Relocation Framework to Improve the Usage Efficiency of Bicycle-sharing Systems

Lee, Chun-Hee; Lee, Jeong-Woo; Jung, YungJoon; Cho, Il-Yeon

doi:10.23919/icact51234.2021.9370711

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2022

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“…The last category involves rebalancing bikes properly to improve user convenience. Many previous studies [2][3][4][5][6][7][8][9]26,27] have investigated the bike rebalancing problem. For example, Dell Amico and others [5] solved the bike rebalancing problem using a stochastic programming model, where the problem was formulated as a two-stage problem.…”

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confidence: 99%

Practical method to improve usage efficiency of bike‐sharing systems

Lee

Jung

2022

ETRI Journal

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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.

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