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Author:

Chen, Chen (Chen, Chen.) | Zhao, Xiaohua (Zhao, Xiaohua.) | Zhang, Yunlong (Zhang, Yunlong.) | Rong, Jian (Rong, Jian.) (Scholars:荣建) | Liu, Xiaoming (Liu, Xiaoming.) (Scholars:刘小明)

Indexed by:

SSCI EI Scopus

Abstract:

Due to differences in driving skills and personal characteristics among drivers, the behaviors of drivers when faced with various driving environments differ, causing different levels of driving safety concerns. In past research, the measurement of safety-related driving behavior mostly focused on classification, while few studies were concerned with individual driving behavior characteristics. However, it is important for drivers to recognize and correct their dangerous behaviors and optimize their driving. This paper presents a graphical method for modeling individual driving behaviors, and the results can be used in driving safety analysis. Based on the assumption that drivers have specific driving habits, typical driving patterns during driving are first detected and extracted. These typical driving patterns are then sorted according to their frequencies, forming a driving behavior graph that can directly illustrate each driver's behavior features. Furthermore, a quantitative analysis method for evaluating driving safety based on the behavior graph is provided. To verify the proposed method, a case study focusing on vehicles' longitudinal motion was conducted using GPS data collected from Beijing taxis. The results demonstrated that the graphical method can describe the individual features of a driver's longitudinal acceleration behavior and distinguish differences among drivers. The development of this method can help understand the individual features of driving behaviors and further support measures to optimize driving safety. (C) 2019 Published by Elsevier Ltd.

Keyword:

Driving safety GPS data Graphical modeling Individual behavior

Author Community:

  • [ 1 ] [Chen, Chen]Beijing Univ Technol, Beijing Key Lab Traff Engn, Pingleyuan 100, Beijing 100124, Peoples R China
  • [ 2 ] [Zhao, Xiaohua]Beijing Univ Technol, Beijing Key Lab Traff Engn, Pingleyuan 100, Beijing 100124, Peoples R China
  • [ 3 ] [Rong, Jian]Beijing Univ Technol, Beijing Key Lab Traff Engn, Pingleyuan 100, Beijing 100124, Peoples R China
  • [ 4 ] [Liu, Xiaoming]Beijing Univ Technol, Beijing Key Lab Traff Engn, Pingleyuan 100, Beijing 100124, Peoples R China
  • [ 5 ] [Zhang, Yunlong]Texas A&M Univ, Zachry Dept Civil Engn, 3136 TAMU, College Stn, TX 77843 USA

Reprint Author's Address:

  • [Zhao, Xiaohua]Beijing Univ Technol, Beijing Key Lab Traff Engn, Pingleyuan 100, Beijing 100124, Peoples R China

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Source :

TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR

ISSN: 1369-8478

Year: 2019

Volume: 63

Page: 118-134

ESI Discipline: PSYCHIATRY/PSYCHOLOGY;

ESI HC Threshold:109

JCR Journal Grade:2

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 61

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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