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


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 5, pp. 169-178, May. 2021
https://doi.org/10.3745/KTSDE.2021.10.5.169,   PDF Download:
Keywords: Bike Sharing System, clustering, demand prediction, Random Forest
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.


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Cite this article
[IEEE Style]
K. Kim and C. H. Lee, "A K-Means-Based Clustering Algorithm for Traffic Prediction in a Bike-Sharing System," KIPS Transactions on Software and Data Engineering, vol. 10, no. 5, pp. 169-178, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.5.169.

[ACM Style]
Kyoungok Kim and Chang Hwan Lee. 2021. A K-Means-Based Clustering Algorithm for Traffic Prediction in a Bike-Sharing System. KIPS Transactions on Software and Data Engineering, 10, 5, (2021), 169-178. DOI: https://doi.org/10.3745/KTSDE.2021.10.5.169.