Who's at risk on the road? Demonstrating young and novice driver risk in low-to middle-income contexts.

INTRODUCTION

Young novice drivers experience higher crash rates, yet most studies focus on high‑income countries, leaving limited evidence from low‑ and middle‑income countries (LMICs). This study examined crash involvement and its risk factors in Colombia to inform improvements to the national driver‑licensing process.

METHODS

We analyzed the national driver‑license registry from 2007 to 2020 (n = 5,822,842) and all police‑reported road crashes during the same period (n = 541,134). Crash probability was modeled with a non‑parametric machine‑learning approach (random forests) and, for interpretation, a logistic regression that incorporated age, driving experience, license category, sex, number of fines, and region.

RESULTS

Main effects explained 51% of the predictive variability, while interactions accounted for the remaining 49%. The random‑forest model achieved an F1 score of 96.38% with 93.9% of precision, indicating a low false‑positive rate and a recall of 98.9%. Driving experience exhibited the strongest interaction effects: interactions with other variables explained ∼ 2.0% of the variability, the pairwise interaction between experience and license category accounted for ∼ 0.4%, and the interaction between experience and region explained ∼ 0.25%.

CONCLUSIONS

Logistic‑regression results corroborated the machine‑learning findings, revealing negative associations of both age and experience with crash probability across multiple model specifications. As expected, younger and less‑experienced drivers faced the highest crash risk. Risk also varied by sex (higher in males), by license category (higher among motorcyclists), and across regions. These findings suggest that implementing a graduated licensing system could reduce crash risk among novice and young drivers in Colombia and, by extension, in other LMICs.