled,
the energy consumption and greenhouse gas (GHG) emissions for transportation purposes
of various groups of travelers. They identify four latent classes in the sample based on
the respondents’ reported use of various travel modes: drivers, active travelers, transit
riders, and car passengers. They further divide each latent class into three groups based
on their reported frequency of ridehailing use: non-users, occasional users (who use
ridehailing less than once a month), and regular users (who use it at least once a month).
The energy consumption and GHG emissions associated with driving a personal vehicle
and using ridehailing services are computed for the individuals in each of these groups
(12 subgroups), and the authors discuss sociodemographics and economic characteristics,
and travel-related and residential choices, of the individuals in each subgroup.
Subject Areas: Mobility patterns; Mode Choice; Travel Multimodality; Driving Behavior
Availability: Circella, G., Lee, Y. and Alemi, F., 2019. Exploring the Relationships Among
Travel Multimodality, Driving Behavior, Use of Ridehailing and Energy Consumption. UC
Davis: National Center for Sustainable Transportation.
https://escholarship.org/uc/item/31v7z2vf

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10.5. Title: Socioeconomic and Usage Characteristics of Transportation Network Company
(TNC) Riders
Author(s): Grahn, R., Harper, C.D., Hendrickson, C., Qian, Z. and Matthews, H.S.
Abstract: The widespread adoption of smartphones followed by an emergence of transportation
network companies (TNC) have influenced the way individuals travel. The authors
use the 2017 National Household Travel Survey to explore socioeconomic, frequency
of use, and spatial characteristics associated with TNC users. The results indicate that
TNC riders tend to be younger, earn higher incomes, have higher levels of education,
and are more likely to reside in urban areas compared to the aggregate United States
population. Of the TNC users, 60% hailed a ride three times or less in the previous month,
indicating that TNC services are primarily used for special occasions. TNC users use
public transit at higher rates and own fewer vehicles compared to the aggregate United
States population. In fact, the TNC user population reported similar frequencies of use
for both TNC services and public transit during the previous month. Approximately
40% of TNC users reside in regions with population densities greater than 10, 000 persons
per square mile compared to only 15% for non-TNC users. Lastly, reported use of public
transit for TNC users living in large cities (>1 million) with access to heavy rail was
almost three times greater when compared to similar sized cities without heavy rail.
The average monthly frequency of TNC use was also elevated when heavy rail was
present.
Subject Areas: Transportation network companies (TNC); Ride sharing; Shared mobility;
Travel behavior; Uber; Lyft
Availability: Grahn, R., Harper, C.D., Hendrickson, C., Qian, Z. and Matthews, H.S.,
2019. Socioeconomic and Usage Characteristics of Transportation Network Company (TNC)
Riders. Transportation, pp.1-21.
https://link.springer.com/article/10.1007/s11116-019-09989-3

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10.6. Title: Location Choice, Life Cycle and Amenities
Author(s): Letdin, M. and Shim, H.S.
Abstract: Our study proposes a housing location choice model where a household faces
a trade-off between proximity to place of employment and proximity to amenities. We
consider subsamples of high amenity cities and low amenity cities and households
with and without children. We show that the roles of gender, education, homeownership,
household composition, and public transportation vary significantly depending on
level of amenities. Households with a female head of household, those with a working
spouse and with older children prefer locating closer to downtown amenities. Female
workers with and without children locate closer to work, in high and low amenity cities.
Subject Areas: Location choice; High and low amenity cities; Housing location choice
model; Employment
Availability: Letdin, M. and Shim, H.S., 2019. Location Choice, Life Cycle and Amenities.
Journal of Regional Science.
https://onlinelibrary.wiley.com/doi/abs/10.1111/jors.12441

112

10.7. Title: The Rise of Long-Distance Trips, in a World of Self-Driving Cars: Anticipating
Trip Counts and Evolving Travel Patterns Across the Texas Triangle Megaregion
Author(s): Kockelman, K., Huang, Y. and Quarles, N.
Abstract: The Texas Triangle megaregion contains Texas’ largest cities and metropolitan
areas, and thereby most of the state’s economic and social activities. This report anticipates
the impacts of self-driving, full automated or ”autonomous” vehicles (AVs), shared AVs
(SAVs), and “autonomous” trucks (Atrucks) on travel across this important megaregion
using Year 2040 land use (and network) forecasts. Various Statewide Analysis Model
(SAM) data are leveraged to anticipate the impacts of AVs’, SAVs’ and Atrucks’ impacts
on destination and mode choices. A travel demand model with feedback is implemented
to forecast changes in vehicle-miles traveled (VMT), congestion, and travel patterns
across the megaregion. Results suggest that people will shift to more distant destinations,
on average (evidenced by the increase in the megaregion’s average travel distance: from
14miles to 16miles). Air travel will fall by more than 82%, with these long-distance travelers
shifting to ground transport options. Without travel demand management (like credit-based
congestion pricing and mandated tight headways between AVs), congestion issues will
grow, thanks to an average VMT increase of 47%, which is more evident in the region’s
major cities: Houston, Dallas-Fort Worth, San Antonio and Austin. Almost 109.6% of
the megaregion’s link flows will suddenly exceed capacity, relative to a no-AV case
which has 4.6% exceed capacity. Automobile travel will rise across all trip distance categories,
with jumps most evident between suburban and urban zones. Six of the 15 commodity
groups simulated are expected to see a >5% increase in their associated truck trips, due
to the introduction of Atrucks, with rising truck trade largely between Houston and
other major Texas employment centers.
Subject Areas: Self-driving vehicle; Passenger and freight travel; Texas Triangle megaregion;
Statewide Analysis Model; Travel Patterns
Availability: Kockelman, K., Huang, Y. and Quarles, N., 2019. The Rise of Long-Distance
Trips, in a World of Self-Driving Cars: Anticipating Trip Counts and Evolving Travel Patterns
Across the Texas Triangle Megaregion. Technical Resport CM2-22, Cooperative Mobility
for Competitive Megaregions, U.S. DOT.
https://trid.trb.org/view/1597755

