A paper titled Explainable macroscopic and microscopic influences of COVID-19 on naturalistic driver aggressiveness derived from telematics through SHAP values of SVM and XGBoost algorithms authored by Apostolos Ziakopoulos, Marios Sekadakis, Christos Katrakazas, Marianthi Kallidoni, Eva Michelaraki and George Yannis  has been published in Journal of Safety Research. This study aims to quantify the impacts of the COVID-19 pandemic on driver behavior as expressed by harsh accelerations (HA) measured from over 35,5000 naturalistic driving trips by smartphone telematics data using advanced machine learning algorithms, including SVM and XGBoost, combined with SHAP values. Key findings indicate that high speeding, total trip distance, and trip duration are associated with increased HA counts. Drivers perform more HAs on speeds between 30–50 km/h, while after 50 km/h, the contributions of speed lead to fewer HAs. Pandemic measurements were more influential on HA counts compared to policy measures taken by the state.