Matching Methods of Shared Parking Slots Considering Overdue Parking Behavior

With the continuous increase in the number of vehicles worldwide, parking challenges have become more severe, making it a shared goal for governments to alleviate parking difficulties in urban centers. Shared parking has emerged as an effective solution to address parking problems and has been widely studied in recent years. However, existing research primarily focuses on static or single-period parking matching, often neglecting the conflicts between overdue parking users and subsequent users. Therefore, addressing the impact of overdue parking on shared parking systems is highly important. This study proposes a multi-period dynamic matching decision model (MDMD), which divides the operation period of the shared parking platform into multiple decision points. At each decision point, parking demands are classified into four categories: newly arriving demands, allocated demands with a start time not within the current decision point, overdue demands during the current decision point, and demands affected by overdue parking. Three decision variables are established to determine matching schemes for the first, second, and fourth types of parking demands, facilitating a dynamic decision-making process that effectively mitigates the impact of overdue parking. A corresponding algorithm is designed to solve the model. Since the single-period model is a linear programming model, the CPLEX solver obtains allocation schemes for each decision point. These schemes, along with new parking demands, are used as input for the next decision point, achieving a dynamic matching process. Simulation experiments are conducted to compare the MDMD model with the traditional First-Book-First-Served (FBFS) model based on platform revenue, parking space utilization, and parking demand acceptance rate. The experimental results show that, compared to FBFS, MDMD improves long-term earnings by 83%, actual profits in recent profits by 6.6%, and parking space utilization by 8% while maintaining a similar parking demand acceptance rate. To validate the robustness of the model, additional simulations are performed under various overdue probability scenarios, demonstrating that MDMD maintains stable system performance across different probabilities. These improvements highlight the advantages of the dynamic matching strategy, distinguishing this study from existing methods lacking adaptability. These findings provide valuable insights for the optimization of shared parking systems, contributing to sustainable transportation solutions and efficient urban mobility management.