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Intelligent simulation and prediction of traffic flow dispersion

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  • Qiao, Fengxiang
  • Yang, Hai
  • Lam, William H. K.

Abstract

Dispersion of traffic flow on urban road segments is often described by some typical statistical models such as the normal distribution model and the geometric distribution model. These probability-based models can fit traffic flow well under ideal physical environments but may not work satisfactory in certain complex cases because of their strict mathematical assumptions. A neural network-based system identification approach is used to establish an auto-adaptive model for simulating traffic flow dispersion. This model, being feasible to a wide variety of traffic circumstances, can be calibrated and used for on-line traffic flow forecasting. Data simulation and field-testing show reliable performance of the proposed intelligent approach.

Suggested Citation

  • Qiao, Fengxiang & Yang, Hai & Lam, William H. K., 2001. "Intelligent simulation and prediction of traffic flow dispersion," Transportation Research Part B: Methodological, Elsevier, vol. 35(9), pages 843-863, November.
    • Handle: RePEc:eee:transb:v:35:y:2001:i:9:p:843-863
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    References listed on IDEAS

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    1. Muriel J. Grace & Renfrey B. Potts, 1964. "A Theory of the Diffusion of Traffic Platoons," Operations Research, INFORMS, vol. 12(2), pages 255-275, April.
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    Cited by:

    1. Ma, Tao & Zhou, Zhou & Abdulhai, Baher, 2016. "Time Series Based Hourly Traffic Flow Prediction on the GTA Freeways Using TSTVEC Model," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319287, Transportation Research Forum.
    2. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    3. Ma, Tao & Zhou, Zhou & Abdulhai, Baher, 2015. "Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 27-47.

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