Graduation Semester and Year
Summer 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Civil Engineering
Department
Civil Engineering
First Advisor
Dr. Kate K. Hyun
Second Advisor
Dr. Stephen P. Mattingly
Third Advisor
Dr. Jinzhu Yu
Fourth Advisor
Dr. Shouyi Wang
Abstract
The rapid growth of online shopping during the COVID-19 pandemic has reshaped consumer behavior, yet many continue to rely on in-store visits. This dissertation presents a comprehensive analysis of shopping and entertainment travel patterns and their impacts on urban mobility. Leveraging machine learning models with two years of smartphone location data, this study identifies key factors influencing in-store shopping trips, including temperature, store accessibility, and online delivery trends. A novel pattern recognition framework classifies weekly shopping and entertainment activities, revealing significant sociodemographic variations in trip behaviors across different population groups. Furthermore, the research investigates the network-wide traffic effects of discretionary trips in the Puget Sound region, highlighting how grocery, mall, and restaurant visits contribute to congestion outside traditional commuting hours. These findings underscore the importance of incorporating dynamic travel behaviors and socio-economic characteristics into transportation planning. The integrated insights from these studies offer valuable guidance for policymakers and urban planners to develop equitable, data-driven strategies that address evolving consumer travel demands and improve urban traffic management.
Keywords
In-store shopping, activity patterns, machine learning, discretionary trips, socioeconomic factors.
Disciplines
Civil Engineering | Transportation Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
IMRAN, MD ASHRAFUL, "NAVIGATING URBAN MOBILITY: INSIGHTS FROM CONSUMER BEHAVIOR AND TRAFFIC PATTERN ANALYSIS" (2025). Civil Engineering Dissertations. 518.
https://mavmatrix.uta.edu/civilengineering_dissertations/518