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Combined Neural Network Approach for Short-Term Urban Freeway Traffic Flow Prediction

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5553))

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Abstract

Short-term traffic flow prediction is an essential component of urban traffic management and information systems. This paper presents a new short-term freeway traffic flow prediction model based on combined neural network approach. This model consists of two modules: a self-organizing feature map neural network and an Elman neural network. The former classifies the traffic conditions in a day into six patterns, and the later specifies the relationship between input and output to provide the prediction value. The inputs of the Elman neural network model include three kinds of data: the several time series of the prediction location, the historic data of the predictive time interval in the same weekdays, the time series of the adjacent location. The performances of the combined neural network model are validated using the real observation data from 3rd ring freeway in Beijing.

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Authors and Affiliations

  1. Institute of Transportation Engineering, Tsinghua University, Room 304, Heshanheng Building, Beijing, 100084, China

    Ruimin Li & Huapu Lu

Authors
  1. Ruimin Li
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  2. Huapu Lu
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Editors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering, South Dakota School of Mines & Technology, 501 E. St. Joseph Street, SD 57701, Rapid City, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Li, R., Lu, H. (2009). Combined Neural Network Approach for Short-Term Urban Freeway Traffic Flow Prediction. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_112

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  • DOI: https://doi.org/10.1007/978-3-642-01513-7_112

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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