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10.8. Title: Factors Associated with Round-trip Carsharing Frequency and Driving-Mileage
Impacts in London
Author(s): Wu, C., Le Vine, S., Clark, M., Gifford, K. and Polak, J.
Abstract: This study draws on respondents from Greater London within the 2016/17
wave (n=2640) of Britain’s Annual Survey of carsharing users, which we enrich with
external data from the 2011 England and Wales Census and small-area income estimates.
Focusing on round-trip carsharing users, we present multivariate analyses of frequency-of-usage
of carsharing vehicles and impacts on annual vehicle miles traveled (VMT). Published
attribute effects from other geographic and contextual circumstances are compiled and
compared (where direct comparison is possible) with the specific attribute effects that
we report in this paper. We demonstrate a statistically significant link between customer
satisfaction with proximity of carsharing vehicles and VMT impacts. Car ownership
(both current, and changes upon joining a carsharing service) is shown to have intuitive
structural impacts. We find that frequent usage is associated ceteris paribus with increasing
VMT after joining a carsharing service, and that subscribing to multiple types of carsharing
is associated with frequent carsharing usage and a reduction in VMT. Interestingly, we
did not find any significant effect of household income on either frequency-of-usage or
VMT impacts.
Subject Areas: Carsharing; Travel behavior; Frequency of usage; Vehicle miles traveled
Availability: Wu, C., Le Vine, S., Clark, M., Gifford, K. and Polak, J., 2019. Factors Associated
with Round-trip Carsharing Frequency and Driving-Mileage Impacts in London. International
Journal of Sustainable Transportation, pp.1-10.
https://www.tandfonline.com/doi/ref/10.1080/15568318.2018.1538401?scroll=top

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10.9. Title: The Impact of Ride Hailing on Parking (and Vice Versa)
Author(s): Henao, A. and Marshall, W.E.
Abstract: Investigating emerging transportation services is critical to forecasting mode
choice and providing appropriate infrastructure. One such infrastructure is parking,
as parking demand may shift with the availability of ride-hailing services. This study
uses ethnographic methods–complemented with passenger surveys collected when
driving for Uber and Lyft in the Denver, Colorado, region–to gather quantitative and
qualitative data on ride-hailing and analyze the impacts of ride-hailing on parking,
including changes in parking demand and parking as a reason to deter driving. The
study also examines relationships between parking time and cost. This includes building
a classification tree-based model to predict the replaced driving trips as a function of
car ownership, destination land type, parking stress, and demographics.
The results suggest that: i) ride-hailing is replacing driving trips and could reduce parking
demand, particularly at land uses such as airports, event venues, restaurants, and bars;
ii) parking stress is a key reason respondents chose not to drive; and iii) respondents
are generally willing to pay more for reduced parking time and distance. Conversely,
parking supply, time, and cost can all influence travel behavior and ride-hailing use.
This study provides insight into potential benefits and disadvantages of ride-hailing as
related to parking.
Subject Areas: Ride-hailing; Ridesourcing; TNC; Uber; Lyft; Parking; Curb space; TDM
Availability: Henao, A. and Marshall, W.E., 2019. The Impact of Ride Hailing on Parking
(and Vice Versa). Journal of Transport and Land Use, 12(1).
https://jtlu.org/index.php/jtlu/article/view/1392

115

10.10. Title: Understand the Multi-Level Effects of the Built Environment on Trip-Chaining
Behavior
Author(s): Pang, H. and Zhang, M.
Abstract: The debate on the effects of the built environment (BE) on travel behavior has
been ongoing despite a large number of studies completed in the past three decades.
This study aims to inform the debate by extending the BE–travel behavior investigation
to the scope of trip-chaining. Specifically, the study conceptualized the contexture frame
for the relationship of BE attributes and trip-chain travel behavior and estimated 2-level
hierarchical linear models (HLM) of chained trip tours with travel survey data from
the Puget Sound region. The results show that travelers who live in areas with better
transit access, higher residential and non-residential density, and higher level of land
use mixture generated low percentage of miles traveled by vehicle (PVMT) during their
daily tours. Furthermore, considering the cross-level interactive effect, the study demonstrates
that the impacts of the non-residential density at work location and the residential density
at home location on PVMT are moderated by vehicle ownership.
Subject Areas: Built Environment; Trip-Chaining Behavior; Hierarchical linear models
(HLM)
Availability: Pang, H. and Zhang, M., 2019. Understand the Multi-Level Effects of the Built
Environment on Trip-Chaining Behavior. Transportation Research Record.
https://journals.sagepub.com/doi/abs/10.1177/0361198119835537

116

10.11. Title: Measuring Mobilities of Care, a Challenge for Transport Agendas: From One
to Many Tracks
Author(s): de Madariaga, I.S. and Zucchini, E.
Abstract: This chapter proposes a methodology for accurately measuring daily travel
associated with care tasks: activities performed by adults for children and other dependants,
and the maintenance of the home. These activities are statistically performed by women,
often as unpaid work. The travel associated with these tasks is not well described in the
transport literature and is still less considered by transport policy agendas. We build
the methodological framework for measuring this kind of travel around the innovative
concept of mobility of care (S`anchez de Madariaga, Transporte metropolitano y grupos
` Madrid: Ministry of Infrastructure,
sociales: propuestas para una mejor planificacion.
2009, Schiebinger et al. Gender innovations in science, health and medicine, engineering
and environment (launched 2011: genderinnovations.stanford.edu), 2013), which provides
an umbrella category for the design of transport statistics that takes into account gender
dimensions in urban transport. The chapter further provides an empirical study that
applies this methodology to analyse the daily mobility of women and men aged 30–45
years in the metropolitan region of Madrid.
Subject Areas: Mobility of care; Gender equality; Transport policy; Transport behaviour;
Mobility survey
Availability: de Madariaga, I.S. and Zucchini, E., 2019. Measuring Mobilities of Care, a
Challenge for Transport Agendas: From One to Many Tracks. In Integrating Gender into
Transport Planning (pp. 145-173). Palgrave Macmillan, Cham.
https://www.researchgate.net/publication/330905894 Measuring Mobilities of Care a
Challenge for Transport Agendas From One to Many Tracks

