Computerized crash reports usability and design investigation : final report.
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Summary
This report details a comprehensive usability and design investigation aimed at redesigning Minnesota’s electronic crash reporting system to improve data accuracy, reliability, and user efficiency. The research was motivated by the limitations of the legacy system, which suffered from poor user acceptance, high error rates, and a rigid interface that did not align with law enforcement officers’ (LEOs) cognitive workflows. As part of Minnesota’s “Toward Zero Deaths” initiative, accurate crash data is critical for identifying safety issues and implementing countermeasures. The project sought to apply human factors principles to create an intuitive, user-centered interface that reduces mental workload, minimizes training requirements, and ensures complete data entry. The study employed an iterative, user-centered design approach involving collaboration between university researchers, state agencies, and a private developer. The methodology included several phases: heuristic analysis of the legacy system, hierarchical task analysis (HTA) to map logical goals, cognitive walkthroughs, and interviews with LEOs. Card sorting exercises with 167 officers informed the logical grouping of report fields. Researchers then developed two prototype interfaces—a sequential “Wizard” interface and a traditional “Form” interface—using Justinmind software. These prototypes underwent rigorous usability testing with 54 officers from 19 agencies using mock crash scenarios. Metrics included error rates, task duration, subjective usability, and mental workload. Following prototype refinement, the vendor (Appriss) conducted beta testing, which was monitored by the research team to ensure adherence to usability standards. Final usability testing was conducted on the vendor prototypes prior to statewide deployment. The findings demonstrated that the user-centric design process successfully produced interfaces with high levels of usability and intuitiveness. The iterative feedback loop allowed for the identification and resolution of usability issues, such as display clutter and complex decision points, leading to features like auto-population, progressive reveal, and clear help guides. The final system, launched in January 2016, received overwhelmingly positive feedback from users. The redesign resulted in improved user satisfaction, increased data completeness and accuracy, and reduced performance time. The study confirmed that involving end-users throughout the design process effectively bridges the gap between technical requirements and practical field application. The significance of this work lies in its demonstration of the value of applying rigorous human factors and usability engineering to critical government infrastructure. By prioritizing the needs and cognitive constraints of law enforcement officers, the project not only improved the efficiency of crash reporting but also enhanced the quality of data available for traffic safety analysis and policy making. The report establishes a precedent for standardized, user-centered reporting systems, offering a replicable model for other states and domains seeking to improve data collection through intuitive interface design.
Key finding
The user-centric iterative design process resulted in a new MNCrash reporting system that launched with overwhelmingly positive feedback from law enforcement users, demonstrating improved usability and data quality compared to the legacy system.
Methodology
mixed_methods
Sample size: 82
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).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
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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: self report data, crash risk outcomes
- Methodological Resource: dataset resource