A SURVEY OF MACHINE LEARNING MODELS FOR TRAFFIC MOVEMENT PREDICTION
DOI:
https://doi.org/10.33003/fjs-2024-0804-2650Keywords:
Gridlock, Vehicular movement, Peak period, PredictionAbstract
In most developed cities globally, traffic congestion has become a major challenge to commuters and road users. In most of the urbanized nations, there are traffic gridlock at certain periods of the day (peak periods). Road users spend alot of time at these gridlocks, wasting a lot of working hours. This gridlock has also resulted to air pollution and accident. Many researchers have develoed different vehicular movement prediction models for better traffic prediction. In this paper, we surveyed different traffic prediction model for congestion management.
References
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Xu, D. and Shi, Y. (2017). A combined model of random forest and multilayer perceptron to forecast expressway traffic flow. In: 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), IEEE. pp. 448-451.
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Wang, J., Cao, Y., Du, Y. and Li, L. (2019). DST: a deep urban traffic flow prediction framework based on spatial-temporal features. KSEM. LNCS (LNAI), vol. 11775, pp. 417–427. Springer, Cham. https://doi.org/10. 1007/978-3-030-29551-6_37
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Zhao, Z., Chen, W., Wu, X., Chen, P. C. and Liu, J. (2017). LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, vol. 11(2), pp. 68-75.
Shao, H., and Soong, B. H. (2016). Traffic flow prediction with long short-term memory networks (LSTMs). In: 2016 IEEE Region 10 Conference (TENCON), IEEE. pp. 2986-2989.
Kang, D., Lv, Y. and Chen, Y.Y. (2017). Short-term traffic flow prediction with LSTM recurrent neural network. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE. pp. 1-6.
Liu, Y., Wang, Y., Yang, X. and Zhang, L. (2017). Short-term travel time prediction by deep learning: A comparison of different LSTM-DNN models. In: IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE. pp. 1-8
Liu, D., Hui, S., Li, L., Liu, Z. and Zhang, Z. (2020). A method for short-term traffic flow forecasting based on GCN-LSTM. In: Proceedings - 2020 International Conference Computer Vision, Image Deep Learning CVIDL no. Cvidl, pp. 364–368 https://doi.org/10.1109/CVI DL51233.2020.00-70
Araujo, M., Barros, J. and Rossetti, R. J. (2015). Short-term real-time traffic prediction methods: A survey. In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 132-139.
Meneguette, R. I., Geraldo-Filho, P. R., Bittencourt, L. F., Ueyama, J., Krishnamachari, B. and Villas, L. A. (2015). Enhancing intelligence in inter-vehicle communications to detect and reduce congestion in urban centers. In: 2015 IEEE Symposium on Computers and Communication (ISCC), IEEE. pp. 1-6.
De Souza, A. M., Yokoyama, R. S., Maia, G., Loureiro, A., and Villas, L. (2016). Real-time path planning to prevent traffic jam through an intelligent transportation system. In: 2016 IEEE Symposium on Computers and Communication (ISCC), IEEE. pp. 726-731.
Hong, W. C. (2011). Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing, vol. 74(12-13), pp. 2096-2107.
Ke, R., Li, Z., Kim, S., Ash, J., Cui, Z. and Wang, Y. (2017). Real-Time Bidirectional Traffic Flow Parameter Estimation from Aerial Videos. IEEE Transactions on Intelligent Transportation Systems, vol. 18(4), pp. 890–901.
Rajesh, K. and Vishu, G. (2017). Intelligent Traffic Light Control for Congestion Management for Smart City Development, IEEE Region 10 Symposium (TENSYMP).
Chung, J. and Sohn, K. (2018). Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network. IEEE Transactions on Intelligent Transportation Systems, vol. 19(5), pp.1670–1675.
Zhang, S., Wu, G., Costeira, J. P. and Moura, J. M. (2017). Fcn-rlstm: Deep spatio-temporal neural networks for vehicle counting in city cameras. In: Proceedings of the IEEE international conference on computer vision, pp. 3667-3676.
Zhang, Y., Yang, Y., Zhou, W., Wang, H. and Ouyang, X. (2021). Multi-city traffic flow forecasting via multi-task learning. Appl. Intell. Vol. 51, pp. 1–19. https://doi.org/10.1007/s10489-020-020 74-8.
Xia, D. (2020). A distributed WND-LSTM model on Map Reduce for short-term traffic flow prediction. Neural Comput. Appl. Vol. 33(7), pp. 2393–2410. https://doi.org/10.1007/s00521- 020-05076-2
Tu, Y., Lin, S., Qiao, J. and Liu, B. (2021). Deep traffic congestion prediction model based on road segment grouping. Appl. Intell. Vol. 51, pp. 1–23. https://doi.org/10.1007/s10489-020-021 52-x
Soua, R., Koesdwiady, A. and Karray, F. (2016). Big-data-generated traffic flow prediction using deep learning and dempster-shafer theory. In: 2016 International joint conference on neural networks (IJCNN), IEEE. pp. 3195-3202.
