Identifying and Optimizing Relocation Regions in One-Way Carsharing System

The imbalance between vehicle supply and user demand in the spatial-temporal dimension is the most relevant problem in a station-based one-way carsharing system (OWCS). To compensate for this disequilibrium, vehicle relocations are highly important. Previous studies have proposed the simulation-optimization model for relocation operations. Little attention has been paid to the clustering problem for intra-regional relocations in OWCS. Based on the K-medoids algorithm and the optimization theory, this paper proposed a two-stage clustering algorithm of dynamically dividing all OWCS stations into a certain number of economic relocation regions, so that there is operational feasibility for dispatchers and a balance between supply and demand within those relocation regions. In the case study, the algorithm has been tested on the largest electric vehicle Car-sharing corporation, EVCARD, in Jiading district, Shanghai. Stations are divided into 15 relocation regions. Results show that it is feasible for dispatchers in management. It also indicates that the supply of each region is greater than the demand, and the dispatcher only needs to relocate vehicles within the region.