What the FMCSA Is Studying About CMV Driver Schedules and Crash Risk — and What Your Safety Program Should Track
The FMCSA is studying how CMV driver schedules drive crash risk. The research will run through 2027 and feed future Hours-of-Service rulemaking. Find out what you should be tracking now.
What FMCSA Is Studying About CMV Driver Schedules and Crash Risk — and What Your Safety Program Should Track
The Federal Motor Carrier Safety Administration (FMCSA) is about to start collecting data on how commercial motor vehicle (CMV) driver schedules — start times, shift patterns, weekly variability, sleep opportunity — relate to crash and inspection-violation risk. The Information Collection Request (ICR) authorizing the study was announced in the Federal Register on April 20, 2026. Carriers that get ahead of the research on the safety program side will have data ready when the rule comes. This piece is the forward-looking implications view for safety managers.
What the FMCSA notice actually authorizes
The ICR is titled "Crash Risks by Commercial Motor Vehicle (CMV) Driver Schedules." Per the Federal Register notice, the study will "answer important questions related to driver schedules and how these factors impact overall driver performance and fatigue." The collected data will be used to "examine the relative risk of crashes and inspection violations based on various factors related to the driver's work schedule and demographics."
The ICR went through the standard Paperwork Reduction Act of 1995 process. The May 20 comment window closing means FMCSA has now consolidated public input and is moving toward Office of Management and Budget (OMB) review and approval of the data collection. The docket is FMCSA-2025-0391.
An ICR is not itself a rule. It authorizes a research project. The output of the research project — typically a published report, sometimes with supporting datasets — becomes the evidence base FMCSA cites when proposing or revising regulations. The current Hours-of-Service (HOS) framework codified at 49 CFR Part 395 went through multiple rounds of evidence-based revision over the last twenty years, each anchored in research like this. Treating an ICR like this as the leading edge of a future rule is the right framing.
Why this matters now
The FMCSA has signaled multiple times over the last decade that the current Hours-of-Service framework — which is anchored in total daily and weekly drive-time and on-duty limits, with some flexibility around the 30-minute break and the sleeper-berth provision — does not fully capture the safety dynamics of schedule variability and sleep opportunity. The Crash Risks by CMV Driver Schedules ICR is the most explicit signal in recent years that the agency wants to evidence-based a future revision in those directions.
The research timeline matters. ICRs of this scope typically run 18 to 36 months from OMB approval to final report. That puts a publishable evidence base in the 2027–2028 window. A proposed rule following the research could come 12 to 24 months after that. Carriers that want to be in a defensible position when the rule comes have a two to four-year runway to start collecting the schedule and fatigue data internally that FMCSA will eventually expect to see.
What the research design implies
The Crash Risks by CMV Driver Schedules ICR collects schedule-pattern variables alongside crash and inspection-violation outcomes. There are three things you can learn from it:
The FMCSA appears to think schedule variability matters, not just total hours. The current Hours-of-Service framework constrains total drive time and total on-duty time. It does not constrain how variable a driver's schedule is from week to week or shift to shift. The ICR's focus on "various factors related to the driver's work schedule" reads as an interest in the variability dimension, not just the volume dimension.
The FMCSA appears to think sleep opportunity matters, not just rest periods. The current framework requires a 10-hour off-duty period before each driving period and constraints on duty cycles. It does not constrain when that off-duty period falls relative to the driver's circadian rhythm. The research on "driver performance and fatigue" tied to schedule factors implies an interest in whether the off-duty period falls in a sleep-opportunity window or not.
The FMCSA prioritizes predictability. Last-minute schedule changes, dispatch-driven start time variability, and irregular weekly rotation patterns are common in the industry and have been linked in safety research to elevated fatigue and crash risk. A study designed to "examine the relative risk of crashes and inspection violations" by schedule factors is well-positioned to surface those signals if they exist in the data.
None of this means the FMCSA has decided what to do about schedule variability, sleep opportunity, or predictability. It means the research is being designed to provide an evidence base for whatever decision the agency reaches. Carriers that have schedule-pattern data of their own — collected the same way the FMCSA study will collect it — will be in a stronger position to engage when the rule comes.
What safety programs should start tracking now
The next step is to start collecting a small number of high-signal schedule variables alongside the crash and inspection data your safety program already tracks. Six metrics that align with the research direction and that most safety programs can collect without major systems work:
- Weekly schedule variability per driver. The standard deviation of a driver's daily start time across a rolling 4-week window. A driver who starts at 5:00 a.m. every shift has near-zero variability. A driver who starts at 4:00 a.m. one day, 11:00 a.m. the next, and 9:00 p.m. the third has high variability. Most ELD systems can produce the underlying timestamp data.
- Sleep-opportunity window. For each driver, in each rolling 4-week window, the percentage of off-duty periods that fall within a standard nighttime sleep-opportunity window (typically 10:00 p.m. to 6:00 a.m. local). Drivers with more off-duty periods overlapping the conventional nighttime sleep window are more likely to be operating at higher sleep efficiency.
