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Neural network modeling of vehicle discharge headway at signalized intersection: model descriptions and results

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  • Tong, H. Y.
  • Hung, W. T.

Abstract

Vehicle discharge headway at signalized intersections is of great importance in junction analysis. However, it is very difficult to simulate the discharge headway of individual queued vehicle because of the great variations in the driver behaviors, vehicle characteristics and traffic environment. The current study proposes a neural network (NN) approach to simulate the queued vehicle discharge headway. A computer-based three-layered (NN) model was developed for the estimation of discharge headway. The widely used backpropagation algorithm has been utilized in training the NN model. The NN model was trained, validated with field data and then compared with other headway models. It was found that the NN model performed better. Model sensitivity analysis was conducted to further validate the applicability of the model. Results showed that the NN model could produce reasonable discharge headway estimates for individual vehicles.

Suggested Citation

  • Tong, H. Y. & Hung, W. T., 2002. "Neural network modeling of vehicle discharge headway at signalized intersection: model descriptions and results," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(1), pages 17-40, January.
    • Handle: RePEc:eee:transa:v:36:y:2002:i:1:p:17-40
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    Cited by:

    1. Tan, Jiyuan & Li, Li & Li, Zhiheng & Zhang, Yi, 2013. "Distribution models for start-up lost time and effective departure flow rate," Transportation Research Part A: Policy and Practice, Elsevier, vol. 51(C), pages 1-11.
    2. Srivastava, Anupam & Jin, Wen-Long & Lebacque, Jean-Patrick, 2015. "A modified Cell Transmission Model with realistic queue discharge features at signalized intersections," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 302-315.

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