Online Identification of Low-Speed Misfit Driving Behavior based on Fuzzy Comprehensive Evaluation Method and XGBoost Algorithm

Huayu Wang

doi:10.54097/1gmq3x93

Authors

Huayu Wang

DOI:

https://doi.org/10.54097/1gmq3x93

Keywords:

Traffic Engineering, Driving Behavior Identification, Fuzzy Comprehensive Evaluation, Low-Speed Outlier Driving Behavior, Moving Bottleneck

Abstract

Timely identification of low-speed misfit driving behavior is essential for enhancing road traffic safety and operational efficiency. This paper proposes an online recognition method for detecting such behavior. First, eight characteristic indicators are selected and quantified from the perspectives of the vehicle itself and the vehicles ahead and behind. Then, thresholds are determined using the interquartile range method, and indicator weights are calculated using the CRITIC method. A membership function is constructed to perform a fuzzy comprehensive evaluation and determine the behavioral state of the vehicle. Finally, the XGBoost algorithm is applied to train an online recognition model using the feature indicators and evaluation results, with the model validated on the highD dataset. The results demonstrate that the fuzzy comprehensive evaluation method, based on the eight defined indicators, effectively identifies low-speed misfit vehicles. The XGBoost model further enhances recognition accuracy. This research provides valuable insights for transportation authorities in managing vehicle behavior and maintaining efficient and stable traffic flow. It also holds practical significance for alleviating congestion and ensuring unimpeded access for critical services such as ambulances and disaster response vehicles.

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References

[1] MENG Hu, GUO Rui, CHEN Yanyan. Risk Characteristics Analysis of Highway Tunnel Based on Trajectory Data[J]. Highway, 2023, 68(09): 289-295.

[2] Wang Tao. Study on the Impact of Speed Dispersion on Highway Traffic Efficiency [D]. Southeast University, 2016.

[3] Zhou Zhaomin. Analysis of Traffic Congestion Characteristics and Suppression Strategies Considering the Impact of Low-Speed Vehicles under T-CPS[D]. Chongqing University, 2020.

[4] Gazis D C, Herman R. The moving and "phantom" bottlenecks [J]. Transportation Science, 1992, 26(3): 223-229.

[5] Newell G F. A moving bottleneck[J]. Transportation Research Part B, 1998, 32(8): 531-537.

[6] Lebacque J P, Lesort J B, Giorgi F. Introducing buses into first-order macroscopic traffic flow models[J]. Transportation Research Record Journal of the Transportation Research Board, 1998, 1644(1): 70-79.

[7] Jia Bin, Gao Ziyou, Li Keping. Traffic System Modeling and Simulation Based on Cellular Automata[M]. Beijing: Science Press, 2007.

[8] LIN Hangfei, FU Qiang, ZHANG Hongjun. Countermeasures against truck impacts on highways based on mobile bottleneck theory [J]. Journal of Tongji University: Natural Science Edition, 2007.35(9): 1209-1213.

[9] CHU Wenhui, WU Chaozhong, ZHANG Hui, et al. A review of driving behavior safety evaluation research[J]. Highway and Transportation Science and Technology, 2017, 34(S2): 8-15+22.

[10] Toledo T, Musicant O, Lotan T. In-vehicle data recorders for monitoring and feedback drivers' behavior[J]. Transportation Research Part C: Emerging Technologies, 2008,16(3): 320-331.

[11] Castignani G, Derrmann T, Frank R, et al. Driver behavior profiling using smartphones: a low-cost platform for driver monitoring[J]. Intelligent Transportation Systems Vagazine, 2015,7(1): 91-102.

[12] CHENG Guozhu, LI Tianyi, WANG Guopeng. A driving risk identification method based on the spectrum of dangerous driving behaviors on snow and ice[J]. Transportation Systems Engineering and Information, 2024, 24(04): 127-138.

[13] WU Jianqing, ZHANG Ziyi, WANG Yubo, et al. A dangerous driving behavior recognition method for heavy trucks considering multimodal data[J]. Transportation Systems Engineering and Information, 2024, 24(02): 63-75.

[14] XUE Qingwen, JIANG Yugming, LU Key. Method for Identifying Dangerous Driving Behavior Based on Trajectory Data [J]. China Journal of Highway and Transportation, 2020, 33(06): 84-94.

[15] Qin Wenwen, Yan Qiyang, Gu Jinjin, et al. Identification and Quantification of Heavy Truck Driver Driving Styles [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(04): 137-148.

[16] Zadeh L A. Fuzzy sets as a basis for a theory of possibility[J]. Fuzzy Sets and Systems, 1978, 1(1): 3-28.

[17] Zadeh L. Fuzzy sets[J]. Information and control, 1965, 8: 338-353.

[18] Wang Xiang. Application of Highway Maintenance Quality Evaluation Model Based on Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation[J]. Transportation World, 2022, No.627(33): 52-55.

[19] GUAN Deyong, WANG Qi, WANG Ke. A PSO-XGBOOST-based method for identifying abnormal driving behaviors in buses[C] // Chinese Society of Highways, Chinese Society of Navigation, Chinese Society of Railway, Chinese Society of Aeronautics, Society of Automotive Engineering. Proceedings of the World Transportation Congress 2024 (WTC2024)

[20] KRAJEWSKI R, BOCK J, KLOEKER L, et al. The highD dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems [C]∥ Proceedings of the 2018 21st International Conference on Intelligent Trans⁃portation Systems. maui: IEEE, 2018: 2118- 2125.

[21] Kong, D. W. Study on the Impact of Large Vehicles on Multilane Highway Traffic Operations [D]. Southeast University, 2018.

[22] ZHU Xinglin, DING Shuangwei, YAO Liang, et al. Bad driving behavior recognition based on the convolutional neural network [J]. Science, Technology and Engineering, 2024, 24 (15): 6493-6501.