Research on Traffic Flow Forecasting Using Multi-Scale Spatial-Temporal Modeling and Spectrum Enhancement.
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| Title: | Research on Traffic Flow Forecasting Using Multi-Scale Spatial-Temporal Modeling and Spectrum Enhancement. |
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| Authors: | Sun, Baoyu1 13897812832@163.com, Dai, Hong2 dear_red9@163.com, Zhang, Jicheng1 839519835@qq.com |
| Source: | Engineering Letters. May2026, Vol. 34 Issue 5, p1649-1657. 9p. |
| Subjects: | Multiscale modeling, Spatiotemporal processes, Signal convolution, Attention, Traffic estimation, Intelligent transportation systems, Deep learning |
| Abstract: | With the rapid development of intelligent transportation systems and smart mobility, accurate traffic flow prediction plays a crucial role in traffic scheduling, congestion management, and travel planning. To address the strong temporal dependencies and complex spatial correlations inherent in traffic flow data, this paper proposes a Transformer based traffic flow prediction model termed Fourier Multi-Scale-Transformer (FMS-Transformer). At the feature embedding stage, a Spectrum Enhancement (SE) mechanism is introduced to integrate time-of-day features, day-of-week features, and node-adaptive embedding, thereby enhancing the representation of periodic and heterogeneous spatial-temporal features. Subsequently, spatial-temporal self-attention mechanisms are employed to model global dependencies along the temporal and spatial dimensions, respectively. Furthermore, a Multi-Scale Deep Separable Temporal Convolution (MS-DS Conv) is incorporated to capture short-term fluctuations and long-term trends using one dimensional temporal convolution kernels with different scales, while an attention fusion mechanism is adopted to adaptively integrate multi-scale temporal features. Experimental results on the PEMS08 dataset demonstrate that the proposed FMS-Transformer consistently outperforms baseline models in terms of root mean square error, mean absolute error, and mean absolute percentage error, thereby validating its effectiveness and superiority in traffic flow prediction tasks. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193453922 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Research on Traffic Flow Forecasting Using Multi-Scale Spatial-Temporal Modeling and Spectrum Enhancement. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Baoyu%22">Sun, Baoyu</searchLink><relatesTo>1</relatesTo><i> 13897812832@163.com</i><br /><searchLink fieldCode="AR" term="%22Dai%2C+Hong%22">Dai, Hong</searchLink><relatesTo>2</relatesTo><i> dear_red9@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jicheng%22">Zhang, Jicheng</searchLink><relatesTo>1</relatesTo><i> 839519835@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. May2026, Vol. 34 Issue 5, p1649-1657. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multiscale+modeling%22">Multiscale modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+convolution%22">Signal convolution</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+estimation%22">Traffic estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the rapid development of intelligent transportation systems and smart mobility, accurate traffic flow prediction plays a crucial role in traffic scheduling, congestion management, and travel planning. To address the strong temporal dependencies and complex spatial correlations inherent in traffic flow data, this paper proposes a Transformer based traffic flow prediction model termed Fourier Multi-Scale-Transformer (FMS-Transformer). At the feature embedding stage, a Spectrum Enhancement (SE) mechanism is introduced to integrate time-of-day features, day-of-week features, and node-adaptive embedding, thereby enhancing the representation of periodic and heterogeneous spatial-temporal features. Subsequently, spatial-temporal self-attention mechanisms are employed to model global dependencies along the temporal and spatial dimensions, respectively. Furthermore, a Multi-Scale Deep Separable Temporal Convolution (MS-DS Conv) is incorporated to capture short-term fluctuations and long-term trends using one dimensional temporal convolution kernels with different scales, while an attention fusion mechanism is adopted to adaptively integrate multi-scale temporal features. Experimental results on the PEMS08 dataset demonstrate that the proposed FMS-Transformer consistently outperforms baseline models in terms of root mean square error, mean absolute error, and mean absolute percentage error, thereby validating its effectiveness and superiority in traffic flow prediction tasks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1649 Subjects: – SubjectFull: Multiscale modeling Type: general – SubjectFull: Spatiotemporal processes Type: general – SubjectFull: Signal convolution Type: general – SubjectFull: Attention Type: general – SubjectFull: Traffic estimation Type: general – SubjectFull: Intelligent transportation systems Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Research on Traffic Flow Forecasting Using Multi-Scale Spatial-Temporal Modeling and Spectrum Enhancement. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Baoyu – PersonEntity: Name: NameFull: Dai, Hong – PersonEntity: Name: NameFull: Zhang, Jicheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 5 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
