Crash prediction for a French highway network with an XAI-informed Bayesian hierarchical model

Worldwide, highway accidents have important social and financial impacts. Crash Predictions Models (CPM) are used to reduce their frequency and gravity. They belong to two main categories: generalized linear models (GLM) and nonparametric machine learning (ML) algorithms. Broadly speaking, the former offer better interpretability but tend to have worse predictive performances than the latter. However, for highway infrastructures managers, efficient predictions of accident count must come with explanations so as to give rise to efficient safety actions. Therefore, to balance predictive power and interpretability, we propose a methodology that combines Bayesian learning of hierarchical GLM with automatic detection of latent structures and interactions through methods borrowed from the field of explainable artificial intelligence (XAI). Promising results are obtained with experiments conducted on crash count data from 2008 to 2017 on a large part of the French highway network. Moreover, we tested our approach on three public datasets covering a broad variety of contexts in terms of volume, data types and tasks (viz. classification and regression). These experiments confirm that our framework outperforms traditional GLM models while getting close to the best ML models and remaining interpretable.