A paper titled “Leveraging Machine Learning Algorithms to Predict and Analyze Single-Vehicle and Multi-Vehicle Crash Occurrences on Motorways” authored by Saumik Sakib Bin Masud, Kirti Mahajan,  Alexandra Kondyli,  Katerina Deliali and George Yannis  has been published in Transportation Research Record. The dataset for this research included various types of roadway design parameters and traffic conditions. Combinations of three feature-selection techniques, namely ANOVA, correlation matrix, and ExtraTreesClassifier algorithm, were utilized to separately select the appropriate variables for single-vehicle (SV) and multi-vehicle (MV) crash analysis. The results confirmed that the crash factors associated with single and multi-vehicle crashes are different and that some parameters have inverse impact. Artificial intelligence and Machine Learning (ML) can assist transportation professionals in better understanding the causes of SV and MV crashes and advance the process toward Vision Zero.