Deep tree neural network for multiple‐time‐step prediction of short‐term speed and confidence estimation

Abstract

To solve the multiple-time-step prediction of traffic speed and confidence for segment types of expressway, a deep tree neural network (DTNN) with multitask learning is proposed. DTNN contains a classification network, regression networks and a confidence network. These sub backbone networks accomplish the tasks of distinguishing segment types, fitting the speed of segments and the confidence estimation on the predicted speed, respectively. Through multitask learning, the sub networks in DTNN share feature representation and complement each other. To further improve the accuracy of speed prediction on congestion, the mean absolute percentage error loss function (MAPE-loss) is applied in DTNN. It makes the learning and extracted features biased to low speed samples. The traffic speed dataset of the Shanghai Expressway is used to test the DTNN and 12 comparison methods. Results show that the proposed DTNN with MAPE-loss can efficiently improve the predictive accuracy of low-speed samples over the other methods. The trained DTNN also gave highly accurate low speed prediction on the dataset of Suzhou expressway. In addition, the smallest reduction in the R-squared value from the training stage to the testing stage illustrates the best generalization of DTNN model.