title: A preliminary investigation of the relationships between historical crash and naturalistic driving.
abstract: 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.
authors.affiliation: Civil & Environmental Engineering, Cal Poly State University, San Luis Obispo, CA 93407, United States. Electronic address: apande@calpoly.edu.
authors.forename: Anurag
authors.initials: A
authors.lastname: Pande
authors.affiliation: Research Centre for Integrated Transport Innovation, School of Civil & Environmental Engineering, University of New South Wales, Sydney, Australia. Electronic address: saichand.chakka@unsw.edu.au.
authors.forename: Sai
authors.initials: S
authors.lastname: Chand
authors.affiliation: Research Centre for Integrated Transport Innovation, School of Civil & Environmental Engineering, University of New South Wales, Sydney, Australia. Electronic address: n.saxena@unsw.edu.au.
authors.forename: Neeraj
authors.initials: N
authors.lastname: Saxena
authors.affiliation: Research Centre for Integrated Transport Innovation, School of Civil & Environmental Engineering, University of New South Wales, Sydney, Australia. Electronic address: v.dixit@unsw.edu.au.
authors.forename: Vinayak
authors.initials: V
authors.lastname: Dixit
authors.affiliation: Civil & Environmental Engineering, Cal Poly State University, San Luis Obispo, CA 93407, United States. Electronic address: jmloy@calpoly.edu.
authors.forename: James
authors.initials: J
authors.lastname: Loy
authors.affiliation: Civil & Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, United States. Electronic address: brian@rsip.lsu.edu.
authors.forename: Brian
authors.initials: B
authors.lastname: Wolshon
authors.affiliation: Louisiana State University, Baton Rouge, LA 70803, United States.
authors.forename: Joshua D
authors.initials: JD
authors.lastname: Kent
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000328:Adult; D001334:Automobile Driving / Q000523:psychology* / Q000706:statistics & numerical data*; D002140:California; D004779:Environment Design; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012044:Regression Analysis; D012449:Safety
keywords: Crash frequency; Naturalistic driving data; Negative binomial model; Random parameter negative binomial model; Traffic safety