Chung, J. and Sohn, K. (2018). Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network. IEEE Transactions on Intelligent Transportation Systems, vol. 19(5), pp.1670–1675.
Zonoozi, A., Kim, J. J., Li, X. L. and Cong, G. (2018). Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns. In: IJCAI, pp. 3732-3738.
Perez-Murueta, P., Gómez-Espinosa, A., Cardenas, C. and Gonzalez-Mendoza, M. (2019) Deep Learning System for Vehicular Re-Routing and Congestion Avoidance. Applied Sciences, vol. 9(13), pp. 2717.
Gupta, B., Awasthi, S., Gupta, R., Ram, L., Kumar, P., Prasad, B. R., and Agarwal, S. (2018). Taxi travel time prediction using ensemble-based random forest and gradient boosting model. In: Advances in Big Data and Cloud Computing, Springer, Singapore. pp. 63-78.
Hamner, B. (2010). Predicting travel times with context-dependent random forests by modelling local and aggregate traffic flow. In: 2010 IEEE International Conference on Data Mining Workshops, IEEE. pp. 1357-1359.
Jenelius, E. and Koutsopoulos, H. N. (2017). Urban network travel time prediction based on a probabilistic principal component analysis model of probe data. IEEE Transactions on Intelligent Transportation Systems, vol. 19(2), pp. 436-445.
Tian, X. (2018). Research on Travel Time Prediction under Internet of Vehicles. In: 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), IEEE. pp. 38-40.
Philip, A. M., Ramadurai, G. and Vanajakshi, L. (2018). Urban Arterial Travel Time Prediction Using Support Vector Regression. Transportation in Developing Economies, vol. 4(1), pp. 7.
Elleuch, W., Wali, A. and Alimi, A. M. (2016). Intelligent Traffic Congestion Prediction System Based on ANN and Decision Tree Using Big GPS Traces. In: International conference on intelligent systems design and applications, Springer, Cham. pp. 478-487.
More, R., Mugal, A., Rajgure, S., Adhao, R.B. and Pachghare, V.K. (2016). Road traffic predictionand congestion control using Artificial Neural Networks. In: 2016 International Conference on Computing, Analytics and Security Trends (CAST), IEEE. pp. 52-57.
Xu, D. and Shi, Y. (2017). A combined model of random forest and multilayer perceptron to forecast expressway traffic flow. In: 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), IEEE. pp. 448-451.
Kong, F., Li, J. and Lv, Z. (2018). Construction of intelligent traffic information recommendation system based on long short-term memory. J. Comput. Sci. vol. 26, pp. 78–86. https://doi.org/10.1016/ j.jocs.2018.03.010
Chou, C. H., Huang, Y., Huang, C. Y. and Tseng, V.S. Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (2019). Long-term traffic time prediction using deep learning with integration of weather effect. (eds.) PAKDD LNCS (LNAI), vol. 11440, pp. 123–135. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_10
Wang, J., Cao, Y., Du, Y. and Li, L. (2019). DST: a deep urban traffic flow prediction framework based on spatial-temporal features. KSEM. LNCS (LNAI), vol. 11775, pp. 417–427. Springer, Cham. https://doi.org/10. 1007/978-3-030-29551-6_37
Jin, W., Lin, Y., Wu, Z. and Wan, H. (2018). Spatio-temporal recurrent convolutional networks for citywide short-term crowd flows prediction. In: ACM International Conference Proceeding Series, pp. 28–35. https://doi.org/10.1145/3193077.3193082
Zhao, Z., Chen, W., Wu, X., Chen, P. C. and Liu, J. (2017). LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, vol. 11(2), pp. 68-75.
Shao, H., and Soong, B. H. (2016). Traffic flow prediction with long short-term memory networks (LSTMs). In: 2016 IEEE Region 10 Conference (TENCON), IEEE. pp. 2986-2989.
Kang, D., Lv, Y. and Chen, Y.Y. (2017). Short-term traffic flow prediction with LSTM recurrent neural network. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE. pp. 1-6.
Published
2024-08-10
How to Cite
Ihama, E. I., & Amenaghawon, V. A. (2024). A SURVEY OF MACHINE LEARNING MODELS FOR TRAFFIC MOVEMENT PREDICTION. FUDMA JOURNAL OF SCIENCES, 8(4), 172 - 178. https://doi.org/10.33003/fjs-2024-0804-2650
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Section
Review Articles
FUDMA Journal of Sciences
