Paper Info
Title
Dynamic cluster-based over-demand prediction in bike sharing systems.
Abstract
Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.
Year
DOI
Venue
2016
10.1145/2971648.2971652
UbiComp
Keywords
Field
DocType
Bike sharing system, over-demand prediction, urban data
Cluster (physics),Monte Carlo method,Computer science,Simulation,Sustainable transport,Human–computer interaction,Distributed computing
Conference
Citations 
PageRank 
References 
26
1.08
16
Authors
10
Name
Order
Citations
PageRank
Longbiao Chen112310.60
Daqing Zhang23619217.31
Leye Wang355136.79
Dingqi Yang454228.79
Xiaojuan Ma532549.27
Shijian Li6115569.34
Zhaohui Wu73121246.32
Gang Pan81501123.57
Thi-Mai-Trang Nguyen9463.27
Jérémie Jakubowicz1022217.95