Incentive-Based Approach to Control Demands for Operation of One-Way Carsharing System

Abstract

One-way carsharing has been regarded as one of the innovative urban transportation modes since the last decade. A fleet imbalance problem frequently occurs in the system, which requires efficient vehicle relocation to cope with. An incentive-based approach could influence the users’ demands and could relieve the pressure on operator-based relocation. This paper presents an incentive-based approach involving a vehicle rewarding policy and a station rewarding policy to attract pick-up demands and drop-off demands respectively. A ranking method is proposed to determine the list of candidate rewarding vehicles and stations. The method acquires the user-app log data and the transaction data in a real operating environment for pick-up and drop-off demand prediction. Five factors for vehicles and four factors for stations are computed from the real-time data. The ranking indices are aggregated from the weighted sum of the factors. The rewarding policy and the ranking method were tested in the real operating environment of an electric vehicle sharing system in two districts of Shanghai. The result suggests that the rewarding policy with ranking method could shorten the vehicle idle time and increase the number of transactions per vehicle and per station, and also resulted in increments on profits.