International Journal of Transportation Engineering

International Journal of Transportation Engineering

Enhancing Short-Term Traffic Flow Forecasting by Hybrid Deep Learning Architectures and Attention Mechanisms (Case Study: High-Density Karaj-Chalous Road, Iran)

Document Type : Research Paper

Authors
Seyed Saber Naseralvai 1
Soheil Rezashoar 2
Akram Mazaheri 3
Saman Shafaati 1
1 Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
2 Department of Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
3 Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
10.22119/ijte.2025.500027.1683
Abstract
The main tool to mitigate congestion and improve travel experiences effectively in intelligent traffic management is to predict the accurate and timely short-term traffic flow on high-volume roads. We present the performances of different deep learning models, such as LSTM, GRU, CNN, their hybrids CNN-LSTM and CNN-GRU, and versions with an attention mechanism for one-hour-ahead traffic flow prediction on mountainous and high-density Karaj-Chalous Road. The input data include the traffic data from two traffic counters. The cited data were derived for a period ranging from 01/01/1401 to 01/01/1403. Besides, the synoptic meteorological data were acquired within three-hour intervals, while the models are compared based on various quantitative accuracy and error metrics. The results showed that the CNN-LSTM model was the best among the rest, with an R² value of 0.83, because it captured complex traffic patterns and temporal dependencies effectively. The other models ranked next were LSTM, GRU, CNN-LSTM-GRU, and CNN-GRU, with R2 values of 0.82, 0.81, 0.80, and 0.80, respectively. While the weakest models, CNN and CNN-MultiHead-Attention, yielded an R² of 0.60 and 0.62, respectively, this is due to a lack of consideration in these models regarding the nature of traffic data as a time series. Employing attention mechanisms improved prediction accuracy in some model architectures. This effect was highly varied based on the model structure itself. The results depict that deep, hybrid models with the integration of attention mechanisms can give more reliable and valuable forecasts to the intelligent transportation management systems for better travel planning and congestion reduction in similar roadways.
Keywords
Short-term
traffic flow prediction
deep learning
attention mechanism
CNN-LSTM
Karaj-Chalous Road
- Ahmed, M. S., & Cook, A. R. (1979). Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques.
 
- Bai, L., Yao, L., Li, C., Wang, X., & Wang, C. (2020). Adaptive graph convolutional recurrent network for traffic forecasting. Advances in Neural Information Processing Systems, 33, 17804–17815.
 
- Bui, K.-H. N., Cho, J., & Yi, H. (2022). Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues. Applied Intelligence, 52(3), 2763–2774.
https://doi.org/10.1007/s10489-021-02587-w
 
- Chen, H., & Grant-Muller, S. (2001). Use of sequential learning for short-term traffic flow forecasting. Transportation Research Part C-Emerging Technologies.
https://doi.org/10.1016/S0968-090X(00)00039-5
 
- Cirstea, R.-G., Yang, B., Guo, C., Kieu, T., & Pan, S. (2022). Towards spatio-temporal aware traffic time series forecasting. 2022 IEEE 38th International Conference on Data Engineering (ICDE).
 
- Do, L. N. N., Taherifar, N., & Vu, H. (2018). Survey of neural network-based models for short-term traffic state prediction. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery.
https://doi.org/10.1002/WIDM.1285
 
- Do, L. N. N., Vu, H. L., Vo, B. Q., Liu, Z., & Phung, D. (2019). An effective spatial-temporal attention based neural network for traffic flow prediction. Transportation Research Part C: Emerging Technologies.
https://doi.org/10.1016/J.TRC.2019.09.008
 
- Dougherty, M., & Cobbett, M. R. (1997). Short-term inter-urban traffic forecasts using neural networks. International Journal of Forecasting.
https://doi.org/10.1016/S0169-2070(96)00697-8
 
- Duan, P., Mao, G., Liang, W., & Zhang, D. (2019). A Unified Spatio-Temporal Model for Short-Term Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems (Print).
https://doi.org/10.1109/TITS.2018.2873137
 
