Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models.
It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.
Kim
Do-Gyeong
DG
Department of Urban Transportation, Seoul Development Institute, Seoul 137-071, Republic of Korea. dokkang@sdi.re.kr
Lee
Yuhwa
Y
Washington
Simon
S
Choi
Keechoo
K
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D016010:Binomial Distribution; D004779:Environment Design; D005845:Georgia / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D008962:Models, Theoretical; D016012:Poisson Distribution; D012424:Rural Population / Q000706:statistics & numerical data*; D012449:Safety / Q000706:statistics & numerical data*; D014186:Transportation / Q000592:standards / Q000706:statistics & numerical data*