Speaker: Yeji Jeon, Ph.D. student, Department of Civil and Architectural Engineering and Mechanics, University of Arizona
When
Join in person or online: https://arizona.zoom.us/j/87815388173?from=addon
Abstract: Accurately capturing long-distance travel patterns remains a challenge for statewide transportation planning due to the limited availability of comprehensive travel behavior data. Traditional travel surveys provide valuable behavioral information but often face limitations in sample size, spatial coverage, and data collection costs. To address these challenges, the Arizona Department of Transportation (ADOT) SPR-813 project, Estimating Long-Distance Travel Patterns Using Data Fusion Methods, aims to develop a cost-effective framework that integrates targeted travel survey data with passively collected mobility data.
This presentation will introduce the project's objectives, scope, and proposed methodological framework. The project involves designing and implementing a small-scale long-distance travel survey, integrating survey responses with Location-Based Services (LBS) and Connected Vehicle Data (CVD), and developing statewide origin-destination (OD) flow matrices. The resulting dataset is intended to capture spatial and temporal travel patterns alongside essential behavioral characteristics, such as trip purposes, travel party composition, and trip-chaining activities. The presentation will also discuss the project's anticipated contributions to enhancing the Arizona Statewide Travel Demand Model (AZTDM) and supporting data-driven statewide transportation planning.
Researcher's Bio: Yeji Jeon is a third-year PhD student at the University of Arizona, working with Dr. Ali Shamshiripour in the Urban Ecosystems Lab at the Center for Applied Transportation Sciences (CATS). Her research focuses on travel behavior, multimodal and active transportation, shared mobility, and data-driven transportation planning. Her work aims to better understand individual travel behaviors and support the development of responsive, people-centered transportation systems.