Investigating the accuracy of Bayesian techniques for before-after safety studies: the case of a "no treatment" evaluation

Accident; Analysis and Prevention
Emanuele Sacchi, Tarek Sayed

Abstract

The main challenge in conducting observational before-after (BA) studies of road safety measures is to use a methodology that accounts for many potential confounding factors. However, it is usually difficult to evaluate and decide on the accuracy of the different safety evaluation techniques available in literature. This is mainly because the outcome of the comparison has no specific target (i.e., the effect of a specific treatment on safety is not precisely known). The objective of this paper is to compare the accuracy of some of the commonly used Bayesian methodologies for BA safety studies by applying them to locations where no safety treatment has been implemented (making the target result to be no effect). This goal was pursued within the setting of a specific case study where a recent set of collision data was available for urban signalized intersections in British Colombia (Canada) with no safety treatments implemented during the time frame considered. An assessment of the temporal stability of the data set was undertaken to exclude the presence of significant BA changes as explanation of the results reported in this paper. Both the well-known empirical Bayes and the full Bayes method with non-linear intervention models ...Continue Reading

References

Jul 9, 2008·Accident; Analysis and Prevention·Wen LiThomas Welch
Nov 6, 2009·Accident; Analysis and Prevention·Bhagwant PersaudRavi Bhim
May 6, 2010·Accident; Analysis and Prevention·Eun Sug ParkTimothy J Lomax
Jan 25, 2012·Accident; Analysis and Prevention·Karim El-Basyouny, Tarek Sayed
Apr 3, 2014·Accident; Analysis and Prevention·Emanuele SacchiKarim El-Basyouny
Jul 18, 2014·Accident; Analysis and Prevention·Emanuele Sacchi, Tarek Sayed

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Citations

Jul 22, 2015·Accident; Analysis and Prevention·Emanuele SacchiAhmed Osama
Jul 23, 2016·Accident; Analysis and Prevention·Tarek Sayed, Emanuele Sacchi

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