Behavioral and Financial Outcomes of Smart Parking Systems

The rapid urbanization of university campuses has heightened the demand for parking management systems capable of addressing user dissatisfaction, operational inefficiencies, and revenue challenges. While smart parking systems (SPS) have shown potential for improving parking operations, limited research has addressed their dual impact on user behavior and financial performance. This study aims to fill this gap by evaluating the effects of an SPS implemented at a university campus, particularly focusing on its influence on parking violations and revenue. The research utilized a mixed-methods approach, combining survey data from 106 users, historical parking violation and revenue records, and predictive modeling techniques. Parking citation data from two campus lots were analyzed, one spanning 1,945 days and the other spanning 1 year. A regression analysis identified the key predictors of reduced violations and increased parking frequency, while machine learning models such as long short-term memory (LSTM) networks and artificial neural networks (ANN) assessed the SPS's impact by comparing actual and predicted outcomes before and after its implementation. The results revealed that smart parking systems, driven by features such as time management tools and greater understanding of parking regulations, significantly reduce the number of parking violations. In addition, due to the system encouraging greater utilization of parking facilities, actual revenues consistently exceeded predicted values post-implementation. These findings underscore SPS's effectiveness in transforming parking behaviors and optimizing financial outcomes. This research provides actionable insights for university administrators, urban planners, and parking managers aiming to implement or refine SPS technologies.