Estimating the effect of proximity to school on cyclist safety using a simultaneous-equations model with heterogeneity in covariance to address potential endogeneity

Traffic safety around schools is a major concern for policy makers and as such safety interventions are often targeted near schools. This paper shows the importance of accounting for the potential endogeneity of proximity to school when attempting to estimate its impact on traffic safety. In this research, we use a Bayesian simultaneous econometric approach with heterogeneity in covariance to disentangle the true effect of proximity to school on cyclist injury frequencies at signalised intersections in an urban setting. We assess the robustness of the bivariate normal assumption, using a scale mixing approach. Notably, we found that proximity to school was associated with an increase in cyclist injuries and this association was stronger when endogeneity was accounted for in the model, confirming the importance of considering endogeneity in studies of traffic safety near schools. Our heterogeneity in covariance specification revealed systematic variations in the covariance structure, which would otherwise go unobserved, providing further insights into sources of heterogeneity with the same set of variables available in the data. A safety-in-numbers effect is also found for cyclists in the study area and period. This research offers policy implications based on the findings of the analysis including the need for safety interventions at intersections with high vehicle turning counts and those in proximity to public transport stops, and better informing decision-makers regarding the magnitude of the impact of proximity to school on cyclist safety at intersections.