A Hybrid Method of Traffic Congestion Prediction and Control

Abstract

With the increasing complexity of urban transportation system, serious traffic congestion brings inconvenience to travel. It is also very difficult to predict and control traffic congestion. Therefore, this paper takes urban traffic condition and traffic congestion as the research object, and conducts in-depth research on traffic condition prediction model and traffic congestion control method. Firstly, a traffic state prediction method based on improved particle swarm optimization (IPSO) optimized radial basis function (RBF) and long and short term memory network (LSTM)/ support vector machine (SVM) feature fusion model was proposed for urban traffic state prediction. Experiments were carried out based on the regional traffic data of Shenyang Station, and compared with other algorithms, which verified the superiority of the feature fusion model based on IPSO-RBF and LSTM/SVM in this paper. Secondly, aiming at the problem of urban traffic congestion, a congestion Section control method based on traffic allocation is proposed. Through VISSIM simulation and comparison with other traffic control schemes, the superiority of the congestion Section optimization method proposed in this paper is verified.

Keywords

• Traffic condition prediction
• feature fusion
• traffic flow allocation
• safety queuing factor