Repoux, MartinKaspi, MorBoyaci, BurakGeroliminis, Nikolas2019-12-112019-12-112019-12-112019-12-0110.1016/j.trb.2019.10.004https://infoscience.epfl.ch/handle/20.500.14299/163918WOS:000498898200005In this paper, we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic staff-based relocation policies. Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. This policy is compared to a state-of-the art staff-based relocation policy and a centralistic relocation model assuming full knowledge of the demand. Numerical results from a real-world implementation and a simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy. (C) 2019 Elsevier Ltd. All rights reserved.EconomicsEngineering, CivilOperations Research & Management ScienceTransportationTransportation Science & TechnologyBusiness & EconomicsEngineeringOperations Research & Management ScienceTransportationcarsharingsimulationmarkov chainpredictionoperationsvehicle sharing systemssimulation-modelelectric vehiclesoptimizationoperationsframeworknetworksswitzerlandmanagementusageDynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservationstext::journal::journal article::research article