01698094

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The evaluation of the effects of road safety measures on road accidents has gained continuous attention among researchers in recent years. Besides the commonly-used empirical Bayes (EB) approach, the propensity score (PS) methods have been widely employed in road safety evaluation studies. However, the conditions under which these methods can provide valid estimates of treatment effects are not well understood. The authors conduct a simulation-based comparison study to provide insight into the performance of the EB and PS methods in settings with and without violation of the key assumptions of the EB and PS methods. The models investigated include the EB, inverse probability weighting (IPW), and the doubly robust (DR) methods with different model specifications and data conditions. The results suggest that most of the methods can provide unbiased estimates of the treatment effect when the models are correctly specified, although the bias of the effect estimates increases slightly for all IPW models and most DR models with a small data sample, indicating that the propensity score methods are “data hungry”. The DR method is less affected by the omission of covariates and consistently provides unbiased estimates even in the scenarios with incorrect model specification, indicating its superiority to other two methods.

This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.

19-00565

English

9p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

References (37) ; Tables

Bayes' theorem; Estimating; Highway safety; Mathematical methods; Safety analysis; Safety audits; Traffic simulation

Highways; Research; Safety and Human Factors

Transportation Research Board Annual Meeting 2019 Paper #19-00565

TRIS, TRB, ATRI

Dec 7 2018 9:46AM