Development and initial testing of a time-related road safety analysis structure

Abstract The increasing availability and implementation of in-vehicle data recorder (IVDR) technologies has resulted in an increased number of safety applications based on the behavior of drivers during actual driving. The objective of this study is to formulate a flexible framework for IVDR data analysis that has the capability to include roadway geometric attributes, driver characteristics, environmental factors and time-dependencies. This research uses the concept of a homogeneous trip segment (HTS), applied with a survival model to estimate the risk of an event occurrence (e.g. crash or warning from on-board monitoring system). The University of Michigan Transportation Research Institute (UMTRI) provided data from the Road Departure Crash Warning System Field Operational Test. The survival-based model allowed the assessment of event hazard for individual drivers on specific routes as well as individual drivers compared to their peers, while controlling for travel in different routes and under different conditions. The authors believe this capability offers opportunities to safety researchers that were not available from existing methods.

[1]  Meinhard Kieser,et al.  A systematic comparison of recurrent event models for application to composite endpoints , 2018, BMC Medical Research Methodology.

[2]  Dominique Lord,et al.  The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. , 2011, Accident; analysis and prevention.

[3]  Venky N. Shankar,et al.  Analysis of Existing Data: Prospective Views on Methodological Paradigms , 2012 .

[4]  Hong Yang,et al.  A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data , 2018, Risk analysis : an official publication of the Society for Risk Analysis.

[5]  Renée de Mutsert,et al.  Survival analysis: time-dependent effects and time-varying risk factors. , 2008, Kidney international.

[6]  Eleni I. Vlahogianni,et al.  Innovative Insurance Schemes: Pay As/how You Drive , 2016 .

[7]  Jeffrey S. Hickman,et al.  Use of a video monitoring approach to reduce at-risk driving behaviors in commercial vehicle operations , 2011 .

[8]  Pei-Sung Lin,et al.  Naturalistic Driving Study: Field Data Collection , 2014 .

[9]  Daniel C Murray,et al.  Predicting Truck Crash Involvement: Developing a Commercial Driver Behavior-Based Model and Recommended Countermeasures , 2005 .

[10]  P J Kelly,et al.  Survival analysis for recurrent event data: an application to childhood infectious diseases. , 2000, Statistics in medicine.

[11]  Tomer Toledo,et al.  The First Year of Driving , 2013 .

[12]  Tomer Toledo,et al.  Driving Exposure of Israeli Young Male Drivers Within Graduated Driver Licensing System , 2014 .

[13]  Nicole van Nes,et al.  The study design of UDRIVE: the naturalistic driving study across Europe for cars, trucks and scooters , 2016 .

[14]  Tomer Toledo,et al.  Safety Correlation and Implications of In-Vehicle Data Recorder on Driver Behavior , 2007 .

[15]  Jennifer D. Davis,et al.  Driving Safety of Cognitively-Impaired Drivers Based on Near Collisions in Naturalistic Driving , 2019, Journal of Alzheimer's disease reports.

[16]  P P Jovanis,et al.  Disaggregate model of highway accident occurrence using survival theory. , 1989, Accident; analysis and prevention.

[17]  Chester G. Wilmot,et al.  EFFECT OF SPEED LIMITS ON SPEED AND SAFETY: A REVIEW , 1999 .

[18]  R. Gill,et al.  Cox's regression model for counting processes: a large sample study : (preprint) , 1982 .

[19]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[20]  Murray Blumenthal,et al.  Dimensions of the traffic safety problem , 1967 .

[21]  P P Jovanis,et al.  Formulating accident occurrence as a survival process. , 1990, Accident; analysis and prevention.

[22]  David M. Baldwin,et al.  Dimensions of the Traffic Safety Problem , 1958 .

[23]  Helai Huang,et al.  County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling , 2010 .

[24]  Paul C Lambert,et al.  Additive and multiplicative covariate regression models for relative survival incorporating fractional polynomials for time‐dependent effects , 2005, Statistics in medicine.

[25]  G. Luria,et al.  Drivers' social-work relationships as antecedents of unsafe driving: A social network perspective. , 2017, Accident; analysis and prevention.

