From Search-for-Parking to Dispatch-for-Parking in an Era of Connected and Automated Vehicles: A Macroscopic Approach

Abstract

The advantage of self-relocation of connected and automated vehicles (CAVs) can eliminate heavy searching-for-parking traffic in areas with limited parking availability. However, the floating trips will exacerbate local traffic congestion and parking competition if relocated CAVs are not well distributed in the network. To address these issues, this paper proposes a centralized dispatching-for-parking system to dynamically dispatch CAVs between different regions to optimize parking resource utilization and traffic distribution. A macroscopic modeling approach is presented with the consideration of mixed traffic flows of human-driven vehicles (HDVs) and CAVs. The system dynamics are modeled with the representation of the macroscopic fundamental diagram (MFD) in a multiregion road network. The objective of the system is to minimize the total network delay, which is formulated by the framework of model predictive control (MPC). Results of the numerical experiments in a two-region network show that the approach improves the performance of system operation and alleviates traffic congestion and imbalance between parking supply and demand in downtown areas. The sensitivity analysis on the level of CAV penetration reveals that the total network delay gradually decreases with the penetration increase, and HDVs benefit more from the MPC controller. The study demonstrates the applicability and implication of the dispatching-for-parking system in an era of CAVs.