Safe-D: Safety through Disruption

Behavior-based Predictive Safety Analytics – Pilot Study

Abstract

This project addresses the emerging field of behavior-based predictive safety analytics, focusing on the prediction of road crash involvement based on individual driver behavior characteristics. This has a range of applications in the areas of fleet safety management and insurance, but may also be used to evaluate the potential safety benefits of automated driving systems. A particular focus in the project is to explore the possibilities of using large sets of naturalistic crash and behavior data collected as part of commercial fleet- and behavior change management programs, collecting tens of thousands of crashes annually. Since this data is subject to legal, ethical and business-related constraints, an important activity in the project is to discuss with relevant stakeholders how barriers in using this data for academic research can be overcome. Moreover, a state-of-the-art review will be conducted and a conceptual framework for relating behavior to crash causation/risk developed. Based on this, a proof-of-concept demonstration of how crash involvement may be predicted on the basis of individual driver behavior will be performed, utilizing an available naturalistic dataset of sufficient size. Finally, a curriculum for undergraduate and graduate studies on behavior-based predictive safety analytics will be developed along with a module in graduate-level course. The present project is designed as an eight-month pilot initiative with the objective to provide the basis for a future more comprehensive research effort.

Final Report

02-020 Final Research Report (PDF)

EWD & T2 Products

Coming Soon!

Presentations/Publications

De Winter, J. C. F, Dreger, F. A., Huang, W., Miller, A., Soccolich, S., Ghanipoor Machiani, S., & Engstrom, J. (2018). The relationship between the Driver Behavior Questionnaire, Sensation Seeking Scale, and recorded crashes: A brief comment on Martinussen et al. (2017) and new data from SHRP2. Accident Analysis and Prevention, 118, 54-56. (Accepted)

Research Investigators (PI*)

Andrew Miller (VTTI)*
Sahar Ghanipoor Machiani (SDSU)

Project Information

Start Date: 2017-05-01
End Date: 2018-7-31
Status: Active
Grant Number: 69A3551747115
Total Funding: $ $72,623
Source Organization: Safe-D National UTC
Project Number: 02-020

Safe-D Theme Areas

Big Data Analytics

Safe-D Application Areas

Risk Assessment
Driver Factors and Interfaces
Vehicle Technology
Freight and Heavy Vehicle

More Information

RiP URL
UTC Project Information Form

Sponsor Organization

Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States

Performing Organization

Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061
USA