Traffic Flow Prediction Based On Cascaded Artificial Neural Network

The prediction of traffic flow is of great significance for the prevention of accidents, the avoidance of congestion and the dispatch of command center. Considering the complexity of traffic data in reality, it is an extraordinarily challenging task to forecast accurately from historical patterns. In this paper, we propose a method based on the cascaded artificial neural network (CANN) to predict traffic flow at positions. In order to express the spatial correlation of traffic data, the actual road network distance is introduced in our model. The real-world data derived from video surveillance cameras in Xiamen is used in the experiment which is compared with five baselines. To the best of our knowledge, this is the first time that CANN is applied to forecast traffic flow. The experimental results demonstrate that the CANN method has superior performance. In addition, We also discuss the impact of some external factors such as temperature, weather and holidays on the prediction results.