Graduation Semester and Year
Spring 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Civil Engineering
Department
Civil Engineering
First Advisor
Dr. Sharareh Kermanshachi
Second Advisor
Dr. Jay Rosenberger
Third Advisor
Dr. Kyeong Rok Ryu
Fourth Advisor
Dr. Karthikeyan Loganathan
Abstract
Parking remains a challenge in urban and university settings, affecting traffic flow, emissions, and user satisfaction. Smart Parking Systems (SPS) offer real-time, data-driven solutions, but their success depends on user perception and behavior as much as technical deployment. This dissertation develops a predictive model for SPS adoption and satisfaction, bridging the gap between system performance and user experience.
The study aimed to: (1) assess satisfaction with traditional parking systems, (2) identify behavioral drivers of SPS adoption, (3) evaluate the impact of SPS on parking experience, and (4) develop predictive models of user satisfaction. Conducted at the University of Texas at Arlington, it focused on the Parking Finder app, which shows real-time occupancy and permit-based parking zones.
A sequential mixed-methods approach was used. Before implementation, 19 interviews highlighted issues like poor communication and enforcement. A survey of 873 users showed availability, real-time info, and fairness as key drivers of satisfaction. Penalties and AI features had limited influence.
Post-implementation, 21 interviews indicated improved sentiment (polarity shift from –0.15 to +0.3), reduced search time by 50%, and behavior changes such as using underutilized lots. Language shifted from frustration to confidence. Occupancy data showed improved lot turnover and peripheral lot use.
A post-implementation survey (n = 105), analyzed using Structural Equation Modeling (SEM), revealed that perceived usefulness, service quality, and ease of use significantly influenced satisfaction and loyalty. Perceived quality boosted value and satisfaction, while complaints reduced both. Satisfaction emerged as the strongest predictor of continued app use.
The study recommends real-time data delivery, user-centered design, and predictive analytics to improve parking outcomes. Institutions should invest in user education and ongoing evaluation. This research provides a validated, user-centered model with practical guidance for universities, city planners, and mobility providers implementing SPS.
Keywords
Smart Parking Systems, User Satisfaction, Parking Behavior, Predictive Modeling, University Parking, Technology Adoption in Mobility, Intelligent Transportation Systems, User-Centered Design
Disciplines
Urban Studies and Planning
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Channamallu, Sai Sneha, "PREDICTIVE MODEL DEVELOPMENT FOR USER BEHAVIOR IN SMART PARKING SYSTEMS ADOPTION" (2025). Civil Engineering Dissertations. 509.
https://mavmatrix.uta.edu/civilengineering_dissertations/509