Spatial Contiguity-Constrained Hierarchical Clustering for Traffic Prediction in Bike Sharing Systems

The critical problem in managing a bike sharing system (BSS) is to solve the imbalance in the number of available bikes by stations and times, which negatively affects the users' riding experience. To address this issue, many BSS operators rearrange bikes using a fleet of trucks. Moreover, the...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-06, Vol.23 (6), p.5754-5764
Main Author: Kim, Kyoungok
Format: Article
Language:English
Subjects:
Algorithms
Bicycles
Bike sharing system
Cluster analysis
Clustering
Clustering algorithms
Clustering methods
Convergence
Heuristic algorithms
hierarchical clustering
hierarchical prediction framework
Prediction algorithms
Predictive models
random forest
System analysis and design
traffic prediction
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Summary:The critical problem in managing a bike sharing system (BSS) is to solve the imbalance in the number of available bikes by stations and times, which negatively affects the users' riding experience. To address this issue, many BSS operators rearrange bikes using a fleet of trucks. Moreover, the effectiveness and efficiency of rebalancing operations heavily rely on accurate traffic prediction in BSS. Hence, many researchers have developed methods to enhance the prediction performance. One approach to increase the accuracy is to hierarchically predict demand from higher level (cluster or system) to station level. In the hierarchical prediction frameworks, the cluster-level to predict the demand of the total usage of groups of stations is introduced and it is crucial to partition stations into clusters that can increase the stability of actual usage and prediction. Therefore, the present work proposes a new spatial contiguity-constrained hierarchical clustering algorithm for BSS. The proposed algorithm is based on hierarchical clustering, which is deterministic, and uses the hourly proportions of check-outs and check-ins to define the temporal usage pattern of each station that does not require cluster assignment. Thus, different from existing methods, our proposed algorithm is deterministic and fast. In addition, the proposed algorithm is proven to be superior to other clustering methods in terms of traffic prediction at both the cluster and station levels.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3057596