Abstract
Recently, non-recurrent congestion caused by road traffic incidents has become a critical concern of road Traffic Management System (TMS). However, incidents can’t be predicted. Hence, modern cities deployed Automatic Incidents Detection Systems (AIDSs) to early detect incidents and to improving road traffic flow efficiency and safety. For this, many AIDS approaches based on Machine Learning (ML) techniques are proposed. Although several reviews about AIDS have been written, a review of ML techniques based incident detection systems is required.
The purpose of this paper is to discuss the recent research contributions in automatic incidents detection systems based on ML techniques. To achieve this goal, a review and a comparison of data sources, datasets, techniques and detection performances in both freeway and urban roads are provided. Finally, the paper concludes by addressing the critical open issues for conducting research in the future as a proposal framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boutaba, R., et al.: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl. 9(1), 16 (2018)
Dey, A.: Machine learning algorithms: a review. Int. J. Comput. Sci. Inf. Technol. 7, 1174–1179 (2016)
Khorashadi, B., Liu, F., Ghosal, D., Zhang, M., Chuah, C.: Distributed automated incident detection with vgrid. IEEE Wirel. Commun. 18, 64–73 (2011)
Farradyne, P.B.: Traffic Incident Management Handbook, FHAOTM, November 2000
Nikolaev, A.B., Sapego, Y.S., Ivakhnenko, A.M., Mel’nikova, T.E., Stroganov, V.Y.: Analysis of the Incident detection technologies and algorithms in intelligent transport systems. Int. J. Appl. Eng. Res. 15(12), 4765–4774 (2017)
El Hatri, C., Boumhidi, J.: Fuzzy deep learning based urban traffic incident detection. Cogn. Syst. Res. 50, 206–213 (2017)
Zou, Y., Shi, G., Shi, G., Wang, Y.: Image sequences based traffic incident detection for signaled intersections using HMM. In: Proceedings of the Ninth International Conference on Hybrid Intelligent Systems, pp. 257–261 (2009)
Parkany, E., Xie, C.: A complete review of incident detection algorithms & their deployment: what works and what doesn’t. Technical report, University of Massachusetts, Transportation Center. 214 Marston Hall, February 2005
Deniz, O., Celikoglu, H.B., Gurcanli, G.E.: Overview to some incident detection algorithms: a comparative evaluation with Istanbul freeway data. In: Proceedings of the 12th International Conference “Reliability and Statistics in Transportation and Communication” (RelStat 2012), Riga, Latvia, 17–20 October 2012, Transport and Telecommunication Institute, Lomonosova 1, LV-1019, Riga, Latvia, pp. 274–284 (2012)
Qingchao, L., Lu, J., Chen, S., Zhao, K.: Multiple naive bayes classifiers ensemble for traffic incident detection. In: Mathematical Problems in Engineering. Hindawi Publishing Corporation (2014)
Gakis, E., Kehagias, D., Tzovaras, D.: Mining traffic data for road incidents detection. In: Proceedings of 17th International Conference on Intelligent Transportation Systems, Qingdao, China, 8–11 October 2014, pp. 930–935 (2014)
Ghosh, B., Smith, D.P.: Customization of automatic incident detection algorithms for signalized urban arterials. J. Intell. Transp. Syst. 4(18), 426–441 (2014)
Zhou, B., Lv, H., Ren, T., Chen, Y., Qiu, N.: Intelligent traffic incidents detection method in freeway corridors. In: Proceedings of International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC 2015), pp. 1623–1626. Atlantis Press (2015)
Agarwal, S., Kachroo, P., Regentova, E.: A hybrid model using logistic regression and wavelet transformation to detect traffic incidents. IATSS Res. 40, 56–63 (2016)
Chlyah, M., Dardor, M., Boumhidi, J.: Multi-agent system based on support vector machine for incident detection in urban roads. In: Proceedings of 2016 11th International Conference on Intelligent Systems: Theories and Applications, Mohammedia, Morocco, 19–20 October 2016. IEEE (2016)
Hawas, Y.E., Ahmed, F.: A binary logit-based incident detection model for urban traffic networks. Transp. Lett. 9(1), 49–62 (2016)
Li, L., Qu, X., Zhang, J., Ran, B.: Traffic incident detection based on extreme machine learning. J. Appl. Sci. Eng. 4(20), 409–416 (2017)
Li, D., Hu, X., Jin, C., Zhou, J.: Learning to detect traffic incidents from data based on tree augmented naive bayesian classifiers. Discrete Dyn. Nat. Soc. 2017, 1–9 (2017). https://doi.org/10.1155/2017/8523495. Article ID 8523495
Li, L., Zhang, J., Zheng, Y., Ran, B.: Real-time traffic incident detection with classification methods. In: Green Intelligent Transportation Systems, pp. 777–788. Springer, Singapore (2018)
Dardor, M., Chlyah, M., Boumhidi, J.: Incident detection in signalized urban roads based on genetic algorithm and support vector machine. In: Proceedings of the 2018 International Conference on Intelligent Systems and Computer Vision, Fez, Morocco, 2–4 April 2018. IEEE (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hireche, S., Dennai, A. (2020). Machine Learning Techniques for Road Traffic Automatic Incident Detection Systems: A Review. In: Hatti, M. (eds) Smart Energy Empowerment in Smart and Resilient Cities. ICAIRES 2019. Lecture Notes in Networks and Systems, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-030-37207-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-37207-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37206-4
Online ISBN: 978-3-030-37207-1
eBook Packages: EngineeringEngineering (R0)