TBD research is centered on understanding evolving travel behaviors due to technological advances, demographic and cultural shifts, and environmental concerns. This requires updating our understanding of travel patterns, as well as the datasets and methods for forecasting future needs and behavioral responses to investments and policies. TBD research activities aim to unravel the underlying and interlinked behavioral processes that shape the movement of passengers and goods.

The Center’s research mission is anchored in three key innovation areas:

Data Collection Mechanisms, emphasizing methods for gathering, compiling, and integrating disparate data,

Data Modeling and Analytic Tools, developing new models and dashboards to provide actionable insights for policymakers,

Equity and Understanding User Needs, advancing policies and investments to meet the needs of a heterogeneous population, including disadvantaged and rural communities.

These areas intersect and foster interdisciplinary collaboration opportunities.

TBD’s Research and Innovation Areas

Within these areas, TBD conducts various innovative research projects. Key topics include commute and location choices in a telework-friendly environment; effects of information and communication technologies on travel demand; methods and data for integrated forecasting of freight and passenger travel in varied contexts; and impacts of emerging transportation technologies on travel behavior and demand.

Thrust 1

Data Collection Mechanisms

Good data is critical to decision-making. A key thrust area of TBD is data collection, focusing on the methods for collecting, compiling, and fusing disparate data. Data may be collected through smartphone apps and other passive means, or through surveys that can be viewed as intensive and intrusive. A few companies are now aggregating trajectory data to provide valuable information about origin-destination flows by purpose, market, and time-of-day. This thrust aims to provide the ability to measure and monitor system behavior and evolution through the integration of disparate data sets and the deployment of sensors to gather information on system states along the continuous time axis. By measuring and monitoring system behaviors, it will be possible to characterize the system of interest, conduct rigorous analysis, and make informed decisions.

Thrust 2

Data Modeling and Analytic Tools

This thrust is aimed at advancing computational analysis and modeling methods – harnessing the power of new algorithms and software platforms for building new models and dashboards capable of providing actionable information to policymakers. A key emphasis of the TBD center will be the development of new hybrid model systems that bring the best of both statistical/econometric modeling techniques and big data mining techniques (e.g., machine learning algorithms) together into new analytical frameworks capable of revealing and visualizing patterns, behaviors, and evolutionary dynamics in insightful ways. With such insights, transportation systems and mobility services can be proactively managed and optimized, providing unprecedented levels of agility and adaptability to the benefit of the traveling public. This thrust area may be described as encompassing methods and models.

Thrust 3

Equity and Understanding User Needs

The information provided by the data and the models needs to be turned into action to advance societal goals of mobility, accessibility, resilience, sustainability, equity, safety, and efficiency. With rich data and even richer models and dashboards, it will be possible to analyze tradeoffs between goals (as a consequence of certain actions) and to determine the best countermeasures to mitigate unintended consequences that may result from certain tradeoffs. In doing so, attention needs to be paid to ensure that disadvantaged populations and rural geographies are not excluded or left behind. Travel demand, unintended consequences, and services, technologies, and pricing structures need to be managed. Thus, this thrust, which focuses on the user (agent) may be viewed through a management lens, with a view to advance policies and investments to address user needs in a heterogeneous population.

Research products

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Analysis of Changes in the Activity Prisms of Individuals to Predict a Shared Life Experience Metric Over Different Regions and Sociodemographic Groups

This project aims to (a) define a new Shared-life Experience (SLE) metric based on the activity prisms of individuals; (b) analyze the changes in the SLE metric in the individual level over multiple years; and (c) run a probabilistic analysis to predict changes in the SLE metrics to identify how different regions and sociodemographic groups will be impacted.

City-Wide Strategic EV Charging Network Design: Demand-Supply Integration via Market Dynamics

The Electric Vehicle Location Selection Problem (EVLSP) addresses the task of identifying optimal locations for installing EV charging stations to achieve maximum coverage, minimize the cost of infrastructure development, and enhance the convenience and accessibility for EV users. This project presents a comprehensive study on the EVLSP with a specific focus on the city of Avondale, AZ.

Future Travel Foresight Catalyst: A Unique Approach to Exploring the Intersection of Transformative Technologies and Future Travel Behavior and Demand

The project aims to combine futures methodologies with cutting-edge use of media platforms such as podcasts, articles, videos, and more, to engage across diverse communities and stimulate new and transformative thinking around future travel behavior and demand.

Exploring the Changing Dynamics of Household Vehicle Ownership and Use in the U.S.

This project aims to design and deploy a comprehensive nationwide survey to collect data on vehicle ownership, use, and preferences in the context of societal and environmental changes as well as related changes in household energy use (e.g. the adoption of residential solar photovoltaics and battery storage).

A Dynamic Analysis of the Built Environment-Travel Behavior Relationship Using Three Activity-Travel Surveys in the Austin, Texas Region

The study will pool three activity-travel surveys (1998, 2007, and 2017) in Austin, TX, and analyze how variations and changes in travel behavior revealed in the surveys relate to built environment variations and changes in the timeframe corresponding to the surveys.

Deep Learning with LiDAR Point Cloud Data for Automatic Roadway Health Monitoring

This project aims to investigate the efficacy of various point cloud-based deep learning models in automating roadway health assessments.

