Field Test of New Mobile System for the Collection of Crash and Citation Data in Nevada
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Summary
This report details the development and field testing of a new mobile system designed to improve the collection of crash and citation data for law enforcement in Nevada. The research was motivated by significant limitations in existing software tools, which often resulted in location errors, inconsistent data entry, and prolonged exposure of officers to dangerous traffic conditions. Accurate and consistent crash data is critical for traffic safety analysis, prevention strategies, and geographic pattern recognition. The proposed system aimed to leverage modern communication networks and software technologies to enhance data accuracy, consistency, real-time reporting, and officer safety by minimizing time spent at crash scenes. The solution comprised three main components: a mobile application for field data collection, a server application for database hosting, and a web portal for data access and analysis. The mobile app utilized GPS for automatic location capture, GIS tagging for highway locations, and barcode scanners to extract driver and vehicle information, thereby reducing manual input and potential errors. Features included adaptive layouts that expanded based on user input, automatic population of citation fields from crash reports, and the ability to sketch scene diagrams. The system was field-tested in cooperation with the Nevada Highway Patrol Southern Command. Four officers with varying roles and device types (tablets and handhelds) participated in the test, collecting a total of 14 crash reports and 16 citations. Researchers accompanied officers to provide guidance and gather feedback, which was categorized by priority and implementation status. The field test yielded substantial feedback, resulting in 121 identified items, including 10 bugs and 111 enhancements. Of these, 96 changes were implemented during the testing phase, prioritizing high-impact improvements such as fixing barcode reading issues, enhancing navigation, and refining data validation. Twenty-five items were designated for future work. Officers reported that the system significantly reduced the effort and time required to collect crash information, while administrative staff confirmed that the system maximized data accuracy and consistency, complying with state and federal requirements. The web portal successfully aggregated data into real-time statistics, maps, and heatmaps, allowing supervisors to monitor incidents and analyze patterns without additional effort from field agents. The study concludes that the proposed mobile system comprehensively improves the crash and citation data collection process. By automating location data, streamlining input through barcode scanning, and providing real-time reporting, the system addresses the key deficiencies of legacy tools. The successful implementation of feedback during the field test demonstrates the system’s adaptability and user-centric design. The findings suggest that adopting such technology can enhance traffic safety studies by providing more reliable data and improve officer safety by reducing time spent at hazardous scenes. Future work includes adding two-factor authentication, enabling multi-officer report editing, and improving catalog updates to further refine the system’s utility and security.
Key finding
Field testing with law enforcement officers demonstrated that the new mobile system significantly reduced the time required for crash data collection and minimized officer exposure to traffic hazards while improving data accuracy and consistency.
Methodology
field_study
Sample size: 4
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.
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Information type
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- Empirical Findings: crash risk outcomes
- Methodological Resource: dataset resource