FMCSA Launches New Data Collection on CMV Driver Schedules and Crash Risks

Washington, D.C. — The Federal Motor Carrier Safety Administration (FMCSA) wants a clearer picture of how commercial motor vehicle (CMV) driver schedules impact fatigue and crash risk. In a notice published Monday in the Federal Register, the agency announced a new information collection to analyze driver schedules, hours-of-service (HOS) duty logs, crash data, inspection violations, and driver demographics.

The study: https://bit.ly/48rewtW

This initiative builds on the FMCSA’s 2011 final HOS rule, where the agency committed to reviewing the relationship between driving hours, restart provisions, and fatigue-related safety outcomes.

Public comments open until January 16, 2026

Stakeholders, carriers and members of the public are invited to submit comments by January 16, 2026, under docket number FMCSA-2025-0391. Submissions may be filed electronically, by mail, fax, or hand delivery at the U.S. Department of Transportation in Washington, D.C.

The study requires a combination of HOS logs, crash and incident reports, violation records, and demographic data. FMCSA will integrate these sources with information from the Motor Carrier Management Information System.

A publicly available, deidentified data set

Pulsar Informatics, contracted by FMCSA, will build a deidentified public dataset after federal and state statutes of limitations related to legal discovery have expired. All personally identifiable information will be removed.

FMCSA notes there is currently no comprehensive dataset capable of fully assessing the relationship between driver schedules, HOS compliance, fatigue and crash risk. Without this collection, the agency would lack critical insights to advance future safety regulations.

Estimated burden on carriers

The proposed data collection includes:

• 60 participating motor carriers

• 15 minutes per response

• A total annual burden of 45 hours

FMCSA is also seeking comments on the necessity of the collection, the accuracy of burden estimates, and suggestions to improve data quality or reduce administrative burden.