[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()

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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

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

Keywords:

driving behavior;  global positioning system(GPS)navigating data;  automatic coding machine;  self-organizing mapping(SOM)

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.

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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.

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