News
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Join Us at the 2026 National Mobility Summit ✨
The TBD Center invites you to the 2026 National Mobility Summit on June 8, 2026, at the USC Capital Campus in Washington, D.C. The Summit will convene leaders from across government, academia, and industry to advance the movement of people and goods across the United States.
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May 2026
Apr 2026
Jan 2026
Dec 2025
Nov 2025
Oct 2025
Data Tool
The Mobility Dashboard (TMD) Now Available
Webinar
TBD Webinar: Telework and Travel Demand — Longitudinal Evidence
Conference
TBD Center at the 2026 TRB Annual Meeting
Publication
New Paper: Data Collection and Weighting for Consistent Population Parameters
Report
Final Report Released: Behavioral Responses to AVs in Four U.S. Metro Areas
Data Brief
New Brief: AV Awareness and Perceptions from THA Wave 2
Events
Upcoming Events
Resources
Data, Tools & Briefs
Flagship surveys, interactive dashboards, and policy-ready outputs from TBD research.
Flagship Projects
Flagship Project #1
Transportation Heartbeat of America (THA) Survey
A nationally representative longitudinal survey tracking travel behavior, attitudes, and mobility trends across the U.S.
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Flagship Project #2
Travel Behavior Data (TBD) Hub
A centralized repository integrating national travel surveys, GPS traces, and administrative data to support open research.
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Interactive Data Tools
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The Mobility Dashboard (TMD)
National travel behavior trends across demographics and geographies.
Launch Tool →
CARE Dashboard
Community resilience explorer for extreme heat and mobility impacts.
Launch Tool →
T3D Dashboard
Travel behavior insights from the THA Survey across metro areas.
Launch Tool →
Data Briefs
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1
Transportation Heartbeat of America (THA) Wave 2 Survey
2025
2
Emerging Travel Behavior Insights from 2024 National Surveys and Count Data
2024
3
Emerging Travel Behavior Insights from 2023 National Surveys
2023
4
COVID Recovery? Changing Travel Behaviors? Insights From the 2022 ACS, ATUS, and CE Data Sets
2022
Research
Discover Our Research
Projects & Reports
Explore Research →
1Understanding AV Acceptance in Low-Income Communities→
2Multimodal Travel Demand Forecasting with Neural Networks→
3Long-Distance Travel Behavior and Extreme Weather Events→
4Equity Implications of Autonomous Ride-Hailing in U.S. Cities→
5Heat Resilience and Non-Motorized Travel in Sun Belt Metros→
6Telework Intensity and Household Vehicle Holdings→
7Rural Mobility Gaps and MaaS Feasibility Assessment→
8Longitudinal Panel Study of AV Attitudes: Wave 3→
9Equity in MaaS Platforms in Mid-Size Cities→
10COVID-19 Effects on Long-Distance Travel Patterns→
11Telework and Non-Work Activity Participation Analysis→
12Freight Demand and Supply Chain Disruption Modeling→
13Micromobility Integration in Transit-Dependent Communities→
14Shared Mobility Adoption Among Older Adults→
15Activity Pattern Analysis Using Machine Learning→
1Understanding Behavioral Responses to AVs in Four U.S. Metro Areas→
2Travel Behavior During and After COVID-19: A National Study→
3Micromobility Implementation Options for Transit Agencies→
4National Survey of Household Travel: Sampling and Weighting Study→
5Activity-Travel Behavior and Extreme Heat: Evidence from the CARE Survey→
6Telework Segments and Their Implications for Travel Demand Modeling→
7AV Public Opinion: Demographic and Experience-Based Predictors→
8Zero-Trip Making: Extent, Correlates, and Trends→
9Equity in Transportation Network Company Usage→
10Public Transit Recovery Post-Pandemic in U.S. Cities→
