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<p>Already today, on-demand mobility services such as Uber and Lyft in the USA, and Didi in China have taken over a noticeable share of the modal split. Furthermore, the market value assessment of these companies is now higher than the one of established vehicle producers, as they are considered to have a very high potential despite current losses with regard to autonomous driving. With the elimination of driver costs, it is expected that ride-hailing and ride-pooling can be offered at a substantially lower cost than today, generating a significant increase in demand. To understand the impact of such automated on-demand systems on future transportation systems, simulations are needed to evaluate both fleet efficiency and their interaction with the overall transportation system.</p>
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Studies to date have focused mainly on efficient vehicle-customer assignments and fleet strategies such as proactive repositioning in static networks. For example, a study comparing a current car-sharing operation with an autonomous ride-hailing system was able to show that although 10% empty trips were generated in the ride-hailing operation, it significantly increased fleet utilization. Additionally, it can be observed that system efficiency increases as fleet and demand scale. These positive scaling effects are even more prominent in ride-pooling systems. Larger demand and fleet increase pooling opportunities, which increases the occupancy rate and reduces additional fleet miles per trip request. In a simulation study for Munich, the evaluation of the trajectories of all fleet vehicles showed that these bundling potentials mainly occur on main arterial roads, while additional mileage is induced on secondary roads. These scaling effects in combination with low-cost operation can lead to a fundamental change in the established traffic system. To prevent undesirable effects on the overall system, regulations such as fleet limits, tolling, or public transport integration can help, but their tools still need to be understood in detail.
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