Improving functional form in cross-sectional regression studies to capture the non-linear safety effects of roadway attributes—Freeway median width case study. (June 2021)

• Record Type: Journal Article
• Authors: Jafari Anarkooli, Alireza; Persaud, Bhagwant; Lyon, Craig
• Is Part Of: Accident analysis and prevention. Volume 156(2021)
• Publication Date: 2021-06
• Subjects: Cross-sectional study -- Median width -- CMFunction -- Functional form; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2021.106130
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: This study aims to improve the functional forms used to derive CMFs in cross-sectional regression models. The estimated CMFs are for target crashes for freeway median width used as a case study. CMFs for a given change in a feature's value depend not only on the amount of the change but also on the original value. The results highlight the importance of using the functional forms that can capture non-linear effects of road attributes. Abstract: Crash modification factors (CMFs) for several roadway attributes are based on cross-sectional regression models, in the main because of the lack of data for the preferred observational before-after study. In developing these models, little attention has been paid to those functional forms that reflect the reality that CMFs should not be single-valued, as most available ones are, but should vary with application circumstance. Using a full Bayesian Markov Chain Monte Carlo (MCMC) approach, this study aimed to improve the functional forms used to derive CMFs in cross-sectional regression models, with a focus on capturing the variability inherent in crash modification functions (CMFunctions). The estimated CMFunction for target crashes for freeway median width, used for a case study, indicates that the approach is capable of developing a function that can capture the logical reality that the CMF for a given change in a feature's value depends not only on the amount of the change but also on the original value. The results highlight the importance of using the functional forms that can capture non-linear effects of road attributes for CMF estimation in cross-sectional models. The case study provides credible CMFs for assessing the safety implications of decisions on freeway median width that could be used in improving current design practice.