Extracting driving volatility from connected vehicle data in exploring Space-Time relationships with crashes in the city of Saint Louis

The analysis of factors that influence the occurrence of roadway crashes within a specified locality have historically been reliant on the assessment of physical infrastructure, historical crash frequency, environmental factors and driver characteristics. The consensus over the years has been drawn to the idea that human factors, specifically regarding driving behaviors, account for the majority of crash outcomes on roadways. With the emergence of connected vehicle data in the last few years, the capacity to analyze real time driving behavior has become a possibility for safety analysts. Driving volatility has emerged as a valuable proxy for driving behavior and indicator of safety. In this study, evidence of the spatial relationship between driving volatility and historical crash hotspots is uncovered. Utilizing an entropy-based analysis, this study discovered generally strong positive spatial relationships between locations of volatile driving events and historical crashes, with R2 values ranging from 0.015 to 0.970 and a mean of 0.612 for hard accelerations, and 0.048 to 0.996 and a mean of 0.678 for hard decelerations. Including temporal context presented insights showing that the relationships are significant for over 60 % of the coverage area usually between the hours of 7 am to 7 pm, with average R2 values of 0.594 for hard accelerations, and 0.629 for hard decelerations.

• Record URL:
  • https://doi.org/10.1016/j.trip.2024.101051
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S259019822400037X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/25901982
• Supplemental Notes:
  • © 2024 The Authors. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Mussah, Abdul Rashid
  • 0000-0002-1084-5598
  • Adu-Gyamfi, Yaw
• Publication Date: 2024-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 101051
• Serial:
  • Transportation Research Interdisciplinary Perspectives
  • Volume: 24
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2590-1982
  • Serial URL: https://www.journals.elsevier.com/transportation-research-interdisciplinary-perspectives
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Connected vehicles; Crash causes; Data quality; High risk locations; Real time information; Safety analysis
• Geographic Terms: Saint Louis (Missouri)
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01913883
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 4 2024 4:58PM