A shared parking optimization framework based on dynamic resource allocation and path planning

Abstract

Cruising-for-parking is a common problem in urban areas due to the limited availability of parking spaces, which leads to increased travel costs and road congestion. To address this issue, this paper proposes a shared parking allocation and guidance optimization framework for autonomous vehicles (AVs) as shared parking and autonomous driving mature. In this framework, facing multi-candidate adjacent parking lots, a rolling-horizon parking allocation model that embeds an adjustment mechanism is first established to optimize the matches dynamically. Then, a global parking routing algorithm (GPR-A*) that considers time-varying link travel times (LTTs) is developed by improving the A* shortest routing method. Results on the Xi’an urban road network show that: (i) compared with the traditional first-book-first-serve (FBFS) model, the established parking allocation model significantly increases the platform revenue and parking utilization, and decreases the travel cost in the shared parking zone; (ii) the developed GPR-A* algorithm that works with the parking allocation can reduce travel time by an average of 14.9% and 5.5% compared to static parking routing (SPR) and rolling parking routing (RPR) methods. The constructed model and algorithm should have a promising application in the intelligent vehicle parking field.