공유자전거 시스템의 이용 예측을 위한K-Means 기반의 군집 알고리즘

Recently, a bike-sharing system (BSS) has become popular as a convenient “last mile” transportation. Rebalancing of bikes is a criticalissue 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 predictionat the station-level and the clustering step is very important in this approach. In this paper, we propose a k-means based clusteringalgorithm that overcomes the drawbacks of the existing clustering methods for BSS; indeterministic and hardly converged. By employingthe centroid initialization and using the temporal proportion of the rents and returns of stations as an input for clustering, the proposedalgorithm can be deterministic and fast.