A Congestion Sub-area Coordinated Control Model for Queue Suppression Based on Improved Evolutionary Algorithm

Iterative optimization of signal control timing is an significant way to alleviate urban traffic congestion. In order to ensure the stability of traffic operation under traffic congestion, this paper constructed a coordinated control algorithm for congestion sub-area facing queue suppression. By introducing path traffic flow and path length indicators, a method for selecting critical traffic paths under congestion conditions was proposed. A coordinated control model for congestion sub-area was established, with the optimization goal of maximizing the throughout traffic volume of critical traffic paths and minimizing the increment of maximum queue length in sub-area. In order to improve the convergence ability of the algorithm, taking the green signal ratio, phase sequence, split and cycle as optimization parameters, a microscopic simulation environment was constructed to solve individual fitness, and an improved adaptive genetic algorithm based on simulated annealing adaptive crossover and mutation strategy was used to obtain optimal signal timing parameters. The simulation results showed the proposed method can effectively improve the sub-area traffic operation efficiency under congestion conditions.