Instantaneous Driving Behavior at Intersections: Insights on Rear-End and Head-On Crash Frequencies Using Connected Vehicles

Connected and automated vehicles have enabled researchers to use big data for development of new metrics that can enhance transportation safety. Emergence of such big data coupled with computational power of modern computers have enabled us to obtain deeper understanding of instantaneous driving behavior by applying the concept of “driving volatility” to quantify variations in driving behavior. Since rear-end and head-on crashes are the most frequent and severe unsafe outcome at intersections, this paper brings in a methodology to quantify variations in vehicular movements utilizing longitudinal and lateral volatilities. More than 125 million real world Basic Safety Message data were analyzed and integrated with historical crash and road inventory data at 167 intersections in Ann Arbor, MI. To capture variations in vehicle movement, the authors quantified and used 24 measures of driving volatility by using speed, longitudinal and lateral acceleration. Rigorous statistical models including fixed parameter, random parameter, and geographically weighted Poisson regressions were developed. The results revealed that controlling for intersection geometry and traffic exposure, and accounting unobserved factors longitudinal volatility is highly correlated with the frequency of rear-end crashes. When it comes to head-on crashes, speed, longitudinal and lateral acceleration volatilities are highly associated with the frequency of crashes. Intersections with high lateral volatility have higher risk of head-on collisions due to risk of deviation from the centerline leading to head-on crash. The developed methodology and volatility measures can be used to proactively identify hotspot intersections where frequency of rear-end/head-on crashes is low but longitudinal/lateral driving volatility is high.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Arvin, Ramin
  • Kamrani, Mohsen
  • Khattak, Asad Jan
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: 7p

Subject/Index Terms

• TRT Terms: Behavior; Connected vehicles; Crash analysis; Driving; Frontal crashes; Intelligent vehicles; Signalized intersections
• Geographic Terms: Ann Arbor (Michigan)
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01698096
• Record Type: Publication
• Report/Paper Numbers: 19-00602
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:51PM