Volume 41 Issue 1
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ZHANG Weichong, YANG Tao, LYU Nengchao. A Method for Classifying Driving Behavior Based on Vehicle Position and Speed[J]. Journal of Transport Information and Safety, 2023, 41(1): 85-94. doi: 10.3963/j.jssn.1674-4861.2023.01.009
Citation: ZHANG Weichong, YANG Tao, LYU Nengchao. A Method for Classifying Driving Behavior Based on Vehicle Position and Speed[J]. Journal of Transport Information and Safety, 2023, 41(1): 85-94. doi: 10.3963/j.jssn.1674-4861.2023.01.009
shu

A Method for Classifying Driving Behavior Based on Vehicle Position and Speed

doi: 10.3963/j.jssn.1674-4861.2023.01.009
  • 1.

    Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

  • 2.

    Wuhan China Communication Transportation Engineering Company, Wuhan 430000, China

  • Received Date: 2022-09-28
    Available Online: 2023-05-13
    Abstract
  • Vehicle trajectory data contains vehicle movement information, including time stamp, vehicle position, speed, etc. By analyzing vehicle trajectory data, driving patterns can be classified. As important features from such data reflect driving behavior, vehicle positioning characteristics have been widely studied, but the others such as speed and acceleration are rarely analyzed. In order to incorporate the multi-dimensional information from vehicle trajectory data into the analysis framework, a method for classifying driving patterns based on the characteristics of vehicle position and speed is studied. To overcome the issue of a single dimension of the existing classification methods, the algorithm for Hausdorff trajectory distance is applied to calculate a comprehensive distance matrix of vehicle position and speed. Given the fact that the robustness of the Hausdorff distance algorithm is low, the algorithm is improved by using 90% percentile value of the one-way Hausdorff distance to reduce the influence of noise. At the same time, vehicle position and speed are introduced to further improve the accuracy of classification, and a multiple hierarchical clustering algorithm is used to classify the trajectory diagrams of position and trajectory diagrams of speed in sequence. At the end, the driving patterns based on vehicle position and speed are obtained. The HighD dataset is used as a sample, the vehicle trajectories on three lanes are extracted to verify the proposed classification method. Study results show that ①the proposed method can provide the comprehensive driving patterns of vehicle position and speed, and the average accuracy of clustering is 94.8%, which is higher than the accuracy of DBTCAN (89.3%) and t-Cluster (86.4%). ②Based on the analysis of trajectory deviation curve of lane changing, four typical driving patterns are obtained. The proposed method can use multidimensional trajectory data to classify driving patterns, which has potentials in trajectory classification and identifying abnormal behavior.

     

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    • References(30)
  • [1]
    ZHAO T, XU Y, MONFORT M, et al. Multi-agent tensor fusion for contextual trajectory prediction[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, New York: IEEE, 2019.
    [2]
    陆德彪, 郭子明, 蔡伯根, 等. 基于深度数据的车辆目标检测与跟踪方法[J]. 交通运输系统工程与信息, 2018, 18(3): 55-62. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201803009.htm

    LU D B, GUO Z M, CAI B G, et al. A vehicle detection and tracking method based on range data[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(3): 55-62. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201803009.htm
    [3]
    杨红红, 曲仕茹. 基于压缩感知尺度自适应的多示例交通目标跟踪算法[J]. 中国公路学报, 2018, 31(6): 281-290, 316. doi: 10.3969/j.issn.1001-7372.2018.06.015

    YANG H H, QU S R. Traffic target tracking algorithm based on scale adaptive multiple instance learning with compressive sensing[J]. China Journal of Highway and Transport, 2018, 31(6): 281-290, 316. (in Chinese) doi: 10.3969/j.issn.1001-7372.2018.06.015
    [4]
    冯汝怡, 李志斌, 吴启范, 等. 航拍视频车辆检测目标关联与时空轨迹匹配[J]. 交通信息与安全, 2021, 39(2): 61-69, 77. doi: 10.3963/j.jssn.1674-4861.2021.02.008

