Data Collection Mechanisms

Analysis of National Travel Trends

Over the past few decades, the transportation landscape has undergone a significant transformation, driven by technological innovations, demographic shifts, and evolving socio-cultural norms. The proliferation of information and communication technologies has revolutionized how and where individuals undertake daily activities, increasingly substituting physical travel with virtual alternatives and reshaping work through telework. Changes in technology have

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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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Micromobility Adoption, Ownership, and Use Patterns in the United States

Micromobility modes—including conventional bicycles, electric bicycles, electric scooters, shared micromobility services, and other emerging personal mobility devices—are becoming increasingly important components of the United States transportation system. These modes offer opportunities to support short-distance travel, improve first- and last-mile access to transit, reduce automobile dependence, and promote healthier and more sustainable travel behavior. Despite growing

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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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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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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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The Home as a Trip Attractor: An Exploratory Study of Residential Service Demand

Taking a two-phase approach, this study aims to investigate the nature and scale of household-based service demands, and to establish a baseline for better data collection and modeling of this under-studied component of travel demand in future research efforts. Part 1 of this project will utilize data from the recently completed Transportation Heartbeat of America

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Quasi-Sparsity in Transportation Origin-Destination Demand

Quasi-sparsity (QS) indicates that for a large-scale transportation network, most origin-destination (OD) demands are concentrated on a small fraction of the OD pairs, while majority of the OD pairs exhibit small (maybe non-zero) travel demands. One example is the King County network (the area that includes the City of Seattle in the State of Washington):

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