Urban Road Travel Time Prediction Considering Impact of Traffic Event

The external environment factors have a great influence on urban traffic prediction, especially in the case of traffic event. Due to the randomness and non-linearity of traffic flow, the prediction accuracy of traffic anomalies is often low. Therefore, based on deep learning theory, a new method of urban road travel time prediction was proposed, which took the sequence-to-sequence (seq2seq) model as the main body and integrated the characteristics of external factors. This study used the seasonal and trend decomposition using loess (STL) to dig the time series cycle law of traffic history data, and deeply analyzed the causes of traffic anomaly combined with traffic event data. Finally, a stacked denosing autoencoder (SDAE) was established to extract the potential characteristics of time attribute and traffic event. Taking the segments of North Fourth Ring Middle Road and G6 Beijing-Tibet Expressway in Beijing as an example, the accuracy and feasibility of the prediction model were verified. And the effectiveness of SDAE model was analyzed through case experiments under recurring traffic event and non-recurring traffic event. The experimental results illustrate that the single step and multi-step prediction results of the model are superior to baseline models, and the highest prediction accuracy reaches 87.71%. In addition, compared with other models with traffic event data input, the model with SDAE has better prediction performance and robustness, and can adapt to the complex and changeable traffic flow. In the short-term prediction of the intelligent transportation system, the model has significant advantages, which can enhance the regulation ability of the management and reduce the congestion cost of the urban traffic. © 2021, Editorial Department of China Journal of Highway and Transport. All right reserved.