Detecting and Modeling Heart Rate Variability for Driving Stress Analysis in Urban Road Network

Driving performance deteriorates with excess driving stress rises, which may increase vehicle accident likelihood. This study aims to quantify the effect of driving stress by monitoring the heart rate increase with various traffic conditions in a real-world road network. The data collection includes electrocardiogram, vehicle GPS trajectories, road conditions from video, and vehicle conditions from CAN bus. The authors propose a machine learning methodology based on Random Forest for the estimation of car driver stress due to different driving events. In contrast to other statistical methods and machine learning methods, Random Forest can handle different types of predictor variables, make a high accurate prediction and give variable importance analysis. Results indicate that average speed, coefficient of covariance of speed, frequency of brake operation and frequency of acceleration operation contribute about 78% relative importance to driving stress. Further sensitivity analysis show that low average speed, large speed variance, frequent operations of brake and acceleration will cause high level of driving stress. Based on the proposed model, a driving heat map is drawn in a large-scale road network, which can be applied to a safety-based route guidance system.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AND30 Standing Committee on Simulation and Measurement of Vehicle and Operator Performance.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Zeng, Weiliang
  • Miwa, Tomio
  • Tashiro, Mutsumi
  • Morikawa, Takayuki
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; Photos; References; Tables;
• Pagination: 17p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Driver performance; Driver support systems; Highway safety; Physiological stress; Psychological stress; Risk assessment; Route guidance; Vehicle trajectories
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01624362
• Record Type: Publication
• Report/Paper Numbers: 17-02363
• Files: TRIS, TRB, ATRI
• Created Date: Jan 30 2017 9:54AM