Two-Stage Allocation Model for Parking Robot Systems Using Cellular Automaton Simulation

Abstract

With increasing vehicle ownership and utilization in urban areas, parking robot systems have become a promising tool to cope with both the parking difficulties and hazards in parking lots. With assistance of the parking robot systems, vehicles are not human-operated in parking lots but fetched by a parking robot to the target places. This study starts by depicting the parking robot allocations and movements consisting of empty-loaded and loaded trips as well as picking-up and setting-down operations. Then, with multi-stage processes aiming to minimize the empty-loaded and loaded travel time, respectively, a two-stage allocation model was proposed to assign parking robots to vehicles and allocate the vehicles with target places. Furthermore, a cellular automaton (CA) simulation was introduced and extended with additional priority rules to solve potential travel conflicts occurring in parking lots. Basing on a campus parking lot in Shanghai, China, the flowchart of the CA simulation was proposed to implement and validate the proposed allocation model. The results demonstrate that, compared with the one-stage allocation model, the proposed allocation model has the potential to decrease queue length and waiting time both at parking lot entrances and parking spaces, which may assist in design and operation of future parking lots.