Macroscopic modeling and dynamic control of on-street cruising-for-parking of autonomous vehicles in a multi-region urban road network

Abstract The spatio-temporal imbalance of parking demand and supply results in unwanted on-street cruising-for-parking traffic of conventional vehicles. Autonomous vehicles (AVs) can self-relocate to alleviate the shortage of parking supplies at the trip destinations. The extra floating trips of vacant AVs have adverse impacts on traffic congestion and the parking demand–supply imbalance may still exist when they are not distributed optimally. This paper presents a centralized parking dispatch approach to optimize the distribution of floating AVs and provide regional route guidance. We apply the concept of macroscopic fundamental diagram to represent the evolution of traffic conditions, cruising-for-parking, and dispatched AVs in a congested multi-region network. A model predictive control is suggested to optimize the control inputs. Numerical experiments in a four-region network demonstrate that the proposed parking dispatch and regional route guidance of AVs are effective in reducing intense cruising-for-parking traffic, and the integration of both has the best control performance by regulating the network towards under-saturated conditions. The performance of the proposed schemes is evaluated via simulations with noise in measurement errors and compliance rate prediction. Results show substantial improvements in terms of total time spent, even for low levels of AV market penetration or AV compliance rate to parking dispatch and route guidance.