Abstract Regarding the issue of unbalanced bike distribution in the bike sharing system (BSS), the accurate prediction of bike demand is of importance to dynamic repositioning. This research focuses on the prediction accuracy of the hourly bike number change on station level. Three frequently used machine learning models, including Random Forest (RF), Gradient Boosting Regression Tree (GBRT) and Neural Network (NN) are trained on the same preprocessed dataset. Meanwhile, two training methods, training a check-in prediction model as well as a check-out prediction model, respectively, and training the model with processed bike number change data directly, are both applied to these three models in order to improve the prediction accuracy. The results show that the method of training on the bike number change data is more effective and the GBRT model trained in this way outperforms other models.
Station-Level Hourly Bike Demand Prediction for Dynamic Repositioning in Bike Sharing Systems
2019-01-01
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Dynamic demand-driven bike station clustering
Elsevier | 2022
|Transportation Research Record | 2021
|