Details:

Alternative Title:
Analysis of the crash level data : final report, April 2009
Creators:
Abdel-Aty, Mohamed ; Pande, Anurag ; Lee, Chris ; Das, Abhishek ; Nevarez, Alexis ; ... More +
Abdel-Aty, Mohamed ; Pande, Anurag ; Lee, Chris ; Das, Abhishek ; Nevarez, Alexis ; Darwiche, Ali ; Devarasetty, Premchand Less -
Corporate Creators:
University of Central Florida. Center for Advanced Transportation Systems Simulation
Corporate Contributors:
Florida. Dept. of Transportation. Research Center
Subject/TRT Terms:
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Crash Injuries--Florida
Crash Injuries--Florida--Prevention
Traffic Accidents--Florida
Traffic Accidents--Florida--Prevention
Traffic Fatalities--Florida
Traffic Fatalities--Florida--Prevention
Traffic Safety--Florida
Arterial Highways
Crash Injuries
Fatalities
Traffic Accidents
Traffic Safety

Publication/ Report Number:
BD-548-22
Resource Type:
Tech Report
Geographical Coverage:
Florida
OCLC Number:
694511172
Edition:
Final report.
Corporate Publisher:
University of Central Florida. Center for Advanced Transportation Systems Simulation
NTL Classification:
NTL-SAFETY AND SECURITY-Highway Safety ; NTL-SAFETY AND SECURITY-Accidents ;
Abstract:
This report presents the analysis conducted to identify the factors that contribute to severe and fatal crash occurrence on multilane corridors. The authors’ preliminary investigation using simultaneous ordered probit model provided enough evidence that a fixed influence area of intersections for all of the corridors is not justified. With the increase of an intersection’s influence area, crash types that are more specific to segments get included and change the crash pattern for the overall intersection. Therefore, for investigation purposes, to treat the corridors in their entirety will result in much more insightful results than when treating the segments and intersections separately. The crash data were grouped into six major types as follows: 1) rear-end, 2) head-on, 3) angle/turning, 4) sideswipe, 5) crashes involving slow moving vehicles (e.g. cycles, mopeds, etc.), and 6) crashes involving single vehicles. Binary severity classification models were developed by using non-parametric conditional inference trees. Parameters like alcohol/drug use came out to be significant across all crash types and clusters. Lane changing on corridors with high truck traffic was found to be risky from a severity point of view. Poor pavement conditions and high permitted speed limits increased the likelihood of severe rear-end crashes. Non-use of safety equipment also increased the severity level provided the crash had occurred. Based on the results of the overall investigation certain recommendations were made taking the 4 Es (Engineering, Education, Enforcement and Emergency Management) into consideration.
Format:
PDF
Collection(s):
US Transportation Collection
University Transportation Centers
Main Document Checksum:
[+]
urn:sha256:1c726f40163decc2718946f7f288fb34d9e79ff6cc6aa88c9f4e4df84018513b
File Type:

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