Improving the Efficiency and Quality of Road Crashes Data in the Sultanate of Oman: Evidence-Based Recommendations
DOI: 10.33492/jrs-d-25-1-2563701
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the critical need for reliable Road Traffic Crash (RTC) data to support evidence-based policy interventions in the Sultanate of Oman. The authors argue that inadequate and inconsistent data hinder the ability to identify risk factors, prioritize interventions, and evaluate outcomes. The study aims to improve the quality of the National Road Traffic Crash (NRTC) database by developing an integrated conceptual framework and providing specific recommendations for data collection and processing. The authors constructed a holistic framework combining the Haddon Matrix and the C3-R3 systems model to analyze RTC dynamics across behavioral, economic, social, technological, and environmental dimensions. This framework accounts for pre-crash, crash, and post-crash phases, as well as the interaction between road users, vehicles, and the road environment. Using this theoretical lens, the researchers conducted a critical evaluation of the existing NRTC database, which is maintained by the Royal Oman Police (ROP). The assessment focused on identifying strengths, weaknesses, and gaps in current data recording, monitoring, and analysis protocols. The evaluation revealed significant limitations in the NRTC database. First, the reliance on manual reporting systems increases the risk of errors and delays. Second, there are inconsistencies in defining RTC-related deaths and a lack of documentation for severe injuries that lead to death later, resulting in underreporting. Third, data collection is biased toward the driver at fault, often omitting details about passengers, other road users, and specific vehicle features such as safety technology or braking systems. Fourth, the database lacks geocoded locations, preventing spatial analysis of high-risk areas, and records crash times only at the hour level, which obscures the impact of time-specific factors like fatigue or traffic volume. Additionally, the database fails to capture secondary contributing factors, such as mobile phone use, which is significantly underreported due to verification difficulties. Finally, the NRTC database operates in isolation, lacking linkage with hospital, insurance, and civil registration records, which prevents a comprehensive understanding of crash outcomes and economic impacts. The study concludes that structured modifications are urgently needed to enhance the NRTC database. Key recommendations include transitioning to digital data collection systems, such as the Integrated Microcomputer Accident Analysis Package (iMAAP), to reduce errors and improve efficiency. The authors advocate for multi-sectoral data integration using civil identification numbers to link police records with health and civil registration data, enabling accurate tracking of injury outcomes and long-term health effects. Furthermore, they recommend incorporating GPS-based location reporting, precise time-stamping, and the use of in-vehicle event data recorders to capture real-time driver behaviors. These improvements are intended to provide policymakers with a more accurate understanding of RTC dynamics, facilitating the implementation of effective, data-driven road safety interventions in Oman and similar regions.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-21 |
| chunk | success | chunk | — | — | 1 | 2026-06-21 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-21 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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Information type
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- Empirical Findings: crash risk outcomes
- Methodological Resource: dataset resource