A paper titled “Comparing Machine Learning Techniques for Predictions of Motorway Segment Crash Risk Level” authored by Dimitris Nikolaou, Apostolos Ziakopoulos, Anastasios Dragomanovits, Julia Roussou and George Yannis has been published in Safety. The techniques considered were Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbours. Among the five techniques, the Random Forest model achieved the best classification performance (overall accuracy: 89.3%). The findings of this study are particularly useful as the Random Forest model could be used as a highly promising proactive road safety tool for identifying potentially hazardous motorway segments.