@article{ref1,
title="Improving functional form in cross-sectional regression studies to capture the non-linear safety effects of roadway attributes-freeway median width case study",
journal="Accident analysis and prevention",
year="2021",
author="Jafari Anarkooli, Alireza and Persaud, Bhagwant and Lyon, Craig",
volume="156",
number="",
pages="e106130-e106130",
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.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2021.106130",
url="http://dx.doi.org/10.1016/j.aap.2021.106130"
}