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

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journal contribution
posted on 27.07.2022, 15:21 authored by Bhagwant PersaudBhagwant Persaud, Alireza Jafari Anarkooli, Craig Lyon


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. 


NSERC (ApplID RGPIN-2017-04457)

Ministry of Transportation Ontario Highway Infrastructure Innovation Funding Program.