Practical method to improve usage efficiency of bike‐sharing systems

Abstract

Bicycle‐ or bike‐sharing systems (BSSs) have received increasing attention as a secondary transportation mode due to their advantages, for example, accessibility, prevention of air pollution, and health promotion. However, in BSSs, due to bias in bike demands, the bike rebalancing problem should be solved. Various methods have been proposed to solve this problem; however, it is difficult to apply such methods to small cities because bike demand is sparse, and there are many practical issues to solve. Thus, we propose a demand prediction model using multiple classifiers, time grouping, categorization, weather analysis, and station correlation information. In addition, we analyze real‐world relocation data by relocation managers and propose a relocation algorithm based on the analytical results to solve the bike rebalancing problem. The proposed system is compared experimentally with the results obtained by the real relocation managers.

Special issue on smart cities and its applications

“…The last paper in this special issue, [11] "Practical method to improve usage efficiency of bike-sharing systems" by Lee and others, explored the bike rebalancing problem in bike-sharing systems. In order to solve the rebalancing problem, the authors designed a bike demand prediction model based on machine leaning techniques.…”