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Using the same vehicles for both passenger and freight transport, to increase vehicle occupancy and decrease their number, is an idea that drives transport planners and is also being addressed by manufacturers. This paper proposes a methodology to simulate the behavior of such vehicles within an urban traffic system and evaluate their performance. The aim is to investigate the impacts of resignation from fleet ownership by a transport service company (TSC) operating on a city-wide scale. In the simulation, the service provider hires private autonomous cars for tour performance. Based on assumptions concerning the operation of such vehicles and TSCs, the software Multi-Agent Transport Simulation (MATSim) is extended to model vehicle and operator behavior. The proposed framework is applied to a case study of a parcel delivery service in Berlin serving a synthetic parcel demand. Results suggest that the vehicle miles traveled for freight purposes increase because of additional access and egress trips. Moreover, the number of vehicles en route is higher throughout the day. The lowering of driver costs can reduce the costs of the operator by approximately 74.5%. If the service provider additionally considers the resignation from fleet ownership, it might lower the operation cost by another 10%, not taking into account the costs of system transfer or risks like vehicle non-availability. From an economic perspective, the reduction of the overall number of vehicles in the system seems to be beneficial.

A major part of urban private vehicles needs to be electrified in order to meet the German climate protection goals in the transport sector. Therefore, charging strategies must be developed and the geographical distribution of infrastructure has to be planned accordingly. We propose a new method to combine a multi agent traffic simulation (MATSim) with real-world vehicle distribution and measured charging behavior. The results of our simulation were analyzed for the exemplary use case of Berlin’s private passenger cars. Three different charging strategies were developed and analyzed: charging at home, at work and during leisure activities. Charging at home was suitable to serve all agents’ charging demand with low charging power (7.4 kW). Charging at work only served 56.23 % of the agents with a charging power of 11 kW, but required fewer charging points. Charging during leisure activities served from 46.36 % to 81.92 % of the agents, depending on the chosen charging power and user behavior.