Data Modeling and Analytic Tools

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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Investigating the Stability and Evolution of Transportation-related Attitudes: Evidence from Two Longitudinal Survey Samples

Despite extensive evidence linking attitudes to behavior in the academic literature, challenges in measuring and forecasting attitudes remain key barriers to incorporating travelers’ attitudes into practice-oriented regional travel demand models. Recent studies have focused primarily on addressing measurement challenges, exploring and validating several approaches including the marker variable approach, which involves including an abbreviated set

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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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The Infrastructure-Perception Gap: Examining the Role of Transportation Infrastructure in Mediating Safety Perceptions for Active Travel

While extensive research demonstrates that perceived safety influences active travel behavior, the relationship between safety perceptions and transportation infrastructure characteristics remains inadequately understood. Traditional safety studies rely primarily on crash data, which often fails to align with how travelers perceive safe environments. This research addresses a critical gap by investigating how transportation infrastructure mediates safety

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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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Integrating Microsimulation in a Multi-scale Local Freight Activity Model

This project builds on results from prior TBD Center projects to advance the development of a multi-scale, synthetic data-based simulation framework for evaluating urban freight policy and infrastructure interventions. In Phase 1, we conducted a comprehensive review of existing freight data sources and machine learning- and generative-AI based approaches to address persistent data gaps for

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Measuring the Motivators of Home Delivery Decisions

Over the last decade, many shoppers in the U.S. and around the world have quickly become reliant on home-based deliveries of household and retail goods. While growth in e-commerce is easily demonstrated from retail sales records, relevant activity trade-offs and resulting impacts on passenger and freight travel activity remain less well-understood. Some home deliveries are

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Who Stays and Who Leaves? Examining Sample-Source Effect on Attrition in a Longitudinal Panel Survey

Longitudinal panel surveys are a cornerstone of travel behavior research, enabling measurement of behavioral change over time and facilitating before-and-after studies of policy interventions. Despite their scientific value, panel surveys suffer from a well-documented challenge: attrition. When respondents exit the panel non-randomly between different waves of longitudinal surveys, the resulting stayer sample becomes biased, thereby

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