LAUSR

As an essential part of the modern intelligent traffic management system, traffic speed prediction is a challenging task. In recent studies, deep neural networks (LSTM and WaveNet) and graph neural networks (GCN and GNN) have been extensively investigated on traffic networks evaluation, which is better than statistical-based models (MA and ARIMA). However, the demerits existing in these deep learning forecasting process include (1) carry out vehicle speed as an individual input and insufficient ability to handle the other related factors, such as the number of equivalent lanes, accident occurrence, and toll data; (2) inadequate capability of considering both linear and nonlinear components as a whole; (3) unstable performance on forecasting task given various heterogeneous series. Therefore, we propose a hybrid end-to-end model to combine both spatio-temporal features and other effective features. First, a heterogeneous graph attention network approach (HetGAT) was proposed, and a temporal dilated convolution architecture (TCN) was adopted to simulate the impacts on traffic flow of the multi-scale context of temporal factors. Then, a weighted graph attention network (GAT) encodes input temporal features, and a decoder predicts the output speed sequence via a freeway network structure. Based on the end-to-end architecture, we integrate multiple Spatio-temporal factors effectively for the prediction. To validate the efficiency of the proposed model, three sets of field-captured data were employed to run the test. Compared with conventional sequence analysis models and deep prediction models, experimental results demonstrated the superiority of HetGAT for all cases with regards to MAE, MAPE, and RMSE.