[26]  Richard D Riley,et al.  Individual patient data meta-analysis of survival data using Poisson regression models , 2012, BMC Medical Research Methodology.

[27]  Paul P Jovanis,et al.  Analysis of Naturalistic Driving Event Data , 2011 .

[28]  Tomer Toledo,et al.  Can providing feedback on driving behavior and training on parental vigilant care affect male teen drivers and their parents? , 2014, Accident; analysis and prevention.

[29]  Tomer Toledo,et al.  In-vehicle data recorders for monitoring and feedback on drivers' behavior , 2008 .

[30]  In-Vehicle Data Recorder for Evaluation of Driving Behavior and Safety , 2006 .

[31]  Thomas A. Dingus,et al.  Near Crashes as Crash Surrogate for Naturalistic Driving Studies , 2010 .

[32]  Dominique Lord,et al.  Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. , 2005, Accident; analysis and prevention.

[33]  F. Mannering.  Temporal instability and the analysis of highway accident data , 2018 .

[34]  Carlo Giacomo Prato,et al.  Modeling the behavior of novice young drivers during the first year after licensure. , 2010, Accident; analysis and prevention.

[35]  Richard J. Cook,et al.  The Statistical Analysis of Recurrent Events , 2007 .

[36]  Fred L. Mannering,et al.  The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .

[37]  Paul P Jovanis,et al.  Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level. , 2014, Accident; analysis and prevention.

[38]  A. V. Peterson,et al.  On the regression analysis of multivariate failure time data , 1981 .

[39]  Paul P Jovanis,et al.  Crashes and crash-surrogate events: exploratory modeling with naturalistic driving data. , 2012, Accident; analysis and prevention.

[40]  F L Mannering,et al.  Male/female driver characteristics and accident risk: some new evidence. , 1993, Accident; analysis and prevention.

[41]  Yulia V. Marchenko,et al.  An Introduction to Survival Analysis Using Stata, Third Edition , 2010 .

[42]  Paul P Jovanis,et al.  Cohort-Based Analysis Structure for Modeling Driver Behavior with an In-Vehicle Data Recorder , 2016 .

[43]  Gang Du,et al.  Time-varying mixed logit model for vehicle merging behavior in work zone merging areas. , 2018, Accident; analysis and prevention.

[44]  Shlomo Bekhor,et al.  Exploring relationships between driving events identified by in-vehicle data recorders, infrastructure characteristics and road crashes , 2018, Transportation Research Part C: Emerging Technologies.

[45]  Feng Guo,et al.  Driver crash risk factors and prevalence evaluation using naturalistic driving data , 2016, Proceedings of the National Academy of Sciences.

[46]  P. Jovanis,et al.  Spatial analysis of fatal and injury crashes in Pennsylvania. , 2006, Accident; analysis and prevention.

[47]  A. Craig,et al.  A critical review of the psychophysiology of driver fatigue , 2001, Biological Psychology.

[48]  Anne T McCartt,et al.  Effects of in-vehicle monitoring on the driving behavior of teenagers. , 2010, Journal of safety research.

[49]  Paul C. Lambert,et al.  Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model , 2011 .

[50]  Thorsten Staake,et al.  Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data , 2014 .

[51]  L. J. Wei,et al.  Regression analysis of multivariate incomplete failure time data by modeling marginal distributions , 1989 .

[52]  Thomas A. Dingus,et al.  The 100-Car Naturalistic Driving Study Phase II – Results of the 100-Car Field Experiment , 2006 .

[53]  Sophia Rabe-Hesketh,et al.  Multilevel and Longitudinal Modeling Using Stata , 2005 .

[54]  M. Quddus.  Modelling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data. , 2008, Accident; analysis and prevention.

[55]  Gaëtan MacGrogan,et al.  Variables with time-varying effects and the Cox model: Some statistical concepts illustrated with a prognostic factor study in breast cancer , 2010, BMC medical research methodology.

[56]  P Fancher,et al.  Intelligent Cruise Control Field Operational Test , 1997 .

[57]  Yoram Shiftan,et al.  Can Feedback from In-vehicle Data Recorders Improve Driver Behavior and Reduce Fuel Consumption? , 2016 .