Selecting exposure measures in crash rate prediction for two-lane highway segments.
A critical part of any risk assessment is identifying how to represent exposure to the risk involved. Recent research shows that the relationship between crash count and traffic volume is non-linear; consequently, a simple crash rate computed as the ratio of crash count to volume is not proper for comparing the safety of sites with different traffic volumes. To solve this problem, we describe a new approach for relating traffic volume and crash incidence. Specifically, we disaggregate crashes into four types: (1) single-vehicle, (2) multi-vehicle same direction, (3) multi-vehicle opposite direction, and (4) multi-vehicle intersecting, and define candidate exposure measures for each that we hypothesize will be linear with respect to each crash type. This paper describes initial investigation using crash and physical characteristics data for highway segments in Michigan from the Highway Safety Information System (HSIS). We use zero-inflated-Poisson (ZIP) modeling to estimate models for predicting counts for each of the above crash types as a function of the daily volume, segment length, speed limit and roadway width. We found that the relationship between crashes and the daily volume (AADT) is non-linear and varies by crash type, and is significantly different from the relationship between crashes and segment length for all crash types. Our research will provide information to improve accuracy of crash predictions and, thus, facilitate more meaningful comparison of the safety record of seemingly similar highway locations.
Qin
Xiao
X
Maricopa Association of Governments, 302 North 1st Avenue Ste. 300, Phoenix, AZ 85003, USA. xqin@mag.maricopa.gov
Ivan
John N
JN
Ravishanker
Nalini
N
D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data; D006801:Humans; D008824:Michigan; D015233:Models, Statistical; D018986:Motor Vehicles / Q000145:classification; D018570:Risk Assessment / Q000379:methods*; D012424:Rural Population / Q000706:statistics & numerical data