Urban Road Network Traffic Congestion Prediction Model Based on Probe Vehicle Technology

Abstract

A model for urban road network traffic congestion forecast based on probe vehicle technology, fuzzy logic judgement and back-propagation (BP) neural network was proposed. A three-layer BP neural network model was built to estimate the real time traffic flow of road network and to obtain BP neural network training specimen for the training by probe vehicle data and video data. Then the congestion possibility, level of congestion and the forming time of the link were estimated based on the road network topology and multifile fuzzy logic reasoning. The in-situ test shows good forecast result by the model.