WT-FCTGN: A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

doi:10.1088/1674-1056/ad3349

中国物理B ›› 2024, Vol. 33 ›› Issue (7): 78901-078901.doi: 10.1088/1674-1056/ad3349

WT-FCTGN: A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

Zhifang Liao(廖志芳)¹, Ke Sun(孙轲)¹, Wenlong Liu(刘文龙)¹, Zhiwu Yu(余志武)^2,3, Chengguang Liu(刘承光)^4,†, and Yucheng Song(宋禹成)^1,‡

1 School of Computer Science and Engineering, Central South University, Changsha 410083, China;
2 National Engineering Research Center of High-speed Railway Construction Technology, Changsha 410075, China;
3 School of Civil Engineering, Central South University, Changsha 410075, China;
4 School of Traffic & Transportation Engineering, Central South University, Changsha 410083, China

收稿日期:2024-01-08 修回日期:2024-02-26 接受日期:2024-03-13 出版日期:2024-06-18 发布日期:2024-06-18
通讯作者: Chengguang Liu, Yucheng Song E-mail:chengguangliu@csu.edu.cn;234703024@csu.edu.cn
基金资助:
The Science and Technology Research and Development Program Project of China Railway Group Ltd provided funding for this study (Project Nos. 2020-Special-02 and 2021-Special-08).

WT-FCTGN: A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

Zhifang Liao(廖志芳)¹, Ke Sun(孙轲)¹, Wenlong Liu(刘文龙)¹, Zhiwu Yu(余志武)^2,3, Chengguang Liu(刘承光)^4,†, and Yucheng Song(宋禹成)^1,‡

1 School of Computer Science and Engineering, Central South University, Changsha 410083, China;
2 National Engineering Research Center of High-speed Railway Construction Technology, Changsha 410075, China;
3 School of Civil Engineering, Central South University, Changsha 410075, China;
4 School of Traffic & Transportation Engineering, Central South University, Changsha 410083, China

Received:2024-01-08 Revised:2024-02-26 Accepted:2024-03-13 Online:2024-06-18 Published:2024-06-18
Contact: Chengguang Liu, Yucheng Song E-mail:chengguangliu@csu.edu.cn;234703024@csu.edu.cn
Supported by:
The Science and Technology Research and Development Program Project of China Railway Group Ltd provided funding for this study (Project Nos. 2020-Special-02 and 2021-Special-08).

摘要/Abstract

摘要： Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction (WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public PeMS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.

关键词: traffic flow modeling, time-series, wavelet reconstruction

Abstract: Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction (WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public PeMS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.

Key words: traffic flow modeling, time-series, wavelet reconstruction

中图分类号: (Structures and organization in complex systems)

89.75.Fb

45.70.Vn (Granular models of complex systems; traffic flow) 05.45.Tp (Time series analysis) 95.75.Wx (Time series analysis, time variability)

Zhifang Liao(廖志芳), Ke Sun(孙轲), Wenlong Liu(刘文龙), Zhiwu Yu(余志武), Chengguang Liu(刘承光), and Yucheng Song(宋禹成). WT-FCTGN: A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling[J]. 中国物理B, 2024, 33(7): 78901-078901.

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WT-FCTGN: A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

WT-FCTGN: A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

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