Quantifying the Benefits of Autonomous On-Demand Ride-Pooling: A Simulation Study for Munich, Germany

Autonomous on-demand mobility systems, especially ride-pooling services, except for providing convenient transportation for the people, could potentially improve the traffic congestion in urban environments by reducing the number of private vehicles. In this paper, we introduce an Autonomous On-Demand Ride-Pooling (AODRP) system, which uses a rather realistic customer-model that is sensitive to waiting times. To quantify the benefits that the AODRP system could have on a city network, a case study in Munich is performed with a shared fleet of vehicles. Different scenarios, in which private vehicle trips are partly replaced with ride-pooling trips until an adoption rate of 15%, are investigated for varying allowed customer detour times. The results show that the benefits of an AODRP service are observed from a certain adoption rate. For low demand level of 1%, the ride-pooling service even increases Vehicle Miles Traveled (VMT) in the system, due to the empty trips generated while going to pick up customers. For higher adoption rates, pooling makes up for the additional empty VMT starting from approximately 5% adoption rate. An analysis of change in VMT per road type reveals that the AODRP system especially reduces traffic on major roads, in which nowadays the highest level of congestion is observed, while extra VMT due to empty pick-up trips are concentrated on minor roads.