Multi-objective analysis of ridesharing in automated mobility-on-demand

Michal Cáp, Javier Alonso-Mora

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Self-driving technology is expected to enable the realization of large-scale mobility-on-demand systems that employ massive ridesharing. The technology is being celebrated as a potential cure for urban congestion and others negative externalities of individual automobile transportation. In this paper, we quantify the potential of ridesharing with a fleet of autonomous vehicles by considering all possible trade-offs between the quality of service and operation cost of the system that can be achieved by sharing rides. We formulate a multi-objective fleet routing problem and present a solution technique that can compute Pareto-optimal fleet operation plans that achieve different trade- offs between the two objectives. Given a set of requests and a set of vehicles, our method can recover a trade-off curve that quantifies the potential of ridesharing with given fleet. We provide a formal optimality proof and demonstrate that the proposed method is scalable and able to compute such trade-off curves for instances with hundreds of vehicles and requests optimally. Such an analytical tool helps with systematic design of shared mobility system, in particular, it can be used to make principled decisions about the required fleet size.
Original languageEnglish
Title of host publicationProceedings of RSS 2018: Robotics - Science and Systems XIV
EditorsHadas Kress-Gazi, Siddhartha Srinivasa, Tom Howard, Nikolay Atanasov
Number of pages9
ISBN (Electronic)978-0-9923747-4-7
Publication statusPublished - 2018
EventRSS 2018: Robotics - Science and Systems XIV - Pittsburgh, United States
Duration: 26 Jun 201830 Jun 2018


ConferenceRSS 2018: Robotics - Science and Systems XIV
Country/TerritoryUnited States

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