Predictive user-based relocation through incentives in one-way car-sharing systems
研究通过激励用户和预测模型来优化单向汽车共享系统的车辆重定位，发现激励能显著提升服务水平和盈利能力，减少员工需求。
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.