A Multiple Factor Bike Usage Prediction Model in Bike-Sharing System
Bike-sharing is becoming popular in the world, providing a convenient service for citizens. The system has to redistribute bikes among different stations frequently to solve the imbalance of spatial distribution. Real-time monitoring doesn’t solve this problem well, since it takes too much time to redistribute the bike and affects the user experience. In this paper, we first analyze the influence of factors such as time, weather, the location of stations. Then we cluster neighboring stations with similar usage pattern, and propose a lagged variable to simulate the effect of weather conditions in usage number. Finally, a multiple factor regression model with ARMA error (MFR-ARMA) is proposed to predict the check-out/in number of bikes in each cluster in a period of time. Evaluation dataset is from New York Bike Sharing System. The prediction results of the model are compared with four baseline methods. The experiments show a lower RMSLE and ER for check-out/in number prediction in our model.
KeywordsBike sharing system Regression prediction model ARMA error Cluster-level
This work is supported by Zhejiang Provincial Natural Science Foundation of China (No. LY17F020008).
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