考虑交通运行条件影响的驾驶员特征聚类

doi:10.16097/j.cnki.1009-6744.2022.02.034

交通运输系统工程与信息 ›› 2022, Vol. 22 ›› Issue (2): 330-336.DOI: 10.16097/j.cnki.1009-6744.2022.02.034

考虑交通运行条件影响的驾驶员特征聚类

张建波^{* 1, 2}，孙建平²，徐春玲²，郭镜霞²，温慧敏²，宋国华¹

1. 北京交通大学，交通运输学院，北京 100044；2. 北京交通发展研究院，城市交通运行仿真与决策支持北京市重点实验室，城市交通北京市国际科技合作基地，北京 100073

收稿日期:2021-12-03 修回日期:2022-01-20 接受日期:2022-01-27 出版日期:2022-04-25 发布日期:2022-04-23
作者简介:张建波(1991- )，男，河北唐山人，工程师，博士
基金资助:
国家重点研发计划

Driver Characteristics Clustering Under Impact of Varying Traffic Operation Conditions

ZHANG Jian-bo^{*1, 2} , SUN Jian-ping² , XU Chun-ling² , GUO Jing-xia², WEN Hui-min² , SONG Guo-hua¹

1. School of Traffic and Transportation, Beijing Jiaotong University, Beijng 100044, China; 2. Beijing International Science and Technology Cooperation Base of Urban Transport, Beijing Key Laboratory of Urban Transport Simulation and Decision Making Support, Beijing Transport Institute, Beijing 100073, China

Received:2021-12-03 Revised:2022-01-20 Accepted:2022-01-27 Online:2022-04-25 Published:2022-04-23
Supported by:
National Key Research and Development Program of China(2018YFB1600700)

摘要/Abstract

摘要： 为提升驾驶员特征聚类方法的适用性与可靠性，本文基于机动车运行轨迹分析提出考虑交通运行条件影响的驾驶员特征聚类改进方法。首先，经过对车辆运行轨迹数据的分析发现，不同道路类型和平均速度条件会显著影响驾驶行为的集计特征；其次，提出改进的驾驶员特征聚类方法，第1步设计考虑道路类型与平均速度因素的车辆轨迹的切片和分类方法，从而稳定提取典型交通条件下的驾驶行为特征参数，第2步选用高斯混合模型聚类驾驶员特征。聚类案例表明，在相同的道路类型和平均速度条件下，驾驶员类型越激进，其速度变异系数、加速度标准差和平均减速度等参数均值越高。不同聚类方法的对比表明，改进方法在驾驶员聚类的类内聚集度和类间分离度方面均表现更好，能有效提升驾驶员聚类的适用性与可靠性。

关键词: 交通工程, 驾驶员聚类, 高斯混合模型, 驾驶行为, 交通运行条件

Abstract: To improve the stability and reliability of driver characteristics clustering, this paper proposes an improved driver clustering method considering the impact of varying traffic conditions using motor vehicle trajectory data. First, the analysis of vehicle trajectories indicates that different road types and average speeds significantly affect the aggregate characteristics of driving behavior. Considering the road types and average speed, a slicing and classification method of vehicle trajectory is designed to steadily extract the driving behavior characteristics under typical traffic conditions. Then, the driver characteristics are clustered using the Gaussian Mixture Model (GMM). Data analysis demonstrates that road types and average speed can significantly affect driving behavior characteristics. The clustering results show that the improved clustering method shows better performance on intra-class aggregation and inter-class separation, and this method can improve the applicability and reliability of driver clustering.

Key words: traffic engineering, driver clustering, Gaussian Mixture Model (GMM), driving behavior, traffic operation condition

中图分类号:

U491.1

张建波, 孙建平, 徐春玲, 郭镜霞, 温慧敏, 宋国华. 考虑交通运行条件影响的驾驶员特征聚类[J]. 交通运输系统工程与信息, 2022, 22(2): 330-336.

ZHANG Jian-bo , SUN Jian-ping , XU Chun-ling , GUO Jing-xia, WEN Hui-min , SONG Guo-hua. Driver Characteristics Clustering Under Impact of Varying Traffic Operation Conditions[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 330-336.

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http://www.tseit.org.cn/CN/Y2022/V22/I2/330

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考虑交通运行条件影响的驾驶员特征聚类

Driver Characteristics Clustering Under Impact of Varying Traffic Operation Conditions

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