FMCSA Crash Data: How AI Predicts Trucking Risk

A crash rate difference of two or three percentage points doesn’t sound dramatic—until you apply it to a fleet with hundreds of drivers running through peak season lanes in December. That’s why the latest FMCSA crash data analysis deserves more than a quick skim. When midsize carriers show year-to-date crash rates above 7% per driver (with some over 11%), while many large fleets cluster around 5–6%, it’s a signal that safety performance is uneven—and that the industry’s current way of measuring risk isn’t keeping up.

Here’s the stance I’ll take: the problem isn’t that we don’t have safety data; it’s that we’re not operationalizing it. Carriers, brokers, and shippers treat crash history like a compliance checkbox, when it should be a living risk model that influences routing, tendering, coaching, and even appointment planning.

This is where AI belongs in transportation and logistics—not as a shiny add-on, but as the system that turns messy crash records, inspection signals, telematics, and operational context into decisions that actually prevent crashes.

What FMCSA crash data is really telling us

Answer first: FMCSA crash data shows that crash frequency varies widely by carrier size, and midsize fleets can carry materially higher per-driver crash rates than large fleets.

Freight market conversations often treat “carrier safety” as binary: safe or unsafe. The FMCSA’s underlying crash data tells a different story. In the analyzed year-to-date window (through Nov. 30, 2025), large fleets (500+ drivers) at the high end hovered around 5–6% crashes per driver, while midsize fleets (250–500 drivers) reached 7–12%.

A few examples from the data snapshot:

• Transdev Services Inc.: 6.56% (61 crashes YTD, 930 drivers)
• Western Express Inc.: 6.15% (270 crashes YTD, 4,390 drivers)
• AD Express Trucking LLC: 11.87% (33 crashes YTD, 278 drivers)
• Hogan Truck Leasing Inc.: 9.85% (32 crashes YTD)
• HMD Trucking Inc.: 9.85% (27 crashes YTD)

This doesn’t prove fault or preventability. It does prove exposure-adjusted frequency, which is what risk teams, underwriters, and procurement leaders ultimately care about.

Why midsize fleets can look worse (even when they’re trying)

Answer first: Midsize carriers often have less safety infrastructure per driver, and small data issues can distort their apparent crash rate.

Large fleets tend to have deeper benches: dedicated safety ops, driver training programs, structured incident review, and more standardized in-cab tech. Midsize carriers may have strong intent but limited capacity—especially when freight is volatile and turnover is high.

There’s also a practical reality: normalization matters. Crash rates are calculated as crashes divided by driver count. If driver counts are stale or underreported in biennial filings, the “crashes per driver” number can spike artificially. That doesn’t excuse bad performance, but it does mean you shouldn’t use a single ratio as the whole truth.

The dirty secret: safety scoring is still too easy to game

Answer first: Inspection-based safety indicators can be managed, but crash outcomes are harder to hide—so your risk model should weight outcomes more heavily.

The FMCSA’s CSA program relies heavily on roadside inspections and violations grouped into BASIC categories. Those signals are useful, but they also create incentives to “manage the score” instead of managing the underlying risk.

Meanwhile, the crash-related picture is fragmented:

• Crash records are fed from state police reports into FMCSA systems.
• Under-reporting can be substantial and inconsistent across states.
• Public-facing views don’t always emphasize crash indicators.
• Preventability determinations exist but aren’t consistently surfaced in a way the market can use for procurement.

If you’re a shipper or broker, this is the uncomfortable takeaway: your “carrier selection” process can look rigorous while still missing the carriers most likely to crash.

A compliance score that’s easy to optimize is not the same thing as a safety score that predicts harm.

Where AI fits: from rearview reporting to forward-looking prevention

Answer first: AI reduces crashes when it’s used to predict risk by lane, time, driver state, and operating conditions—and then changes daily decisions.

Most teams use safety data like a report card. AI works better as a navigation system: constantly recalculating risk and recommending actions.

Here are three practical, high-impact ways AI improves transportation safety without turning operations upside down.

1) Predictive carrier risk scoring (beyond “rate shopping”)

Answer first: AI can combine crash history with operational context to predict near-term risk, not just historical performance.

A useful model doesn’t stop at “Carrier A has a higher crash rate than Carrier B.” It adds context that procurement and operations actually control:

• Lane difficulty (weather volatility, terrain, urban density)
• Appointment pressure (tight dwell buffers create speed and fatigue incentives)
• Equipment type and maintenance signals
• Driver tenure mix (new-hire concentration is a real risk amplifier)
• Historical performance on similar lanes, not just network-wide averages

In practice, this becomes a carrier-lane pairing score. The AI doesn’t just rank carriers; it ranks carrier suitability for this load, this route, and this time window.

2) Route risk optimization (not just shortest or fastest)

Answer first: AI route optimization should minimize crash probability, not only miles or ETA.

