Short-term traffic flow prediction based on vehicle trip chain features

Abstract

Short-term traffic flow prediction can improve the efficiency of transportation operations. Historical data-driven prediction methods have been proved to perform well. However, saturated or oversaturated traffic operations cannot be accurately predicted based only on detector data from a single intersection. This study proposes a short-term traffic prediction method based on vehicle trip chain features. First, the video data is pre-processed and quality assessed. Then, vehicle trip chain features are mined to correlate upstream and downstream intersections.Convolutional neural networks and long-short-term-memory model are built next. The model is launched to train the predictor and output the traffic flow for all turns at each approach to the intersection. After cases we demonstrate that the prediction accuracy of CNNs-LSTM is usually better than other methods, especially during oversaturation. In addition, we demonstrate that vehicle trip chain features can improve prediction accuracy and shorten the time consumed by the model.