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Ride-hailing services have the potential to play a major role within future transport systems. While the operation as well as environmental, social and economic consequences are research subjects, the focus in the literature lies on system efficiency and profitability. In this study, vehicle relocation strategies are investigated with respect to service provision equity using an agent-based simulation approach. The aim is to identify ways of operation that support social fairness while maintaining the profitability and effectiveness of the service. The results show that a typical demand-anticipatory relocation strategy leads to an imbalance in the spatial distribution of mean wait times for the use case of Berlin. This can be significantly improved by applying different relocation strategies. As a consequence, the overall demand for the system rises while the ratio of empty vehicle kilometres increases slightly.

Darstellung relevanter Projektergebnisse des vom Bundesministerium für Verkehr und digitale Infrastruktur geförderten Forschungsprojektes “Räumlich und zeitlich hochauflösende Evaluation und Optimierung automatisierter und vernetzter Bedienkonzepte im öffentlichen Verkehr” (AVÖV) Presentation of relevant results of the research project “Räumlich und zeitlich hochauflösende Evaluation und Optimierung automatisierter und vernetzter Bedienkonzepte im öffentlichen Verkehr” (AVÖV: spatially and temporally highly resolved evaluation and optimization of autonomous and networked public transit service schemes) funded by the Bundesministerium für Verkehr und digitale Infrastruktur (German Federal Ministry of Transport and Digital Infrastructures)

This study explores today's and future ride-sharing services by combining real-world user data with state-of-the-art methods of agent-based simulation. First, an existing ride-sharing service in Berlin, Germany, is implemented in an agent-based model and real-world user data is used to calibrate the model. Then the service is extended and transferred to other regions in Germany (Gladbeck, Vulkaneifel) and to a future scenario where the ride-sharing mode is operated by autonomous vehicles and the service is integrated into the regular (schedule-based) public transit system. The simulation experiments reveal ride-sharing potentials for two different perspectives: From the operator's point of view, urban areas with high population densities (Berlin inner-city center, Gladbeck) are found to be most promising since travel demand can be served more efficiently compared to low-demand areas. From the users' point of view, high potentials are observed in areas (and times) with rather poor schedule-based public transit provision (rural Vulkaneifel district, Spandau, entire Berlin area including outskirts) where the on-demand mobility concept is an attractive alternative to the existing modes of transportation. In the today's scenario, ride-sharing trip shares are rather low, whereas, in the future scenario, there is a strong mode shift effect to the ride-sharing mode and the overall road traffic increases strongly. In the urban area, most users switch from schedule-based public transit to ride-sharing, whereas, in the rural area, most users switch from car to ride-sharing. Yet, in all cases, there are undesired mode shift effects, e.g. from bicycle and walk to ride-sharing. For the today's situation, a profitable service is only observed in some of the simulation experiments. In contrast, in the future scenario, a profitable service is found for all simulated services.

The overall transportation speed is a significant factor influencing the attractiveness as well as the profitability of the transit system. If a vehicle needs less time to complete a tour, it can serve more tours and thus more passengers within the same time. Likewise, the passengers benefit from a decreased in-vehicle travel time. In this paper, the factors affecting the passenger transfer time are discussed for the case of Berlin, Germany. Furthermore, the paper presents the results of a survey that focuses (i) on the average time needed for passengers to board and alight a vehicle, (ii) its deviation, and (iii) the impact of the vehicle’s occupancy and number of boarding/alighting passengers. Such data can also be used to model the boarding and alighting process at stops in transport simulations in a more realistic way. For buses and subways, more passengers standing in the door area of a vehicle are found to slow down the boarding and alighting process. The Berlin specific policy to allow the boarding of a bus only at the first door induces a significantly higher boarding time per passenger.