Graph Attention Informer for Long-Term Traffic Flow Prediction under the Impact of Sports Events

期限(时间) 流量(计算机网络) 图形 计算机科学 模拟 计算机安全 理论计算机科学 量子力学 物理
作者
Yaofeng Song,Ruikang Luo,Tianchen Zhou,Changgen Zhou,Rong Su
出处
期刊:Sensors [Multidisciplinary Digital Publishing Institute]
卷期号:24 (15): 4796-4796
标识
DOI:10.3390/s24154796
摘要

Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation and smart cities. With the development 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, a 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 factors. The GAT-Informer model was tested on real-world data collected in London, and the experimental results showed that our model has better performance in long-term traffic flow prediction compared to other baseline models.
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