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Machine Learning Techniques for Road Traffic Automatic Incident Detection Systems: A Review

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 102)

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.

Keywords

  • Machine Learning
  • Automatic incident detection systems
  • Detection Rate
  • False alarm rates
  • Mean Time to Detect

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References

  1. 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)

    CrossRef  Google Scholar 

  2. Dey, A.: Machine learning algorithms: a review. Int. J. Comput. Sci. Inf. Technol. 7, 1174–1179 (2016)

    Google Scholar 

  3. Khorashadi, B., Liu, F., Ghosal, D., Zhang, M., Chuah, C.: Distributed automated incident detection with vgrid. IEEE Wirel. Commun. 18, 64–73 (2011)

    CrossRef  Google Scholar 

  4. Farradyne, P.B.: Traffic Incident Management Handbook, FHAOTM, November 2000

    Google Scholar 

  5. 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)

    Google Scholar 

  6. El Hatri, C., Boumhidi, J.: Fuzzy deep learning based urban traffic incident detection. Cogn. Syst. Res. 50, 206–213 (2017)

    CrossRef  Google Scholar 

  7. 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)

    Google Scholar 

  8. 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

    Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. Ghosh, B., Smith, D.P.: Customization of automatic incident detection algorithms for signalized urban arterials. J. Intell. Transp. Syst. 4(18), 426–441 (2014)

    CrossRef  Google Scholar 

  13. 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)

    Google Scholar 

  14. 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)

    CrossRef  Google Scholar 

  15. 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)

    Google Scholar 

  16. Hawas, Y.E., Ahmed, F.: A binary logit-based incident detection model for urban traffic networks. Transp. Lett. 9(1), 49–62 (2016)

    CrossRef  Google Scholar 

  17. 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)

    Google Scholar 

  18. 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

    CrossRef  Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Google Scholar 

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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

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  • DOI: https://doi.org/10.1007/978-3-030-37207-1_7

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