Predicting crash occurrence at intersections in Texas: an opportunity for machine learning

This paper studies the frequency of traffic crashes at intersections across Texas by employing Zero-inflated Negative Binomial (ZINB) and Negative Binomial-Lindley (NB-L) generalized linear models, as well as various tree-based machine learning (ML) methods, namely Random Forests (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Bayesian Additive Regression Trees (BART) to predict the frequency of crashes at intersections. Official crash reports from 2010 through 2019 were linked to Texas' over 700,000 intersections. RF provided best prediction performance (using R-square and Root Mean Square Error metrics) while serving well for highly imbalanced crash data (with many zero cases). Sensitivity analysis highlights the practical significance of signalized intersection, annual average daily traffic, number of lanes at intersection approach, and other covariates.