Dynamic Ride-Hailing with Electric Vehicles

Nicholas D. Kullman (), Martin Cousineau (), Justin C. Goodson () and Jorge E. Mendoza ()
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Nicholas D. Kullman: Laboratory of Fundamental and Applied Computer Science (LIFAT), Université de Tours, 37200 Tours, France
Martin Cousineau: HEC Montréal, Montréal, Québec H3T 2A7, Canada
Justin C. Goodson: Richard A. Chaifetz School of Business, Saint Louis University, St. Louis, Missouri 63103
Jorge E. Mendoza: HEC Montréal, Montréal, Québec H3T 2A7, Canada; Centre Interuniversitaire de Recherche sur les Réseaux d'Entreprise, la Logistique et le Transport (CIRRELT), Montréal, Québec H3T 1J4, Canada

Transportation Science, 2022, vol. 56, issue 3, 775-794

Abstract: We consider the problem of an operator controlling a fleet of electric vehicles for use in a ride-hailing service. The operator, seeking to maximize profit, must assign vehicles to requests as they arise as well as recharge and reposition vehicles in anticipation of future requests. To solve this problem, we employ deep reinforcement learning, developing policies whose decision making uses Q -value approximations learned by deep neural networks. We compare these policies against a reoptimization-based policy and against dual bounds on the value of an optimal policy, including the value of an optimal policy with perfect information, which we establish using a Benders-based decomposition. We assess performance on instances derived from real data for the island of Manhattan in New York City. We find that, across instances of varying size, our best policy trained with deep reinforcement learning outperforms the reoptimization approach. We also provide evidence that this policy may be effectively scaled and deployed on larger instances without retraining.

Keywords: ride-hailing; electric vehicles; neural networks; deep reinforcement learning; dynamic vehicle routing (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:56:y:2022:i:3:p:775-794

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