• ETRI Journal
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• v.44 no.2
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• pp.244-259
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• 2022

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.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.169-178
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• 2021

Recently, a bike-sharing system (BSS) has become popular as a convenient "last mile" transportation. Rebalancing of bikes is a critical issue to manage BSS because the rents and returns of bikes are not balanced by stations and periods. For efficient and effective rebalancing, accurate traffic prediction is important. Recently, cluster-based traffic prediction has been utilized to enhance the accuracy of prediction at the station-level and the clustering step is very important in this approach. In this paper, we propose a k-means based clustering algorithm that overcomes the drawbacks of the existing clustering methods for BSS; indeterministic and hardly converged. By employing the centroid initialization and using the temporal proportion of the rents and returns of stations as an input for clustering, the proposed algorithm can be deterministic and fast.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.2
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• pp.83-92
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• 2024

Currently, various countries are enhancing accessibility by providing bicycle rental services for convenient usage within daily life. This paper introduces the Nubija public bicycle service in Changwon, South Korea, aiming to address the imbalance between demand and supply of Nubija bicycles. We propose a Highest Priority Reallocation Scheme to prevent this disparity. Comparing this scheme with others that randomly visit terminals for redistribution and those that prioritize terminals closest to current locations, we illustrate its superior efficiency. Our proposed Highest Priority Reallocation Scheme prioritizes terminals with the highest demand and shortest distances nearby. Through experiments, our proposed scheme demonstrates superior performance, with the lowest average of 817.44km distance and an average of 6437.45 times, i.e., 88.14% successful rental occurrences. This highlights its superiority over the other two algorithms.