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The market share of mobility on-demand (MoD) services strongly increased in recent years and is expected to rise even higher once vehicle automation is fully available. These services might reduce space consumption in cities as fewer parking spaces are required if private vehicle trips are replaced. If rides are shared additionally, occupancy related traffic efficiency is increased. Simulations help to identify the actual impact of MoD on a traffic system, evaluate new control algorithms for improved service efficiency and develop guidelines for regulatory measures. This paper presents the open-source agent-based simulation framework FleetPy. FleetPy (written in the programming language "Python") is explicitly developed to model MoD services in a high level of detail. It specially focuses on the modeling of interactions of users with operators while its flexibility allows the integration and embedding of multiple operators in the overall transportation system. Its modular structure ensures the transferabillity of previously developed elements and the selection of an appropriate level of modeling detail. This paper compares existing simulation frameworks for MoD services and highlights exclusive features of FleetPy. The upper level simulation flows are presented, followed by required input data for the simulation and the output data FleetPy produces. Additionally, the modules within FleetPy and high-level descriptions of current implementations are provided. Finally, an example showcase for Manhattan, NYC provides insights into the impacts of different modules for simulation flow, fleet optimization, traveler behavior and network representation.

This paper examines the integration of freight delivery into the passenger transport of an on-demand ride-pooling service. The goal of this research is to use existing passenger trips for logistics services and thus reduce additional vehicle kilometers for freight delivery and the total number of vehicles on the road network. This is achieved by merging the need for two separate fleets into a single one by combining the services. To evaluate the potential of such a mobility-on-demand service, this paper uses an agent-based simulation framework and integrates three heuristic parcel assignment strategies into a ride-pooling fleet control algorithm. Two integration scenarios (moderate and full) are set up. While in both scenarios passengers and parcels share rides in one vehicle, in the moderate scenario no stops for parcel pick-up and delivery are allowed during a passenger ride to decrease customer inconvenience. Using real-world demand data for a case study of Munich, Germany, the two integration scenarios together with the three assignment strategies are compared to the status quo, which uses two separate vehicle fleets for passenger and logistics transport. The results indicate that the integration of logistics services into a ride-pooling service is possible and can exploit unused system capacities without deteriorating passenger transport. Depending on the assignment strategies nearly all parcels can be served until a parcel to passenger demand ratio of 1:10 while the overall fleet kilometers can be deceased compared to the status quo.