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10.12. Title: Eliciting Preferences of Ridehailing Users and Drivers: Evidence from the
United States
Author(s): Bansal, P., Sinha, A., Dua, R. and Daziano, R.
Abstract: Transportation Network Companies (TNCs) are changing the transportation
ecosystem, but micro-decisions of drivers and users need to be better understood to
assess the system-level impacts of TNCs. In this regard, we contribute to the literature
by estimating a) individuals’ preferences of being a rider, a driver, or a non-user of TNC
services; b) preferences of ridehailing users for ridepooling; c) TNC drivers’ choice to
switch to vehicles with better fuel economy, and also d) the drivers’ decision to buy,
rent or lease new vehicles with driving for TNCs being a major consideration. Elicitation
of drivers’ preferences using a unique sample (N=11,902) of the U.S. population residing
in TNC-served areas is the key feature of this study. The statistical analysis indicates
that ridehailing services are mainly attracting personal vehicle users as riders, without
substantially affecting demand for transit. Moreover, around 10% of ridehailing users
reported postponing the purchase of a new car due to the availability of TNC services.
The model estimation results indicate that the likelihood of being a TNC user increases
with the increase in age for someone younger than 44 years, but the pattern is reversed
post 44 years. This change in direction of the marginal effect of age is insightful as the
previous studies have reported a negative association. We also find that postgraduate
drivers who live in metropolitan regions are more likely to switch to fuel-efficient vehicles.
These findings would inform transportation planners and TNCs in developing policies
to improve the fuel economy of the fleet.
Subject Areas: Transportation Network Companies (TNCs); Ridehailing; Drivers
Availability: Bansal, P., Sinha, A., Dua, R. and Daziano, R., 2019. Eliciting Preferences of
Ridehailing Users and Drivers: Evidence from the United States. Cornell University, arXiv
preprint arXiv:1904.06695.
https://arxiv.org/abs/1904.06695

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10.13. Title: Rider-to-rider Discriminatory Attitudes and Ridesharing Behavior
Author(s): Moody, J., Middleton, S. and Zhao, J.
Abstract: Using online survey data from N=2041 Uber and Lyft users in the United
States collected in 2016 and 2018, this paper establishes the validity, reliability, and invariance
of a measure of rider-to-rider race and social class discrimination. This measure is then
incorporated into three structural models that investigate associations between rider-to-rider
discriminatory attitudes and four aspects of ridesharing behavior. We find no significant
relationship between rider-to-rider discriminatory attitudes and whether a TNC user
has ever used a ridesharing service (such as uberPOOL or Lyft Line). However, among
those who have used ridesharing services before, rider-to-rider discriminatory attitudes
are strongly negatively predictive of an individual’s level of satisfaction with the sharing
option, and marginally negatively predictive of an individual’s percentage of shared
TNC trips. Furthermore, among those who have not yet used ridesharing services, rider-to-rider
discriminatory attitudes are strongly negatively predictive of willingness to consider
using uberPOOL or Lyft Line in the future. Together, these findings suggest that rider-to-rider
discriminatory attitudes may discourage sustained and frequent use of ridesharing
services among TNC users. Further research is required to identify strategies for addressing
discriminatory attitudes in the ridesharing context and overcoming reluctance to sharing.
Subject Areas: Dynamic ridesharing; Race; Class Discrimination; Transportation Network
Companies (TNCs)
Availability: Moody, J., Middleton, S. and Zhao, J., 2019. Rider-to-rider Discriminatory
Attitudes and Ridesharing Behavior. Transportation Research Part F: Traffic Psychology
and Behaviour, 62, pp.258-273.
https://www.sciencedirect.com/science/article/abs/pii/S1369847818306181

