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 Impairment (Modifier)

Alcohol status	Crash risk multiplier
BAC = 0.00	1.0
BAC ≥ 0.08	4.0
BAC ≥ 0.15	12.0

Drivers with a Blood Alcohol Content (BAC) of 0.08 are about 4× more likely to be in a crash than sober drivers, and at 0.15, at least 12× more likely. For simplicity, this multiplier is applied to both fatalities and injuries. If future research provides separate injury vs. fatality multipliers, this table can be expanded.

• NHTSA: Drunk driving statistics

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.

• NHTSA: Speeding-related fatalities ~29%

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  RiskModel: Driving/Data:fatality_model
Calculation: 1 - (2.718281828 ^ ( - ( (1.26 / 100000000) * distance_miles * time_fatality_multiplier * belt_fatality_multiplier * alcohol_multiplier * 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 * alcohol_multiplier * distraction_multiplier * speeding_multiplier ) ))
    Content: 
Your estimated chance of being injured in a crash is {{One_in_X:{result}}}.

Calculation note: Each model first computes the expected number of events λ from the verbatim base rates (per 100 million vehicle miles traveled), scaled by distance and modifiers. It then converts λ into a probability of at least one event using the Poisson formula \(p = 1 - e^{-λ}\). Because RiskModel only supports numeric constants and math operators, the constant *e* is approximated as 2.718281828 and exponentiation is written with the `^` operator.

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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