Understanding crashes involving roadway objects with SHRP 2 naturalistic driving study data

被引:18
|
作者
Hao, Haiyan [1 ]
Li, Yingfeng [1 ]
Medina, Alejandra [1 ]
Gibbons, Ronald B. [1 ]
Wang, Linbing [2 ]
机构
[1] Virginia Tech, Transportat Inst, 3500 Transportat Res Plaza, Blacksburg, VA 24060 USA
[2] Virginia Polytech Inst & State Univ, 750 Drillfield Dr,200 Patton Hall, Blacksburg, VA 24061 USA
关键词
Naturalistic driving study; Logistic regression; Support vector machine; Fixed object; Animal; Crash; INJURY SEVERITY;
D O I
10.1016/j.jsr.2020.03.005
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Introduction: Crashes involving roadway objects and animals can cause severe injuries and property damages and are a major concern for the traveling public, state transportation agencies, and the automotive industry. This project involved an in-depth investigation of such crashes based on the second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) data including detailed information and videos about 2,689 events. Methods: The research team conducted a variety of logistic regression analyses, complemented by Support Vector Machine (SVM) analyses and detailed case studies. Results: The logistic regression results indicated that driver behavior/errors, involvement of secondary tasks, roadway characteristics, lighting condition, and pavement surface condition are among the factors that contributed significantly to the occurrence and/or increased severity outcomes of crashes involving roadway objects and animals. Among these factors, improper turning movements (odds ratio = 88), avoiding animal or other vehicle (odds ratio = 38), and reaching/moving object in vehicle (odds ratio = 29) particularly increased the odds of crash occurrence. Factors such as open country roadways, sign/signal violation, unfamiliar with roadway, fatigue/drowsiness, and speeding significantly increased the severity outcomes when such crashes occurred. The sensitivity analysis of the three SVM classifiers confirmed that driver behavior/errors, critical speed, struck object type, and reaction time were major factors affecting the occurrence and severity outcomes of events involving roadway objects and animals. Published by Elsevier Ltd.
引用
收藏
页码:199 / 209
页数:11
相关论文
共 50 条
  • [41] Sleep disorders and risk of traffic crashes: A naturalistic driving study analysis
    Bharadwaj, Nipjyoti
    Edara, Praveen
    Sun, Carlos
    SAFETY SCIENCE, 2021, 140
  • [42] Crashes and crash-surrogate events: Exploratory modeling with naturalistic driving data
    Wu, Kun-Feng
    Jovanis, Paul P.
    ACCIDENT ANALYSIS AND PREVENTION, 2012, 45 : 507 - 516
  • [43] Using SHRP 2 naturalistic driving data to assess drivers' speed choice while being engaged in different secondary tasks
    Schneidereit, Tina
    Petzoldt, Tibor
    Keinath, Andreas
    Krems, Josef F.
    JOURNAL OF SAFETY RESEARCH, 2017, 62 : 33 - 42
  • [44] Evaluation of the Impact of Work Zone Traffic Control Devices on Change of Speed Using the SHRP 2 Naturalistic Driving Study
    Hallmark, Shauna
    Basulto-Elias, Guillermo
    Oneyear, Nicole
    Goswamy, Amrita
    Thapa, Raju
    Chrysler, Susan T. T.
    Smadi, Omar
    TRANSPORTATION RESEARCH RECORD, 2023, 2677 (10) : 757 - 765
  • [45] Drivers' Lane-Keeping Ability in Heavy Rain Preliminary Investigation Using SHRP 2 Naturalistic Driving Study
    Ghasemzadeh, Ali
    Ahmed, Mohamed M.
    TRANSPORTATION RESEARCH RECORD, 2017, (2663) : 99 - 108
  • [46] Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data
    Li, Yingfeng
    Hao, Haiyan
    Gibbons, Ronald B.
    Medina, Alejandra
    TRANSPORTATION RESEARCH RECORD, 2021, 2675 (09) : 1345 - 1358
  • [47] Quantifying regional heterogeneity effect on drivers' speeding behavior using SHRP2 naturalistic driving data: A multilevel modeling approach
    Ghasemzadeh, Ali
    Ahmed, Mohamed M.
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 106 : 29 - 40
  • [48] Efficacy of automatic emergency braking among risky drivers using counterfactual simulations from the SHRP 2 naturalistic driving study
    Seacrist, Thomas
    Sahani, Ridhi
    Chingas, Gregory
    Douglas, Ethan C.
    Graci, Valentina
    Loeb, Helen
    SAFETY SCIENCE, 2020, 128
  • [49] Exploring stop sign running at all-way stop-controlled intersections with the SHRP2 naturalistic driving data
    Liu, Chenhui
    Zhang, Wei
    JOURNAL OF SAFETY RESEARCH, 2022, 81 : 190 - 196
  • [50] Machine and Deep Learning Techniques for Daytime Fog Detection in Real Time with In-Vehicle Vision Systems Using the SHRP 2 Naturalistic Driving Study Data
    Khan, Md Nasim
    Ahmed, Mohamed M.
    TRANSPORTATION RESEARCH RECORD, 2022,