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10.14. Title: Predicting the Ownership, Use, and Environmental Impacts of New Vehicle
Technologies with a Focus on the Relationship between Travel Behavior and the
Built Environment
Author(s): Nodjomian, A.T.
Abstract: The field of transportation is on the cusp of major change. Innovations in
how vehicles operate and are powered have the potential to elicit changes not seen
since the introduction of the interstate highway system more than half a century ago.
Predicting the impacts of new vehicle technologies has interested researchers and practitioners
across disciplines and continents. This thesis makes a handful of such predictions. It
is divided into three parts. In the first part, the results of two large-scale preference
surveys and data from the U.S. Environmental Protection Agency’s (EPA) Smart Location
Database (EPA, 2014) are used to estimate how land use characteristics impact Americans’
perceptions of, interest in, and willingness to pay for new vehicle technologies, while
controlling for demographic attributes. The surveys were conducted by Quarles and
Kockelman (2018) and Gurumurthy and Kockelman (2018) in 2017 and together represented
over 4,000 U.S. households. Statistical models like the ordered probit and multinomial
logit are used to estimate the impacts of demographics and land use characteristics on
vehicle-related behavior. Various land use variables arise as significant depending on
the question being asked of the respondents. For example, poor job accessibility via
automobile is associated with higher levels of interest in automated vehicles (AVs),
higher anticipated use of AV technology, a willingness-to-pay (WTP) for self-driving
capability, and a greater reliance on AVs for some long-distance travel. No land use
variable arises as significantly more predictive than the others at this national-level
scale of analysis. The results emphasize the fact that land use policy must be considered
at the local level, and that there is no “one size fits all” solution for managing future
transportation behavior with land use action. The second part of this thesis evaluates
the connection between land use and current travel behavior. Census tract-level measures
of population and employment density (provided once again by the EPA’s Smart Location
Database [EPA, 2014]) are evaluated across the nation to investigate the connection
between the development conditions one experiences and his or her travel behavior.
Travel data comes from the 2009 National Household Travel Survey (NHTS). The results
highlight a stronger connection between population density and vehicle-miles traveled
(VMT) and vehicle ownership, than with employment density. For both VMT and vehicle
ownership, an improvement of only two to one can be expected by changing population
density conditions in a census tract. In other words increasing population from the
lowest density conditions to the highest results in a decline of VMT per capita per day
from 20 miles to 10 miles. Similarly, vehicle ownership per capita generally ranges from
0.4 to 0.8. Notably, these improvements are not realized until the highest decile of population
density (18 people per acre), thus indicating that simply building homes in rural or
low-density suburban regions will likely have a negligible impact on transportation
demand. Employment density was found to be less indicative of travel behavior. The
third and final piece of the thesis predicts how an evolving light-duty vehicle (LDV)
fleet will impact the amount of energy consumed by Americans and the emissions they
create. Here, the results of a fleet evolution simulation, developed by Quarles et al.
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(2019), are used to project what a vehicle fleet with more electric (and fewer gasoline-powered)
vehicles will mean for energy consumption and emissions on a per capita basis. Projections
are based on historic fuel efficiency data and emission production rates from the Bureau
of Transportation Statistics (BTS) and EPA (BTS, 2018b; BTS, 2018c; EPA, 2018a). Conclusions
from these findings highlight the need for more efficient vehicles, better emissions control
technologies on existing vehicle models and power plants, and a decreased reliance
on highly-polluting energy sources for power generation. Policies aimed at achieving
these objectives will help ensure that Americans’ future vehicle behavior and ownership
will not create an undue burden on themselves or the environment in which they live.
Although the analyses discussed in this thesis cover diverse topics such as human behavior,
urban planning, and air quality, they establish the need for a proactive approach to
cutting-edge vehicle technologies. If left to develop without any oversight or action,
transportation network congestion will worsen, development will continue to sprawl,
and the environment and public health will suffer. Policies aimed at limiting “empty”
driving with AVs, increasing population density, and curbing vehicle and power plant
emissions can help ensure the benefits of vehicle technology innovation are not realized
at the expense of other considerations.
Subject Areas: Autonomous vehicles; Shared; Travel behavior survey; Models; Willingness
to pay; Mode choice; Population density; Employment density; Fleet evolution; Energy
usage; Emissions
Availability: Nodjomian, A.T., 2019. Predicting the Ownership, Use, and Environmental
Impacts of New Vehicle Technologies with a Focus on the Relationship between Travel Behavior
and the Built Environment. Doctoral dissertation, University of Texas.
https://repositories.lib.utexas.edu/handle/2152/74510

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10.15. Title: What Drives the use of Ridehailing In California? Ordered Probit Models
of the Usage Frequency of Uber and Lyft
Author(s): Alemi, F., Circella, G., Mokhtarian, P. and Handy, S.
Abstract: The availability of ridehailing services, such as those provided by Uber and
Lyft in the U.S. market, as well as the share of trips made by these services, are continuously
growing. Yet, the factors affecting the frequency of use of these services are not well
understood. In this paper, we investigate how the frequency of use of ridehailing varies
across segments of the California population and under various circumstances. We
analyze data from the California Millennials Dataset (N=1975), collected in fall 2015
through an online survey administered to both millennials and members of the preceding
Generation X. We estimate an ordered probit model with sample selection and a zero-inflated
ordered probit model with correlated error terms to distinguish the factors affecting
the frequency of use of ridehailing from those affecting the adoption of these services.
The results are consistent across models: sociodemographic variables are important
predictors of service adoption but do not explain much of the variation in the frequency
of use. Land use mix and activity density respectively decrease and increase the frequency
of ridehailing. The results also confirm that individuals who frequently use smartphone
apps to manage other aspects of their travel (e.g. to select a route or check traffic) are
more likely to adopt ridehailing and use it more often. This is also true for long-distance
travelers, in particular, those who frequently travel by plane for leisure purposes. Individuals
with higher willingness to pay to reduce their travel time use ridehailing more often.
Those with stronger preferences to own a personal vehicle and those with stronger concerns
about the safety/security of ridehailing are less likely to be frequent users. These results
provide new insights into the adoption and use of ridehailing that could help to inform
planning and forecasting efforts.
Subject Areas: Uber/Lyft; Ridehailing; Travel behavior; Frequency model; Ordered
probit model with sample selection; Zero-inflated probit ordered model with correlated
error terms
Availability: Alemi, F., Circella, G., Mokhtarian, P. and Handy, S., 2019. What Drives the
use of Ridehailing In California? Ordered Probit Models of the Usage Frequency of Uber and
Lyft. Transportation Research Part C: Emerging Technologies, 102, pp.233-248.
https://www.sciencedirect.com/science/article/pii/S0968090X18318849

