Skip to main content

Abstract

The theory and application of railway passenger traffic volume (RPTV) forecasting is one of the focuses in the research areas of traffic transportation. In China, the passenger traffic system is composed of railways, highways, waterways and civil airlines. However, the railways have been played a main role in the national traffic system and the RPTV has attracted more and more attention because of its vital effect in economic evaluation of railway project, national resources allocation, and readjustment of investment structure within railway enterprises.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Feng Q, Xiyu L, Yinghong M (2009) Prediction of railway passenger traffic volume based on neural tree model. In: Second international conference on intelligent computation technology and automation. ICICTA’09

    Google Scholar 

  2. Xiaogang C (2009) Railway passenger volume forecasting based on support vector machine and genetic algorithm. In: International conference on future computer and communication. FCC’09, p 244

    Google Scholar 

  3. Qing C, Cuihong L, Wei G (2009) Railway passenger volume forecast based on IPSO-BP neural network. In: International conference on information technology and computer science. ITCS 2009

    Google Scholar 

  4. Qing C, Wei G, Cuihong L (2009) Railway passenger volume forecast by GA-SA-BP neural network. In: International workshop on intelligent systems and applications. ISA 2009

    Google Scholar 

  5. Dougherty MS, Cobbett MR (1997) Short-term inter-urban traffic fore-casts using neural networks. In: Int J Forecast 13(1):21–31

    Google Scholar 

  6. Ledoux C (1997) An urban traffic flow model integrating neural networks. Transp Res Part C–Emerg Technol 5(5):287–300

    Article  Google Scholar 

  7. Shmueli D (1998) Applications of neural networks in transportation planning. Prog Plann 50(3):141–204

    Article  Google Scholar 

  8. Geng Y (2008) Based on support vector machine research and application of least squares power system short-term load forecasting[D]. Shandong University, Shandong

    Google Scholar 

  9. Yang YQ (2006) Research on least squares support vector machine power system short-term load forecasting[D]. Sichuan University, Sichuan

    Google Scholar 

  10. Yao ZS (2007) Short time prediction theory and method of real-time data traffic on the road network[D]. Beijing Jiaotong University, Beijing

    Google Scholar 

  11. Dia H (2001) An object-oriented neural network approach to short-term traffic forecasting. Eur J Oper Res 131(2):253–261

    Article  Google Scholar 

  12. Xia Q (2011) Research on fluctuation of holiday railway passenger flow and application on forecast of passenger flow[D]. Beijing Jiaotong University, Beijing

    Google Scholar 

Download references

Acknowledgment

We wish to acknowledge the support of Beijing Laboratory for Mass Transit, the National High-Technology Research and Development Program (“863”Program) of China No. 2012AA112801, The Project of the Fundamental Research Funds for Central Universities No. 2011JB2004.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haifeng Song .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Song, H., Tang, T., Li, C., Ding, Y. (2014). Short Time Forecasting of Rail Transit Passenger Volume. In: Xia, H., Zhang, Y. (eds) The 2nd International Symposium on Rail Transit Comprehensive Development (ISRTCD) Proceedings. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37589-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37589-7_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37588-0

  • Online ISBN: 978-3-642-37589-7

  • eBook Packages: Business and EconomicsEconomics and Finance (R0)

Publish with us

Policies and ethics