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| Short Term Bike-sharing Ridership Prediction under the Big-data Condition: Comparison of Machine Learning Models |
| JIAO Zhilun1,JIN Hong2,LIU Binglian1,ZHANG Zihao2 |
1. College of Economic and Social Development
2. Analytic Partners, Inc. New York
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Abstract Using the large and multidimensional data released by the bike-sharing project, and employing the Machine Learning Models, this article discussed the factors influenced short-term demand prediction of a bike-haring business. The results showed that the major factors that affected the short-term demand of bike sharing include specific location, time, and weather conditions. Meanwhile, compared with the Ordinary Linear Regression, Lasso Regression and Ridge Regression model, the Random Forest and Gradient Boosting Decision Tree models had higher goodness of fit (R2) and lower standard error (RMSE) in both in sample and the out sample predictions, which shed lights on the machine learning models and are more suitable for short-term precise demand predictions.
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Received: 25 March 2018
Published: 15 August 2018
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Corresponding Authors:
JIAO Zhilun
E-mail: zjiao@nankai.edu.cn
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2018, Vol. 38 
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