Dynamic Modeling for the Parking Allocation Problem: A Framework for Real-Time Optimization

Parking problems pose significant challenges in urban transportation planning and management, exacerbating congestion, economic strain, and environmental degradation. This study introduces a dynamic parking allocation framework aimed at minimizing total vehicular travel time. Our model innovatively integrates a network flow-based method and a greedy heuristic to prioritize parking assignments based on real-time vehicle arrival times. The proposed framework swiftly adjusts to fluctuations in parking demand and supply by incorporating both vehicles' stay durations and anticipated parking requests. Through rigorous numerical experiments, utilizing a real-world data, we validate the model's efficacy and flexibility across various urban settings. The results reveal the model's potential to significantly improve urban parking management by optimizing space utilization and reducing unnecessary vehicular travel, thereby contributing to more sustainable urban transportation systems.