11Micromobility Integration with Transit Systems→
12Long-Distance Travel Behavior During COVID-19 Era→
Journal Articles
View All Publications →
1Alhassan, V.O., Yu, F., Batur, I. et al. Investigating the Influence of Alternative Survey Participant Recruitment Strategies on Measurement and Inference of Mobility Patterns.→
2Batur, I., Mondal, A., Alhassan, V.O. et al. The Induced Demand Implications of Alternative Adoption Modalities of Automated Vehicles.→
3Bhat, C.R. A New Flexible Skewed Bimodal Distribution with Multivariate Extensions: Theory and Application to Traffic Crash Injury Severity Analysis.→
4Chen, C., Wang, R., Bansal, P. et al. From Biases to Opportunities: Leveraging Location-Based-Service (LBS) Data for Next-Generation Transportation Planning.→
5Choi, S.-E., Kim, I., Wang, X. et al. How Has the Importance of Factors Influencing Telework Adoption Changed Over Time?→
6Conway, A. and Conway, M. The Community Impacts of Ecommerce Warehousing Growth: A Case Study of Berks County, Pennsylvania, USA.→
7Feng, G., Li, Y., Tok, A.Y.C., and Ritchie, S.G. Domain Informed Vision Language Model for Sustainable Freight with Drayage Truck Powertrain and Cargo Classification.→
8Hwang, U., Mokhtarian, P.L., Koo, B.W., and Guhathakurta, S. Perceived Streetscape Quality and Bike Lane Effectiveness: A Computer Vision Approach.→
9Kim, I. and Mokhtarian, P.L. How Much Do Attitudinal Variables Improve Travel Demand Models?→
10Kim, S.H., Wang, X., and Mokhtarian, P.L. Quantifying Aggregate-Level Telework Occasions and Their Impacts on Vehicle-Miles Traveled in the US.→
11Li, Y. and Zhang, M. Understanding the Emerging Interregional Travel Amid Shifting Societal and Technological Trends.→
12Oshanreh, M.M., Khan, N.A., and MacKenzie, D. Propagating Synthetic Populations with Dynamic Bayesian Networks: A Framework for Long-Horizon Demographic Forecasting.→
13Rezapour Fardin, F., Morvan-Chevestre, M.L., and Conway, A. How Teleworking Affects Online Shopping and Home Delivery: A Joint Modeling Perspective from Post-Pandemic New York City.→
14Robbennolt, D., Haddad, A.J., and Bhat, C.R. A Rank-Based Model of Residential Location Preferences Before and During the COVID-19 Pandemic.→
15Robbennolt, D., Pendyala, R.M., and Bhat, C.R. Data Collection, Weighting, and Modeling Techniques to Estimate Consistent Population Parameters.→
16Tu, Y., Oshanreh, M.M., Khaloei, M. et al. Effect of Trip Attributes on Ridehailing Driver Trip Request Acceptance.→
17Wang, Z., Oshanreh, M.M., and MacKenzie, D. A Hybrid Temporal-Spatial Framework for Understanding Public EV Charging Usage Patterns: Evidence from Bay Area.→
18Yang, Y., Li, X., Guo, Y. et al. Understanding Joint Travel–Charging Decisions of Battery Electric Vehicle Users During Typhoons.→
19Robbennolt, D., Pendyala, R.M., and Bhat, C.R. Data Collection, Weighting, and Modeling Techniques to Estimate Unbiased Population Parameters.→
Our Focus Areas
Research Innovation Areas
TBD’s research aims to unravel the interlinked behavioral processes shaping the movement of passengers and goods, anchored in three key innovation areas. Explore Our Research Areas →
Thrust 1
Data Collection Mechanisms
Good data is critical to decision-making. A key focus of TBD is data collection, focusing on the methods for collecting, compiling, and fusing disparate data sources to build a robust evidence base for transportation research.
Thrust 2
Data Modeling and Analytic Tools
Harnessing new algorithms and software platforms to build models and dashboards capable of providing actionable information to policymakers and transportation planners.
Thrust 3
Understanding User Needs
Focusing on the user through a management lens, advancing policies and investments to address user needs in a heterogeneous population with a view toward equity and accessibility.