Medium and long-term traffic flow prediction
based on ACNN-Trans model

doi:10.19682/j.cnki.1005-8885.2023.2013

中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (4): 21-32.doi: 10.19682/j.cnki.1005-8885.2023.2013

• Artificial Intelligence • 上一篇下一篇

Medium and long-term traffic flow prediction based on ACNN-Trans model

Zhang Xijun, Zhang Guannan, Zhang Xianli, Zhang Hong, Zhang Minghu

School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China

收稿日期:2022-08-17 修回日期:2023-06-19 接受日期:2023-08-31 出版日期:2023-08-31 发布日期:2023-08-31
通讯作者: Zhang Xijun, E-mail: zhangxijun198079@sina.com E-mail:zhangxijun198079@sina.com
基金资助:
This work was supported by the National Natural Science Foundation of China ( 62162040 ), Gansu Province Higher Education Innovation Fund-Funded Project ( 2021A-028 ), Gansu Provincial Science and Technology Program Funding Project ( 21ZD4GA028 ) and Gansu Provincial Science and Technology Plan Funding Key Project of Natural Science Foundation of China (22JR5RA226).

Medium and long-term traffic flow prediction based on ACNN-Trans model

Zhang Xijun, Zhang Guannan, Zhang Xianli, Zhang Hong, Zhang Minghu

School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China

Received:2022-08-17 Revised:2023-06-19 Accepted:2023-08-31 Online:2023-08-31 Published:2023-08-31
Contact: Zhang Xijun, E-mail: zhangxijun198079@sina.com E-mail:zhangxijun198079@sina.com
Supported by:
This work was supported by the National Natural Science Foundation of China ( 62162040 ), Gansu Province Higher Education Innovation Fund-Funded Project ( 2021A-028 ), Gansu Provincial Science and Technology Program Funding Project ( 21ZD4GA028 ) and Gansu Provincial Science and Technology Plan Funding Key Project of Natural Science Foundation of China (22JR5RA226).

摘要/Abstract

摘要： Aiming at the failure of traditional medium and long-term traffic flow forecasting methods to effectively process long-time series data and extract the spatio-temporal characteristics between road nodes, a com-bined prediction model attention based on convolutional neural network (CNN) and transformer ( ACNN-Trans ) network is proposed. The spatial features between nodes on complex roads are extracted by CNN. The embedded attention mechanism is adaptively pay attention to the results of feature extraction. The spatial features of the node traffic flow are dug by the embedded attention mechanism. The traffic flow sequence is calculated by the transformer network. The temporal correlation of long-term series data is captured by the relative position weight between the data. Finally, the 30-min and 60-min traffic flow predictions are respectively modeled by the extracted temporal and spatial features. The results show that the prediction results of the ACNN-Trans combined model are better than other models on two different real datasets. Comparing to the baseline models, the root mean square error (RMSE) index of the prediction results is reduced by an average of 34.58%, which verifies the effectiveness of ACNN-Trans model.

关键词: traffic flow prediction, intelligent transportation, attention mechanism, transformer

Abstract: Aiming at the failure of traditional medium and long-term traffic flow forecasting methods to effectively process long-time series data and extract the spatio-temporal characteristics between road nodes, a com-bined prediction model attention based on convolutional neural network (CNN) and transformer ( ACNN-Trans ) network is proposed. The spatial features between nodes on complex roads are extracted by CNN. The embedded attention mechanism is adaptively pay attention to the results of feature extraction. The spatial features of the node traffic flow are dug by the embedded attention mechanism. The traffic flow sequence is calculated by the transformer network. The temporal correlation of long-term series data is captured by the relative position weight between the data. Finally, the 30-min and 60-min traffic flow predictions are respectively modeled by the extracted temporal and spatial features. The results show that the prediction results of the ACNN-Trans combined model are better than other models on two different real datasets. Comparing to the baseline models, the root mean square error (RMSE) index of the prediction results is reduced by an average of 34.58%, which verifies the effectiveness of ACNN-Trans model.

Key words: traffic flow prediction, intelligent transportation, attention mechanism, transformer

Zhang Xijun, Zhang Guannan, Zhang Xianli, Zhang Hong, Zhang Minghu. Medium and long-term traffic flow prediction based on ACNN-Trans model[J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(4): 21-32.

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Medium and long-term traffic flow prediction based on ACNN-Trans model

Medium and long-term traffic flow prediction based on ACNN-Trans model

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