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http://dx.doi.org/10.3745/KTSDE.2021.10.5.169

A K-Means-Based Clustering Algorithm for Traffic Prediction in a Bike-Sharing System  

Kim, Kyoungok (서울과학기술대학교 산업공학과)
Lee, Chang Hwan (서울과학기술대학교 데이터사이언스학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.5, 2021 , pp. 169-178 More about this Journal
Abstract
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.
Keywords
Bike Sharing System; Clustering; Demand Prediction; Random Forest;
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