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10.16. Title: Nudging People towards More Sustainable Residential Choice Decisions:
An Intervention Based on Focalism and Visualization
Author(s): Bhattacharyya, A., Jin, W., Le Floch, C., Chatman, D.G. and Walker, J.L.
Abstract: There have been numerous behavior change studies focused on sustainable
travel mode choices. In this study we focused on the residential choices that in turn
influence travel habits. We designed and implemented two interventions, which we
term the “focalism” and “visualization” interventions, based on literature in psychological
economics. The focalism intervention was motivated by literature that suggests people
make suboptimal choices when looking for a new home. While focus is given to immediately
tangible features like the quality of the house, important but less tangible factors like
access to transportation are relatively overlooked. The visualization intervention was
based on literature showing that providing information at decision points when long-ingrained
habits are vulnerable to change, such as at the time of a residential move, can be influential
on choices. We designed both interventions to be interactive so that the intervention
was “discovered” by respondents rather than presented directly as information. With
the focalism intervention, we pointed out differences in how respondents ranked their
search priorities for new housing and neighborhoods, versus how they ranked what
they reported makes them happy. With the visualization intervention, we explained
to respondents that moving is an opportunity to make changes in one’s life, and we
prompted them to think through what they desired to change. We evaluated the influence
of these interventions on residential housing decisions by surveying respondents about
their priorities in residential search before and after the interventions, and by collecting
information about their housing, neighborhoods, travel patterns, and reported well-being.
The surveys were web-based, with one survey conducted before respondents moved
and a second survey conducted afterward. Participants were randomly assigned to
a focalism treatment group, a visualization treatment group, or a control group. 380
respondents answered the pre-move survey, and 184 of these answered the post-move
survey. In the pre-move survey, we found that both the focalism and visualization interventions
resulted in a significant increase in the fraction of people who planned to travel more
sustainably relative to the control group. More importantly, we found that after the
post-move survey, respondents in the focalism group, but not the visualization group,
significantly reduced their travel time to work and increased their cycling, walking,
carpooling, carsharing and transit use in comparison to the control group. Meanwhile,
those in the visualization treatment group had significantly higher reported well-being
after the move; those in the focalism treatment group also improved their stated well-being,
though less significantly; and there was no change in the control group. These results
suggest that it might be relatively easy to nudge residential choices towards both more
sustainable travel and greater well-being.
Subject Areas: Travel behavior; Choice modeling; Urban Intervention
Availability: Bhattacharyya, A., Jin, W., Le Floch, C., Chatman, D.G. and Walker, J.L.,
2019. Nudging People towards More Sustainable Residential Choice Decisions: An Intervention
Based on Focalism and Visualization. Transportation, 46(2), pp.373-393.
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https://link.springer.com/article/10.1007/s11116-018-9936-x

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10.17. Title: Are Americans Driving Older Cars Or Just Leaving Them In The Driveway?
Author(s): Hernandez, K., Batbold, G. and Bin-Nun, A.
Abstract: Blog
Subject Areas: Travel behavior; Older Vehicles; VMT; NHTS vehicles
Availability: Hernandez, K., Batbold, G. and Bin-Nun, A., 2019. Are Americans Driving
Older Cars Or Just Leaving Them In The Driveway? The Fuse.
http://energyfuse.org/are-americans-driving-older-cars-or-just-leaving-them-inthe-driveway/

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10.18. Title: Why do we Trust Ridesharing Apps so much?
Author(s): Grinberg, E.
Abstract: Blog
Subject Areas: Travel behavior; Older Vehicles; VMT; NHTS vehicles
Availability: Grinberg, E., 2019. Why do we Trust Ridesharing Apps so much? KVIA.com.
https://www.kvia.com/news/.../why-do-we-trust...apps.../1065648802

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10.19. Title: Modeling Individuals’ Willingness to Share Trips with Strangers in an Autonomous
Vehicle Future
Author(s): Lavieri, P.S. and Bhat, C.R.
Abstract: With the era of fully automated vehicles (AVs) quickly approaching, ridesharing
services could have an important role in increasing vehicle occupancy, reducing vehicle
miles traveled, and improving traffic conditions. However, the extent to which these
potentials can be achieved depends on consumers’ disposition to sharing rides. From a
travel behavior perspective, two essential elements to the adoption of shared rides are
individuals’ acceptance of increased travel times associated with pick-up/drop-off of
other passengers and their approval of strangers sharing the same vehicle. The current
study develops the notion of willingness to share (WTS), which represents the money
value attributed by an individual to traveling alone compared to riding with strangers,
to investigate the adoption of shared rides. Using a multivariate integrated choice and
latent variable approach, we examine current choices and future intentions regarding
the use of shared rides and estimate individuals’ WTS as well as their values of travel
time for two distinct trip purposes. Results show that users are less sensitive to the
presence of strangers when in a commute trip compared to a leisure-activity trip. We
also observe that the travel time added to the trip to serve other passengers may be
a greater barrier to the use of shared services compared to the presence of a stranger.
However, the potential to use travel time productively may help overcome this barrier
especially for high-income individuals.
Subject Areas: Travel behavior; Value of travel time; Willingness-to-share; Ride-hailing;
Dynamic ridesharing; Automated vehicles
Availability: Lavieri, P.S. and Bhat, C.R., 2019. Modeling Individuals’ Willingness to Share
Trips with Strangers in an Autonomous Vehicle Future. Transportation Research Board 98th
Annual Meeting.
https://trid.trb.org/view/1572421

