Adaptive Iterative Learning Identification Strategy for Macroscopic Traffic Flow Model

Abstract

The traffic flow system of urban road network has strong randomness and time-varying nature, and it is difficult for a single fixed traffic flow model to accurately describe the actual operation of urban road network. In order to describe the actual operation of traffic flow in urban road networks more accurately, a nonlinear macroscopic traffic flow model was proposed with unknown time-varying multi-parameter by taking into account the steady-state and dynamic characteristics of traffic flow, and a time-varying multi-parameter iterative learning identification strategy was designed 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 an optimal tracking control problem, so that the number of queued vehicles at the entrance of each intersection converges on the true value, the real-time adaptive ability of unfalsified control algorithm is used to adjust the learning law gain of the iterative learning identification strategy, which improves the anti-interference ability of the identification strategy. The convergence of the algorithm is proved by a rigorous mathematical theoretical derivation, and finally the effectiveness of the method was further verified by simulation experiments using the model-based control method.

Keywords

• urban road networks
• iterative learning
• parameter identification
• nonlinear models
• macroscopic traffic flow