Safe-D: Safety through Disruption

Behavior-based Predictive Safety Analytics Phase II

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 an automated driving systems. This project continues work from a pilot study that created a proof-of-concept demonstration of how crash involvement may be predicted on the basis of individual driver behavior utilizing SHRP2 naturalistic data. The current project largely focuses on understanding and identifying the risks from a driver based on their real-time driving behaviors, personal characteristics, and environmental influences. This project seeks to analyze large scale continuous naturalistic data as well as event data, both public and proprietary, to study the role of different driving behaviors in the buildup of a safety critical event. This knowledge is transferred to build a continuous road-safety prediction system—a smartphone app developed and designed to best convey actionable analytics results. This involves issues such as identifying the most relevant type of information that should be displayed and in what format to the different end users, the driver or managers. In addition, a curriculum for undergraduate and graduate studies on behavior-based predictive safety analytics will be developed along with a module in graduate-level course.

Project Highlights

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Final Report

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EWD & T2 Products

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Presentations/Publications

Coming Soon!

Research Investigators (PI*)

Andrew Miller (VTTI)*
Abhijit Sarkar (VTTI)
Tony McDonald (TTI/TAMU)
Sahar Ghanipoor-Machiani (SDSU)
Arash Jahangiri (SDSU)

Project Information

Start Date: 2019-03-01
End Date: 2020-08-31
Status: Active
Grant Number: 69A3551747115
Total Funding: $191,534
Source Organization: Safe-D National UTC
Project Number: 04-114

Safe-D Theme Areas

Big Data Analytics

Safe-D Application Areas

Risk Assessment
Driver Factors and Interfaces
Vehicle Technology
Freight and Heavy Vehicles
Performance Measures

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 Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061
USA

Texas A&M University
Texas A&M Transportation Institute
3135 TAMU
College Station, Texas 77843-3135
USA

San Diego State University
5500 Campanile Dr
San Diego, CA 92182
USA