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10.20. Title: How do Activities Conducted while Commuting influence Mode Choice?
Using Revealed Preference Models to Inform Public Transportation Advantage
and Autonomous Vehicle Scenarios
Author(s): Malokin, A., Circella, G. and Mokhtarian, P.L.
Abstract: From early studies of time allocation onward, it has been acknowledged that
the “productive” nature of travel could affect its utility. Currently, at the margin an
individual may choose transit over a shorter automobile trip, if thereby she is able to
use the travel time more productively. On the other hand, recent advancements toward
partly/fully automated vehicles are poised to revolutionize the perception and utilization
of travel time in cars, and are further blurring the role of travel as a crisp transition
between location-based activities. To quantify these effects, we created and administered
a survey to measure travel multitasking attitudes and behaviors, together with general
attitudes, mode-specific perceptions, and standard socioeconomic traits (N=2229 Northern
California commuters). In this paper, we present a revealed preference mode choice
model that accounts for the impact of multitasking attitudes and behavior on the utility
of various alternatives. We find that the propensity to engage in productive activities
on the commute, operationalized as using a laptop/tablet, significantly influences utility
and accounts for a small but non-trivial portion of the current mode shares. For example,
the model estimates that commuter rail, transit, and car/vanpool shares would respectively
be 0.11, 0.23, and 1.18 percentage points lower, and the drive-alone share 1.49 percentage
points higher, if the option to use a laptop or tablet while commuting were not available.
Conversely, in a hypothetical autonomous vehicles scenario, where the car would allow
a high level of engagement in productive activities, the drive-alone share would increase
by 1.48 percentage points. The results empirically demonstrate the potential of a multitasking
propensity to reduce the disutility of travel time. Further, the methodology can be generalized
to account for other properties of autonomous vehicles, among other applications.
Subject Areas: Travel behavior; Multitasking; Activities while traveling; Autonomous
vehicles; Mode choice; Attitudes; Value of travel time
Availability: Malokin, A., Circella, G. and Mokhtarian, P.L., 2019. How do Activities
Conducted while Commuting influence Mode Choice? Using Revealed Preference Models to
Inform Public Transportation Advantage and Autonomous Vehicle Scenarios. Transportation
Research Part A: Policy and Practice, 124, pp.82-114.
https://www.sciencedirect.com/science/article/pii/S0965856416306772

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Chapter 11. Trend Analysis and Market Segmentation
11.1. Title: Trends of Home Deliveries in the U.S.: Changes from 2009 to 2017
Author(s): Schmid, J. and Wang, X.
Abstract: Most aspects of modern life have been significantly influenced by the internet
and shopping is not immune from this. This is quite evident when analyzing the 2009
and 2017 National Household Travel Surveys (NHTS). Between 2009 and 2017, the number
of online shopping deliveries received by the average American each month more than
doubled. Zero-inflated negative binomial models are applied to both NHTS datasets.
In both 2009 and 2017, age, education level, and technology use are major indicators
of online shopping patterns. Both NHTS datasets were pooled to create a comparison
model, with the main differences in demographic effects being the effect of education
and large metropolitan areas on purchasing patterns. The results here reflect the trend
of home deliveries. For transportation planners, it implies an increased amount of freight
traffic on residential streets that communities will need to accommodate.
Subject Areas: Data analysis; Delivery service; Electronic commerce; Home shopping;
Trend (Statistics)
Availability: Schmid, J. and Wang, X., 2019. Trends of Home Deliveries in the U.S.: Changes
from 2009 to 2017. Transportation Research Board 98th Annual Meeting.
https://pubsindex.trb.org/view/2019/C/1572327

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11.2. Title: Modeling the Willingness to Work as Crowd-Shippers and Travel Time Tolerance
in Emerging Logistics Services
Author(s): Le, T.V. and Ukkusuri, S.V.
Abstract: The objective of this study is to understand the different behavioral considerations
that govern the choice of people to engage in a crowd-shipping market. A binary logit
model and an ordinary least-square regression model have been developed. Those models
are integrated by a selectivity-bias term. The results suggest that socio-demographic
characteristics, freight transportation experience, and social media usage significantly
influence respondents’ decisions to participate in the crowd-shipping market. The selectivity
is found available in the dataset and has strong heterogeneity. Moreover, the crowd-shippers’
expect to-be-paid rate is found concurrent with value-of-time literature. Findings from
this research are valuable to crowd-shipping companies recruiting employees and developing
business strategies.
Subject Areas: Crowd-shipping; Willingness to work; Last-mile delivery; On-demand
delivery; Selectivity correction; Discrete–continuous model
Availability: Le, T.V. and Ukkusuri, S.V., 2019. Modeling the Willingness to Work as Crowd-Shippers
and Travel Time Tolerance in Emerging Logistics Services. ravel Behaviour and Society, 15,
pp.123-132.
https://www.sciencedirect.com/science/article/pii/S2214367X18300620

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11.3. Title: Generational Trends in Vehicle Ownership and Use: Are Millennials Any Different?
Author(s): Knittel, C.R. and Murphy, E.
Abstract: Anecdotes that Millennials fundamentally differ from prior generations are
numerous in the popular press. One claim is that Millennials, happy to rely on public
transit or ride-hailing, are less likely to own vehicles and Travel less in personal vehicles
than previous generations. However, in this discussion it is unclear whether these perceived
differences are driven by changes in preferences or the impact of forces beyond the
control of Millennials, such as the Great Recession. We empirically test whether Millennials’
vehicle ownership and use preferences differ from those of previous generations using
data from the US National Household Travel Survey, Census, and American Community
Survey. We estimate both regression and nearest-neighbor matching models to control
for the confounding effect of demographic and macroeconomic variables. We find little
difference in preferences for vehicle ownership between Millennials and prior generations
once we control for confounding variables. In contrast to the anecdotes, we find higher
usage in terms of vehicle miles traveled (VMT) compared to Baby Boomers. Next we
test whether Millennials are altering endogenous life choices that may, themselves,
affect vehicles ownership and use. We find that Millennials are more likely to live in
urban settings and less likely to marry by age 35, but tend to have larger families, controlling
for age. On net, these other choices have a small effect on vehicle ownership, reducing
the number of vehicles per household by less than one percent.
Subject Areas: Millennials; Trends; Vehicle Ownership; Prior Generations
Availability: Knittel, C.R. and Murphy, E., 2019. Generational Trends in Vehicle Ownership
and Use: Are Millennials Any Different? (No. w25674). National Bureau of Economic
Research.
https://www.nber.org/papers/w25674