    FENG R Y, LI Z B, WU Q F, et al. Association of vehicle object detection and the time-space trajectory matching from aerial videos[J]. Journal of Transport Information and Safety, 2021, 39(2): 61-69, 77. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.02.008
    [5]
    CHOI S, KIM J, YEO H. Trajgail: generating urban vehicle trajectories using generative adversarial imitation learning[J]. Transportation Research Part C: Emerging Technologies, 2021(128): 91-113.
    [6]
    LI X C, ZHAO K Q, C G, et al. Deep representation learning for trajectory similarity computation[C]. 34th International Conference on Data Engineering (ICDE), Paris, France: IEEE, 2018.
    [7]
    马文耀, 吴兆麟, 杨家轩, 等. 基于单向距离的谱聚类船舶运动模式辨识[J]. 重庆交通大学学报(自然科学版), 2015, 34 (5): 130-134. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT201505026.htm

    MA W Y, WU Z L, YANG J X, et al. Vessel motion pattern recognition based on one-way distance spectral clustering algorithm[J]. Journal of Chongqing Jiaotong University(Natural Science), 2015, 34(5): 130-134. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT201505026.htm
    [8]
    王培, 江南, 万幼, 等. 应用Hausdorff距离的时空轨迹相似性度量方法[J]. 计算机辅助设计与图形学学报, 2019, 31 (4): 647-658. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201904016.htm

    WANG P, JIANG N, WAN Y, et al. Measuring similarity of spatio-temporal trajectory using hausdorff distance[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(4): 647-658. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201904016.htm
    [9]
    CHOONG M Y, ANGELINE L, CHIN R K Y, et al. Modeling of vehicle trajectory clustering based on LCSS for traffic pattern extraction[C]. 2nd International Conference on Automatic Control and Intelligent Systems, Kota Kinabalu, Malaysia: IEEE, 2017.
    [10]
    李颖, 赵莉, 赵祥模, 等. 基于大货车GPS数据的轨迹相似性度量有效性研究[J]. 中国公路学报, 2020, 33(2): 146-157. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202002014.htm

    LI Y, ZHAO L, ZHAO X M, et al. Effectiveness of trajectory similarity measures based on truck GPS data[J]. China Journal of Highway and Transport, 2020, 33(2): 146-157. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202002014.htm
    [11]
    ANDRE S F, DESPINA K, LUIS O A, et al. Multidimensional similarity measuring for semantic trajectories[J]. Transactions in Gis, 2016, 20(2): 280-298. doi: 10.1111/tgis.12156
    [12]
    FHWA, Department of Transportation, America. NGSIM: Next generation simulation[EB/OL]. (2007-5-5)[2023-02-15]. http://www.ngsim-com-munity.org/.
    [13]
    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]. 21st International Conference on Intelligent Transportation Systems(ITSC), Hawaii: IEEE, 2018.
    [14]
    WANG J H, FU T, XUE J T, et al. Realtime wide-area vehicle trajectory tracking using millimeter-wave radar sensors and the open TJRD TS dataset, [EB/OL]. (2021)[2023-02-15]. https://www.tjrdts.com.
    [15]
    潘晓, 马昂, 郭景峰, 等. 基于时间序列的轨迹数据相似性度量方法研究及应用综述[J]. 燕山大学学报, 2019, 43 (6): 531-545.

    PAN X, MA A, GUO J F, et al. A survey of research and application of track data similarity measurement based on time series[J]. Journal of Yanshan University, 2019, 43(6): 531-545. (in Chinese)
    [16]
    丁华, 杨文杰, 姜超. 考虑轨迹分析的车辆异常行为辨识[J]. 重庆理工大学学报(自然科学版), 2022, 36(7): 62-69.

    DING H, YANG W J, JIANG C. Vehicle abnormal behavior identification considering trajectory analysis[J]. Journal of Chongqing University of Technology(Natural Science), 2022, 36(7): 63-69. (in Chinese)
    [17]
    LI H P. Typical trajectory extraction method for ships based on AIS data and trajectory clustering[C]. 2nd International Conference on Artificial Intelligence and Information Systems, chongqing: ACM, 2021.
    [18]
    杨家轩, 刘元. 基于DBTCAN算法的船舶轨迹聚类与航路识别[J]. 上海海事大学学报, 2022, 43(3): 7-12.

