A preliminary investigation of the relationships between historical crash and naturalistic driving

This paper describes a project that was undertaken using naturalistic driving data collected via Global Positioning System (GPS) devices to demonstrate a proof-of-concept for proactive safety assessments of crash-prone locations. The main hypothesis for the study is that the segments where drivers have to apply hard braking (higher jerks) more frequently might be the “unsafe” segments with more crashes over a long-term. The linear referencing methodology in ArcMap was used to link the GPS data with roadway characteristic data of US Highway 101 northbound (NB) and southbound (SB) in San Luis Obispo, California. The process used to merge GPS data with quarter-mile freeway segments for traditional crash frequency analysis is also discussed in the paper. A negative binomial regression analyses showed that proportion of high magnitude jerks while decelerating on freeway segments (from the driving data) was significantly related with the long-term crash frequency of those segments. A random parameter negative binomial model with uniformly distributed parameter for ADT and a fixed parameter for jerk provided a statistically significant estimate for quarter-mile segments. The results also indicated that roadway curvature and the presence of auxiliary lane are not significantly related with crash frequency for the highway segments under consideration. The results from this exploration are promising since the data used to derive the explanatory variable(s) can be collected using most off-the-shelf GPS devices, including many smartphones.

• Record URL:
  https://doi.org/10.1016/j.aap.2017.01.023
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0001457517300507
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Pande, Anurag
  • 0000-0002-3456-7932
  • Chand, Sai
  • Saxena, Neeraj
  • Dixit, Vinayak
  • Loy, James
  • Wolshon, Brian
  • Kent, Joshua D
• Publication Date: 2017-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 107-116
• Serial:
  • Accident Analysis & Prevention
  • Volume: 101
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Crash rates; Global Positioning System; High risk locations; Traffic crashes; Traffic safety
• Geographic Terms: San Luis Obispo (California)
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01629907
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 9 2017 8:48AM