Proactive empty vehicle rebalancing for Demand Responsive Transport services

Worldwide, ridesharing business is steadily growing and has started to receive attention also by public transport operators. With future fleets of Autonomous Vehicles, new business models connecting schedule-based public transport and feeder fleets might become a feasible transport mode. However, such fleets require a good management to warrant a high level of service. One of the key aspects of this is proactive vehicle rebalancing based on the expected demand for trips. In this paper we model vehicle rebalancing as the Dynamic Transportation Problem. Results suggest that waiting times can be cut by around 30 % without increasing the overall vehicle miles travelled for a feeder fleet in rural Switzerland

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