Artificial neural network and wavelet analysis application in speed forecast of Beijing urban freeway

Speed is an important factor for traffic safety evolution.The use of ITS technology in speed management with real-time information of urban freeway is one of strategies to enhance road safety. This paper presents methods for prediction short-term traffic flow speed on Beijing urban freeway with real time information from inductive loops. The source data sets including traffic volumes, speed and occupancy which are collected 24 h/day over several years on Beijing ring road. Traffic flow is divided into three statuses, including free flow, transition status and congestion according to occupancy. To achieve objective prediction results, wavelet technology is applied to de-noising process. The artificial neural network, which does not require any pre-defined underlying relationship between dependent and independent variables, is a powerful tool in dealing with prediction problems. In this paper, RBF network is designed for predicting speed for future five minutes. Results show that the proposed RBF network model produces reliable estimates of vehicle speed for three various traffic conditions, especially congestion condition.