A paper titled “Analysis of harsh braking and harsh acceleration occurrence via explainable imbalanced machine learning using high-resolution smartphone telematics and traffic data” authored by Apostolos Ziakopoulos has been published in Accident Analysis & Prevention. Subsequently, Synthetic Minority Oversampling TEchnique (SMOTE) was applied due to class imbalance and then binary classification was conducted to detect factors leading to harsh brakes (HB) and harsh accelerations (HA) occurrence.  Results reveal strong nonlinear effects on harsh event occurrence, with individual speed and traffic flow parameters showing the highest influence, followed by exposure parameters such as segment length and pass count. However, network characteristics such as number of lanes, and speed limit had limited influence on harsh events occurrence, as did behaviors such as mobile phone engagement and speeding.