Exploring contributory factors to highway accidents: A nonparametric multivariate adaptive regression spline approach. Issue 4 (2nd October 2017)

• Record Type: Journal Article
• Authors: Chang, Li-Yen
• Is Part Of: Journal of transportation safety & security. Volume 9:Issue 4(2017)
• Page Start: 419
• Page End: 438
• Publication Date: 2017-10-02
• Subjects: highway -- accident frequency -- Poisson regression -- multivariate adaptive regression splines (MARS); Transportation -- United States -- Safety measures -- Periodicals; Transportation -- Security measures -- United States -- Periodicals; Transportation -- Safety measures; Transportation -- Security measures; United States; Periodicals; 353.9805
• DOI: 10.1080/19439962.2016.1237601
• Languages: English
• ISSNs: 1943-9970

Abstract

ABSTRACT: Poisson and negative binomial regression models have been commonly applied to identify the significant factors that affect accident frequency on highways or at intersections. This study explores the application of an alternate method, the nonparametric multivariate adaptive regression spline (MARS) technique, to identify the effects of highway geometric characteristics, traffic factors, and environmental conditions on the frequency of highway accidents. The 2007 to 2008 accident data for National Freeway 1 in Taiwan were collected to develop MARS models. Results indicate that horizontal alignment, vertical alignment, average daily traffic volume (ADT), heavy vehicle ADT, and precipitation all have nonlinear effects on highway accidents.