    YANG J X, LIU Y. Ship trajectory clustering and route recognition based on DBTCAN algorithm[J]. Journal of Shanghai Maritime University, 2022, 43(3): 7-12. (in Chinese)
    [19]
    姜乔文, 刘瑜, 潭大宁, 等. 时空轨迹多维特征融合的行为规律挖掘算法[J/OL]. (2021-11-23)[2023-02-15]. https://kns.cnki.net/kcms/detail/11.1929.v.20211123.1336.014.html

    JIANG Q W, LIU Y, TAN D N, et al. Behavior rule mining algorithm based on multi-dimensional feature fusion of spatio-temporal trajectory[J/OL]. (2021-11-23)[2023-02-15]. https://kns.cnki.net/kcms/detail/11.1929. v. 20211123.1336.014. html. (in Chinese)
    [20]
    ARIF A S, DEBI P D, SAMARJIT K, et al. Video trajectory analysis using unsupervised clustering and multi-criteria ranking[J]. Soft Computing, 2020, 24(21): 16643-16654.
    [21]
    ZHAO P X, LIU X T, SHEN J W, et al. A network distance and graph-partitioning-based clustering method for improving the accuracy of urban hotspot detection[J]. Geocarto International, 2019, 34(3): 293-315.
    [22]
    SHI Y, WANG D, TANG J B, et al. Detecting spatiotemporal extents of traffic congestion: A density-based moving object clustering approach[J]. International Journal of Geographical Information Science, 2021, 35(7): 1449-1473.
    [23]
    YUE H Q, GUAN Q F, PAN Y T, et al. Detecting clusters over intercity transportation networks using k-shortest paths and hierarchical clustering: a case study of mainland china[J]. International Journal of Geographical Information Science, 2019, 33(5): 1082-1105.
    [24]
    赵怀鑫, 邓然然, 张英杰, 等. 1种用于高速公路通行情况分析的收费数据挖掘方法[J]. 中国公路学报, 2018, 31(8): 155-164.

    ZHAO H X, DENG R R, ZHANG Y J, et al. Method of mining fee data for expressway traffic analysis[J]. China Journal of Highway and Transport, 2018, 31(8): 155-164. (in Chinese)
    [25]
    RODRIGUEZ M, COMIN C, CASANOVA D, et al. Clustering algorithms: a comparative approach[J]. PLoS ONE, 2019, 14(1): 107-141.
    [26]
    王灵丽, 黄敏, 高亮. 基于聚类算法的交通网络节点重要性评价方法研究[J]. 交通信息与安全, 2020, 38(2): 80-88. doi: 10.3963/j.jssn.1674-4861.2020.02.010

    WANG L L, HUANG M, GAO L. Methods of importance evaluation of traffic network node based on clustering algorithms[J]. Journal of Transport Information and Safety, 2020, 38(2): 80-88. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.02.010
    [27]
    YANG J X, LIU Y, MA L Q, et al. Maritime traffic flow clustering analysis by density based trajectory clustering with noise[J]. Ocean Engineering, 2022(249): 111001.
    [28]
    JIANG R Q, CHEN L L. Driving stress estimation in physiological signals based on hierarchical clustering and multi-view intact space learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 13141-13154.
    [29]
    徐进, 陈莹, 陈海源, 等. 回头曲线路段的轨迹行为模式与事故风险[J]. 东南大学学报(自然科学版), 2020, 50(5): 973-982. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX202005025.htm

    XU J, CHEN Y, CHEN H Y, et al. Vehicle track patterns and accident risk on hairpin curves[J]. Journal of Southeast University(Natural Science Edition), 2020, 50(5): 973-982. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX202005025.htm
    [30]
    PU Z Y, CUI Z Y, TANG J J, et al. Multimodal traffic speed monitoring: A real-time system based on passive wi-fi and bluetooth sensing technology[J]. IEEE Internet of Things Journal, 2022, 9(14): 12413-12424.
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