- Dynamic Graph Convolutional Networks Based on Spatiotemporal Data Embedding for Traffic Flow Forecasting, 250 Knowledge-Based Systems 109028 (Elsevier 2022).
https://www.sciencedirect.com/science/article/pii/S0950705122005032
 
- Guo, J., Huang, W., & Williams, B. M. (2014). Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C-Emerging Technologies.
https://doi.org/10.1016/J.TRC.2014.02.006
 
- Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019). Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 922–929. https://ojs.aaai.org/index.php/AAAI/article/view/3881
 
- Iran Meteorological Organization (IRIMO). (2024). IRIMO Official Website.
https://www.irimo.ir
 
- Iran Road Maintenance and Transportation Organization. (2024). RMTO Official Website.
https://www.rmto.ir
 
- Jeong, Y., Byon, Y.-J., Castro-Neto, M., & Easa, S. (2013). Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems (Print).
https://doi.org/10.1109/TITS.2013.2267735
 
- Jia, Y., Wu, J., & Xu, M. (2017). Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method.
https://doi.org/10.1155/2017/6575947
- Karlaftis, M. G., & Vlahogianni, E. I. (2011). Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C-Emerging Technologies.
https://doi.org/10.1016/J.TRC.2010.10.004
 
- Khorshidi, N., Zargari, S. A., Rezashoar, S., & Mirzahossein, H. (2025). Optimizing Travel Time Reliability with XAI: A Virginia Interstate Network Case Using Machine Learning and Meta-Heuristics. Machine Learning with Applications, 100709.
https://doi.org/10.1016/j.mlwa.2025.100709
 
- Kong, J., Fan, X., Zuo, M., Deveci, M., Jin, X., & Zhong, K. (2024). ADCT-Net: Adaptive traffic forecasting neural network via dual-graphic cross-fused transformer. Information Fusion, 103, 102122.
 
- Li, M., & Zhu, Z. (2021). Spatial-temporal fusion graph neural networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4189–4196. https://ojs.aaai.org/index.php/AAAI/article/view/16542
 
- Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (No. arXiv:1707.01926). arXiv.
https://doi.org/10.48550/arXiv.1707.01926
 
- Liu, H., Zhu, C., Zhang, D., & Li, Q. (2023). Attention-based spatial-temporal graph convolutional recurrent networks for traffic forecasting. International conference on advanced data mining and applications.
 
- Lippi, M., Bertini, M., & Frasconi, P. (2013). Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2013.2247040
 
- Liu, T., Wang, Y., Zhou, H., Luo, J., & Deng, F. (2024). Distributed Short-Term Traffic Flow Prediction Based on Integrating Federated Learning and TCN. IEEE Access, 12, 148026–148036. IEEE Access.
https://doi.org/10.1109/ACCESS.2024.3474300
 
- Luo, X., Zhu, C., Zhang, D., & Li, Q. (2023). Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction (No. arXiv:2302.12598). arXiv.
https://doi.org/10.48550/arXiv.2302.12598
 
- Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. (2015). Traffic Flow Prediction With Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems (Print).
https://doi.org/10.1109/TITS.2014.2345663
 
- Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015). Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C-Emerging Technologies.
https://doi.org/10.1016/J.TRC.2015.03.014
 
- Miglani, A., & Kumar, N. (2019). Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges. Vehicular Communications.
https://doi.org/10.1016/J.VEHCOM.2019.100184
 
- Naheliya, B., Redhu, P., & Kumar, K. (2023). A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM. SRS Journal. https://indjst.org/
 
- Okutani, I., & Stephenades, Y. J. (1984). Dynamic prediction of traffic volume through Kalman Filtering.
https://doi.org/10.1016/0191-2615(84)90002-X
 
- Park, B., Messer, C., & Urbanik, T. (1998). Short-Term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Network.
https://doi.org/10.3141/1651-06
 
- Polson, N. G., & Sokolov, V. O. (2016). Deep learning for short-term traffic flow prediction.
https://doi.org/10.1016/J.TRC.2017.02.024
 
