Academic Journal
Who's at risk on the road? Demonstrating young and novice driver risk in low-to middle-income contexts.
| Title: | Who's at risk on the road? Demonstrating young and novice driver risk in low-to middle-income contexts. |
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| Authors: | Guzman LA; Grupo de Sostenibilidad Urbana y Regional, SUR, Departamento de Ingeniería Civil y Ambiental, Universidad de los Andes, Colombia. Electronic address: la.guzman@uniandes.edu.co., Sarmiento-Barbieri I; Facultad de Economía, Universidad de los Andes, Bogotá, Colombia. Electronic address: i.sarmiento@uniandes.edu.co., Sarmiento OL; Facultad de Medicina, Universidad de los Andes, Bogotá, Colombia. Electronic address: osarmien@uniandes.edu.co., Hidalgo D; Departamento de Ingeniería Industrial, Pontificia Universidad Javeriana, Bogotá, Colombia. Electronic address: dariohidalgo@javeriana.edu.co., Quistberg A; Department of Environmental & Occupational Health, Dornsife School of Public Health, Drexel University, Colombia. Electronic address: daq26@drexel.edu. |
| Source: | Journal of safety research [J Safety Res] 2026 Jun; Vol. 97, pp. 406-417. Date of Electronic Publication: 2026 Apr 15. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 1264241 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1247 (Electronic) Linking ISSN: 00224375 NLM ISO Abbreviation: J Safety Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: Elmsford, NY : Pergamon Press Original Publication: [Chicago] National Safety Council. |
| MeSH Terms: | Accidents, Traffic*/statistics & numerical data , Automobile Driving*/statistics & numerical data, Licensure/statistics & numerical data ; Humans ; Male ; Female ; Colombia ; Adult ; Risk Factors ; Young Adult ; Adolescent ; Logistic Models ; Middle Aged ; Developing Countries ; Age Factors ; Machine Learning ; Risk Assessment ; Random Forest |
| Abstract: | 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. (Copyright © 2026 The Author(s). Published by Elsevier Ltd.. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Colombia; Crash risks; Logistic Regression; Low- and middle-income countries; Machine learning; Novice young drivers; Random forests; Road safety |
| Entry Date(s): | Date Created: 20260615 Date Completed: 20260615 Latest Revision: 20260615 |
| Update Code: | 20260616 |
| DOI: | 10.1016/j.jsr.2026.03.015 |
| PMID: | 42297493 |
| Database: | MEDLINE |
| ISSN: | 1879-1247 |
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| DOI: | 10.1016/j.jsr.2026.03.015 |