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An iterative learning identification strategy for nonlinear macroscopic traffic flow model

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  • Yan, Fei
  • Qiu, Jiangchen
  • Tian, Jianyan

Abstract

The parameters of traffic flow model are important to the management and control of urban road network. In order to more accurately describe the actual operation of traffic flow in urban road network, this paper proposes a nonlinear macroscopic traffic flow model containing unknown time-varying multi-parameter on the basis of the steady-state and dynamic characteristics of traffic flow, and designs a time-varying multi-parameter iterative learning identification strategy by using the inherent repetitive characteristics of traffic flow. In the finite time interval, the iterative learning identification strategy is used to transform the parameter identification problem into the optimal tracking control problem, so that the number of queuing vehicles at each entrance of intersection tends to the true value, thus improving the accuracy of the model. Finally, the convergence of the algorithm is proved by a strict theoretical derivation, and the effectiveness of the method is further verified by simulation experiments using the model-based control method.

Suggested Citation

  • Yan, Fei & Qiu, Jiangchen & Tian, Jianyan, 2022. "An iterative learning identification strategy for nonlinear macroscopic traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s0378437122005775
    DOI: 10.1016/j.physa.2022.127901
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    References listed on IDEAS

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