Exploring the Effects of Shared Autonomous Vehicles and the Feasibility of a Novel Parking Pricing Policy: An Agent-Based Simulation Approach

Shared autonomous vehicles (SAVs) combine autonomous driving and sharing mobility, offering potential to reshape future travel modes. Their self-driving attribute is expected to significantly reduce parking demand and alter the parking landscape. Previous studies have primarily focused on the quantity of parking demand, with parking choice based solely on parking prices. The spatial distribution of parking demand remains unclear, especially during the transition period with both conventional vehicles and SAVs mixed on roads. An agent-based simulation model was developed to evaluate the impact of SAVs on parking demand from quantity and spatial distribution perspectives. A generalized cost function considered not only parking prices, but also the road toll and energy fee, was developed to alleviate the negative effect of SAVs’ endless cruising. We also explored the trend of such effects with varying SAVs’ market penetration rates. The results indicated a substantial decrease in parking demand (nearly 80%), spreading from the central business district (CBD) to the periphery, leading to a significant increase in vehicle miles traveled (VMT) within the entire network. We also found that the additional VMT is mainly due to SAVs’ empty travel during providing continuous services. To relieve the poor utilization of parking lots in CBD due to outspreading demand, a parking policy which can dynamically adjust parking price based on its real-time occupancy rate was proposed and verified. Results suggested that it would increase of the average utilization rate of overall parking lots by 8% and reduce the VMT caused by SAVs’ parking by 29.5% if the policy applied with a large pricing change coefficient.