Author
Listed:
- Nisha Singh
(Dr. B. R. Ambedkar University Delhi)
- Kranti Kumar
(Dr. B. R. Ambedkar University Delhi)
- Bhawna Pokhriyal
(Dr. B. R. Ambedkar University Delhi)
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.
Suggested Citation
Nisha Singh & Kranti Kumar & Bhawna Pokhriyal, 2025.
"Attention based spatiotemporal model for short-term traffic flow prediction,"
International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(4), pages 1517-1531, April.
Handle:
RePEc:spr:ijsaem:v:16:y:2025:i:4:d:10.1007_s13198-025-02784-7
DOI: 10.1007/s13198-025-02784-7
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