title: How the choice of safety performance function affects the identification of important crash prediction variables.
abstract: Across the nation, researchers and transportation engineers are developing safety performance functions (SPFs) to predict crash rates and develop crash modification factors to improve traffic safety at roadway segments and intersections. Generalized linear models (GLMs), such as Poisson or negative binomial regression, are most commonly used to develop SPFs with annual average daily traffic as the primary roadway characteristic to predict crashes. However, while more complex to interpret, data mining models such as boosted regression trees have improved upon GLMs crash prediction performance due to their ability to handle more data characteristics, accommodate non-linearities, and include interaction effects between the characteristics. An intersection data inventory of 36 safety relevant parameters for three- and four-legged non-signalized intersections along state routes in Alabama was used to study the importance of intersection characteristics on crash rate and the interaction effects between key characteristics. Four different SPFs were investigated and compared: Poisson regression, negative binomial regression, regularized generalized linear model, and boosted regression trees. The models did not agree on which intersection characteristics were most related to the crash rate. The boosted regression tree model significantly outperformed the other models and identified several intersection characteristics as having strong interaction effects.
authors.affiliation: Department of Information Systems, Statistics, and Management Science, The University of Alabama, USA. Electronic address: kwang18@crimson.ua.edu.
authors.forename: Ketong
authors.initials: K
authors.lastname: Wang
authors.affiliation: Department of Civil, Construction, and Environmental Engineering, The University of Alabama, USA.
authors.forename: Jenna K
authors.initials: JK
authors.lastname: Simandl
authors.affiliation: Department of Information Systems, Statistics, and Management Science, The University of Alabama, USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Porter
authors.affiliation: Department of Civil, Construction, and Environmental Engineering, The University of Alabama, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Graettinger
authors.affiliation: Department of Computer Science, The University of Alabama, USA.
authors.forename: Randy K
authors.initials: RK
authors.lastname: Smith
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000407:Alabama; D004779:Environment Design / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D015233:Models, Statistical; D008962:Models, Theoretical; D016012:Poisson Distribution; D012449:Safety; D014186:Transportation
keywords: Boosted regression trees; Crash frequency; Intersection characteristic importance; Non-signalized intersections; Safety performance functions