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11.4. Title: Estimating the Cost and Utility of Statewide Travel Models using Scenario-Based
Interviews
Author(s): Francis, D., Tsang, F. and Erhardt, G.D.
Abstract: Statewide travel models are analysis tools that simulate transportation system
conditions and are used to answer “what if” questions about proposed plans and policies.
In the United States, they are in use or in development in 39 out of 50 state departments
of transportation (DOTs). States without a statewide model are faced with the decision
of whether to invest in one, whereas states with models need to decide when and whether
to upgrade. Prior efforts to aid this decision making provided detailed synthesis on the
cost of statewide modeling, but it has been difficult for other states to use the lessons
learned, because cost is largely driven by each state’s specific circumstances. There has
also been very little research on quantifying the value of models. To address these gaps,
the present research uses a novel scenario-based interview approach. Representatives
from 29 DOTs and five consultancies participated in our scenario-based interviews,
from which we collected cost estimates for three archetypical statewide models and
willingness-to-pay estimates (i.e., perceived value) under nine model development and
policy focus scenarios. Our results show that cost ranges from $500,000 for an archetypical
Basic 3-Step Model to between $2.8million and $5million for an Activity-Based Model
for a large state, with data collection comprising a large portion of the cost (36–66%).
Further, the perceived value of statewide models exceeds the costs by a factor of 2.4–11.3,
with the cost-benefit ratio being higher when a DOT is interested in a broader set of
policy issues.
Subject Areas: Statewide travel models; Costs; Invest; Willingness-to-pay estimates;
Cost-benefit Ratio
Availability: Francis, D., Tsang, F. and Erhardt, G.D., 2019. Estimating the Cost and Utility
of Statewide Travel Models using Scenario-Based Interviews. Transportation Research Record,
p.0361198118821684.
https://journals.sagepub.com/doi/abs/10.1177/0361198118821684

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11.5. Title: Forecast Households at the County Level: An Application of the ProFamy Extended
Cohort-Component Method in Six Counties of Southern California, 2010 to 2040
Author(s): Feng, Q., Wang, Z., Choi, S. and Zeng, Y.
Abstract: Policymakers and market analysts have long been interested in future trends
of households. Among household projection methods, the ProFamy extended cohortcomponent
method, as one alternative to the traditional headship-rate method, has recently been
extended to the subnational levels. This paper illustrates the application of the ProFamy
method at the county level by projecting household types, sizes, and elderly living arrangements
for six counties of Southern California from 2010 to 2040, including Imperial, Los Angeles,
Orange, Riverside, San Bernardino, and Ventura.Using this specific case, this paper
introduces the rationales and procedure of the county-level application of the ProFamy
method. The validation test for the ProFamy to project the 2010 population and households
using the 2000 census data support the use of the ProFamy at the county level. And the
ProFamy method also yields satisfactory results in comparison with the projections of
headship-rate methods. The ProFamy forecasts on the six county of Southern California
provide detailed information on the county-level trends of households and elderly living
arrangement in this region, which are valuable information for the local planning agency
but usually beyond the capacity of the traditional methods.
Subject Areas: Household; Household projection; Forecast; ProFamy; Headship rate;
County
Availability: Feng, Q., Wang, Z., Choi, S. and Zeng, Y., 2019. Forecast Households at the
County Level: An Application of the ProFamy Extended Cohort-Component Method in Six
Counties of Southern California, 2010 to 2040. Population Research and Policy Review,
pp.1-29.
https://link.springer.com/article/10.1007/s11113-019-09531-4

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11.6. Title: Transportation Network Companies and Taxis: The Case of Seattle
Author(s): Leisy, C.A.
Abstract: Transportation Network Companies and Taxis: The Case of Seattle is a modern
economic case history and thorough analysis of the devastating impact of the transportation
network company (TNC) industry (Uber and Lyft) on the taxicab industry in Seattle,
Washington, beginning in 2014. The events that transpired and lessons learned are applicable
to most large cities in North America, Europe and Australia.
As the regulator of the taxicab and TNC industries in Seattle during this period, the
author offers a unique insider perspective. The book also provides internal operating
statistics on the TNC industry, which are available here for the first time. Despite the
spectacular growth of the TNC industry, growth rates have steadily declined and may
fall to zero by 2019 or 2020, while the taxicab industry appears to have begun a modest
recovery. This book offers a thorough explanation of how and why this decline has
happened. It explains the taxicab industry, economic deregulation, competitive market
failure, market disruption, price elasticity of demand and other concepts. There is also
a wealth of data, computations and analysis for the specialized reader.
This book considers the past, present and future of the taxicab and TNC industries in
Seattle, It is recommended for both the general reader and industry professionals.
Subject Areas: Transportation Network Companies; Taxis; Seattle; Industry
Availability: Leisy, C.A., 2019. Transportation Network Companies and Taxis: The Case of
Seattle. Routledge.
https://www.taylorfrancis.com/books/9780429265129

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11.7. Title: Estimating Preference Heterogeneity in Discrete Choice Models of Product
Differentiation
Author(s): Leard, B.
Abstract: Modeling preference heterogeneity in discrete choice models of product differentiation
remains computationally challenging. I derive a new method for estimating preference
heterogeneity in these models. A key advantage of the method is its simplicity: preference
heterogeneity parameters are estimated with a closed-form expression or with a linear
regression. I apply the method to estimate parameters of new vehicle demand and to
simulate the effects of new vehicle fuel economy standards. The simulation results suggest
that a marginal tightening of the standards has a modest impact on total new vehicle
sales.
Subject Areas: Discrete Choice Models; Preference Heterogeneity; Microdata; Substitution
Patterns
Availability: Leard, B., 2019. Estimating Preference Heterogeneity in Discrete Choice Models
of Product Differentiation. Resources for the Future Working Paper, pp.19-02.
https://www.rff.org/publications/working-papers

