Highlights Ex-post road safety evaluation with multiple treatments is studied. Potential outcome notations are used to define causal estimands. Various methods are compared with simulations based on semi-synthetic data. PS-based weighting improves robustness against outcome model misspecifications. GRF performs well in the case with heterogeneous treatment effects.

    Abstract This paper focuses on ex-post road safety evaluation with multiple treatments. The potential outcome framework for causal inference is introduced to formalize the causal estimands of interest. Various estimation methods are compared via performing simulation experiments based on semi-synthetic data constructed from a London 20 mph zones dataset. The methods under evaluation include regressions, propensity score (PS) based methods, and a machine learning-based method termed generalized random forests (GRF). Both PS-based methods and GRF show higher flexibility with respect to functional specifications of outcome models. Moreover, GRF shows great superiority in the cases where road safety treatments are assigned following specific criteria and/or where there are heterogeneous treatment effects. Considering the ex-post evaluation of combined effects of multiple treatments has significant practical value, the potential outcome framework and the estimation methods presented in this paper are highly recommended for road safety studies.


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    Titel :

    Road safety evaluation with multiple treatments: A comparison of methods based on simulations


    Beteiligte:
    Zhang, Yingheng (Autor:in) / Li, Haojie (Autor:in) / Ren, Gang (Autor:in)


    Erscheinungsdatum :

    2023-06-08




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch