Advertisement

Passenger Flow Prediction Model of Intercity Railway Based on G-BP Network

  • Hai-lian LiEmail author
  • Meng-kai Lin
  • Qi-cai Wang
Conference paper
  • 21 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 617)

Abstract

Inter-city railway as the city’s comprehensive transportation system, the development of urban industrial economy and the image of the overall improve greatly boost. However, scientific and reasonable forecast traffic is the focus on the study of the inter-city railway construction project, which aim is to obtain the characteristics and rules of passenger flow, planning area to provide comprehensive system for railway planning and the actual resources and foundation of real and reliable data. Based on the grey relational analysis method influence the traffic data and the relationship between influencing factors, choose the main influence factors of traffic influence factors of the BP neural network model is established. Finally combined Lanzhou to Zhongchuan Airport inter-city railway project to traffic prediction research and survey data, it is concluded that the influence factors of the BP neural network model has good predictability to the traffic.

Keywords

Passenger traffic volume Prediction Grey theory BP neural network 

Notes

Acknowledgements

The authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China (Grant No. 51868042); the Changjiang Scholars and Innovation Team Development Program of Ministry of Education (IRT_15R29); Youth Science Foundation of Gansu (17JR5RA087); Youth Science Foundation of Lanzhou Jiaotong University (2017016); Foundation of A Hundred Young Talents Training Program of Lanzhou Jiaotong University (2018103).

References

  1. 1.
    Wang C (2008) Prediction of civil aviation passenger traffic volume on grey theory and RBF neural network. Beijing Jiaotong UniversityGoogle Scholar
  2. 2.
    Liu Q (2008) Research on travel demand forecasting based on complex system. China Academy of Railway SciencesGoogle Scholar
  3. 3.
    Mou Z (2008) The prophecy and analyse about Shen Yang area’s highway-flow. Northeastern UniversityGoogle Scholar
  4. 4.
    Deng L (2009) Research on the influencing factors on intercity rail construction. J Railway Eng Soc 10:99–101Google Scholar
  5. 5.
    Liu S, Dang Y, Fang Z (2010) Grey system theory and its application, 3rd edn. Science Press, BeijingGoogle Scholar
  6. 6.
    Xian M (2016) The research of railway passenger flow forecasting method based on greyneural network models. Southwest Jiaotong UniversityGoogle Scholar
  7. 7.
    Feng B, Bao X, Wang Q, Dong X (2015) A new combination model for forecasting railway passenger volume. Railway Stan Des 59(12):6–9Google Scholar
  8. 8.
    Feng B, Bao X, Wang Q (2015) Research of railway passenger volume forecast based on grey and neural network. J Railway Sci Eng 12(05):1227–1231Google Scholar
  9. 9.
    Ting G, Li W (2013) Prediction research of highway traveling passenger volume based on wavelet neural network. Appl Mech Mater 401–403:1401–1405Google Scholar
  10. 10.
    Meng T, Xiang F, Jing Z, Bin R (2014) FA-BP neural network-based forecast for railway passenger volume. Appl Mech Mater 641(8):673–677Google Scholar
  11. 11.
    Wang H, Wang X, Dang J (2010) Comprehensive evaluation of comfort of high-speed trains based on fuzzy reduction. J China Railway Soc 32(05):98–102Google Scholar
  12. 12.
    Wu H, Zhen J, Wang Y, Wang F (2014) Railway passenger and freight prediction based on RBF neural network theory. J Railway Sci Eng 11(4):110–113Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Key Laboratory of Road & Bridge and Underground Engineering of Gansu ProvinceLanzhou Jiaotong UniversityLanzhouChina
  2. 2.National and Provincial Joint Engineering Laboratory of Road & Bridge Disaster Prevention and ControlLanzhou Jiaotong UniversityLanzhouChina

Personalised recommendations