TRAFFIC FLOW PREDICTION DISPLAY AND STUDY BASED ON NEURAL NETWORK
Abstract
Intelligent Transportation System (ITS) is recognized as one of the most efficient measures to solve the city traffic jam and traffic safety problems. Accurate real-time prediction of traffic flow is the key technology in ITS. The real-time traffic flow prediction model with neural network is established to meet the theoretical needs for traffic flow guidance systems based on simple introduction the real-time detection device in ITS. Traffic volume is important in the traffic planning and construction, which is important technical indicator of them, but the present traffic volume methods almost have many problems, so in this paper a model of traffic volume forecast has been founded in artificial neural network BP model. Also, the paper takes real road as an example to verify this model which can predict traffic flow fast and correctly, still has higher precision and feasibility. The model provides a good method for traffic flow guidance systems to predict traffic condition.
