Assessment of future parking systems with autonomous vehicles through agent-based simulation: A case study of Hangzhou, China

Abstract

Autonomous vehicles (AVs) are expected to transform urban parking. AVs may not require dedicated parking spaces near their destination. Instead, AVs can drive to the optimal parking lot, cruise, or return to the origin after the passenger gets off. Previous research on AV parking strategies has largely overlooked actual trip patterns and road network conditions that can affect the effectiveness and feasibility of these strategies. In this paper, we construct an agent-based simulation model to assess the potential impacts of AVs on urban parking, focusing on new AV parking strategies. Using the real traffic network and trip data of Hangzhou, we assess the impacts on overall network efficiency, personal trip, and parking demand under different AV penetration rates. Results show that AVs could decrease the total parking cost from 23 to 6 RMB and increase parking convenience for both AVs and human-driven vehicles (HDVs). 21% of AVs will still choose to park, while 70% will return to the origin. On average, each trip with AV will generate an additional zero-occupant distance of 8.8 km, exacerbating the congestion. The findings indicate the benefits and challenges of new AV parking strategies, providing valuable insights for future urban parking planning and policy making.