A multi-agent parking simulation framework was constructed in order to formulate autonomous vehicle(AV)parking demand management strategies.Two charging strategies for empty-load driving were proposed:a static charge based on driving distance and a dynamic charge based on road congestion levels.Rate calculation method was analyzed.Cost functions for parking lots,residential parking,and continuous empty cruising were established under these charging policies.A logit model was used to describe the choice behavior under different parking modes.The simulation of urban mobility(SUMO)was used to conduct a large-scale road network simulation experiment in Nanning's main urban area.AV parking behavior and road network operation under both strategies were analyzed.The simulation results showed that the empty-load driving mileage of AVs decreased by 20.16%and 10.85%under the static and dynamic charging strategies,respectively.Total vehicle delay decreased by 39.80%and 43.52%,respectively.The dynamic charging strategy was adjustable in real-time based on road conditions,and operational efficiency of the road network was significantly enhanced.
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