Data Modeling and Analytic Tools

Are We Ready? Evaluating Evacuation Preparedness, Behavior, and Vulnerability During Wildfires in Washington

As wildfires intensify across the western United States, communities in the wildland-urban interface face growing risks to life, infrastructure, and mobility. Wildfire evacuations, often occurring under rapidly changing conditions with limited warning, require coordinated responses informed by real-world human behavior. However, current evacuation planning models often rely on assumptions that do not reflect the variability […]

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Opportunities and Limits of Individual Behavioral Changes: Phase I

Individual travel behavior changes such as changing from driving to non-car-based modes of transportation can have overarching implications at the system level in reducing congestion and improving the mobility for all. And yet, individual behavioral changes are hard due to a variety of personal and system-level factors. Efforts to change individual travel behaviors have been

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Real-Time Transportation Origin-Destination Demand Estimation Using Multimodality Data

Real-time origin-destination (OD) demand estimation is essential for urban transportation management, enabling responsive traffic control, dynamic transit scheduling, e-hailing services, and emergency operations. Traditional OD estimation methods rely heavily on traffic sensor data and statistical modeling; but the limited coverage of sensors in most cities poses a significant challenge. Emerging data sources—such as mobile trajectories

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Do Millennials and Zoomers Participate in Teleactivities More and Travel Less Than Older Generations?

This project seeks to bridge two lines of inquiry, both concerning the future trajectory of travel behavior. The first one explores shifts in travel patterns amid the rise of home-based teleactivities – defined here as activities traditionally conducted outside the home but increasingly performed at home through digital technological applications, such as working from home,

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Estimating Latent Bicyclist and Pedestrian Demand for Shared Use Path Design

This is a human behavioral study designed to develop methodology to predict demand for bicycle and pedestrian facilities. The current design guidance for bicycle and pedestrian facilities does not include specific guidance for predicting numbers of bicycle and pedestrian users.  The available guidance for such facilities suggests designers should consider the number of users, and

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Multimodal Freight Network Capacity and Resilience Under Demand Shifts

This project will develop methods for identifying bottlenecks in multimodal maritime freight systems as demand patterns shift. Examples of such changes are increased demand due to onshoring of manufacturing industries; competition from alternative shipping modes (rail, overland); and demand changes during natural or man-made disruptions (which may simultaneously cause capacity changes). To address this goal,

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Data Collection, Weighting, and Modeling Techniques to Estimate Unbiased Population Parameters

Empirical research studies across multiple fields employ data from large surveys for their analysis. In doing so, studies must address such sampling-related issues as non-response, missing data, unequal sampling, and other survey biases. The voluntary nature of most surveys means that, in many empirical applications, data are not randomly selected from the population. Instead, researchers

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A Virtual Reality Framework for Analyzing Pedestrian Crossing Behavior and Decision-Making Factors

Pedestrian safety remains a critical concern, with a significant rise in pedestrian fatalities in recent years. In 2022 alone, 7,522 pedestrians died in crashes, marking a troubling 57% rise from 2013 (Smart Growth America, 2024). However, traditional safety research focusing on crash outcomes and infrastructure factors has failed to capture the nuanced behavioral aspects that

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Potential Use of Large Language Models (LLMs) for Travel Behavior Survey Research

Traditional travel behavior surveys are resource-intensive and constrained by challenges such as small sample sizes, response bias, and high costs. With the rapid advancements in artificial intelligence, particularly LLMs, we now have the opportunity to explore novel, cost-efficient methods for creating synthetic data comparable to real-world survey results. However, the potential of LLMs to contribute

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