Arterial Coordination Control Signal Transition Optimization Based on Volatility Analysis

In order to reduce the adverse effects of the arterial intersections signal control strategy dynamic changes during arterial coordination control signal transition. This paper analyzes the fluctuation characteristics during the transition of control signals. The phase offset variation volatility model, signal cycle fluctuation volatility model, green ratio volatility model are constructed, and the volatility model analysis is conducted on the two aspects of the multi-period transition and multi-intersection coordination cycle transitions. The particle swarm optimization algorithm was used to solve the multi-objective optimization problem in the arterial coordination transition, and the best transition control strategy was finally obtained. The simulation results show that the proposed algorithm is optimized compared with the traditional Add algorithm and the Subtract algorithm in terms of vehicle average delay, vehicle average stop times, and average queue length, which can make the signal control effect better.