STTF: An Efficient Transformer Model for Traffic Congestion Prediction
Abstract With the rapid development of economy, the sharp increase in the number of urban cars and the backwardness of urban road construction lead to serious traffic congestion of urban roads. Many scholars have tried their best to solve this problem by predicting traffic congestion. Some tradition...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-01-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-022-00177-3 |
| _version_ | 1797958465184858112 |
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| author | Xing Wang Ruihao Zeng Fumin Zou Lyuchao Liao Faliang Huang |
| author_facet | Xing Wang Ruihao Zeng Fumin Zou Lyuchao Liao Faliang Huang |
| author_sort | Xing Wang |
| collection | DOAJ |
| description | Abstract With the rapid development of economy, the sharp increase in the number of urban cars and the backwardness of urban road construction lead to serious traffic congestion of urban roads. Many scholars have tried their best to solve this problem by predicting traffic congestion. Some traditional models such as linear models and nonlinear models have been proved to have a good prediction effect. However, with the increasing complexity of urban traffic network, these models can no longer meet the higher demand of congestion prediction without considering more complex comprehensive factors, such as the spatio-temporal correlation information between roads. In this paper, we propose a traffic congestion index and devise a new traffic congestion prediction model spatio-temporal transformer (STTF) based on transformer, a deep learning model. The model comprehensively considers the traffic speed of road segments, road network structure, the spatio-temporal correlation between road sections and so on. We embed temporal and spatial information into the model through the embedding layer for learning, and use the spatio-temporal attention module to mine the hidden spatio-temporal information within the data to improve the accuracy of traffic congestion prediction. Experimental results based on real-world datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches. |
| first_indexed | 2024-04-11T00:20:29Z |
| format | Article |
| id | doaj.art-29bdd9e3ebc041809018f8a7c22ee8a4 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-11T00:20:29Z |
| publishDate | 2023-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-29bdd9e3ebc041809018f8a7c22ee8a42023-01-08T12:20:24ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-01-0116111610.1007/s44196-022-00177-3STTF: An Efficient Transformer Model for Traffic Congestion PredictionXing Wang0Ruihao Zeng1Fumin Zou2Lyuchao Liao3Faliang Huang4College of Computer and Cyber Security, Fujian Normal UniversitySchool of Civil Engineering, The University of SydneyFujian Key Laboratory of Automotive Electronic and Electrical Drive Technology, Fujian University of TechnologyFujian Key Laboratory of Automotive Electronic and Electrical Drive Technology, Fujian University of TechnologySchool of Computer and Information Engineering, Nanning Normal UniversityAbstract With the rapid development of economy, the sharp increase in the number of urban cars and the backwardness of urban road construction lead to serious traffic congestion of urban roads. Many scholars have tried their best to solve this problem by predicting traffic congestion. Some traditional models such as linear models and nonlinear models have been proved to have a good prediction effect. However, with the increasing complexity of urban traffic network, these models can no longer meet the higher demand of congestion prediction without considering more complex comprehensive factors, such as the spatio-temporal correlation information between roads. In this paper, we propose a traffic congestion index and devise a new traffic congestion prediction model spatio-temporal transformer (STTF) based on transformer, a deep learning model. The model comprehensively considers the traffic speed of road segments, road network structure, the spatio-temporal correlation between road sections and so on. We embed temporal and spatial information into the model through the embedding layer for learning, and use the spatio-temporal attention module to mine the hidden spatio-temporal information within the data to improve the accuracy of traffic congestion prediction. Experimental results based on real-world datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.https://doi.org/10.1007/s44196-022-00177-3Traffic congestion predictionFree-stream velocityRoad network structureSpatio-temporal informationTransformer |
| spellingShingle | Xing Wang Ruihao Zeng Fumin Zou Lyuchao Liao Faliang Huang STTF: An Efficient Transformer Model for Traffic Congestion Prediction International Journal of Computational Intelligence Systems Traffic congestion prediction Free-stream velocity Road network structure Spatio-temporal information Transformer |
| title | STTF: An Efficient Transformer Model for Traffic Congestion Prediction |
| title_full | STTF: An Efficient Transformer Model for Traffic Congestion Prediction |
| title_fullStr | STTF: An Efficient Transformer Model for Traffic Congestion Prediction |
| title_full_unstemmed | STTF: An Efficient Transformer Model for Traffic Congestion Prediction |
| title_short | STTF: An Efficient Transformer Model for Traffic Congestion Prediction |
| title_sort | sttf an efficient transformer model for traffic congestion prediction |
| topic | Traffic congestion prediction Free-stream velocity Road network structure Spatio-temporal information Transformer |
| url | https://doi.org/10.1007/s44196-022-00177-3 |
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