Georgia Institute of Technology

Enhancing the Use of Attitudinal Marker Variables in Travel Behavior Models: Evaluation of Latent Class Modeling Approaches Using a Nationwide Travel Survey

Recent studies have shown that an abbreviated set of attitudinal marker variables (MVs) can serve as proxies for attitudinal factor scores from the full set of attitudinal variables, either directly or through machine learning-based imputation. Compared to models without attitudes, these MVs enhance model fit, identify additional significant explanatory variables, and improve prediction of less-often […]

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Neutralizing Onerous Heat Effects on Active Transportation (NO-HEAT) in Atlanta

This project advances research at the intersection of heat resilience and multimodal transportation by combining urban microclimate modeling and sensing tools with big data and travel behavioral frameworks. We aim to explore how, and to what extent, people modify their activity-mobility patterns during periods of extreme heat in Atlanta, GA. We will extend recently developed

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Empirical Investigation of Post-Disaster Travel Behavior to Points of Distribution of Relief Supplies

The world has seen a surge in extreme weather events and increased challenges of disaster response. One overlooked factor complicating the distribution of relief supplies is the immediate decisions survivors make when seeking aid, which impacts the realized demand at various points of distribution (PODs) of relief supplies. This project aims to understand these critical

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How Much Do Attitudinal Variables Improve Travel Demand Models? Evaluation Using an Overlap Sample from an Attitude-rich Survey and the 2017 National Household Travel Survey

A line of research has recently been launched on attitude imputation using machine learning (ML) functions trained on variables common to two survey datasets (Mokhtarian, 2024). It was discovered that using a handful of attitudinal marker variables (i.e., the one or two attitudinal items most strongly associated with each attitude) as common variables for imputation

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Improving Mobility Options through Transit Signal Priority (TSP)

TSP seeks to optimize the interaction between busses and the infrastructure, creating a minimum resistance path for transit buses through signalized intersections. TSP may improve travel time reliability (TTR), schedule adherence, and ultimately the quality of service and ridership for transit systems. Bhat and Sardesai explicitly includes of TTR in mode choice, demonstrating the significance

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How Complete are Your City’s Streets? Evaluating the Completeness of Urban Streets Using Big Data and Computer Vision

The main objectives of this project are: (1) development and validation of detection methods on the presence and width of individual elements of complete streets at street level, (2) development of a numeric index and typology to rate the completeness of streets, (3) curation of a publicly accessible database of various elements of complete street

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Travel Behavior Data (TBD) Hub

In an era characterized by transformative shifts in demographics, lifestyles, work patterns, technological advances, societal values, and climate and environmental conditions, decision-makers are now confronted with ever-increasing, multifaceted uncertainties. The TBD National Center has launched a flagship initiative, called the TBD Hub, to provide transportation decision-makers information and deep insights about the state of the

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Promoting Sustainable Travel Within Communities Through Behavioral Interventions and Emerging Mobility Solutions

Traditionally, interventions to change travel behavior have relied on penalty-based approaches (such as tolls). Recent discussions have shifted towards monetary incentive-based approaches to promote sustainable modes. However, due to limited funding, relying on monetary incentives alone is not sustainable. Hence, it is important to explore the potential of sustainable non-monetary incentives such as gamification, nudges,

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

Despite the fact that our existing models are not up to the job of predicting travel behavior in today’s rapidly changing landscape, and despite considerable evidence that attitudes help us explain behavior more completely and more meaningfully, attitudes are nowhere to be found in practice-oriented travel demand forecasting models.  Two main objections have been raised

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

Walking for transportation, health, and pleasure is an essential part of people’s lives in most cities. Knowing where people linger, the destinations that attract them, and how those places are accessed could assist in optimizing business locations and providing better security. In addition, predicting and sharing congestion times and locations (perhaps in real-time as in

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