Prediction and Analysis of Train Passenger Load Factor of High-Speed Railway Based on LightGBM Algorithm

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.

• Record URL:
  https://doi.org/10.1155/2021/9963394
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
• Supplemental Notes:
  • © 2021 Bing Wang et al.
• Authors:
  • Wang, Bing
  • Wu, Peixiu
  • Chen, Quanchao
  • Ni, Shaoquan
• Publication Date: 2021-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: Article ID 9963394
• Serial:
  • Journal of Advanced Transportation
  • Volume: 2021
  • Publisher: John Wiley & Sons, Incorporated
  • ISSN: 0197-6729
  • EISSN: 2042-3195
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Algorithms; Analysis; High speed rail; Load factor; Machine learning; Mathematical prediction; Passenger trains; Passenger volume
• Subject Areas: Passenger Transportation; Planning and Forecasting; Railroads;

Filing Info

• Accession Number: 01777195
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 23 2021 3:25PM