Simulation-Based Assessment of Parking Constraints for Automated Mobility on Demand: A Case Study of Zurich

Abstract

In a coordinated mobility-on-demand system, a fleet of vehicles is controlled by a central unit and serves transportation requests in an on-demand fashion. An emerging field of research aims at finding the best way to operate these systems given certain targets, e.g., customer service level or the minimization of fleet distance. In this work, we introduce a new element of fleet operation: the assignment of idle vehicles to a limited set of parking spots. We present two different parking operating policies governing this process and then evaluate them individually and together on different parking space distributions. We show that even for a highly restricted number of available parking spaces, the system can perform quite well, even though the total fleet distance is increased by 20% and waiting time by 10%. With only one parking space available per vehicle, the waiting times can be reduced by 30% with 20% increase in total fleet distance. Our findings suggest that increasing the parking capacity beyond one parking space per vehicle does not bring additional benefits. Finally, we also highlight possible directions for future research such as to find the best distribution of parking spaces for a given mobility-on-demand system and city.

Analysis and Control of Autonomous Mobility-on-Demand Systems

“…Therefore, planning studies also focus on interactions between AMoD systems and existing infrastructure. Aspects of interest include the allocation of parking space (48,49), the optimal placement of charging stations, charge scheduling for electric AVs (50)(51)(52)(53)(54), contributions to congestion (55), joint operation with other modes of transportation (56,57), autonomous calibration (58), smart infrastructure (59), and the achievable performance of the overall mobility system (45).…”

“…Parking policies typically relocate idling AVs while not causing deterioration in metrics such as waiting times, distance traveled, and congestion. In graph-theoretic models, effects of parking are captured by additional costs when idling at a node (price of parking) (11,100,101) or via upper bounds on the number of AVs at a node (capacity of parking lots) (48,69,70,74). In queuing-theoretic models, stations are usually assumed to have an infinite number of parking spaces (82).…”

Analysis and Control of Autonomous Mobility-on-Demand Systems

“…Therefore, planning studies also focus on interactions between AMoD systems and existing infrastructure. Aspects of interest include allocation of parking space (48,49), optimal placement of charging stations, charge scheduling for electric AVs (50,51,52,53,54), contribution to congestion (55), joint operation with other modes of transportation (56,57), autonomous calibration (58), smart infrastructure (59), and achievable performance of the overall mobility system (45).…”

“…Parking policies typically relocate idling AVs, while not deteriorating metrics such as waiting times, traveled distance, and congestion. In graph-theoretic models, effects of parking are captured by additional costs when idling at a node (price of parking) (11,100,101) or via upper bounds on the number of AVs at a node (capacity of parking lots) (48,69,70,74). In queueing-theoretic models, stations are usually assumed to have infinite parking spaces (82).…”

Evaluation of Parking Demand and Future Requirement in the Urban Area

“…The study found that the system can function successfully even when there are a limited number of parking spaces available, and that expanding capacity of parking by one parking place per automobile cannot provide any advantages. Furthermore, it ensures optimum parking spaces' distribution for a certain on-demands and city transportations system [2].…”