Evaluating the Impact of Smart Parking Systems on Parking Violations

Abstract

The pursuit of convenient parking presents a pervasive challenge that is exacerbated by growing populations and an increasing number of vehicles in urban areas. This study investigates the impact of smart parking systems (SPSes) on parking violations, addressing a critical gap in existing research that often overlooks the dynamic influence of real-time parking management technologies on user behavior and compliance. The primary objectives of the study were to evaluate the effectiveness of SPSes in reducing the number of parking violations, analyze behavioral shifts among campus drivers, and provide data-driven insights for policy and enforcement improvements. The methodology employed a long short-term memory (LSTM) model to predict parking violations and compare predictions with actual ticket data collected on a university campus from 2018 to 2023. Descriptive statistics and visual analyses illustrate the distribution of parking violations across various facilities, while the Kolmogorov-Smirnov (KS) test statistically validates the impact of SPSes on parking behavior. The findings revealed a significant reduction in parking violations post-SPS implementation, with actual violations consistently lower than predicted values, and indicate enhanced compliance driven by the system’s real-time monitoring capabilities. Feature importance analysis revealed that holidays and exam periods significantly influence parking violations, demonstrating the LSTM model’s effectiveness in capturing temporal shifts in parking behavior post-SPS implementation. These findings will benefit urban planners, university administrators, parking management professionals, and policymakers by providing empirical evidence that supports the integration of SPSes into parking management strategies.

Keywords

• Long short-term memory model
• parking compliance
• parking management
• parking violations
• smart parking systems