Temporal perspective on individual driver behavior using electronic records of undesirable events.

This paper explores In-Vehicle Data Recorders (IVDRs) information about the count of undesirable driving events (such as hard braking, lane changing, and sharp turning) of 148 individuals. The information was logged over three years and included time stamp information about the occurrence of undesirable driving events in each trip (N=573,238). The objective was to gain deeper understanding about the heterogeneity among drivers with respect to behavior change over time, the effect of trip duration and the distribution of events count. Our findings show that in some respects drivers are similar: for all drivers, the variance of the events count was larger than the mean, indicating that the negative binomial distribution is suitable to model the distribution of events count per trip. Most drivers (95%) had lower events rate during longer trips, suggesting that a 'simple' events rate index is problematic when comparing between those driving longer trips and drivers driving short trips. In addition, most drivers (87%) improved their driving behavior throughout the measurement period. However, there are important differences among drivers in terms of the frequency of behavior change and the trends in behavior over time. These findings demonstrate the need for personalized examination of individual drivers. Several tools for such personalized examination were developed and discussed in this study.

[1]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[2]  Carlo Giacomo Prato,et al.  Intrafamilial Transmission of Driving Behavior , 2009 .

[3]  John D Lee,et al.  Extending parental mentoring using an event-triggered video intervention in rural teen drivers. , 2007, Journal of safety research.

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

[5]  Samir Bouaziz,et al.  Data collection and processing tools for naturalistic study of powered two-wheelers users' behaviours. , 2013, Accident; analysis and prevention.

[6]  Charles M Farmer,et al.  Trends Over Time in the Risk of Driver Death: What If Vehicle Designs Had Not Improved? , 2006, Traffic injury prevention.

[7]  Hillel Bar-Gera,et al.  Electronic Records of Undesirable Driving Events , 2010 .

[8]  S. Wood Generalized Additive Models: An Introduction with R , 2006 .

[9]  John D Lee,et al.  Technology and teen drivers. , 2007, Journal of safety research.

[10]  Todd Litman Distance-Based Vehicle Insurance , 2008 .

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

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

[13]  M. Sako,et al.  Profiting from business model innovation: Evidence from Pay-As-You-Drive auto insurance , 2013 .

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

[15]  Ezra Hauer,et al.  OBSERVATIONAL BEFORE-AFTER STUDIES IN ROAD SAFETY -- ESTIMATING THE EFFECT OF HIGHWAY AND TRAFFIC ENGINEERING MEASURES ON ROAD SAFETY , 1997 .

[16]  Todd Litman Pay-As-You-Drive Pricing For Insurance Affordability , 2011 .

[17]  George Yannis,et al.  Critical power two wheeler driving patterns at the emergence of an incident. , 2013, Accident; analysis and prevention.

[18]  Larry Head,et al.  Surrogate Safety Measures from Traffic Simulation Models , 2003 .

[19]  Ruth Welsh,et al.  Is naturalistic driving research possible with highly instrumented cars? Lessons learnt in three research centres. , 2013, Accident; analysis and prevention.

[20]  Ido Erev,et al.  The effect of workers' visibility on effectiveness of intervention programs: supervisory-based safety interventions. , 2008, Journal of safety research.

[21]  Neville A Stanton,et al.  Safe driving in a green world: a review of driver performance benchmarks and technologies to support 'smart' driving. , 2011, Applied ergonomics.

[22]  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.

[23]  Oren Musicant,et al.  When Technology Tells Novice Drivers how to Drive , 2010 .

[24]  Chih-Sheng Hsu,et al.  Onboard Measurement and Warning Module for Irregular Vehicle Behavior , 2008, IEEE Transactions on Intelligent Transportation Systems.