Highlights Design an innovative user-based relocation policy to solve the vehicle relocation problem in oneway car-sharing systems. Integrate a Markovian prediction method to forecast future demand losses due to vehicle imbalances. Propose an adaptive method that determines the optimal incentive based on customers’ value of time. Illustrate the benefits and performance of user-based relocation compared to staff-based relocation in terms of service level, cost and sustainability. Describe the performance of a hybrid operator-user-based relocation policy in terms of service level, cost and sustainability.

    Abstract Car-sharing systems are an attractive alternative to private vehicles due to their benefits in terms of mobility and sustainability. However, the distribution of vehicles throughout the network in one-way systems is disturbed due to asymmetry and stochasticity in demand. As a consequence, vehicles need to be relocated to maintain an adequate service level. In this paper, we develop a user-based vehicle relocation approach through the incentivization of customers and a predictive model for the state of the system based on Markov chains. Our methods determine the optimal incentive as a trade-off between the cost of an incentive and the expected omitted demand loss while taking into account the value of time of customers. We introduce a learning algorithm that allows the operator to estimate unknown customer preferences to find the optimal incentive. Experimental results in an event-based simulation of a real system show that the use of incentives can significantly increase the service level and profitability of a car-sharing system and decrease the number of staff members needed to balance the vehicles in the system. Thereby, incentives are a more sustainable alternative to staff-based relocations. Extensive sensitivity analyses show the prospective benefits in terms of customer flexibility and the robustness of our results to varying customer preferences.


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    Titel :

    Predictive user-based relocation through incentives in one-way car-sharing systems


    Beteiligte:


    Erscheinungsdatum :

    2021-05-07


    Format / Umfang :

    20 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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