Designing a Study to Investigate Older Novice Drivers

Wright, Tim; Ehsani, Johnathon P; Blomberg, Richard D.; Gershon, Pnina; Watson, Christine E · 2023 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

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Summary

This report outlines the design of a hypothetical naturalistic driving study (NDS) to investigate the safety and driving behaviors of older novice drivers (ages 18–20) compared to younger novices (ages 15.5–16.5). The research was motivated by the disproportionate crash risk among young drivers and the increasing trend of delayed licensure, particularly among Latino, Black, and lower socioeconomic status populations. While Graduated Driver Licensing (GDL) laws are effective for younger novices, few states apply these protections to those licensed at 18 or older. Consequently, there is a significant knowledge gap regarding the risk trajectories and driving exposure of older novices, hindering the development of targeted safety interventions. The research team conducted a comprehensive literature review to inform the study design, analyzing existing NDS data, crash statistics, and demographic trends. They identified critical limitations in prior research, noting that previous NDS samples were predominantly White, high-income, and recruited via convenience sampling, leading to poor generalizability. To address these biases, the team designed a hypothetical study featuring intentional sampling through partnerships with state licensing agencies to ensure demographic diversity. The proposed methodology employs a "hybrid" data collection approach: most participants would use smartphone apps to record driving data, while a smaller subgroup would be equipped with traditional in-vehicle data acquisition systems. This design aims to reduce participation barriers related to vehicle instrumentation and allow for a comparison of data quality between smartphone and in-vehicle methods. The study plan includes detailed protocols for recruitment, data management, and analysis, including group-based trajectory modeling to identify distinct risk profiles over a 12-month period. Key findings from the literature review indicate that while novice crash rates generally decline with experience, older novices may exhibit slower declines in risk compared to younger peers. Additionally, existing data suggests that older novices are more likely to face economic barriers to licensure and vehicle access. The report highlights that previous studies often suffer from "good driver" bias, where high-risk participants drop out early, skewing results. The proposed design seeks to mitigate this by ensuring a representative sample and sufficient duration to capture seasonal variations and long-term behavioral trajectories. The team also developed draft questionnaires and assessed potential challenges, such as maintaining engagement among diverse populations and handling data from varied vehicle types. The significance of this work lies in its potential to inform the extension of GDL provisions to older novices and the development of tailored safety interventions. By addressing the lack of representative data on older novices, the study design provides a framework for understanding how demographic factors and licensure age influence driving risk. The hybrid data collection method offers a scalable solution for future research, enabling more inclusive studies that can better identify predictors of risky driving across different socioeconomic groups. Ultimately, this report serves as a blueprint for conducting rigorous, equitable research to reduce crash rates among a vulnerable and understudied segment of the driving population.

Key finding

The report proposes a hypothetical naturalistic driving study design that utilizes intentional recruitment and hybrid data collection methods to address the lack of representative data on older novice drivers and the limitations of previous convenience samples.

Methodology

theoretical

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• novice drivers
• learner drivers
• induced exposure
• generational effects
• exposure measurement
• parental management

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: observational prevalence
• Methodological Resource: dataset resource, validation psychometrics