Research projects
Future Travel Foresight Catalyst: Phase 2
This project aims to catalyze innovative thinking and knowledge mobilization around future travel behavior and demand through the Future Travel Foresight Catalyst project, integrating cutting-edge technologies, futures methodologies, and public engagement via diverse media platforms.
Disabled Parking CV: Scalable Methods to Analyze Disability Parking Using Computer Vision and High-Resolution Aerial and Streetscape Images
This project aims to develop and evaluate computer vision methods for analyzing the allocation and characteristics of disability parking spaces across the U.S. using high-resolution aerial imagery, and to create open datasets and analytics for ADA-accessible parking.
Imputing Socio-Demographics for Mobile Trajectors
This project aims to develop a model that imputes socio-demographics from census data into mobile trajectory data, addressing the uncertainty in linking socio-demographics to travel behaviors and unlocking the potential of big, passively-generated mobile data for real-time applications.
Quasi-Sparsity in Transportation Origin-Destination Demand
This project aims to investigate quasi-sparsity (QS) in origin-destination (OD) travel demands across various transportation modes and networks, including vehicular traffic, transit, ride-hailing, and freight, and explore how QS can improve OD demand estimation and synthesis methods.
Michigan Mobility Metrics (M3): An Outcome-Focused, Multi-Year Survey Deployment and Data Collection Effort
This project aims to augment the statewide household travel survey in Michigan by conducting a detailed research-oriented survey that examines residents’ attitudes, preferences, and intended adoption of emerging transportation modes and technologies, enhancing demand forecasting and policy development for the SEMCOG region and the state.
A Multidimensional Analysis for Understanding Walking Habits in Older Adults Post-Pandemic
This project aims to analyze post-pandemic walking behaviors among older adults (50+) in the U.S., focusing on walking frequency, duration, and companionship, using data from the Foresight 50+ Consumer Omnibus panel survey to inform policies promoting physical activity for older populations.
The Reverse Side of Online Shopping: Examining Sociodemographic and Built-Environment Determinants of Delivery Returns
This project aims to examine the influence of sociodemographic and built-environment factors on the frequency and channel choice for returning online purchases, using data from the NHTS 2022 survey, and to provide insights for strategic urban logistics planning and policy interventions.
An Evaluation of the Long-Term Effects of the COVID-19 Pandemic on Public Transportation Use
This project aims to explore the ongoing and long-term impacts of the COVID-19 pandemic on public transportation use, focusing on changes in ridership, expectations about the permanence of these changes, and current transit usage, while offering insights for transportation planners and policymakers.
A Model of EV Adoption and Rank-Based Contributing Factors
This project aims to examine individual-level electric vehicle (EV) adoption behavior, focusing on the differences between plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), using a survey of California households to inform policies promoting EV adoption and the design of EV charging infrastructure.
Empirical Investigation of Post-Disaster Travel Behavior to Points of Distribution of Relief Supplies
This project aims to empirically investigate the relief supply-seeking travel behaviors of disaster-affected populations, focusing on the decisions regarding which points of distribution (PODs) to visit and the factors influencing transportation mode and route choices, with the goal of improving disaster response logistics.
Promoting Sustainable Travel within Communities through Behavioral Interventions and Emerging Mobility Solutions: Stage 2
This project aims to nudge communities toward societal travel goals of mobility, accessibility, environmental sustainability, and equity by leveraging behavioral interventions and emerging mobility solutions, with a focus on disadvantaged groups and low-adoption of sustainable travel modes.
How Effective Are Attitudinal Variables at Improving Travel Behavior Models? Evaluation Using an Overlapping Sample From an Attitude-Rich Survey and the 2017 National Household Travel Survey
This project aims to impute attitudinal factor scores into a survey dataset using machine learning functions trained on attitudinal marker variables from a donor sample, and assess the impact of these imputed attitudes on travel behavior modeling.
Improving Mobility Options through Transit Signal Priority (TSP)
This research project develops and tests novel adaptive TSP algorithms based on AI/ML and CV in a simulated environment.
How Complete are Your City’s Streets? Evaluating the Completeness of Urban Streets Using Big Data and Computer Vision
This project plans to collect data on both the presence of complete streets elements and their cross-sectional width where applicable.
Exploring Top-Down Visual Attention for Transportation Behavior Analysis
This project aims to develop best practices for safe and efficient interaction of automated roadway vehicles with existing vehicles, roadside hardware, pedestrians, cyclists, and motorcyclists by performing human behavior analysis with visual attention.
Investigation of Emerging Sensing and AI/ML Technologies to Enhance the Safety of Vulnerable Roadway Users at Signalized Intersection
This project studies the state-of-the-art methods of VRU data collection, image- and LiDAR-based VRU object detection and classification, and dynamic VRU trajectory estimation methods.
Blockchain Application on Smart Transportation Systems
The project aims at comprehensively studying the application of blockchain technology within various domains of transportation systems.
Addressing Mobility-Related Challenges for AAPI Older Adults
This project will use qualitative and quantitative research methods to better understand mobility-related challenges for Asian Americans and Pacific Islanders (AAPI) older adults in order to provide government agencies and organizations such as National Asian Pacific Center on Aging (NAPCA) with recommendations for policy and program changes to pursue.
Vehicle Edge Computing for Travel Behavior and Demand in Future Intelligent Transportation Systems (ITS)
The project investigates how edge computing impacts travel behavior. Field studies and simulations will measure travelers' responsiveness to real-time data and how it influences their travel choices and demand patterns. This ensures the research is relevant to travel behavior studies.
Smart Transportation Digital Infrastructure: Advancing System Equity, Resilience, and Safety through Multi-Source Open-Standard Data Integration
This project aims to develop an Open data hub and Open-source data analysis platform for transportation-focused Open-STDI applications.
From Cross-Sectional to Longitudinal: The Impact of Sampling Strategies on Measuring Mobility Choices
This project aims to analyze the impact of survey sampling strategies on data representativeness and understanding of travel behavior over time.
Time Use, Travel, and Telework Dashboard (T3D)
This project endeavors to develop a web-based data dashboard named the Time Use, Travel, and Telework Dashboard (T3D), aimed at democratizing ATUS data to make it more accessible and interpretable for everyone
The Travel Behavior and Data (TBD) Hub
This project aims to develop a travel behavior data hub that brings a variety of data sets into a single unified platform, thus serving as a one-stop shop for data-driven insights on travel behavior and demand.
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.