Classifying and predicting risky driving among novice drivers: A group-based trajectory approach. (February 2019)

• Record Type: Journal Article
• Authors: Missikpode, Celestin; Peek-Asa, Corinne; McGehee, Daniel V.; Wallace, Robert
• Is Part Of: Journal of safety research. Volume 68(2019)
• Page Start: 215
• Page End: 222
• Publication Date: 2019-02
• Subjects: Novice drivers -- Dangerous driving events -- Risky driving behaviors -- Group-based trajectory modeling -- And dual trajectory modeling; Industrial safety -- Periodicals; Accidents -- Prevention -- Periodicals; Safety -- Periodicals; Accidents, Occupational -- Periodicals; Sécurité du travail -- Périodiques; Accidents -- Prévention -- Périodiques; Accidents -- Prevention; Industrial safety; Periodicals; 363.1
• DOI: 10.1016/j.jsr.2018.10.014
• Languages: English
• ISSNs: 0022-4375

Abstract

Abstract: Introduction : Classifying risky driving among new teenage drivers is important for efficiently targeting driving interventions. We thoroughly investigated whether novice drivers can be clustered by their driving outcome profiles over time. Methods : A sample of 51 newly licensed teen drivers was recruited and followed over a period of 20 weeks. An in-vehicle video recording system was used to gather data on dangerous driving events referred to as DDEs (elevated g-force, near-crash, and crash events), risky driving behaviors referred to as RDBs (e.g., running stop signs, cell phone use while driving), and miles traveled. The DDE and RDB weekly rates rate were determined by dividing the number of DDEs and RDBs in a week by the number of miles traveled in that week, respectively. Group-based trajectory modeling was used to map the clustering of DDE rate and RDB rate patterns over time and their associated covariates. Results : Two distinct DDE rate patterns were found. The first group (69.1% of the study population) had a lower DDE rate which was consistent over time. The second had a higher DDE rate pattern (30.9%) and characterized by a rising trend in DDE rate followed by a steady decrease (inverted U-shaped pattern). Two RDB rate patterns were also identified: a lower RDB rate pattern (83.4% of the study population) and a higher RDB rate pattern (16.6%). RDB and DDE rate patterns were positively related, and therefore, co-occurred. The results also showed that males were more likely than females to be in the higher DDE and RDB rate patterns. Conclusion : The groups identified by trajectory models may be useful for targeting driving interventions to teens that would mostly benefit as the different trajectories may represent different crash risk levels. Practical applications: Parents using feedback devices to monitor the driving performance of their teens can use the initial weeks of independent driving to classify their teens as low or high-risk drivers. Teens making a very few DDEs during their early weeks of independent driving are likely to remain in the lower risk group over time and can be spared from monitoring and interventions. However, teens making many DDEs during their initial weeks of unsupervised driving are likely to continue to make even more DDEs and would require careful monitoring and targeted interventions. Highlights: Classifying risky driving among new teenage drivers is important for efficiently targeting driving interventions We investigated whether novice drivers can be clustered by their driving outcome profiles over time Novice drivers can be classified into two groups: a lower risk group and a higher risk group Teens making a few errors during their early driving experiences are likely to remain less risky over time Teens making many errors during their early driving experiences are likely to remain risky over time Parents can use the initial weeks of independent driving to classify their teens as low or high-risk drivers Teens making many errors during their initial weeks of unsupervised driving would require targeted interventions