Bayesian latent class safety performance function for identifying motor vehicle crash black spots

Abstract

The state of the practice in black spot identification uses safety performance functions based on total crash counts to identify high-risk crash sites. This paper postulates that total crash count is a result of multiple distinct risk-generating processes (RGPs), including geometric characteristics of the road, spatial features of the surrounding environment, and driver behavior factors. However, these multiple sources are ignored in current modeling methodologies that try to explain or predict crash frequencies across sites. Instead, current practice uses models that imply that a single RGP exists. This misspecification may lead to correlation of crashes with incorrect sources of contributing factors (e.g., concluding a crash is predominately caused by a geometric feature when the cause is a behavioral issue), which may ultimately lead to inefficient use of public funds and misidentification of true black spots. This study proposes a latent class model consistent with a multiple risk process theory and investigates the influence this model has on correctly identifying crash black spots. The paper presents the theoretical and corresponding methodological approach in which a Bayesian latent class model is estimated with the assumption that crashes arise from two distinct RGPs, including engineering and unobserved spatial factors. The methodology was applied to state-controlled roads in Queensland, Australia. The results were compared with an empirical Bayesian negative binomial (EB-NB) model. A comparison of goodness-of-fit measures illustrated superiority of the proposed model compared with the NB model. The detection of black spots was improved compared with the EB-NB model. In addition, modeling crashes as the result of two fundamentally separate RGPs reveals more detailed information about unobserved crash causes.