- Schedule predictability score. How far in advance is each driver's schedule set? A program that sets schedules a full week ahead and rarely changes them scores high. A program that posts schedules 24 hours ahead and changes them in flight scores low.
- Fatigue-event correlation by shift pattern. When fatigue events occur — driver self-reports, hard-braking events, lane-departure events, dashcam-flagged drowsiness signals — how do they correlate with the driver's schedule pattern over the prior week? This requires no new data collection beyond what most safety programs already have; it requires joining the fatigue-event data to the schedule data.
- Dispatch-driven last-minute schedule changes. The count per week of schedule changes communicated to the driver less than 24 hours before the affected shift. Track who initiates each change (dispatch, customer, driver) for the cleanest pattern read.
- Weekend and late-night start patterns. Frequency of shifts starting between 10:00 p.m. and 4:00 a.m., or starting on the back end of a weekend off-duty period. These are the windows where the misalignment between schedule and circadian rhythm is most pronounced.
Tracking these six metrics over 12 to 24 months gives a safety program a baseline that can be compared against incident outcomes. Most importantly, it gives the program the dataset it needs to engage with whatever rule comes out of the FMCSA research — either to demonstrate compliance from a position of evidence, or to make a substantive case in a future notice-and-comment process.
How to use scheduling data internally
Carriers that track schedule-pattern data this way typically find three uses for it. First, it surfaces drivers operating in higher-risk schedule patterns earlier than incident-only tracking does. Schedule pattern is a leading indicator; incident is a lagging indicator. Second, it makes safety briefings more concrete. Telling a driver "your schedule pattern over the last 4 weeks puts you in a higher-risk window than your peer median" is a different conversation than "you had a fatigue event last week." Third, it gives insurance partners and brokers a concrete narrative about how the carrier manages fatigue beyond Hours-of-Service compliance.
The internal-use case for the data is independent of what FMCSA ultimately does with the research. Carriers that track schedule-pattern data tend to see lower fatigue-event rates and lower preventable-crash rates over time, on the order of single-digit to low-double-digit percentage improvements depending on the baseline.
What the FMCSA research timeline implies for your safety committee
The research will produce findings during 2027 or 2028. A rule could follow 12 to 24 months later. That gives most carriers two windows to engage:
The 2027–2028 research findings window. If the FMCSA publishes the research findings as a report, that report becomes citable evidence in future safety-program decisions, insurance underwriting conversations, and contract negotiations with customers who care about safety performance. Safety committees should plan to read the findings closely when they come out.
The proposed-rule comment window. Any rule that follows the research will go through a Notice-and-Comment process. Carriers with their own schedule-pattern data will be in the strongest position to engage substantively — to support, contest, or refine the proposed rule based on what their internal data shows. The comment window is the moment to translate two-plus years of internal tracking into regulatory influence.
Frequently asked questions
When will the FMCSA publish a rule from this research?
The ICR is in OMB review now, post the May 20 comment close. Data collection typically begins 3 to 9 months after OMB approval. Research of this scope usually runs 18 to 36 months. Publishable findings most likely land in late 2027 or 2028. A proposed rule that uses the findings could follow 12 to 24 months after that. None of these timelines is a commitment — the FMCSA can move faster or slower depending on research outcomes and broader rulemaking priorities.
Can my fleet voluntarily contribute data to the FMCSA study?
The ICR notice does not detail voluntary participation pathways. Carriers interested in participating in FMCSA safety research can monitor the docket (FMCSA-2025-0391) and reach out to the FMCSA Office of Research and Analysis if a participation pathway opens. Voluntary participation is more common in subsequent rule-development phases than in the initial data collection phase.
Does this affect current Hours-of-Service compliance?
No. The ICR does not change the regulation. The current Hours-of-Service framework at 49 CFR Part 395 remains in effect. Carriers must continue to comply with the existing daily and weekly drive-time and on-duty limits and with the 30-minute break and sleeper-berth provisions.
How does schedule variability differ from total hours?
Total hours measures how much a driver is driving and on duty across a day or week — the lever the current Hours-of-Service framework primarily uses. Schedule variability measures the pattern of when those hours occur. A driver who works exactly 60 hours each week on a 6:00 a.m. start every day has the same total hours as a driver who works 60 hours with start times bouncing from 4:00 a.m. to 9:00 p.m. The current framework treats those two drivers the same; safety research increasingly suggests they should not be treated the same.
What is the relationship between this ICR and ELD data?
Electronic Logging Device data is the most likely raw input for both the FMCSA research and any internal schedule-pattern tracking a carrier does. ELDs capture start time, duty status changes, drive time, and rest periods at high resolution. The schedule variability and sleep-opportunity metrics described above can be computed from standard ELD output.
How does this connect to CSA scores?
The Hours-of-Service Compliance BASIC is one of seven BASICs in the FMCSA Compliance, Safety, Accountability program. Better Hours-of-Service compliance reduces HOS Compliance BASIC severity weighting; a safety program that runs lower fatigue rates tends to see fewer crashes and fewer driver-attributable inspection violations across multiple BASICs. The FMCSA research, if it leads to a rule, would likely also feed into the SMS methodology for the HOS Compliance BASIC.