Driving/Data
Baseline rates (verbatim; used directly in models)
The following U.S. national rates are taken verbatim from authoritative sources and are **not** converted here. Conversions to per-miles exposure are performed only inside the RiskModel calculations below.
- Fatalities: **1.26 deaths per 100 million vehicle miles traveled (VMT)** in 2023 (national estimate).
* NHTSA 2023 Traffic Fatalities Estimates * IIHS: Fatality statistics
- Injuries: **75 injuries per 100 million VMT** in 2022 (police-reported injuries, national estimate).
* NHTSA: Traffic Safety Facts 2022
Distance Options
| Distance choice | Miles |
|---|---|
|
100 miles |
100 |
|
1,000 miles |
1000 |
|
100,000 miles |
100000 |
These rows provide user-friendly exposure choices. The RiskModels convert the per-100M-VMT base rates into expected counts for the chosen miles.
Time of Day (modifier)
| Time of day | Fatality risk multiplier | Injury risk multiplier |
|---|---|---|
|
Day |
1.0 |
1.0 |
|
Night |
3.56 |
1.47 |
Per-mile risk is higher at night: in 2022, ~53.9% of fatalities and ~32.9% of injury crashes occurred during roughly ~25% of VMT (nighttime). Using exposure-adjusted ratios yields ≈3.56× (fatalities) and ≈1.47× (injuries).
- Night/day crash distribution and injuries: NHTSA 2022
- Background on time-of-day involvement: NHTSA
- Exposure assumption (≈25% of VMT at night): FHWA Lighting Guidance
- Supplemental analysis: University of Michigan per-mile risk
Seat-belt Usage (modifier)
| Seat-belt usage | Fatality risk multiplier | Injury risk multiplier |
|---|---|---|
|
Worn (car) |
1.0 |
1.0 |
|
Not worn (car) |
1.82 |
2.0 |
|
Worn (SUV/van/truck) |
1.0 |
1.0 |
|
Not worn (SUV/van/truck) |
2.5 |
2.857 |
Seat belts reduce fatal injury risk by ~45% in cars and ~60% in light trucks; moderate-to-critical injury by ~50% in cars and ~65% in light trucks. The multipliers above are the inverse of those reductions (i.e., increased risk when not wearing a belt).
- Effectiveness summary: IIHS: Seat belts
- Technical background: NHTSA: Seat belt effectiveness
Alcohol-impaired driving frequency (share of miles while impaired)
| How often does the driver drive after drinking too much? | Approximate share of miles while impaired |
|---|---|
|
Never |
0 |
|
A few times per year |
0.001 |
|
About once a month |
0.005 |
|
About once a week |
0.02 |
|
Several times a week |
0.05 |
These shares approximate the fraction of total miles driven while impaired (not the fraction of people). They translate intuitive frequencies (“a few times per year”, “once a month”, etc.) into small exposure shares so the multiplier applies only to that portion of miles.
Calibration notes (assumptions; adjust as needed):
- “A few times per year” ≈ 0.1% of miles (e.g., ~10–20 impaired miles in ~10,000 annual miles).
- “About once a month” ≈ 0.5% of miles.
- “About once a week” ≈ 2% of miles.
- “Several times a week” ≈ 5% of miles.
Background/prevalence:
- CDC summary: self-reported alcohol-impaired driving episodes occur at a non-zero rate in the population.
Distraction (Modifier)
| Distraction type | Relative crash risk multiplier |
|---|---|
|
Model driving (no distraction) |
1.0 |
|
Any handheld cell use |
3.6 |
|
Texting (visual-manual) |
6.1 |
|
Dialing (visual-manual) |
12.2 |
Naturalistic driving data (SHRP2 study) showed that any handheld phone use increases crash risk by ~3.6×; texting by ~6.1×; dialing by ~12.2×.
- Summary of SHRP2 findings; texting risk ~23× for heavy vehicles, but dialing/texting relative to model driving is 12.2×/6.1× :contentReference[oaicite:1]{index=1}
Speeding (Modifier)
| Speeding status | Relative crash severity multiplier |
|---|---|
|
At or below limit |
1.0 |
|
Above limit |
2.0 |
Speeding increases crash severity and overall risk—doubling speed quadruples kinetic energy, and speeding was involved in ~29% of U.S. traffic fatalities. Using a simplified 2× severity multiplier as a proxy.
RiskModel: Driving/Data:fatality_model
Calculation: 1 - (2.718281828 ^ ( - ( (1.26 / 100000000) * distance_miles * time_fatality_multiplier * belt_fatality_multiplier * ( (1 - alcohol_frequency_share) + (alcohol_frequency_share * 4.0) ) * distraction_multiplier * speeding_multiplier ) ))
Content:
Your estimated chance of being in a fatal crash is {{One_In_X:{result}}}.
RiskModel: Driving/Data:injury_model
Calculation: 1 - (2.718281828 ^ ( - ( (75 / 100000000) * distance_miles * time_injury_multiplier * belt_injury_multiplier * ( (1 - alcohol_frequency_share) + (alcohol_frequency_share * 4.0) ) * distraction_multiplier * speeding_multiplier ) ))
Content:
Your estimated chance of being injured in a crash is {{One_In_X:{result}}}.
Calculation note: Each model computes an expected count λ from verbatim base rates (per 100 million vehicle miles traveled, VMT), scaled by distance and modifiers, then converts to a probability \(p = 1 - e^{-λ}\) using \(2.718281828^{-\lambda}\). Alcohol risk is applied via an exposure share: the model blends \((1 - \text{share})\times 1 + \text{share}\times 4.0\) so the higher crash risk applies only to the fraction of miles driven after drinking “too much.” If future research provides more precise multipliers by impairment level or distinct injury vs. fatality effects, the constant 4.0 can be revised.
Note on uncertainty: These are national averages and approximate modifiers. Actual risks vary by state, roadway type (urban/rural), weather, vehicle, driver demographics, and year-to-year changes. Modifiers are population-level and not individual predictions.
Mostly generated by GPT-5 Thinking