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Use of Fuzzy Neural Networks for a Short Term Forecasting of Traffic Flow Performance

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Creativity in Intelligent Technologies and Data Science (CIT&DS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1083))

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Abstract

The method for a short term forecasting of the traffic in the urban road network and of the average vehicle speed is suggested. The author’s method is based on a regulatory approach to the calculation of the traffic capacity of the city road network. This method is completed with the methodology of forecasting the changes in the hourly traffic intensity. As the mathematical tool for the implementation of the forecasting methodology, the fuzzy neural networks are taken. It is suggested to make the forecast of short-term traffic intensity taking into account time of day, day of the week and season. On the basis of the data on the traffic capacity, the authors provide the relationships of an average speed change. The example of the calculation of transport flow performance is made in one of the motorways in the city of Volgograd.

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Author information

Authors and Affiliations

  1. Chair “Systems of Computerized Design Engineering and Exploratory Engineering”, Volgograd State University, Volgograd, Russia

    Skorobogatchenko Dmitry

  2. Chair “Construction and Operation of Transport Facilities”, Volgograd State University, Volgograd, Russia

    Viselskiy Sergey

Authors
  1. Skorobogatchenko Dmitry
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  2. Viselskiy Sergey
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Correspondence to Skorobogatchenko Dmitry .

Editor information

Editors and Affiliations

  1. CAD & Research Department, Volgograd State Technical University, Volgograd, Russia

    Alla G. Kravets

  2. Electrical and Computer Engineering, University of Patras, Rion, Patras, Greece

    Peter P. Groumpos

  3. Volgograd State Technical University, Volgograd, Russia

    Maxim Shcherbakov

  4. Volgograd State Technical University, Volgograd, Russia

    Marina Kultsova

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Dmitry, S., Sergey, V. (2019). Use of Fuzzy Neural Networks for a Short Term Forecasting of Traffic Flow Performance. In: Kravets, A., Groumpos, P., Shcherbakov, M., Kultsova, M. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2019. Communications in Computer and Information Science, vol 1083. Springer, Cham. https://doi.org/10.1007/978-3-030-29743-5_32

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  • DOI: https://doi.org/10.1007/978-3-030-29743-5_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29742-8

  • Online ISBN: 978-3-030-29743-5

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