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Prediction and Analysis of Train Passenger Load Factor of High-Speed Railway Based on LightGBM Algorithm
Journal of Advanced Transportation ( IF 2.3 ) Pub Date : 2021-06-15 , DOI: 10.1155/2021/9963394
Bing Wang 1 , Peixiu Wu 1 , Quanchao Chen 1, 2, 3 , Shaoquan Ni 1, 2, 3
Affiliation  

In order to improve the prediction accuracy of train passenger load factor of high-speed railway and meet the demand of different levels of passenger load factor prediction and analysis, the influence factor of the train passenger load factor is analyzed in depth. Taking into account the weather factor, train attribute, and passenger flow time sequence, this paper proposed a forecasting method of train passenger load factor of high-speed railway based on LightGBM algorithm of machine learning. Considering the difference of the influence factor of the passenger load factor of a single train and group trains, a single train passenger load factor prediction model based on the weather factor and passenger flow time sequence and a group of trains’ passenger load factor prediction model based on the weather factor, the train attribute, and passenger flow time sequence factor were constructed, respectively. Taking the train passenger load factor data of high-speed railway in a certain area as an example, the feasibility and effectiveness of the proposed method were verified and compared. It is verified that LightGBM algorithm of machine learning proposed in this paper has higher prediction accuracy than the traditional models, and its scientific and accurate prediction can provide an important reference for the calculation of passenger ticket revenue, operation benefit analysis, etc.

中文翻译:

基于LightGBM算法的高速铁路列车客座率预测与分析

为提高高铁列车客座率预测精度,满足不同层次客座率预测分析的需求,对列车客座率影响因素进行了深入分析。综合考虑天气因素、列车属性和客流时序,提出一种基于机器学习LightGBM算法的高速铁路列车客座率预测方法。考虑单列列车和群列列车客座率影响因素的差异,基于天气因素和客流时序的单列列车客座率预测模型和基于群列列车客座率预测模型关于天气因素,火车属性,分别构建客流时序因子。以某地区高速铁路列车客座率数据为例,验证并比较了所提方法的可行性和有效性。经验证,本文提出的机器学习LightGBM算法比传统模型具有更高的预测精度,其科学准确的预测可为客票收入计算、运营效益分析等提供重要参考。
更新日期:2021-06-15
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