ENHANCING LARGE SCALE TRAFFIC CONGESTION PREDICTION WITH ATTENTION-AUGMENTED BILSTM NEURAL NETWORK
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.1824Keywords:
Neural Network, Traffic Congestion, Sustainable GrowthAbstract [English]
One major issue impeding the sustainable growth of urban traffic is traffic congestion. To avoid traffic congestion, it is crucial to assess the current state of the traffic and project future traffic patterns. Identifying and creating prediction techniques for a city wide in urban setting is the key objective of this study. Predicting long-term levels of road congestion can help commuters avoid crowded regions and allow traffic agencies to take the necessary measures. In this paper, we present attention-augmented BiLSTM neural network approach for hourly based monthly traffic congestion prediction. Our experiments show that the proposed model outperforms the baselines in terms of accuracy and error.
An increasing number of studies are using real -time data-which is gathered using various devices including GPS, loop detectors and fixed location traffic sensors-to improve the prediction impact. Fixed location traffic sensors are more affordable, therefore, in our research we estimate the traffic congestion using real-time data obtained by these sensors.
References
Immers, L., Logghe, S., 2002. Traffic Flow Theory, Vol. 40 (21). Faculty of Engineering, Department of Civil Engineering, Section Traffic and Infrastructure, Kasteelpark Arenberg.
Gerlough, D.L., Huber, M.J., 1976. Traffic flow theory. In : Transportation Research Board (TRB) Special Report. Transportation Research Board.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions. In : Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. DOI: https://doi.org/10.1109/CVPR.2015.7298594
Daganzo, C., Daganzo, C., 1997. Fundamentals of Transportation and Traffic Operations, Vol. 30. Pergamon Oxford DOI: https://doi.org/10.1108/9780585475301
Gartner, N.H., Messer, C.J., Rathi, A., 2002. Traffic flow theory-a State-of-the-art Report : Revised Monograph on Traffic Flow Theory. In : Transportation Research Board (TRB) Special Report. Turner-Fairbank Highway Research Center.
De Fabritiis, C., Ragona, R., Valenti, G., 2008. Traffic Estimation and Prediction Based on Real Time Floating Car Data. In : 2008 11th International IEEE Conference on Intelligent Transportation Systems. IEEE, pp. 197–203. DOI: https://doi.org/10.1109/ITSC.2008.4732534
Afrin, T., Yodo, N., 2020. A survey of Road Traffic Congestion Measures Towards a Sustainable and Resilient Transportation System. Sustainability 12 (11), 4660. DOI: https://doi.org/10.3390/su12114660
Fouladgar, M., Parchami, M., Elmasri, R., Ghaderi, A., 2017. Scalable deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction. In : 2017 International Joint Conference on Neural Networks, IJCNN, pp. 2251–2258. DOI: https://doi.org/10.1109/IJCNN.2017.7966128
Liu, J., Barreau, M., Cicic, M., Johansson, K.H., 2020. Learning-Based Traffic State Reconstruction Using Probe Vehicles. arXiv Preprint arXiv:2011.05031.
T. Zhang, J. Xu, S. Cong, C. Qu and W. Zhao, "A Hybrid Method of Traffic Congestion Prediction and Control," in IEEE Access, vol. 11, pp. 36471-36491, 2023, doi: 10.1109/ACCESS.2023.3266291 DOI: https://doi.org/10.1109/ACCESS.2023.3266291
] Walid Fahs, Fadlallah Chbib, Abbas Rammal, Rida Khatoun, Ali El Attar, Issam Zaytoun, Joel Hachem, 2023.Traffic Congestion Prediction Based on Multivariate Modelling and Neural Networks Regressions, Procedia Computer Science, Volume 220, Pages 202-209, ISSN 1877-0509, DOI: https://doi.org/10.1016/j.procs.2023.03.028
H. Yi and K. -H. N. Bui, "An Automated Hyperparameter Search-Based Deep Learning Model for Highway Traffic Prediction," in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 9, pp. 5486-5495, Sept. 2021, doi: 10.1109/TITS.2020.2987614. DOI: https://doi.org/10.1109/TITS.2020.2987614
Saleem, Muhammad & Abbas, Sagheer & Ghazal, Taher & Khan, Muhammad & Sahawneh, Nizar & Ahmad, Munir. (2022). Smart cities : Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal. In Press. 1-10. 10.1016/j.eij.2022.03.003. DOI: https://doi.org/10.1016/j.eij.2022.03.003
G. Liu et al., "Smart Traffic Monitoring System Using Computer Vision and Edge Computing," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 12027-12038, Aug. 2022, doi: 10.1109/TITS.2021.3109481. DOI: https://doi.org/10.1109/TITS.2021.3109481
G. Kothai, E. Poovammal, Gaurav Dhiman, Kadiyala Ramana, Ashutosh Sharma, Mohammed A. AlZain, Gurjot Singh Gaba, Mehedi Masud, and Vimal Shanmuganathan. 2021. A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities. Wirel. Commun. Mob. Comput. 2021 (2021). DOI: https://doi.org/10.1155/2021/5583874
HCM 2010 : highway capacity manual. (2010). Washington, D.C. : Transportation Research Board.
Schuster, M. and Paliwal, K.K. : Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), pp.2673-2681 (1997). DOI: https://doi.org/10.1109/78.650093
Brownlee, J. : Long Short-term Memory Networks with Python : Develop Sequence Prediction Models with Deep Learning. Machine Learning Mastery (2017).
Tian, C. and Chan, W. K. (2021). Spatial‐Temporal Attention Wavenet: a Deep Learning Framework for Traffic Prediction Considering Spatial‐Temporal Dependencies. IET Intelligent Transport Systems, 15(4), 549-561. DOI: https://doi.org/10.1049/itr2.12044
Zhai, Y., Wan, Y., & Wang, X. (2022). Optimization of Traffic Congestion Management in Smart Cities under Bidirectional Long and Short‐Term Memory Model. Journal of Advanced Transportation, 2022(1), 3305400. DOI: https://doi.org/10.1155/2022/3305400
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Deepti Soni, Dr. Shraddha Masih
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
