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[1] Sun Chao, Chen Xiaohong, Zhang H. Michael, et al. Evaluation of driving behaviorbased on massive vehicle trajectory data [J]. Journal of Southeast University (English Edition), 2019, 35 (4): 502-508. [doi:10.3969/j.issn.1003-7985.2019.04.013]
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Evaluation of driving behaviorbased on massive vehicle trajectory data()
基于海量车辆轨迹数据的机动车驾驶员驾驶行为评价
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
35
Issue:
2019 4
Page:
502-508
Research Field:
Traffic and Transportation Engineering
Publishing date:
2019-12-30

Info

Title:
Evaluation of driving behaviorbased on massive vehicle trajectory data
基于海量车辆轨迹数据的机动车驾驶员驾驶行为评价
Author(s):
Sun Chao1;  2;  Chen Xiaohong1;  Zhang H. Michael1;  3;  Zhang Junfeng2
1 Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
2 Shenzhen Urban Transport Planning Center Co., Ltd., Shenzhen 518021, China
3 College of Civil and Environmental Engineering, University of California Davis, Davis 95616, USA
孙超1;  2;  陈小鸿1;  张红军1;  3;  张俊峰2
1同济大学道路与交通工程教育部重点实验室, 上海201804; 2深圳市城市交通规划设计研究中心股份有限公司, 深圳518021; 3College of Civil and Environmental Engineering, University of California Davis, Davis 95616, USA
Keywords:
driving behavior;  global positioning system(GPS)navigating data;  automatic coding machine;  self-organizing mapping(SOM)
驾驶行为;  GPS导航数据;  自动编码机;  自组织映射
PACS:
U491
DOI:
10.3969/j.issn.1003-7985.2019.04.013
Abstract:
Based on the driver surveillance video data and controller area network(CAN)data, the methods of studying commercial vehicles’ driving behavior is relatively advanced. However, these methods have difficulty in covering private vehicles. Naturalistic driving studies have disadvantages of small sample size and high cost, one new driving behavior evaluation method using massive vehicle trajectory data is put forward. An automatic encoding machine is used to reduce the noise of raw data, and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed, rapid speed change, rapid turning and rapid lane changing. The proportion of different driving behaviors and typical dangerous driving behaviors is calculated, then the temporal and spatial distribution of drivers’ driving behavior and the driving behavior characteristics on typical roads are analyzed. Driving behaviors on accident-prone road sections and normal road sections are compared. Results show that in Shenzhen, frequent lane changing and overspeed are the most common unsafe driving behaviors; 16.1% drivers have relatively aggressive driving behavior; the proportion of dangerous driving behavior is higher outside the original economic special zone; dangerous driving behavior is highly correlated with traffic accident frequency.
基于监控数据及车辆总线数据的营运车驾驶行为评价研究方法已相对成熟, 但难以推广至私家车, 针对自然驾驶试验样本容量小、设备昂贵的缺点, 提出了一种基于海量秒级GPS导航数据的驾驶行为评价方法.运用自动编码机对数据进行降噪处理, 结合行车动力学、SOM自组织映射分类等方法给出超速、急变速、频繁变道、急转弯等行为的判定方法及阈值, 计算不同驾驶特性驾驶员及危险驾驶行为比例.在此基础上, 分析得到深圳市机动车驾驶员驾驶行为时空分布特征、典型通道驾驶行为特征, 并选取典型道路进行事故多发段与正常段的驾驶行为对比.结果表明, 深圳危险驾驶行为以频繁变道和超速为主, 16.1%的驾驶员驾驶行为偏冒进, 原特区外相对原特区内危险驾驶行为比例更高, 危险驾驶行为与事故密度呈高度相关.

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Memo

Memo:
Biographies: Sun Chao(1985—), male, Ph.D. candidate, senior engineer; Chen Xiaohong(corresponding author), female, doctor, professor, Tongjicxh@163.com.
Foundation items: The National Natural Science Foundation of China(No.71641005), the National Key Research and Development Program of China(No.2018YFB1601105).
Citation: Sun Chao, Chen Xiaohong, Zhang H. Michael, et al.Evaluation of driving behavior based on massive vehicle trajectory data[J].Journal of Southeast University(English Edition), 2019, 35(4):502-508.DOI:10.3969/j.issn.1003-7985.2019.04.013.
Last Update: 2019-12-20