NA-DGRU: A Dual-GRU Traffic Speed Prediction Model Based on Neighborhood Aggregation and Attention Mechanism

Abstract

Traffic prediction is an important part of the Intelligent Transportation System (ITS) and has broad application prospects. However, traffic data are affected not only by time, but also by the traffic status of other nearby roads. They have complex temporal and spatial correlations. Developing a means for extracting specific features from them and effectively predicting traffic status such as road speed remains a huge challenge. Therefore, in order to reduce the speed prediction error and improve the prediction accuracy, this paper proposes a dual-GRU traffic speed prediction model based on neighborhood aggregation and the attention mechanism: NA-DGRU (Neighborhood aggregation and Attention mechanism–Dual GRU). NA-DGRU uses the neighborhood aggregation method to extract spatial features from the neighborhood space of the road, and it extracts the correlation between speed and time from the original features and neighborhood aggregation features through two GRUs, respectively. Finally, the attention model is introduced to collect and summarize the information of the road and its neighborhood in the global time to perform traffic prediction. In this paper, the prediction performance of NA-DGRU is tested on two real-world datasets, SZ-taxi and Los-loop. In the 15-, 30-, 45- and 60-min speed prediction results of NA-DGRU on the SZ-taxi dataset, the RMSE values were 4.0587, 4.0683, 4.0777 and 4.0851, respectively, and the MAE values were 2.7387, 2.728, 2.7393 and 2.7487; on the Los-loop dataset, the RMSE values for the speed prediction results were 5.1348, 6.1358, 6.7604 and 7.2776, respectively, and the MAE values were 3.0281, 3.6692, 4.0567 and 4.4256, respectively. On the SZ-taxi dataset, compared with other baseline methods, NA-DGRU demonstrated a maximum reduction in RMSE of 6.49% and a maximum reduction in MAE of 6.17%; on the Los-loop dataset, the maximum reduction in RMSE was 31.01%, and the maximum reduction in MAE reached 24.89%.