Data Collection Mechanisms

Does AI based synthetic travel behavior data replicate human cognitive biases?

This project will develop methods to test for cognitive biases in AI-generated synthetic data used as substitutes for human survey responses in travel behavior analyses. Moving beyond recent benchmarking and prompt engineering work, this project will ask: even when synthetic data look statistically reasonable, do they reproduce canonical cognitive biases that influence travel choices? In

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Evaluating Impacts of Street Repurposing Interventions in U.S. Cities (Phase II)

Cities are increasingly repurposing street space to support pedestrian activity and economic vitality through interventions such as sidewalk cafés, parklets, streateries, and pedestrian plazas. While these interventions are widely implemented, their impacts are often evaluated using limited case studies or coarse spatial data. There is a need for scalable, data-driven approaches to evaluate their effects

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Leveraging Location-Based Services (LBS) Data and Video Analytics for Scalable Intersection Safety Assessment

Intersections are high-risk locations where a significant share of severe crashes occur, particularly those involving pedestrians and other vulnerable road users. Traditional approaches to safety analysis rely on crash records, field observations, or fixed camera systems. While valuable, these methods are often costly, spatially limited, and retrospective, limiting their ability to support proactive, system-wide safety

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Public Transit Automation and the Future of Service Delivery: Scenario Planning for Operations and Workforce Readiness

Public transit agencies are beginning to confront a new generation of automation technologies that will almost certainly alter how service is planned, operated, staffed, and delivered. These technologies include fully automated transit vehicles as well as nearer-term applications in scheduling, dispatching, maintenance, customer information, and operations management. In this project, we will examine how transit

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Integrating Transportation Data to Address Representation and Measurement Limitations in Travel Behavior and Demand Analysis

Expanding transportation data availability is creating new opportunities for a richer, potentially more representative, and broader data landscape to support the measurement and modeling of evolving travel behaviors and outcomes. This project addresses the need for clearer guidance on when, where, and how multiple data sources can be integrated to overcome common data limitations in

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Characterizing Operational Disruptions in Urban Freight Networks: Patterns, Propagation, and Recovery Behaviors in Atlanta

Urban freight systems are subject to recurring disruptions that can degrade travel time reliability and operational efficiency. Despite their cumulative effects on freight movement and supply chain performance, these disruptions remain relatively understudied at the network level, particularly when only publicly funded and openly accessible data are used. This project develops a data-driven analytical framework

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Generative AI-based Framework for Modeling Longitudinal Travel Behavior Adaptation Under Transportation Interventions

Transportation agencies deploy a wide range of interventions to influence travel behavior and manage demand. While a broad range of approaches have been used to model travel behavior dynamics, modeling how behavioral responses manifest, strengthen, or decay over repeated exposure to transportation interventions remains relatively underexplored. This project will address this gap by developing a

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Towards Building-Level Modeling: Human-Driven Agentic Workflows for Multi-Source Data Synthesis

Travel demand modeling is limited by the aggregation problem: data are often synthesized at census-tract or traffic-zone scales that obscure building-level mobility patterns. This project develops a human-directed agentic workflow in which a transportation planner orchestrates specialized artificial intelligence agents that discover, evaluate, and integrate open geospatial data from sources such as OpenStreetMap, building footprint

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Beyond Pedestrian Flow: Evaluating Urban Sidewalk Friction and Accessibility via Efficient State Space Models

Traditional sidewalk performance evaluation has primarily relied on flow metrics, such as level of service, that treat pedestrians as uniform units, similar to vehicles on a highway. These methods typically rely on basic pedestrian intersection or screenline counts, which capture pedestrians at a single point location. More recently, advanced sensors such as video- and LIDAR

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Decoding Road-User Intent: Advancing Surrogate Safety Measures through Behavior-Aware Generative World Models

This project investigates how roadside LiDAR sensing and generative scene prediction models can support more informative and uncertainty-aware traffic safety assessment at urban intersections. Conventional surrogate safety measures, such as time-to-collision and post-encroachment time, are widely used to identify potential traffic conflicts. However, these measures are primarily based on observed kinematic relationships and may not

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