Data Modeling and Analytic Tools

An Application-Agnostic Investigation of Location-Based Services Data Quality: Using Synthetic Data and Empirical Benchmarking

Over the past decade, the widespread adoption and use of mobile services and devices have enabled the large‑scale, passive collection of human location data. The most widely available of these, termed location‑based services (LBS) data, are largely GPS enabled location “pings” collected from third party smartphone applications. These data have already proven to be useful […]

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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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Spatiotemporal Heterogeneous Change of Travel Behavior during Wildfires in California

California suffers from frequent and intense wildfires every year due to its unique weather patterns and dense, flammable vegetation. Wildfires often cause significant infrastructure damage, disrupt roadway networks, interrupt business operations, and generate hazardous smoke, all of which can deteriorate the driving environment, elevate perceived safety risks, limit individuals’ ability to meet daily work-life demands,

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Exploring Top-Down Visual Attention for Transportation Behavior Analysis: Walkability and Pedestrian Behaviors

Employing the state-of-the-art research on attention and feedback mechanisms, especially with vision language models (VLMs), has not been fully explored previously for transportation behavior analysis, especially for the analysis of a variety of pedestrian behaviors related to sidewalks and streets. This project will expand our previous project exploring top-down visual attention for transportation behavior analysis

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Leveraging Vision-Language Models for Efficient Understanding of Vulnerable Roadway Users via a Multimodal Traffic Sensing Approach

The proliferation of 3D and video data from urban intersections offers a unique opportunity to analyze and protect vulnerable road users (VRUs). However, the effectiveness of modern detection models like PointPillar or CenterPoint is limited by the availability of high-quality labeled data. In Year 2, we demonstrated the feasibility of multimodal sensing using LiDAR and

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Measuring the Last-Mile: Leveraging Synthetic Data to Evaluate the Effects of Urban Freight Interventions

In most urban areas, there is currently a misalignment between difficult-to-measure freight vehicle demands and infrastructure capacities (e.g., parking and loading zones) to accommodate these demands. This misalignment typically results in high costs for industry (e.g., wasted time, wasted fuel, parking fines) as well as in congestion, traffic conflicts, and other externalities for the surrounding

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From Reactive to Predictive: Modeling Urban Event Impacts on Transportation Systems

Every day, New York City hosts countless events ranging from street festivals and protest marches to unexpected incidents and major sporting events. Each of these events may create ripples through the city’s complex transportation network, affecting how millions of New Yorkers move around their city. But what if we could predict these ripples? This proposal

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Behavior-Aware Evaluation of Emerging Vehicular Technologies

Emerging vehicular technologies such as vehicle-to-everything (V2X) and cooperative driving automation (CDA) have the potential to improve traffic operation and safety. However, their effectiveness hinges not only on technical performance but also on how users (mainly drivers) perceive, interpret, and respond to these systems. Past USDOT-sponsored pilot deployments have demonstrated promising benefits but also revealed

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The Mobility Dashboard (TMD)

The transportation landscape is undergoing rapid and far-reaching change, fueled by emerging technologies, shifting work arrangements, demographic transitions, and evolving lifestyles and consumer attitudes. These developments are challenging long-standing assumptions in transportation planning – particularly those related to the stability of travel behavior, the predictability of demand, and the foundational drivers of mobility choices. As

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