Multi-Step Short-Term Traffic Flow Prediction of Urban Road Network Based on ISTA-Transformer Model

Key words:  urban road network  traffic flow prediction  spatio-temporal feature  ISTA-transformer model

Abstract:

Short-term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems. Nowadays, there is a large amount of traffic flow data generated from the monitoring devices of urban road networks, which contains road network traffic information with high application value. In this study, an improved spatio-temporal attention transformer model (ISTA-transformer model) is proposed to provide a more accurate method for predicting multi-step short-term traffic flow based on monitoring data. By embedding a temporal attention layer and a spatial attention layer in the model, the model learns the relationship between traffic flows at different time intervals and different geographic locations, and realizes more accurate multi-step short-time flow prediction. Finally, we validate the superiority of the model with monitoring data spanning 15 days from 620 monitoring points in Qingdao, China. In the four time steps of prediction, the MAPE (Mean Absolute Percentage Error) values of ISTA-transformer's prediction results are 0.22, 0.29, 0.37, and 0.38, respectively, and its prediction accuracy is usually better than that of six baseline models (Transformer, GRU, CNN, LSTM, Seq2Seq and LightGBM), which indicates that the proposed model in this paper always has a better ability to explain the prediction results with the time steps in the multi-step prediction.