A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems

Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2018; no. 2018; pp. 1 - 8
Main Authors Tan, Yulong, Zhong, Rong, Wang, Lilei, Zhou, Yajun
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
Hindawi Limited
Subjects
Accuracy
Artificial intelligence
Bicycles
Bicycling
Demand
Economic forecasting
Gravitation
Markov chains
Mathematical problems
Methods
Traffic models
Transfer stations
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Summary:Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of the number of public bikes within the station. Traditional traffic demand prediction methods, such as gravity model, cannot be easily adapted to the problem of forecasting bike station transfer demand due to the difficulty in defining impedance and distinct characteristics of bike stations (Xu et al. 2013). Therefore, this paper proposes a prediction method based on Markov chain model. The proposed model is evaluated based on field data collected from Zhongshan City bike sharing system. The daily production and attraction of stations are forecasted. The experimental results show that the model of this paper performs higher forecasting accuracy and better generalization ability.
ISSN:1024-123X
1563-5147
DOI:10.1155/2018/8028714