- Shu, W., Cai, K., & Xiong, N. (2021). A Short-Term Traffic Flow Prediction Model Based on an Improved Gate Recurrent Unit Neural Network. IEEE Transactions on Intelligent Transportation Systems (Print).
https://doi.org/10.1109/TITS.2021.3094659
 
- Smith, B. L., & Demetsky, M. (1997). Traffic Flow Forecasting: Comparison of Modeling Approaches. https://doi.org/10.1061/(ASCE)0733-947X(1997)123:4(261)
 
- Song, C., Lin, Y., Guo, S., & Wan, H. (2020). Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 914–921.
https://ojs.aaai.org/index.php/AAAI/article/view/5438
- Stathopoulos, A., & Karlaftis, M. G. (2003). A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C-Emerging Technologies.
https://doi.org/10.1016/S0968-090X(03)00004-4
 
- Sun, S., Zhang, C., & Yu, G. (2006). A bayesian network approach to traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems.
https://doi.org/10.1109/TITS.2006.869623
 
- Tedjopurnomo, D. A., Bao, Z., Zheng, B., Choudhury, F., & Qin, A. K. (2020). A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2020.3001195
 
- Vlahogianni, E., Golias, J., & Karlaftis, M. (2004). Short-term traffic forecasting: Overview of objectives and methods.
https://doi.org/10.1080/0144164042000195072
 
- Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. (2014). Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C-Emerging Technologies.
https://doi.org/10.1016/J.TRC.2014.01.005
 
- Wang, Y., Xu, S., & Di Feng. (2020). A New Method for Short-term Traffic Flow Prediction Based on Multi-segments Features. ICMLC.
https://doi.org/10.1145/3383972.3384038
 
- Williams, B. M., & Hoel, L. (2003). Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results. Journal of Transportation Engineering.
https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)
 
- Wu, Y., Tan, H., Qin, L., Bin Ran, & Jiang, Z. (2018). A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C-Emerging Technologies. https://doi.org/10.1016/J.TRC.2018.03.001
 
- Xia, M., Jin, D., & Chen, J. (2023). Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning. IEEE Transactions on Intelligent Transportation Systems (Print).
https://doi.org/10.1109/TITS.2022.3179391
 
- Xie, Y., Zhang, Y., & Ye, Z. (2007). Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition. Computer-Aided Civil and Infrastructure Engineering.
https://doi.org/10.1111/J.1467-8667.2007.00489.X
 
- Yu, B., Yin, H., & Zhu, Z. (2018). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. https://doi.org/10.24963/ijcai.2018/505
 
- Yuan, H., & Li, G. (2021). A Survey of Traffic Prediction: From Spatio-Temporal Data to Intelligent Transportation. Data Science and Engineering, 6(1), 63–85.
https://doi.org/10.1007/s41019-020-00151-z
 
- Zargari, S. A., Khorshidi, N., Mirzahossein, H., & Jin, X. (2025). Application of data augmentation techniques in predicting travel time reliability: evidence from England. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 49(1), 921-933.
https://doi.org/10.1007/s40996-024-01383-z
 
- Zhang, W., Yu, Y., Qi, Y., Shu, F., & Wang, Y. (2019). Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transportmetrica A: Transport Science. https://doi.org/10.1080/23249935.2019.1637966
 
- Zhang, Y., & Ye, Z. (2008). Short-Term Traffic Flow Forecasting Using Fuzzy Logic System Methods. J. Intell. Transp. Syst.
https://doi.org/10.1080/15472450802262281
 
Zhao, Z., Chen, W., Wu, X., Chen, P. C. Y., & Liu, J. (2017). LSTM network: A deep learning approach for short-term traffic forecast.
https://doi.org/10.1049/IET-ITS.2016.0208
 
- Zheng, C., Fan, X., Wang, C., & Qi, J. (2020). Gman: A graph multi-attention network for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 1234–1241. https://aaai.org/ojs/index.php/AAAI/article/view/5477
International Journal of Transportation Engineering
Volume 13, Issue 2 - Serial Number 50
Autumn 2025
Pages 2173-2192