Linking Oregon Driver Records and Crash Data to Evaluate Interventions and Mitigate Driver Risk

Hurwitz, David; Jashami, Hisham; Gray, Aiden · 2026 · ROSA P / Oregon. Department of Transportation. Research Section

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Summary

This report addresses the critical need to evaluate the efficacy of Oregon’s driver intervention programs, which aim to mitigate risky driving behaviors but have rarely been assessed since the 1980s. Motivated by high collision rates in Oregon, including 554 fatal collisions in 2022, the study seeks to provide the Oregon Department of Transportation (ODOT) and the Driver and Motor Vehicles Services (DMV) with a robust tool to analyze program performance and identify demographic trends among risky drivers. The research focuses on linking disparate data sources to create a comprehensive view of driver behavior, crash outcomes, and administrative interventions. The methodology involved constructing a linked database combining four distinct datasets: crash data, accident data, verdict data, and driver records spanning from 1995 to 2024. The researchers employed deterministic linkage techniques to merge these sources, ensuring data quality through rigorous cleaning and validation protocols. The analysis plan included generating visualizations of citation and crash data to identify shifts in administrative procedures and demographic trends. Additionally, statistical models, specifically logistic regression, were built to predict the probability of various crash types based on demographic factors and citation history. The study also reviewed existing literature on Oregon’s Habitual Traffic Offender (HTO), Driver Improvement Program (DIP), DUII diversion, and At-Risk Driver programs, comparing them to similar initiatives in California, Montana, and Washington. Key findings include detailed visualizations of citation trends, revealing the ten most common citation types and their distribution across different years, sexes, and age groups. The analysis highlighted demographic patterns, such as the share of yearly citations by sex and the relationship between driving experience and risk categories. Logistic regression models provided specific insights into crash involvement probabilities, particularly for speed-related and DUII-related crashes. The results demonstrated how citation history correlates with the likelihood of future crash involvement, offering empirical evidence on which groups are most prone to risky driving behaviors. The study also documented challenges in data merging, such as inconsistencies in data fields, which informed recommendations for future data standardization. The significance of this work lies in its provision of a foundational linked database that enables ODOT to systematically evaluate the effectiveness of its driver improvement programs. By identifying specific demographic and behavioral risk factors, the findings support the tailoring of future intervention strategies to target high-risk groups more effectively. The report recommends optimizing data workflows, creating comprehensive data dictionaries, and documenting current data practices to enhance future research capabilities. Ultimately, this study offers a critical step toward evidence-based traffic safety policy, allowing for more precise mitigation of driver risk and improved safety outcomes in Oregon.

Key finding

The creation of a linked database spanning 1995 to 2024 enabled the identification of demographic trends and the prediction of crash probabilities based on citation history, providing a basis for tailoring driver intervention strategies.

Methodology

dataset

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 (5 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.

• sex gender
• incidence prevalence
• naturalistic crash near crash
• induced exposure
• driver education effectiveness
• telematics crash prediction

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: crash risk outcomes, observational prevalence
• Methodological Resource: dataset resource