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 […]
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
Potential Use of Large Language Models (LLMs) for Travel Behavior Survey Research Read More »
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
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
The Home as a Trip Attractor: An Exploratory Study of Residential Service Demand Read More »
Ubiquitous mobile devices have resulted in massive amount of location- and time-stamped traces that can be used to infer people’s mobility patterns for various applications. Unlike household travel survey data that is small but rich (short but wide data), mobile data, is often massive but shallow (long but thin data) whose meanings in terms of
Imputing Socio-Demographics for Mobile Trajectors Read More »
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):
Quasi-Sparsity in Transportation Origin-Destination Demand Read More »
Transportation modes, technologies, and the broader context within which people travel have evolved rapidly over the last decade. Examples of such changes include the introduction of: new/emerging modes like ridesharing and micromobility, electric and automated vehicle technologies, information and communication devices; and the increase in remote and hybrid work due to COVID-19. It is critical
Accurately identifying and analyzing vulnerable roadway users (VRUs) such as pedestrians, bicyclists, and other non-vehicle occupants, are a crucial yet difficult undertaking. VRUs’ behavior is influenced by localized factors such as land use, and their movements are not confined to predefined paths. This study will investigate the use of emerging technologies such as LiDAR, network
Blockchain technology, predominantly utilized within cryptocurrency, is being increasingly adapted across diverse sectors, and transportation systems is not an exception. Despite presenting several challenges, blockchain technology also offers various advantages. Understanding Blockchain’s potential applications and benefits in addressing future urban challenges is an emerging field of research which has not been fully investigated. In fact,
Blockchain Application on Smart Transportation Systems Read More »
The transportation profession has long relied on surveys as a main source of data. These surveys are used across a broad range of applications, including but not limited to, travel demand forecasting, travel behavior analysis, policy evaluation, environmental impact assessment, accessibility analysis, and economic evaluation. Despite the increasing accessibility of passive data collection methods, such
