No. The paper behind the viral claim reports 98% accuracy for a machine-learning model—not that 98% of truck drivers drive dangerously. Those are different kinds of numbers: one describes how a classifier performed on evaluation data; the other would describe how common a behavior is among drivers.
What the 98% figure measures
Aldhahri et al.’s 2025 PLOS ONE paper, “GNN-RMNet: Leveraging graph neural networks and GPS analytics for driver behavior and route optimization in logistics”, reports 98% accuracy for its GNN-RMNet model on a real-world GPS and vehicle-sensor dataset. The study concerns driver-behavior and route-anomaly detection in logistics. GNN-RMNet combines graph neural networks with ResNet and MobileNet.
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Accuracy is the fraction of evaluated examples the model classified correctly. It describes performance against the labels in the evaluation data. It does not mean that the same fraction of all truck drivers—or all trips—has a particular behavior.
Why model accuracy is not a rate of dangerous drivers
A population rate needs a denominator and a defined outcome: for example, the share of drivers in a specified population who meet a specified, consistently applied definition of dangerous driving. A classifier’s accuracy instead compares its predictions with reference labels for the examples it was evaluated on.
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The paper’s reported accuracy does not establish the sampling frame needed to estimate the prevalence of dangerous driving among truck drivers. Nor does the score by itself establish which driving behaviors were counted, how often they occur across the wider population, or whether the evaluation data represent that population. The wording “98% of truck drivers drive dangerously” therefore cannot be inferred from this result.
Accuracy, precision, recall and F1 answer different questions
The PLOS ONE paper reports four classification metrics. They are related, but their denominators differ, so one cannot be substituted for another.
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| Metric | What it asks | Reported result |
|---|---|---|
| Accuracy | What fraction of all evaluated examples did the model classify correctly? | 98% |
| Precision | Among examples the model predicted as positive, what fraction were positive under the reference labels? | 98% |
| Recall | Among examples labeled positive, what fraction did the model identify? | 97% |
| F1 score | What is the combined score summarizing precision and recall? | 97.5% |
These are results reported by Aldhahri et al. for GNN-RMNet, not population rates. Precision and recall depend on what counts as a positive case and on the reference labels. F1 combines precision and recall into one measure; it does not estimate how common the behavior is in the real world.
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The paper also discusses its plotted learning curves, reporting 99% training accuracy and 98% testing accuracy at the final epoch. Those figures refer to separate training and testing results, not to the percentage of drivers classified as dangerous.
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What to check before trusting a headline about a model score
- Find the metric name. Accuracy, precision, recall and F1 describe different aspects of classification performance.
- Ask what was counted. The definition of a positive or anomalous case, and how the reference labels were assigned, determine what a result means.
- Check the evaluation data. Training, validation and held-out test results are not interchangeable. A test score describes performance on the test examples, not automatically on a different fleet or the full population of drivers.
- Look for class-specific errors and prevalence. Accuracy can appear high even when a model misses many cases in a rare class. Knowing the class distribution and the model’s errors helps put the score in context.
- Separate benchmark performance from operational use. The paper identifies testing under live logistics conditions—including road closures, traffic, weather and sensor variability—as future work. Its reported benchmark alone does not prove effectiveness in every fleet or road context.
A separate PLOS ONE methods paper on AI-based scores discusses evaluating reliability, validity and fairness, including performance on training, validation and test data and across relevant groups. These checks can help assess a model, but even strong results do not convert accuracy into a population rate.
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