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  • Technical advances in autonomous driving will enable the large scale implementation of mobility-on-demand systems in existing cities. In this work, we investigate their competitive interaction with fixed-route invariant public transportation systems and we provide a theoretical framework to formally answer vehicle fleet sizing and operational unknowns and assess the impact on public transport line usages. We achieve this by first generalizing a fluidic modeling formulation integrating both mobility-on-demand and public transport modal flows. Hence, we derive a multi-commodity optimal flow problem that allows us to maximise the efficiency of the mobility system. Application of our theory on travel data for the city of Zu¨ rich reveals that the minimal and most efficient fleet size is reduced by up to 31% compared to previously estimated required number of vehicles. Lastly, we show how our model allows to readily identify critical links in the public transportation network for efficient combined operation along the mobility-on-demand system. This result is of great significance with respect to current public transportation infrastructure investment decisions. (xsd:string)
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  • Integrating Autonomous Mobility-on-Demand Systems in Existing Cities (xsd:string)
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  • (09574), 15, ETH Zurich, 2019 (xsd:string)
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