ML Crash Model
Predicting Which Trucks Are Most Likely to Crash Next Year
Published: February 2026
The Big Idea
Can we look at a truck's safety record from last year and predict whether that same truck will be involved in a crash this year?
We trained a machine learning model on 1.67 million individual trucks using publicly available federal safety data. The model learned which patterns in a truck's inspection history, violation record, and carrier profile are early warning signs for future crashes.
Why this matters:
- ● Insurance underwriters can identify higher-risk trucks before writing a policy
- ● Fleet managers can spot problem areas and take corrective action
- ● Safety regulators can focus limited resources where they matter most
How It Works
Gather the Data
We collected every inspection, violation, and crash report from 2024 for 1.67 million individual trucks across the U.S., all from FMCSA public records.
Teach the Model
The model studied which trucks that had certain patterns in 2024 actually went on to crash in 2025. It learned from 270 different data points per truck.
Test on Unseen Data
We held back 15% of the trucks to test the model on data it had never seen. This ensures the results are genuine, not just memorization.
Key Results
How do we measure accuracy?
We use a metric called AUC (Area Under the Curve). Think of it as the model's ability to correctly rank trucks from safest to riskiest. A score of 0.50 means the model is no better than flipping a coin. A score of 1.00 would mean perfect prediction. In the real world, scores above 0.60 for individual-vehicle crash prediction are considered strong, because crashes are rare and inherently unpredictable events.
Uses every available data point. Maximum predictive power, but complex to manage.
Uses only the 20 most important factors. Nearly as accurate, but far easier to understand, audit, and explain.
Model Accuracy Comparison
What the Model Looks At
The simplified model uses 20 factors drawn from public FMCSA data. Here are the most important ones, ranked by how much they influence the prediction. Blue bars increase crash risk; red bars decrease it.
| Factor | What It Means | Effect |
|---|---|---|
| Hours-of-Service Driving Measure | How severely a carrier's drivers violate rest and driving-time rules | Increases risk |
| Authorized-for-Hire Status | Whether the carrier operates as a for-hire business (vs. private) | Increases risk |
| Hazmat Violations | Number of hazardous materials violations found during inspections | Decreases risk |
| Driver Out-of-Service Rate | How often a carrier's drivers are pulled off the road during inspections | Increases risk |
| Controlled Substance Measure | Carrier's score on drug and alcohol testing violations | Decreases risk |
| Driver Fitness Violations | Inspections where driver qualifications (license, medical cert) were deficient | Decreases risk |
| Vehicle Maintenance Violations | Number of vehicle defects (brakes, tires, lights) found on this truck | Decreases risk |
| Violation Severity Score | A time-weighted measure of how serious and recent the violations are | Increases risk |
| Unsafe Driving Violations | Inspections where unsafe driving behavior (speeding, lane changes) was cited | Increases risk |
| Total Miles Driven | Self-reported total mileage of the carrier's fleet | Decreases risk |
Factor Importance (Top 12)
Full Model vs. Simplified Model
We built two versions of the model. The full version uses 270 different data points and squeezes out every bit of predictive power. The simplified version uses just 20 carefully selected factors. Here's how we chose them:
The Selection Process
- We ran the model 8 separate times on different random samples of the data
- We tracked which factors were consistently chosen as important across all 8 runs
- Factors that were important in every run made the shortlist
- We tested models with 20, 25, and 30 factors to find the sweet spot
Why Simplify?
- ✓ Transparency: Regulators and clients can understand exactly how scores are calculated
- ✓ Auditability: Easier to verify the model isn't using biased or inappropriate inputs
- ✓ Reliability: Fewer factors means less chance of the model breaking when data changes
- ✓ Minimal cost: Only gives up ~2.5 points of accuracy for all these benefits
Accuracy by Number of Factors
Adding more factors beyond 20 actually made the model slightly less accurate on validation data, confirming that 20 is the optimal choice.
What This Means in Practice
This model doesn't predict crashes with certainty — no model can, because crashes involve unpredictable human behaviour and road conditions. What it does do is reliably rank trucks from lowest to highest risk.
For Underwriters
When pricing a fleet policy, the model can flag which specific trucks in the fleet carry the most risk. This allows more accurate pricing at the individual vehicle level rather than averaging across an entire fleet.
For Fleet Operators
Understanding which factors drive crash risk gives fleet managers a clear checklist: ensure drivers comply with hours-of-service rules, address maintenance issues promptly, and monitor unsafe driving citations.
The Strongest Signal
The single biggest predictor of future crashes is hours-of-service driving violations — how severely a carrier's drivers violate rest and driving-time rules. This factor alone has 3x more influence on the prediction than any other, reinforcing what safety experts have long known: tired drivers are dangerous drivers.
Data Source and Integrity
Where the data comes from
All data is sourced from the FMCSA Safety Measurement System (SMS), the U.S. federal government's primary system for monitoring commercial vehicle safety. This is the same data that FMCSA uses for its own safety ratings.
We used inspection reports, violation records, crash reports, and carrier profile information — all publicly available.
How we prevent cheating
- ● No future data: The model only sees 2024 data when predicting 2025 crashes
- ● Held-out test set: 15% of trucks were never seen during training
- ● Stratified sampling: Both crash and non-crash trucks are proportionally represented
By the Numbers
For the full technical details, see the Technical Crash Model page.