Title

Multi-Level Analysis Of Severe Crashes Along Arterials Including The Effect Of Road Entity And Collision Type

Keywords

Access management; Arterial roads; Driver crash involvements; Driver injury; Logistic regression; Model reliability; Multilane roads; Severity analysis

Abstract

A large-scale injury severity analysis for arterial roads in Florida was conducted using roadway and crash information. The objective was to compare the reliability of different arterial road crash involvement modeling frameworks (combining crash locations vs. traditional analysis by location) and to find new contributing factors using newly available data. In the first phase of the investigation, logistic regression models of all driver involvements by roadway locations (i.e., segments and intersections) were developed and compared. The second phase involved an extension of these models, controlling by crash types, including rear-end, angle, and left turn. The results suggest a method for arterial road systematic analysis by modeling all crashes combined complemented by two models of involvements at signalized intersections and on segments combined with access points (nonsignalized intersections). The crash type models showed some possible safety strategies not clearly shown by the road entity models alone. Important contributing factors were significant, including driver age, gender, speeding, aggressive driving, seat belt use, alcohol or drug use, driver ejection, type of collision, type of vehicle, and point of impact. Significant road-related factors included speed limit, ADT per lane, access management class, land use, lane and sidewalk width, roadway lighting, curves, and friction characteristics. Suggested applications for future crash analysis and roadway design guidelines are discussed. © 2009 Taylor & Francis Group, LLC.

Publication Date

1-1-2009

Publication Title

Journal of Transportation Safety and Security

Volume

1

Issue

3

Number of Pages

203-223

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/19439960903149678

Socpus ID

85023838227 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85023838227

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