Abstract
Anomaly detection is an important part of machine learning. Detection of outliers in the field of transportation can provide valid data for future traffic predictions or traffic flow analysis. This paper builds a model based on XGBoost to detect outliers in IoT data. The data is preprocessed first, followed by model building. Then we use the grid search to adjust the parameters and substitute the optimal parameters into the building model. To validate the model, we cross-checked it with two benchmark models, iFroset and Random Forest. The final experimental results show that the model constructed in this paper can accurately detect outliers in traffic flow and the accuracy is better than that of the baseline model.
This work is supported by National Key R &D Program of China 2018AAA0101703, Shandong Key Technology R &D Program 2019JZZY021005 and Natural Science Foundation of Shandong ZR2020MF067.
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References
Wang, Y.: Research on abnormal data identification and repair method of highway traffic flow. Beijing Jiaotong University (2015)
Philip, L., Wang, L.L., Ngan, H.Y.T.: Outlier detection in large-scale traffic data by Naitve Bayes method and gaussian mixture model method. In: Electronic Imaging, Intelligent Robotics and Industrial Applications Using Computer Vision 2017, pp. 73–78. Society for Imaging Science and Technology (2017)
Hongfei, X.U., Jing, L.I.: Traffic flow anomaly detection algorithm based on spatio-temporal convolutional neural network GL-GCN. Mod. Inf. Sci. Technol. 5(02), 70–75 (2021)
Wen, H.Y., Luo, J.: Traffic incident detection for urban arterial road based on data fusion and learning vector quantization. In: First International Conference on Transportation Information and Safety. American Society of Civil Engineers, Wuhan (2011)
Chen, T.Q., He, T.: Higgs boson discovery with boosted trees. In: JMLR: Workshop And Conference Proceedings, Proceedings of the NIPS 2014–4th Workshop on High energy Physics and Machine Learning, Montreal, Quebec, Canada, pp. 69–80 (2014)
Haidong, S., Jing, L., Liangwei, Z., Diego, G., Uday, K.: A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance. Inf. Fusion (2021, prepublish)
He, Z., Shao, H., Jing, L., Cheng, J., Yang, Y.: Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder. Measurement 152(C) (2020)
Xu, Y., Cao, J., Shmaliy, Y.S., Zhuang, Y.: Distributed Kalman filter for UWB/INS integrated pedestrian localization under colored measurement noise. Satell. Navig. 2(1) (2021)
Li, X., Cheng, J., Shao, H., Liu, K., Cai, B.: A fusion CWSMM-based framework for rotating machinery fault diagnosis under strong interference and imbalanced case. IEEE Trans. Ind. Inf. 18(8), 5180–5189 (2022). https://doi.org/10.1109/TII.2021.3125385
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Li, S., Sun, B., Geng, R., Zhang, L., Shen, T. (2022). Grid-Search Enhanced Tree-Based Machine Learning for Traffic IoT Data Anomaly Detection. In: Liu, Q., Liu, X., Cheng, J., Shen, T., Tian, Y. (eds) Proceedings of the 12th International Conference on Computer Engineering and Networks. CENet 2022. Lecture Notes in Electrical Engineering, vol 961. Springer, Singapore. https://doi.org/10.1007/978-981-19-6901-0_1
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DOI: https://doi.org/10.1007/978-981-19-6901-0_1
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