Short-Time Traffic Flow Prediction Based on Chaos Time Series Theory

Traffic flow prediction has become a kernel study in intelligent transportation system. A prediction model of short-time traffic flow is presented based on chaotic time series analysis method. After the phase space reconstruction using traffic flow data, a two-step optimized selection method is proposed, which considers Euclidean distance and equal coefficient between neighboring point and predicted point. Then the prediction model is educed with the local polynomial method to approximate the neighboring points. The model proposed in this paper is applied to predict the real traffic flow in Dongjiang road, Dongguan city in Guangdong province, China. Comparing the predicted traffic flow value with the flow measured in reality, the results show that the maximal relative error is 0.445%, whereas, the minimal one is 0.038%. Moreover, the single-step forward prediction only requires 38.52 seconds. As a result, it is proved that the method can significantly improve the prediction accuracy and meet the requirement of the real-time prediction.