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Traffic Flow Modeling of Urban Expressway Using Artificial Neural Networks

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

From the viewpoint of macroscopic dynamic characteristics of urban expressway traffic flow, a commonly used macroscopic dynamic deterministic traffic flow model is analyzed, and the 1.5-layer feed-forward network modeling for the urban expressway traffic flow is established. Hangzhou urban expressway is simulated and the result demonstrates that the neural network model is able to reproduce traffic congestion built in realty with considerable accuracy, thus making it suitable for evaluating various control strategies and performing further modeling and simulation tasks.

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References

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Author information

Authors and Affiliations

  1. The National Key Lab. of Industrial Control Technology, Zhejiang University, Yuquan Campus, Hangzhou, China

    Guo-Jiang Shen

Authors
  1. Guo-Jiang Shen
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Shen, GJ. (2006). Traffic Flow Modeling of Urban Expressway Using Artificial Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_3

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  • DOI: https://doi.org/10.1007/11760191_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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