Abstract
In recent years, the rapid development of global artificial intelligence technology has become an important force in promoting the accelerated development of science and technology, and industry. From finance, medical care, and education to intelligent manufacturing and smart cities, artificial intelligence technology is penetrating into all walks of life, bringing convenience to people's lives. Through traffic flow forecasting, people can better plan their own travel routes and reduce the risk of traffic congestion. Accurate traffic forecast information can help to improve people's travel efficiency and improve urban service level, which is the core technology in the construction of intelligent transportation systems. The task of traffic flow forecasting aims at forecasting the future traffic flow trend according to the historical data information in the traffic network. The key challenge is how to model the temporal and spatial dependence in traffic networks. In the time domain, traffic flow has obvious periodicity characteristics, especially based on the daily periodicity and weekly periodicity characteristics of human social laws. We use the time series Transformer with predefined periodicity information to extract time dependence. In the spatial domain, there are similar traffic patterns in some areas on the macro level and the relationship between adjacent road sections on the micro level. We use the graph convolutional network based on the geographical adjacency matrix and the graph convolutional network based on the sequence semantic similarity matrix to extract the micro and macro spatial dependence respectively. We combine the two deeply and design a traffic flow forecasting model based on a graph convolutional network and Transformer to extract the deep temporal-spatial correlation. Finally, we experiment on several real-world traffic data sets to verify the effectiveness of the model.
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