This study takes the intelligent transportation information management system as the basic framework and focuses on the technical scheme of the traffic flow regulation module in the system. Taking the intersection in urban traffic as the research scenario, we extract the characteristics of urban traffic time and regulation objective function to construct the traffic flow scheduling model. The particle swarm algorithm (PSO) is used to optimize the traffic flow 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 traffic flow 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 traffic flow regulation model based on MPSO algorithm has significant 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 traffic scheduling scenario. The application of the method in this paper can effectively solve the problems of vehicle congestion and frequent traffic accidents in urban intersections.