Developing real-time traffic prediction with deep neural networks

Abstract

Traffic flow prediction is one of the challenges in the development of Intelligent Transportation System (ITS). Precise traffic flow prediction aids in mitigating urban traffic congestion and enhancing urban traffic efficiency, which is vital for fostering the integrated development of intelligent transportation and smart cities. With the advancement of deep learning, many deep neural networks have been proposed to address this problem. However, due to the complexity of traffic maps and external factors, such as sports events, these models cannot perform well in long-term prediction. In order to enhance the accuracy and robustness of the model on long-term time series prediction, Graph Attention Informer (GAT-Informer) structure is proposed by combining the graph attention layer and Informer layer to capture the intrinsic features and external factors in spatial-temporal correlation. The external factors are represented as sports events impact factor. The GAT-Informer model was tested on the real-world data, and the experimental results showed that the proposed model had a better performance in long-term traffic flow prediction compared with other baseline models.