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Analysis and Prediction of Passenger Flow of High-Speed Night Train

  • Bo Li
  • Xiang-chun Qi
  • Qiao Li
  • Xiao Yang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

High-speed night train is a new personalized product of high-speed railway to meet the market demand in China. Based on the existing operation conditions of high-speed night train, by recapitulating its relevant advantages and comparing with characteristics of high-speed day train, conventional train, and civil flight products, it comes to a conclusion that the high-speed night train has certain competitiveness and has well expanded the high-speed railway service portfolio. By analyzing the passenger flow characteristics of high-speed night train according to the ticketing data, it is found that the passenger flow characteristics of Beijing–Guangzhou and Shanghai–Shenzhen high-speed night trains are similar, both have relatively stable passenger flows, and also have a certain share in the passenger transportation market. Through analysis on passenger flow composition of high-speed night train, it is found that the high-speed night train is a supplement to high-speed day trains, and the passenger flow basically consists of transfer passengers. At last, based on calculation of passenger transfer inclination, neural network method, time series method, curve estimation method, and multiple regression method are used to, respectively, predict the passenger flow of high-speed night train during the “13th Five-Year Plan”. The result can provide certain data support to the reasonable operation of high-speed night train under network conditions.

Keywords

Railway transportation Passenger flow prediction High-speed night train Passenger flow characteristics 

References

  1. 1.
    Wang C (2014) Schematic study on operation of night train on Beijing-Guangzhou High-speed Railway, Beijing Jiaotong UniversityGoogle Scholar
  2. 2.
    Wang W (2016) Prediction and study of railway passenger flow under influence of high-speed railway. Railway Transp Econ 38(4):42–46MathSciNetGoogle Scholar
  3. 3.
    Aimin Wang (1995) Study on application of neural network to fuzzy comprehensive evaluation. Syst Eng Theor Pract 15(10):37–42Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.China Academy of Railway ScienceBeijingChina

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