Skip to main content
SpringerLink
Log in

Prediction Traffic Flow with Combination Arima and PageRank

  • Conference paper
  • First Online:
Smart Grid and Internet of Things (SGIoT 2019)

Included in the following conference series:

Abstract

Modern traffic network information is similar to the complex network structure in that the links between the sections are quite complex. Therefore, predicting the traffic flow between sections can effectively relieve traffic congestion. To solve this problem, this paper proposes a combined model of Arima and PageRank to predict the traffic flow of each section of the road network. First, the trained Armia model is used to predict the average speed and traffic flow of each section, and then the PageRank model is used to calculate the weight of each section. The product of traffic flow and weight is output as the final result. Through the experiment of highway traffic data in PeMS database, this method is verified to be able to predict the traffic flow of the whole road network.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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. Cheol, O., Jun-seok, O., Ritchie, S.G.: Real-time hazardous traffic condition warning system: framework and evaluation. IEEE Trans. Intell. Transp. Syst. 6(3), 265–272 (2005)

    Article  Google Scholar 

  2. Ding, W., Gong, Y., Nan, N.: Toward cognitive vehicles. IEEE Intell. Syst. 26(3), 76–80 (2011)

    Article  Google Scholar 

  3. Wu, C., Zhao, G., Ou, B.: A fuel economy optimization system with applications in vehicles with human drivers and autonomous vehicles. Transp. Res. Part D Transp. Environ. 16(7), 515–524 (2011)

    Article  Google Scholar 

  4. Luettel, T., Himmelsbach, M., Wuensche, H.J.: Autonomous ground vehicles-concepts and a path to the future. Proc. IEEE 100, 1831–1839 (2012)

    Article  Google Scholar 

  5. Grant-muller, S., Usher, M.: Intelligent transport systems: the propensity for environmental and economic benefits. Technol. Forecast. Soc. Chang. 8(2), 149–166 (2014)

    Article  Google Scholar 

  6. Nair, A.S., Liu, J-C., Rileft, L, et al.: Non-linear analysis of traffic flow. In: Proceedings of the Intelligent Transportation Systems (2001)

    Google Scholar 

  7. Hua, J., Faghri, A.: Dynamic traffic pattern classification using artificial neural networks. The TRIS and ITRD Database (1993)

    Google Scholar 

  8. Wei, W., Wu, H., Ma, H.: An Auto Encoder and LSTM-Based Traffic Flow Prediction Method. Sensors (Basel, Switzerland) 19(13), 2946 (2019)

    Article  Google Scholar 

  9. Davis, G.A., Nihan, N.L.: Nonparametric regression and short-term freeway traffic forecasting. J. Transp. Eng. 117(2), 178–188 (1991)

    Article  Google Scholar 

  10. Dougherty, M.S., Cobbett, M.R.: Short-term inter-urban traffic forecasts using neural networks. Int. J. Forecast. 13(1), 21–23 (1997)

    Article  Google Scholar 

  11. Abdulhal, B., Ritchie, S.G.: Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network. Transp. Res. Part C Emerg. Technol. 7(5), 26–280 (1999)

    Google Scholar 

  12. Park, B.B.: Hybrid neuro-fuzzy application in short-term freeway traffic volume forecasting. Transp. Res. Rec. J. Transp. Res. Board 1802(1), 190–196 (2002)

    Article  Google Scholar 

  13. Tang, J., Chen, X., Hu, Z., Zong, F., Han, C., Li, L.: Traffic flow prediction based on combination of support vector machine and data denoising schemes. Physica A Stat. Mech. Appl. 534, 120642 (2019)

    Article  Google Scholar 

  14. Payandeh, S., Chiu, E.: Application of modified pagerank algorithm for anomaly detection in movements of older adults. Int. J. Telemed. Appl. 2019, 1–9 (2019)

    Google Scholar 

Download references

Acknowledgement

The work was supported by the Graduate Innovation and Entrepreneurship Program in Shanghai University in China under Grant No. 2019GY04.

Author information

Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Cheng-fan Li, Jia-xin Huang & Shao-chun Wu

  2. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, 200044, China

    Cheng-fan Li

Authors
  1. Cheng-fan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia-xin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shao-chun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shao-chun Wu .

Editor information

Editors and Affiliations

  1. National Changhua University of Education, Changhua City, Taiwan

    Der-Jiunn Deng

  2. National Taiwan Normal University, Taipei, Taiwan

    Ai-Chun Pang

  3. Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan

    Chun-Cheng Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Cf., Huang, Jx., Wu, Sc. (2020). Prediction Traffic Flow with Combination Arima and PageRank. In: Deng, DJ., Pang, AC., Lin, CC. (eds) Smart Grid and Internet of Things. SGIoT 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 324. Springer, Cham. https://doi.org/10.1007/978-3-030-49610-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-49610-4_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49609-8

  • Online ISBN: 978-3-030-49610-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation