Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlation
Traffic forecasting is crucial for a variety of applications, including route optimization, signal management, and travel time estimation. However, many existing prediction models struggle to accurately capture the spatiotemporal patterns in traffic data due to its inherent nonlinearity, high dimens...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neurorobotics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1527908/full |
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| author | Ande Chang Yuting Ji Yiming Bie |
| author_facet | Ande Chang Yuting Ji Yiming Bie |
| author_sort | Ande Chang |
| collection | DOAJ |
| description | Traffic forecasting is crucial for a variety of applications, including route optimization, signal management, and travel time estimation. However, many existing prediction models struggle to accurately capture the spatiotemporal patterns in traffic data due to its inherent nonlinearity, high dimensionality, and complex dependencies. To address these challenges, a short-term traffic forecasting model, Trafficformer, is proposed based on the Transformer framework. The model first uses a multilayer perceptron to extract features from historical traffic data, then enhances spatial interactions through Transformer-based encoding. By incorporating road network topology, a spatial mask filters out noise and irrelevant interactions, improving prediction accuracy. Finally, traffic speed is predicted using another multilayer perceptron. In the experiments, Trafficformer is evaluated on the Seattle Loop Detector dataset. It is compared with six baseline methods, with Mean Absolute Error, Mean Absolute Percentage Error, and Root Mean Square Error used as metrics. The results show that Trafficformer not only has higher prediction accuracy, but also can effectively identify key sections, and has great potential in intelligent traffic control optimization and refined traffic resource allocation. |
| format | Article |
| id | doaj-art-5a12b63e34ad4d0c8894ad97b8d228bb |
| institution | Kabale University |
| issn | 1662-5218 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj-art-5a12b63e34ad4d0c8894ad97b8d228bb2025-01-23T06:56:29ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182025-01-011910.3389/fnbot.2025.15279081527908Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlationAnde Chang0Yuting Ji1Yiming Bie2College of Forensic Sciences, Criminal Investigation Police University of China, Shenyang, ChinaSchool of Transportation, Jilin University, Changchun, ChinaSchool of Transportation, Jilin University, Changchun, ChinaTraffic forecasting is crucial for a variety of applications, including route optimization, signal management, and travel time estimation. However, many existing prediction models struggle to accurately capture the spatiotemporal patterns in traffic data due to its inherent nonlinearity, high dimensionality, and complex dependencies. To address these challenges, a short-term traffic forecasting model, Trafficformer, is proposed based on the Transformer framework. The model first uses a multilayer perceptron to extract features from historical traffic data, then enhances spatial interactions through Transformer-based encoding. By incorporating road network topology, a spatial mask filters out noise and irrelevant interactions, improving prediction accuracy. Finally, traffic speed is predicted using another multilayer perceptron. In the experiments, Trafficformer is evaluated on the Seattle Loop Detector dataset. It is compared with six baseline methods, with Mean Absolute Error, Mean Absolute Percentage Error, and Root Mean Square Error used as metrics. The results show that Trafficformer not only has higher prediction accuracy, but also can effectively identify key sections, and has great potential in intelligent traffic control optimization and refined traffic resource allocation.https://www.frontiersin.org/articles/10.3389/fnbot.2025.1527908/fullintelligent transportation systemshort-term traffic forecastingTransformertraffic spatiotemporal correlationdeep learning |
| spellingShingle | Ande Chang Yuting Ji Yiming Bie Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlation Frontiers in Neurorobotics intelligent transportation system short-term traffic forecasting Transformer traffic spatiotemporal correlation deep learning |
| title | Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlation |
| title_full | Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlation |
| title_fullStr | Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlation |
| title_full_unstemmed | Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlation |
| title_short | Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlation |
| title_sort | transformer based short term traffic forecasting model considering traffic spatiotemporal correlation |
| topic | intelligent transportation system short-term traffic forecasting Transformer traffic spatiotemporal correlation deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1527908/full |
| work_keys_str_mv | AT andechang transformerbasedshorttermtrafficforecastingmodelconsideringtrafficspatiotemporalcorrelation AT yutingji transformerbasedshorttermtrafficforecastingmodelconsideringtrafficspatiotemporalcorrelation AT yimingbie transformerbasedshorttermtrafficforecastingmodelconsideringtrafficspatiotemporalcorrelation |