Mode choice and ride-pooling simulation: A comparison of mobiTopp, Fleetpy, and MATSim

On-demand ride-pooling systems have gained a lot of attraction in the past years as they promise to reduce traffic and vehicle fleets compared to private vehicles. Transport simulations show that automation of vehicles and resulting fare reductions enable large-scale ride-pooling systems to have a high potential to drastically change urban transportation. For a realistic simulation of the new transport mode it is essential to model the interplay of ride-pooling demand and supply. Hence, these simulations should incorporate (1) a mode choice model to measure demand levels and (2) a dynamic model of the on-demand ride-pooling system to measure the service level and fleet performance. We compare two different simulation frameworks that both incorporate both aspects and compare their results with an identical input. It is shown that both systems are capable of generating realistic results and assessing mode choice and ride-pooling schemes. Commonalities and differences are identified and discussed

Flow-inflated selective sampling: Efficient agent-based dynamic ride-sharing simulations

Agent-based simulations have become a popular and powerful tool for simulating emergent mobil- ity modes. Often times, the memory and computing requirements are daunting. Scaling down agent populations by simulating only a fraction of all agents is a frequently used option to reduce these burdens. However, recent studies have pointed out the difficulty of scaling ride-sharing simulations as these rely heavily on demand density and do not scale linearly. In this study, we introduce a simple yet effective methodology for simulating dynamic ride-sharing services, which we call flow-inflated selective sampling (FISS). The basic operation is that, similar to scaling agent-based populations, only a fraction of the actual agents are explicitly assigned. However, here only trips of private car transport are sampled, while public transport as well as ride-sharing vehicles are fully represented. In contrast to scaling in previous studies, the network capacity is not adjusted. Rather, the capacity consumption of the cars is scaled up to obtain realistic traffic flows. We implement this approach in the MATSim simulation environment for a large scenario in the region of Munich, Germany and show that our approach preserves traffic flows while keeping key performance indicators of a ride-sharing service stable and mostly unbiased. Mode choice decisions based on this approach also remain stable. By introducing our approach, the run-times of the actual assignment can be almost halved

Mode choice and ride-pooling simulation: A comparison of mobiTopp, Fleetpy, and MATSim

On-demand ride-pooling systems have gained a lot of attraction in the past years as they promise to reduce traffic and vehicle fleets compared to private vehicles. Transport simulations show that automation of vehicles and resulting fare reductions enable large-scale ride-pooling systems to have a high potential to drastically change urban transportation. For a realistic simulation of the new transport mode it is essential to model the interplay of ride-pooling demand and supply. Hence, these simulations should incorporate (1) a mode choice model to measure demand levels and (2) a dynamic model of the on-demand ride-pooling system to measure the service level and fleet performance. We compare two different simulation frameworks that both incorporate both aspects and compare their results with an identical input. It is shown that both systems are capable of generating realistic results and assessing mode choice and ride-pooling schemes. Commonalities and differences are identified and discussed.ISSN:1877-050