Predicting Changes in Driving Safety Performance on an Individualized Level Under Naturalistic Driving Conditions

Transportation incidents remain a pressing public safety issue in the United States and throughout the world, despite significant advancements in vehicle safety technologies. The National Highway Traffic Safety Administration (NHTSA) estimates that about 20% of all crashes are fatigue-related, and as such has begun an initiative to reduce drowsy and distracted driving. Of particular interest are commercial truck drivers. In order to reduce the likelihood of incidents, it is important to understand the factors that affect driver safety performance in order to predict future changes in performance. The goal of this project is to examine how driver safety performance varies by location, time of day, hours on duty, and/or driver workload and to model the rate of change in performance to predict hazardous behaviors. To meet the overall goal, the following tasks will be completed: 1) model input parameters for characterizing workload: tasks performed, cognitive load, miles driven, road locations, driving characteristics; 2) quantify changes in driving performance based on mirror checks and system alerts and evaluate these changes with respect to gold standard guidelines; and 3) investigate data-driven modeling approaches for driving safety performance prediction, including structural analysis and machine learning approaches. This research makes use of data collected, through Maven Machines, during naturalistic conditions for a fleet of over 200 drivers and over 9 million driving events.

• Record URL:
  https://www.buffalo.edu/transinfo/Research/safety/DrivingSafetyPerfomance.html

Language

• English

Project

• Status: Active
• Funding: $75000
• Contract Numbers:

  DTRT13-G-UTC48

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Performing Organizations:

  State University of New York, Buffalo

  212 Ketter Hall
  Buffalo, NY  United States  14260
• Principal Investigators:

  Cavuoto, Lora

• Start Date: 20170901
• Expected Completion Date: 20180831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Driver performance; Hours of labor; Location; Periods of the day; Traffic safety; Workload
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01652097
• Record Type: Research project
• Source Agency: Transportation Informatics University Transportation Center
• Contract Numbers: DTRT13-G-UTC48
• Files: UTC, RiP
• Created Date: Nov 28 2017 2:57PM