Estimating Motor Carrier Management Information System Crash File Underreporting from Carrier Records
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Summary
This study investigates the extent of underreporting in the Federal Motor Carrier Safety Administration’s (FMCSA) Motor Carrier Management Information System (MCMIS) crash file. The MCMIS file is critical for FMCSA’s mission to reduce large truck and bus crashes, yet it is widely believed to be incomplete. The research specifically addresses claims by motor carriers that they possess records of crashes meeting MCMIS reporting criteria that are absent from the federal database. The study aims to quantify this underreporting and identify the factors contributing to missing data. The methodology involved a sample of six motor carriers, selected for diversity in fleet type, operation nature, and geographic scope, who provided 58,333 crash records from 2012–2014. Researchers matched these carrier records against the MCMIS crash file. For crashes identified by carriers as reportable but missing from MCMIS, researchers searched crash databases from 15 representative U.S. states. Finally, records absent from both MCMIS and state files were manually reviewed to determine if they met reporting thresholds. This multi-stage linkage allowed for the categorization of crashes based on their presence in carrier, state, and federal records. The findings reveal significant underreporting. For the studied carriers, the MCMIS file contained only about 66% of crashes that met reporting criteria. Approximately 25% of crashes identified by carriers as reportable did not actually meet the criteria, while 6% of MCMIS-reportable crashes were missing from carrier records entirely. Among the crashes missing from MCMIS, 56% had no police report filed, often involving minor incidents like animal strikes that technically qualified due to vehicle disablement. Of the crashes present in state files but missing from MCMIS, half were due to missing or incorrect USDOT numbers, and 24% involved trucks misclassified as light vehicles. Two-axle single-unit trucks and intrastate vehicles were disproportionately underreported, suggesting difficulties in applying gross vehicle weight rating thresholds. The study concludes that underreporting stems largely from human error at the scene and systemic issues in state data processing. Police officers often fail to capture necessary vehicle identification data or correctly classify vehicle weights, particularly for medium-duty trucks. The authors recommend reducing reliance on manual police reporting by implementing automated data collection or linking with other systems to verify vehicle criteria. Additionally, raising the severity threshold for reportable crashes could improve data completeness, as reporting is more consistent for severe incidents. The study notes limitations, including the non-random selection of carriers and the use of data from only 15 states, cautioning against generalizing the results to the entire national fleet.
Key finding
The MCMIS crash file contained approximately 66 percent of crashes that met the reporting criteria according to carrier records.
Methodology
dataset
Sample size: 58333
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