Key Takeaways
- $380 billion estimate for the economic cost of motor vehicle crashes in the United States in 2022
- 33,651 fatalities in motor-vehicle crashes in 2019 in the United States
- 38,680 deaths in passenger vehicle crashes in 2019 in the United States
- In 2022, 3,200 fatal crashes involved alcohol impairment (NHTSA estimate framing)
- In 2022, 1,900 fatal crashes involved no seat belt use by the fatally injured occupant (when belt-use data available)
- Front seat belts reduce the risk of death for front-seat passenger vehicle occupants by about 45–50% (NHTSA)
- 42,795 motor-vehicle crash fatalities in the United States in 2022 (preliminary total; includes all road users)
- 4,300+ people killed in crashes involving wrong-way driving in the United States in 2017
- Driver and occupant behavior contributes to about 90% of traffic crashes in the United States (broad U.S. safety literature consensus)
- 1.19 million people died on the world’s roads in 2016 (global deaths estimate)
- Road traffic injuries were responsible for 2.3% of total global disability-adjusted life years (DALYs) in 2019 (GBD estimate share)
- The World Bank estimated global annual economic costs of road injuries at about $1.8 trillion (global cost estimate)
- In the United States, speeding is estimated to be a factor in about 26% of fatal crashes (FHWA/NHTSA-reported estimate)
- Electronic Stability Control (ESC) reduces fatal single-vehicle crashes by about 50% for passenger cars (meta-analysis estimate)
- Automatic emergency braking (AEB) reduces rear-end crashes by about 38% for passenger vehicles (system performance estimate)
Seat belt use, speed management, and safer vehicle technologies could prevent many road deaths globally and in the US.
Related reading
Safety Burden
Safety Burden Interpretation
Collision Dynamics
Collision Dynamics Interpretation
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Fatalities & Injuries
Fatalities & Injuries Interpretation
Risk Factors
Risk Factors Interpretation
Global Burden
Global Burden Interpretation
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Policy & Mitigation
Policy & Mitigation Interpretation
Infrastructure & Technology
Infrastructure & Technology Interpretation
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Road Fatalities
Road Fatalities Interpretation
Risk Behaviors
Risk Behaviors Interpretation
Economic Impact
Economic Impact Interpretation
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Injuries & Morbidity
Injuries & Morbidity Interpretation
Vehicle Technology & Policy
Vehicle Technology & Policy Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Alexander Schmidt. (2026, February 13). Car Crash Statistics. Gitnux. https://gitnux.org/car-crash-statistics
Alexander Schmidt. "Car Crash Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/car-crash-statistics.
Alexander Schmidt. 2026. "Car Crash Statistics." Gitnux. https://gitnux.org/car-crash-statistics.
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