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SVM and KNN ensemble learning for traffic incident detection

Author

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  • Xiao, Jianli

Abstract

Traffic incident detection is a very important research area of intelligent transportation systems. Many methods have obtained good performance in traffic incident detection. However, the robustness of these methods is not satisfactory. Namely, when one method is applied on another data set again, its performance is not always good, even it had obtained good performance on one data set once. In this paper, we propose an ensemble learning method to improve the robustness in traffic incident detection. The proposed method trains individual SVM and KNN models firstly. And then, it takes a strategy to combine them for better final output. Experimental results show that the propose method has achieved the best performance among all the compared methods. Also, the ensemble learning strategy in the proposed method has improved the robustness of the individual models.

Suggested Citation

  • Xiao, Jianli, 2019. "SVM and KNN ensemble learning for traffic incident detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 29-35.
    • Handle: RePEc:eee:phsmap:v:517:y:2019:i:c:p:29-35
    DOI: 10.1016/j.physa.2018.10.060
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    References listed on IDEAS

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    1. Sheu, Jiuh-Biing, 2006. "A composite traffic flow modeling approach for incident-responsive network traffic assignment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 461-478.
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    Cited by:

    1. Lixin Cheng & Qiuhua Tang & Zikai Zhang & Shiqian Wu, 2021. "Data mining for fast and accurate makespan estimation in machining workshops," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 483-500, February.
    2. Li, Yuni & Xiao, Jianli, 2020. "Traffic peak period detection using traffic index cloud maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    3. Nicholas Fiorentini & Massimo Losa, 2020. "Long-Term-Based Road Blackspot Screening Procedures by Machine Learning Algorithms," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
    4. Samami, Maryam & Akbari, Ebrahim & Abdar, Moloud & Plawiak, Pawel & Nematzadeh, Hossein & Basiri, Mohammad Ehsan & Makarenkov, Vladimir, 2020. "A mixed solution-based high agreement filtering method for class noise detection in binary classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    5. Sheikh, Muhammad Sameer & Regan, Amelia, 2022. "A complex network analysis approach for estimation and detection of traffic incidents based on independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

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