FMCSA Data Quality and Limitations

Why Data Quality Matters

FMCSA safety data drives consequential decisions: regulatory enforcement actions, carrier safety scores, shipper and broker vetting decisions, and insurance underwriting. When this data contains errors, omissions, or biases, those decisions may be flawed. Understanding the limitations of FMCSA data is essential for anyone who relies on it for business or safety purposes. TruckCodes provides tools to help users navigate these data quality challenges through the research portal.

Inspection Data Variability

Roadside inspection data quality varies across jurisdictions. Each state has its own inspection program with different staffing levels, emphasis areas, and training standards. Some states conduct significantly more inspections per CMV-mile than others, creating geographic bias in the data. Inspectors exercise judgment when identifying and coding violations, introducing variability in how similar conditions are documented. Some states focus on vehicle mechanical conditions while others emphasize driver compliance. These variations mean that a carrier's inspection record reflects not only its actual safety performance but also where and how frequently its vehicles are inspected.

Violation Coding Accuracy

Each inspection violation is assigned a specific violation code that maps to a federal regulation section. Coding errors occur when inspectors select incorrect codes, misidentify the violation severity, or apply codes that do not precisely match the observed condition. Some violations can reasonably be coded under multiple sections, leading to inconsistency. The severity weight assigned to each violation directly impacts CSA BASIC scores, so coding errors can meaningfully affect a carrier's safety measurement. Carriers should review inspection reports promptly and use the DataQs system to challenge errors.

Crash Data Limitations

FMCSA crash data comes from police accident reports (PARs) submitted by law enforcement. Not all CMV-involved crashes are captured in the database; reporting depends on the crash meeting DOT-reportable criteria and on law enforcement accurately identifying and reporting the CMV involvement. Crash data does not include fault determination, meaning a carrier is scored on crashes regardless of responsibility. The lag between a crash event and its appearance in FMCSA systems can be weeks or months. These factors make crash data a blunt instrument for measuring carrier safety.

MCS-150 Self-Reported Data

Carrier profile information in FMCSA systems comes largely from the MCS-150 form, which carriers are required to update biennially but which many fail to update on schedule. Self-reported fields including fleet size (power units and drivers), operations classification, and cargo types may be outdated or inaccurate. Since CSA scoring uses carrier size as a peer comparison factor, inaccurate fleet size data can distort percentile rankings. Verify that your carrier's public profile data is current and accurate.

The DataQs Challenge Process

FMCSA provides the DataQs system for carriers to request reviews of data they believe is inaccurate. Common DataQs challenges include incorrect carrier identification on inspection reports, improperly coded violations, crashes attributed to the wrong carrier, and inspections with procedural errors. Filing a DataQs request triggers a review by the state that originated the record. Successful challenges result in correction or removal of the erroneous data. The process can take weeks to months, during which the disputed data continues to affect safety scores.

Exposure and Normalization Challenges

A fundamental challenge in FMCSA data is the lack of consistent exposure data. Carriers self-report annual mileage on the MCS-150, but this data is updated infrequently and not verified. Without accurate mileage data, it is difficult to calculate meaningful safety rates (crashes per million miles, violations per inspection). The CSA system normalizes by number of inspections rather than by miles traveled, which can disadvantage carriers that operate in high-inspection states or on routes with frequent enforcement activity. Understanding these normalization limitations is important when comparing carriers through TruckCodes analytical tools.

Best Practices for Data Users

Users of FMCSA data should treat it as an important but imperfect signal. Use multiple data sources rather than relying on a single metric. Look for patterns across time rather than reacting to individual data points. Consider the carrier's inspection sample size before drawing conclusions from OOS rates or BASIC percentiles. Supplement FMCSA data with direct carrier assessments, insurance loss runs, and industry references. Review violation trends in context and account for the known limitations described here when making risk-based decisions.