Traffic Speed Prediction of Urban Road Network Based on High Importance Links Using XGB and SHAP

As the intelligent transportation system has been introduced, traffic speed prediction has become one of the foremost challenging tasks within complex urban road networks. The main idea of this study is to identify links that have a significant impact on the target link and develop a high-performance travel speed prediction model using those links. This study proposes the Extreme gradient boosting model with high importance links (HI-XGB) to predict traffic speed in the urban area. High importance links for predicting the target link speed are selected using Shapley additive explanations. With the selected input features, extreme gradient boosting is used to predict traffic speed. The results show that the performance of the HI-XGB model with one- and 12-time steps ahead achieved 98.5% and 90.7% accuracy, respectively. Feature analysis and link classification analysis are performed to identify the impact of the spatial characteristic on predicted speed. Among the eight features, the speed of the target link at t and the speed change of the target link at t–1 have the most impact on the predicted target link speed. In addition, link classification analysis is performed to identify the impact of the spatial characteristic of the input feature on predicted speed. The result indicates that links other than upstream or downstream could have a greater impact on traffic speed prediction.