Have they taken effect as expected? Unpacking the black box of road safety countermeasure effects

Countermeasure evaluation is an important subfield of road traffic safety research. Numerous works have been carried out to establish the cause-effect relationship between countermeasures and crash-related outcomes. However, a single estimate of the countermeasure effect may not always reflect their actual road safety benefits since the underlying causal mechanisms are neglected. To be specific, conventional evaluation frameworks cannot provide a comprehensive view of how a countermeasure influences defined outcomes of interest, e.g., via influencing a specific intermediate variable (mediator). This paper aims to unpack the black box by exploiting the causal inference framework for mediation analysis, which is able to decompose the total causal effect into the indirect effect that relays through the putative mediator and the direct effect. In particular, this framework builds on the potential outcome paradigm, which offers general definitions for the causal estimands of interest and clarifies the core assumptions for nonparametric identification. As a major superiority over traditional mediation analysis, evaluators are permitted to choose from a wide range of statistical methods to estimate the component models (e.g., the mediator model and the outcome model). Simulation experiments are conducted to show the applicability of the causal mediation method to count data analysis with negative binomial models. Also, the method is applied to a speed enforcement camera case study to examine the presence of crash migration phenomena by modelling traffic volume as mediator. Our empirical analysis has found a null indirect effect on crash frequency at enforced road sites, implying that crash migration due to drivers choosing alternative routes to avoid speed cameras is unlikely in this particular case. In summary, by properly applying the causal mediation method to countermeasure evaluation, we could gain a deeper insight into systematic causality.