Attention based spatiotemporal model for short-term traffic flow prediction

Nisha Singh (), Kranti Kumar () and Bhawna Pokhriyal ()
Additional contact information
Nisha Singh: Dr. B. R. Ambedkar University Delhi
Kranti Kumar: Dr. B. R. Ambedkar University Delhi
Bhawna Pokhriyal: Dr. B. R. Ambedkar University Delhi

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 4, No 12, 1517-1531

Abstract: Abstract This study presents the development of AST-Deep, an attention-based deep learning model aimed at improving the accuracy and reliability of short-term traffic flow forecasting. Traffic forecasting plays a critical role in managing road operations, reducing congestion, enhancing safety, and optimizing transportation resources. The AST-Deep model consists of three key steps: (1) Spatial Correlation Analysis, where an enhanced version of ResNet is utilized to capture spatial dependencies between mileposts; (2) Temporal Correlation Modeling, where an attention-driven LSTM network is employed to model the temporal dynamics of traffic flow; and (3) Weighted Feature Fusion, which integrates the spatial and temporal features to generate the final traffic flow predictions. The model incorporates three traffic flow patterns-real-time, daily, and weekly-allowing it to account for periodic traffic behavior and improve forecasting precision. Experiments conducted on real-world traffic datasets show that AST-Deep consistently outperforms nine baseline models, including traditional and machine learning approaches, by a significant margin in terms of forecasting accuracy. Specifically, the AST-Deep model achieves a 1 to 5 % improvement in mean absolute error and root mean square error over the best-performing baseline model as the prediction horizon increases. These results demonstrate the effectiveness of AST-Deep in capturing both spatial and temporal dependencies to provide more accurate and reliable short-term traffic flow predictions.

Keywords: Traffic flow prediction; Resnet; Periodic; Attention; LSTM (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-025-02784-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:16:y:2025:i:4:d:10.1007_s13198-025-02784-7

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-025-02784-7

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().