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Predicting Two-lane Highway Crashes Using Crash Opportunities: A Newly Defined Measure of Exposure

Zhang, Chen; Ivan, John N. and Jonsson, Thomas LU (2006) 85th annual meeting of the Transportation Research Board In Proceedings of the 85th Annual meeting of TRB, CD-ROM
Abstract
There is general consensus among road safety researchers that the crash count is not linear with respect to the traffic volume. However, the now-preferred practice of estimating exponents on traffic volume not only results in parameters that cannot be confidently applied in other contexts, but also offers very little insight into the relationship. This paper describes a preliminary investigation of a new exposure metric called “crash opportunities,” which aims to learn more about the relationship between crashes and traffic volume by accounting for the number of times two vehicles meet in opposite directions along a road segment. Hourly directional traffic volumes were gathered for 95 rural, two-lane road segments in Connecticut, along... (More)
There is general consensus among road safety researchers that the crash count is not linear with respect to the traffic volume. However, the now-preferred practice of estimating exponents on traffic volume not only results in parameters that cannot be confidently applied in other contexts, but also offers very little insight into the relationship. This paper describes a preliminary investigation of a new exposure metric called “crash opportunities,” which aims to learn more about the relationship between crashes and traffic volume by accounting for the number of times two vehicles meet in opposite directions along a road segment. Hourly directional traffic volumes were gathered for 95 rural, two-lane road segments in Connecticut, along with geometric characteristics. Negative binomial regression models were estimated to predict the number of head-on, opposite direction sideswipe and single vehicle crashes on each segment using either the new opportunities measure or the more traditional vehicle-kilometers-traveled as exposure measures. The annual average daily traffic was also included as a covariate to account for additional effects on crash risk due to the traffic flow state, along with covariates describing the physical characteristics of the road and accounting for time of day effects. The results suggest that the new exposure measure does not explain variation in crash incidence better than the more traditional exposure measure, likely because a different representation of traffic volume in the crash risk function is needed. Further investigation of the new exposure measure using new model forms is the subject of continuing research by the authors. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
modelling, opportunities, modeling, safety, accident, crash, road, traffic
in
Proceedings of the 85th Annual meeting of TRB, CD-ROM
pages
19 pages
publisher
Transportation Research Board, Washington DC, USA
conference name
85th annual meeting of the Transportation Research Board
language
English
LU publication?
yes
id
398b4962-d1ac-4c9b-880d-b56b97f85b4f (old id 838758)
date added to LUP
2008-01-08 13:59:47
date last changed
2016-04-16 09:13:09
@misc{398b4962-d1ac-4c9b-880d-b56b97f85b4f,
  abstract     = {There is general consensus among road safety researchers that the crash count is not linear with respect to the traffic volume. However, the now-preferred practice of estimating exponents on traffic volume not only results in parameters that cannot be confidently applied in other contexts, but also offers very little insight into the relationship. This paper describes a preliminary investigation of a new exposure metric called “crash opportunities,” which aims to learn more about the relationship between crashes and traffic volume by accounting for the number of times two vehicles meet in opposite directions along a road segment. Hourly directional traffic volumes were gathered for 95 rural, two-lane road segments in Connecticut, along with geometric characteristics. Negative binomial regression models were estimated to predict the number of head-on, opposite direction sideswipe and single vehicle crashes on each segment using either the new opportunities measure or the more traditional vehicle-kilometers-traveled as exposure measures. The annual average daily traffic was also included as a covariate to account for additional effects on crash risk due to the traffic flow state, along with covariates describing the physical characteristics of the road and accounting for time of day effects. The results suggest that the new exposure measure does not explain variation in crash incidence better than the more traditional exposure measure, likely because a different representation of traffic volume in the crash risk function is needed. Further investigation of the new exposure measure using new model forms is the subject of continuing research by the authors.},
  author       = {Zhang, Chen and Ivan, John N. and Jonsson, Thomas},
  keyword      = {modelling,opportunities,modeling,safety,accident,crash,road,traffic},
  language     = {eng},
  pages        = {19},
  publisher    = {ARRAY(0xad346a8)},
  series       = {Proceedings of the 85th Annual meeting of TRB, CD-ROM},
  title        = {Predicting Two-lane Highway Crashes Using Crash Opportunities: A Newly Defined Measure of Exposure},
  year         = {2006},
}