title: Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections.
abstract: Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations.
authors.affiliation: Department of Civil and Environmental Engineering, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854, United States. Electronic address: power.julian.chen@gmail.com.
authors.forename: Chen
authors.initials: C
authors.lastname: Chen
authors.affiliation: Department of Civil and Environmental Engineering, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854, United States. Electronic address: yuanchang_xie@uml.edu.
authors.forename: Yuanchang
authors.initials: Y
authors.lastname: Xie
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D004779:Environment Design / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D008962:Models, Theoretical; D012449:Safety; D013383:Suburban Population / Q000706:statistics & numerical data*; D014505:Urban Population / Q000706:statistics & numerical data*
keywords: Functional form; Generalized additive model; Intersection safety; Piecewise Linear Negative Binomial regression; Safety Performance Function