Assessing The Safety Effects Of Multiple Roadside Treatments Using Parametric And Nonparametric Approaches

Keywords

Crash modification factors and functions; Cross-sectional method; Generalized nonlinear models; Multiple treatments; Multivariate adaptive regression splines; Safety effectiveness

Abstract

This study evaluates the safety effectiveness of multiple roadside elements on roadway segments by estimating crash modification factors (CMFs) using the cross-sectional method. To consider the nonlinearity in crash predictors, the study develops generalized nonlinear models (GNMs) and multivariate adaptive regression splines (MARS) models. The MARS is one of the promising data mining techniques due to its ability to consider the interaction impact of more than one variables and nonlinearity of predictors simultaneously. The CMFs were developed for four roadside elements (driveway density, poles density, distance to poles, and distance to trees) and combined safety effects of multiple treatments were interpreted by the interaction terms from the MARS models. Five years of crash data from 2008 to 2012 were collected for rural undivided four-lane roadways in Florida for different crash types and severity levels. The results show that the safety effects decrease as density of driveways and roadside poles increase. The estimated CMFs also indicate that increasing distance to roadside poles and trees reduces crashes. The study demonstrates that the GNMs show slightly better model fitness than negative binomial (NB) models. Moreover, the MARS models outperformed NB and GNM models due to its strength to reflect the nonlinearity of crash predictors and interaction impacts among variables under different ranges. Therefore, it can be recommended that the CMFs are estimated using MARS when there are nonlinear relationships between crash rate and roadway characteristics, and interaction impacts among multiple treatments.

Publication Date

8-24-2015

Publication Title

Accident Analysis and Prevention

Volume

83

Number of Pages

203-213

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aap.2015.07.008

Socpus ID

84939811643 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84939811643

This document is currently not available here.

Share

COinS