Optimization Research on Traffic Flow Scheduling of Intelligent Transportation Information Management System Based on MPSO Algorithm

This study takes the intelligent transportation information management system as the basic framework and focuses on the technical scheme of the traf ic low regulation module in the system.Taking the intersection in urban traf ic as the research scenario, we extract the characteristics of urban traf ic time and regulation objective function to construct the traf ic low scheduling model.The particle swarm algorithm (PSO) is used to optimize the traf ic low control model, and the inertia weights and the four degree and position update mechanism are improved for the problems of PSO algorithm, such as easy to fall into local optimization.The improved particle swarm algorithm (MPSO) in this paper is utilized to solve the traf ic low scheduling problem, and compared with the PSO algorithm to highlight the effectiveness of the improved operation in this paper.The results show that the optimized traf ic low regulation model based on MPSO algorithm has signi icant performance advantages in indicators such as average parking delay.Compared with the PSO algorithm, the MPSO algorithm in this paper obviously has higher convergence accuracy and can achieve more excellent regulation solution set in the intersection traf ic scheduling scenario.The application of the method in this paper can effectively solve the problems of vehicle congestion and frequent traf ic accidents in urban intersections.