Abstract
The predication of short-term passenger flow plays a very important role for improving service quality and revenue High-speed railway operation. To precisely predict the short-term passenger flow, impact factors need to be deeply analyzed and a reasonable predication model is required. This chapter analyzed the impact factors for short-term passenger flow and proposed a prediction model based on random forest regression. With the passenger flow data between Beijing and Shanghai from July to August in 2015, a predication model is trained and reached 91% accuracy for daily passenger flow. Finally, the importance of each impact factor has been analyzed, and this information can also help high-speed railway operation. It is shown that the prediction model based on random forest regression for predicting short-term passenger flow can help to improve the high-speed railway operation.
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Li, Lh., Zhu, Js., Shan, Xh., Zhang, X. (2019). Prediction Modeling of Railway Short-Term Passenger Flow Based on Random Forest Regression. In: Wang, W., Bengler, K., Jiang, X. (eds) Green Intelligent Transportation Systems. GITSS 2017. Lecture Notes in Electrical Engineering, vol 503. Springer, Singapore. https://doi.org/10.1007/978-981-13-0302-9_84
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DOI: https://doi.org/10.1007/978-981-13-0302-9_84
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