Traditional routing optimizes cost and time. Safety-aware routing adds variables like:

• Known high-incident corridors
• Construction and merge complexity
• Night-driving exposure
• Weather and wind risk for high-profile trailers

That matters in December. Holiday retail surges increase traffic density, schedule compression, and fatigue exposure—exactly the conditions where small risk differences become costly outcomes.

The goal isn’t to avoid every “risky road.” It’s to quantify tradeoffs.

• If Route A is 18 minutes faster but increases predicted incident risk by 22%, do you still take it?
• If a different appointment window reduces night driving by 40%, is the dock willing to flex?

AI is how you ask—and answer—those questions at scale.

3) Real-time driver risk monitoring that drivers don’t hate

Answer first: The best AI coaching focuses on a few measurable behaviors that strongly correlate with crashes, and it delivers feedback in a way drivers accept.

Driver-facing safety tools fail when they feel punitive or noisy. The systems that get adopted do three things well:

• Prioritize: they focus on the handful of behaviors that meaningfully change risk (following distance, harsh braking, speeding in specific contexts, distraction signals).
• Personalize: they compare a driver to their own baseline and to peers on the same route types.
• Time the feedback: they avoid mid-task nagging and instead deliver post-trip coaching with specific clips and clear thresholds.

AI also helps safety teams allocate time where it counts.

• Instead of coaching 300 drivers lightly, coach the 30 drivers whose risk scores are trending up.
• Instead of weekly generic training, push micro-modules tied to the driver’s actual behavior patterns.

A practical “AI + safety data” playbook for shippers and brokers

Answer first: If you tender freight, you can reduce crash exposure by treating safety as a procurement constraint and an operations variable—then automating enforcement.

If you want fewer incidents in 2026, you don’t need a 12-month transformation. You need a few strong operating rules, backed by data and automation.

Step 1: Stop using one safety metric as a gate

Use a multi-signal approach:

• Crash frequency (normalized by exposure)
• Inspection trends (not just percentile snapshots)
• Claims and incident reports (your own history matters)
• Lane-level performance

If your system can’t do this automatically, you’ll fall back to the simplest metric. That’s how risky capacity keeps getting booked.

Step 2: Add “safety buffers” to appointment scheduling

Most companies underestimate how often schedule pressure translates into unsafe behavior. Build operational slack intentionally:

• More realistic pickup windows in winter weather lanes
• Minimum buffer between multi-stop appointments
• Rules limiting night driving on specific corridors

AI can recommend where buffers buy the most risk reduction per dollar.

Step 3: Treat data quality as a safety control

FMCSA records rely on state reporting and carrier-submitted exposure data. So do your own hygiene:

• Require carrier profile refresh on a set cadence
• Ask carriers to attest that fleet/driver counts are current
• Create a process to dispute obvious errors promptly

Bad data doesn’t just distort dashboards—it distorts decisions.

Step 4: Automate “do not tender” conditions for high-risk scenarios

Even good carriers have bad fits. Common examples:

• High winds + light loads + high-profile trailers
• Tight appointment windows + congested metros + night arrival
• New driver cohorts assigned to the hardest lanes

AI can flag these combinations and force an ops review before tender.

What carriers should do with this (if you’re tired of paying for accidents)

Answer first: Carriers cut crash rates fastest by using AI to target coaching, maintenance, and lane assignment—not by rolling out blanket policies.

If you’re a carrier leader, you’ve probably felt the squeeze: insurance pressure, nuclear verdict risk, shipper scorecards, and rising expectations—often without rising margins.

Three actions tend to pay back quickly:

1. Targeted intervention: Use AI to identify the small slice of drivers, routes, and time windows driving a big share of exposure.
2. Preventability workflows: Build a clean internal process for incident review, video evidence capture, and rapid submission where applicable.
3. Risk-based dispatching: Newer drivers shouldn’t be “earning their stripes” on the most complex lanes during the busiest weeks of the year.

I’ve found that carriers who operationalize this don’t just get fewer crashes—they get fewer service failures. Safety and service share the same root causes: planning quality, fatigue, and exception handling.

The bigger point: crash data should change who gets the load

FMCSA crash data is surfacing a real gap: risk isn’t evenly distributed, and the market doesn’t consistently price or manage that risk. That’s why the conversation keeps circling back to transparency, preventability, and better public scoring.

But even if regulators improved what’s visible tomorrow, the real advantage would still go to the teams that can act on the data in hours—not review it in quarterly meetings.

If you’re a shipper, broker, or carrier trying to reduce incidents in 2026, the next step is straightforward: build an AI-driven risk layer that sits next to cost and service in every routing and tendering decision. Because the companies that treat safety data as operational intelligence will spend less time reacting to crashes—and more time preventing the next one.

What would change in your network if every load had to “pass” a safety risk check the same way it has to pass a rate check?