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11.8. Title: Consumer Myopia in Vehicle Purchases: Evidence from a Natural Experiment
Author(s): Gillingham, K., Houde, S. and Van Benthem, A.
Abstract: A central question in the analysis of fuel-economy policy is whether consumers
are myopic with regards to future fuel costs. We provide the first evidence on consumer
valuation of fuel economy from a natural experiment. We examine the short-run equilibrium
effects of an exogenous restatement of fuel-economy ratings that affected 1.6 million
vehicles. Using the implied changes in willingness-to-pay, we find that consumers act
myopically: consumers are indifferent between $1 in discounted fuel costs and 15–38cents
in the vehicle purchase price when discounting at 4%. This myopia persists under a
wide range of assumptions.
Subject Areas: Fuel-economy policy; Consumer; Myopia
Availability: Gillingham, K., Houde, S. and Van Benthem, A., 2019. Consumer Myopia
in Vehicle Purchases: Evidence from a Natural Experiment. National Bureau of Economic
Research, Working Paper No. 25845.
https://www.nber.org/papers/w25845

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11.9. Title: Selection of Optimal Target Reliability in RBDO through Reliability-Based
Design for Market Systems (RBDMS) and Application to Electric Vehicle Design
Author(s): Lee, U., Kang, N. and Lee, I.
Abstract: Reliability-based design optimization (RBDO) allows decision-makers to achieve
target reliability in product performance under engineering uncertainties. However,
existing RBDO studies assume the target reliability as a given parameter and do not
explain how to determine the optimal target reliability. From the perspective of the
market, designing a product with high target reliability can satisfy many customers and
increase market demand, but it can generate a large cost leading to profit reduction of
the company. Therefore, the target reliability should be a decision variable which needs
to be found to maximize the company profit. This paper proposes a reliability-based
design for market systems (RBDMS) framework by integrating RBDO and design for
market system (DMS) approaches to find the optimal target reliability. The proposed
RBDMS framework is applied to electric vehicle (EV) design problems to validate effect
of the target reliability on company profit–or market share–and engineering performances
of EV. Several observations about the optimal target reliability are presented from the
case study with various scenarios. From the EV design case study, it is verified that the
proposed RBDMS framework is an effective way of finding the optimal target reliability
that maximizes the company profit, and the optimal target reliability varies depending
on the situation of market and competitors.
Subject Areas: Reliability-based design optimization (RBDO); Design for market systems
(DMS); Electric vehicles; Target reliability; Uncertainty
Availability: Lee, U., Kang, N. and Lee, I., 2019. Selection of Optimal Target Reliability
in RBDO through Reliability-Based Design for Market Systems (RBDMS) and Application to
Electric Vehicle Design. Structural and Multidisciplinary Optimization, pp.1-15.
https://link.springer.com/article/10.1007/s00158-019-02245-3

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11.10. Title: Game Theory Approach on Modeling of Residential Electricity Market by
Considering the Uncertainty due to the Battery Electric Vehicles (BEVs)
Author(s): Fijani, R.F., Azimian, B. and Ghotbi, E.
Abstract: Based on sophisticated metering infrastructure (AMI), one can use big data
to provide demand-response (DR) solutions. There is a need to develop optimized cost
structures for consumers. In this paper, Stackelberg game approaches are utilized, and
residential loads are considered including battery electric vehicles (BEVs) equipped
with BEV communication controllers and vehicle-to-grid (V2G) technologies. Efficient
and effective optimized algorithms are developed for users (followers) based on time
dependent pricing schemes. In the game, besides the followers, other participant is an
electricity retailer company (leader), with a two-way bilateral communication procedure
accepted and established by all participants. The user side of the games is related to
the demand side management (DSM). Real-time pricing (RTP) from time-of-use (TOU)
companies is used to achieve better results. Monte Carlo simulations (MCS) represent
uncertain behaviors of BEV drivers. Results indicate that customers demands can be
met while reaching the best efficiency.
Subject Areas: Game Theory Approach; Residential Electricity Market; Battery Electric
Vehicles (BEVs)
Availability: Fijani, R.F., Azimian, B. and Ghotbi, E., 2019. Game Theory Approach on
Modeling of Residential Electricity Market by Considering the Uncertainty due to the Battery
Electric Vehicles (BEVs). Cornell University.
https://arxiv.org/abs/1902.05028

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11.11. Title: Quantifying the Electric Vehicle Charging Infrastructure Gap across US Markets
Author(s): Nicholas, M., Hall, D. and Lutsey, N.
Abstract: The electrification of the United States vehicle market continues, with the
most growth occurring in markets where barriers are addressed through policy, charging
infrastructure, and consumer incentives. Key questions about electric vehicle market
growth include how much charging infrastructure will be needed to sustain growth
and whether to invest in various types of this infrastructure. This report quantifies the
gap in charging infrastructure from what was deployed through 2017 to what is needed
to power more than 3 million expected electric vehicles by 2025, consistent with automaker,
policy, and underlying market trends. Based on the expected growth across the 100
most populous U.S. metropolitan areas, we estimate the amount of charging of various
types that will be needed to power these vehicles. Our evaluation of charging needs is
based on best available observed data on the growing electric vehicle market, charging
availability, and emerging charging behavior patterns. Figure ES-1 illustrates the deployment
of public and workplace charging infrastructure through 2017 as a percentage of what
will be needed by 2025 across the 100 most populous U.S. metropolitan areas (the 50
most populous are labeled). Shades of red indicate that less than 50% of the needed
charging has been installed through the end of 2017, while blues indicate that more
than 50% of charging needed in 2025 was in place by 2017. Of the 100 areas, 88 had less
than half of the total needed charging infrastructure in place, based on their expected
electric vehicle growth.
Subject Areas: US Markets; Electric Vehicle; Trends; Charging infrastructure
Availability: Nicholas, M., Hall, D. and Lutsey, N., 2019. Quantifying the Electric Vehicle
Charging Infrastructure Gap across US Markets. International Council on Clean Transportation.
https://theicct.org/publications/charging-gap-US

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