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Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

Author

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  • Aderemi Adewumi
  • Jimmy Kagamba
  • Alex Alochukwu

Abstract

In recent times, urban road networks are faced with severe congestion problems as a result of the accelerating demand for mobility. One of the ways to mitigate the congestion problems on urban traffic road network is by predicting the traffic flow pattern. Accurate prediction of the dynamics of a highly complex system such as traffic flow requires a robust methodology. An approach for predicting Motorised Traffic Flow on Urban Road Networks based on Chaos Theory is presented in this paper. Nonlinear time series modeling techniques were used for the analysis of the traffic flow prediction with emphasis on the technique of computation of the Largest Lyapunov Exponent to aid in the prediction of traffic flow. The study concludes that algorithms based on the computation of the Lyapunov time seem promising as regards facilitating the control of congestion because of the technique’s effectiveness in predicting the dynamics of complex systems especially traffic flow.

Suggested Citation

  • Aderemi Adewumi & Jimmy Kagamba & Alex Alochukwu, 2016. "Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-15, January.
  • Handle: RePEc:hin:jnlmpe:5656734
    DOI: 10.1155/2016/5656734
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    Cited by:

    1. Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    2. Inoue, Kei & Tani, Kazuki, 2023. "Quantification of chaos in a time series generated from a traffic flow model using the extended entropic chaos degree," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Juan Francisco Sánchez-Pérez & Santiago Oviedo-Casado & Gonzalo García-Ros & Manuel Conesa & Enrique Castro, 2024. "Understanding Complex Traffic Dynamics with the Nondimensionalisation Technique," Mathematics, MDPI, vol. 12(4), pages 1-14, February.

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