title: Driver crash risk factors and prevalence evaluation using naturalistic driving data.
abstract: The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only. The results show that crash causation has shifted dramatically in recent years, with driver-related factors (i.e., error, impairment, fatigue, and distraction) present in almost 90% of crashes. The results also definitively show that distraction is detrimental to driver safety, with handheld electronic devices having high use rates and risk.
authors.affiliation: Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061; tdingus@vtti.vt.edu.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Dingus
authors.affiliation: Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061; Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061.
authors.forename: Feng
authors.initials: F
authors.lastname: Guo
authors.affiliation: Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061;
authors.forename: Suzie
authors.initials: S
authors.lastname: Lee
authors.affiliation: Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061;
authors.forename: Jonathan F
authors.initials: JF
authors.lastname: Antin
authors.affiliation: Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061;
authors.forename: Miguel
authors.initials: M
authors.lastname: Perez
authors.affiliation: Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061;
authors.forename: Mindy
authors.initials: M
authors.lastname: Buchanan-King
authors.affiliation: Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061;
authors.forename: Jonathan
authors.initials: J
authors.lastname: Hankey
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001288:Attention; D001334:Automobile Driving / Q000706:statistics & numerical data*; D002947:Cities; D016208:Databases, Factual / Q000706:statistics & numerical data*; D005221:Fatigue; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D016017:Odds Ratio; D012307:Risk Factors; D012894:Sleep Stages; D013315:Stress, Psychological / Q000523:psychology; D014481:United States; D055815:Young Adult
keywords: crash risk; driver distraction; driver error; driver impairment; naturalistic driving