This paper explores the minimum lateral parking distance and parking acceleration/deceleration distance of vehicles to improve the efficiency of automated valet parking (AVP) lots and save urban land. Specifically, the paper focuses on designing parking lots for automated guided vehicles (AGVs) and their parking attributes. To ensure AGV accessibility and maximize AVP capacity, graph theories and unique path-driving methods are used in designing mobile priority parking lots and decision spaces. Additionally, the paper proposes an optimization design for parking lots with obstacles, considering the layout of load-bearing columns and charging resources for electric vehicles in underground parking lots. The article further proposes an optimization design for hybrid parking lots based on spatio-temporal resource conversion in traffic design and the principle of traffic separation in traffic control since hybrid parking lots that accommodate both conventional vehicles and AGVs are crucial to the future development of urban parking lots. The experimental results show that the proposed optimization design for urban parking lots in automated environments is superior to the traditional parking lots design in terms of capacity and density. This paper provides an optimal layout scheme of urban parking lots in multiple scenarios, which can improve the service level of urban static traffic systems.

To study the impact of traffic conditions of urban road networks and the distribution of potential demand of charging users on the charging distribution in the region. In this paper, the long short-time memory (LSTM) neural network is used to learn the historical user charging behavior data and predict the distribution of user charging behavior in real-time. An integer programming model for charging allocation of electric vehicles in the rolling time domain under time-varying demand is established by taking multiple charging stations and road networks in the region as research objects. Aiming at the highest comprehensive return, the model rejects some users with a long charging residence time before charging is allocated at the peak time and carries out the rolling time domain allocation of charging. When the demand for charging is high during peak charging hours, early rejection of long-staying users during the real-time allocation of charging appointments can increase the revenue of charging stations and the utilization rate of charging piles. In the case that the charging pile can be saturated at peak times, the allocation based on LSTM neural can increase the revenue of charging stations by 20.45% on average and the utilization rate of charging piles by 2.35% on average throughout the day. During peak hours, the revenue of the charging station can be increased by 36.63% on average, and the utilization rate of the charging pile can be increased by 13.54% on average.