The Differential Accessibility Effects of Work from Home: Travel Behavior Outcomes and Transportation Equity Implications

The research is expected to develop new tools and methods for measuring the accessibility for working and living activities in the new work-from-home era while informing policies and practices that could help improve accessibility and equity for essential workers and disadvantaged groups.

Consumer Preferences for Restaurant and Grocery Delivery Services in Seattle: Impacts on Travel Behavior

The project is focused on consumers in Seattle who use restaurant and grocery delivery services and the related impacts on the safety of delivery drivers (in automobiles, motorcycles, bicycles, e-bikes, e-scooters and on foot) and pedestrians in dense urban areas.

Promoting Sustainable Travel within Communities through Behavioral Interventions and Emerging Mobility Solutions

This project will draw on methods from behavioral economics, data analytics, machine learning, multiobjective optimization, and simulation to generate solutions to achieve various societal travel goals.

How Effective Are Marker Variables at Predicting Attitudinal Factor Scores? An Out-of-Sample Evaluation

This project would continue a line of research that focuses on overcoming the objection as to why attitudes are nowhere to be found in practice-oriented travel demand forecasting models.

Identifying Targets for Electric Vehicle Industry Improvement

This research will examine the complete spectrum of the EV industry to identify all the issues that should be identified as targets for improvement.

Identifying Travel Needs, Barriers, and Solutions

Historically and currently disadvantaged population groups often face unique travel needs and barriers that can significantly impact their daily lives. This research project aims to investigate and address these challenges within these communities, ultimately contributing to improved access to essential services and enhanced quality of life.

A Pilot Study to Integrate Mobility Data Collection APPs with Personalized Recommendation Systems

This research project aims to launch a new smartphone application on travel behaviors and provide more personalized recommendations based on the collected data, while the end tasks (i.e., the personalized recommendation algorithms) could also help improve data collection as well.

A Pilot Experimental Project for Predicting Pedestrian Flows using Computer Vision and Deep Learning

This research project aims to develop a graph convolutional network model (GCN) based only on pedestrian counts at various intersections and segments to predict pedestrian traffic flows.

Measuring the Last-Mile: A Comprehensive Evaluation of Synthesis Approaches to Address Data Gaps for Local Freight Decision-Making (Phase 1)

This project represents the first phase of an expected multi-year effort to design and construct one or more synthetic last-mile freight datasets that can address existing data gaps to inform planning and operational decision-making by local transportation agencies.

The Effects of Changing Commutes on Home Delivery Activity

Relying on the New York City Department of Transportation’s forthcoming 2022 Citywide Mobility Survey (CMS) and publicly-available land-use and employment data, this project will explicitly investigate the relationship between work-related travel activity (or lack thereof) and propensity for home delivery.

The Effect of Urban Infrastructure Change on Movement

The project aims to fill an important gap in the current built environment and travel behavior literature, which is to understand how block-level changes in our urban infrastructure can result in changes in people’s visit patterns and business activities.

Enhanced Network Models for Multimodal Resiliency

This project will develop next-generation multimodal network resilience models. We will examine the performance of networked transportation systems in disrupted conditions using field data, generate mathematical models to describe system performance and user behavior, and develop mitigation strategies based on this model.

Teleworking to Play or Playing to Telework? A Latent Segmentation Approach to Exploring the Relationship Between Telework and Nonwork Travel

This study explores the causal direction/jointness issue underlying the interplay of teleworking choice and nonwork travel, within the context of the telework landscape in the aftermath of the pandemic.

Telemedicine Adoption Before, During, and After COVID-19: The Role of Socioeconomic and Built Environment Variables

In this research, we focus our investigation on the telemedicine adoption preferences of patients/consumers. Our comprehensive approach contributes to advancing the existing body of knowledge in distinct ways. Our study will open up additional research in studying the travel implications of tele-participation in medical-related activities. This should be of particular interest in the context of medical accessibility for the increasingly aging population of many countries, including the United States.

Trends in Time, Travel, Transit, Telework, and Treasure (T5)

This project explores the evolving trends in time, travel, transit, telework, and treasure (T5) over the last two decades, examining how advancements in technology, changing demographics, and evolving cultural norms have reshaped how people manage their time, travel, and resources.

The Travel Behavior and Data (TBD) Hub

TBD Center aims to build a travel behavior data hub that the public, planners, and policymakers alike can leverage to understand the state of the transportation system, with built-in quality of life, energy footprint, and mobility poverty calculators to aid in advancing system performance, economic development, community wellbeing, sustainability, and equity.

COVID Recovery? Changing Travel Behaviors? Insights From the 2022 ACS, ATUS, and CE Data Sets

The dramatic transportation impact of the COVID-19 pandemic coupled with ongoing changes in demographics, transportation technologies, and culture and values make it particularly important to review the available data to discern emerging new normal behaviors. This brief reviews the American Community Survey (ACS), the Consumer Expenditure (CE) Survey, and the American Time Use Survey (ATUS), with respect to questions that give insight into travel behaviors.

The Transportation Heartbeat of America Survey

TBD will deploy a comprehensive longitudinal travel behavior and demand survey across the nation for six years to obtain a statistically representative depiction of attitudes, values, choices, socio-economics, demographics, well-being, and mobility and accessibility.

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