In a two-vehicle injury crash, how much does the class of the other vehicle change what happens to the car driver? Every figure on this page has a written estimand, a stated sampling frame, and a query behind it.
Victorian two-vehicle injury crashes, 2012 – Oct 2025, where both vehicles were being driven. The bar is the share of car drivers killed or hospitalised. Whiskers are 95% Wilson intervals.
associational A car driver is killed or hospitalised in 12.7% of collisions with another car, 16.1% against an SUV or station wagon, 21.5% against a ute or light commercial, and 35.4% against a truck. Under this pipeline the four Wilson intervals do not overlap. Disjoint sampling intervals do not imply the remaining gaps would survive adjustment for speed zone, road class or reporting threshold.
descriptive In 2,873 coded car–truck injury crashes with both vehicles driven, 100 car drivers and 3 truck drivers were recorded killed. That is a raw driver-fatality count under this filter. It is not a causal responsibility split and not a model-level vulnerability rating.
Conditional on an injury crash, so this is not population risk. Not adjusted for speed zone or road class, and speed-zone exposure rises with opponent class — see the measured breakdown below. Vehicle class is a body-type proxy, not measured collision mass. This is not a per-model safety rating. Rating how well a model protects its own occupants, or how much harm it does to the other party, needs a risk denominator this dataset does not contain. MUARC Used Car Safety Ratings remain the reference.
A single crash configuration can be explained by geometry. Four cannot. These are crash-code bundles, not instrumented collision types.
| Crash-code bundle | Another car | SUV / station wagon | Ute / light commercial | Truck |
|---|---|---|---|---|
| Head-on (DCA 120) | 31.8% 29.9–33.8 · n=2,230 | 38.7% 35.9–41.7 · n=1,092 | 44.8% 41.4–48.3 · n=781 | 75.0% ⚠ 68.9–80.2 · n=224 |
| Side/intersection | 14.4% 13.9–14.8 · n=23,262 | 17.7% 16.9–18.5 · n=9,271 | 25.6% 24.3–26.9 · n=4,452 | 45.1% 41.1–49.1 · n=597 |
| Car ran into their rear | 6.8% 6.2–7.4 · n=7,107 | 10.2% 9.2–11.3 · n=3,196 | 16.6% 14.5–18.8 · n=1,159 | 53.4% ⚠ 45.7–60.9 · n=163 |
| Car struck from behind | 7.7% 7.1–8.4 · n=6,844 | 9.5% 8.4–10.7 · n=2,537 | 10.3% 9.1–11.7 · n=2,147 | 17.8% 14.4–21.7 · n=422 |
Cells show the percentage, the 95%% Wilson interval and n. ⚠ marks cells with fewer than 300 car drivers: the truck bundles are small (head-on n=224, car-into-rear n=163) and those percentages are unstable relative to the car cells.
descriptive The ordering is monotone in every bundle we defined, with no sign flip. The gradient is largest in the head-on group and smallest when the car is struck from behind. That pattern is consistent with a mass or geometry contribution; it does not identify one. Vehicle class is a body-type proxy. Mass, structural stiffness and impact speed are not measured in this table, and neither is speed zone or road class.
The adjacent set pools nine DCA codes with different geometry, so it is the most likely place for a pooled result to fray. Its three largest components, shown separately:
| Largest adjacent components | Another car | SUV / station wagon | Ute / light commercial | Truck |
|---|---|---|---|---|
| Cross traffic (110) | 13.6% | 17.4% | 26.4% | 48.3% |
| Right near (113) | 16.7% | 19.3% | 25.9% | 50.3% |
| Right through (121) | 14.5% | 18.4% | 26.1% | 38.2% |
All three are monotone on their own, so the bundle is not concealing a reversal here.
| Area | Another car | SUV / station wagon | Ute / light commercial | Truck |
|---|---|---|---|---|
| Melbourne | 11.8% | 14.3% | 18.6% | 30.2% |
| Rest of state | 15.2% | 20.2% | 25.9% | 44.6% |
Monotone in both, and the geography split carries the same caveat as the bundles: it is not adjusted for speed zone or road class, and the non-metropolitan rows carry more high-speed-zone exposure.
On the rear-end split. We split on the car's coded initial impact point rather than the DCA vehicle-slot code. Impact point is still an officer-coded field, not a measured crash pulse; we use it because it tracks which vehicle struck which more directly than the slot does when the two disagree. In rear-end crashes the two agree closely (slot 1: 31,883 front against 158 rear; slot 2: 30,801 rear against 354 front) but not perfectly, and the disagreements are exactly where a slot-only analysis would mislabel the role. This is a construct tightening for role. It is not a measurement of mass, and it does not make anything else on this page identified.
Share of light-vehicle drivers killed or hospitalised, by the age of the vehicle, split by driver age band — because pooling the two produces a bigger number than either band supports.
| Vehicle age | driver 16-25 | driver 26-59 | driver 60+ |
|---|---|---|---|
| 0-4 years | 11.96% | 10.45% | 19.66% |
| 5-9 years | 12.64% | 11.55% | 21.75% |
| 10-14 years | 14.97% | 13.92% | 24.13% |
| 15-19 years | 16.88% | 16.83% | 26.48% |
| 20+ years | 19.12% | 20.29% | 28.99% |
associational Crashes of older vehicles are more often severe, and the gradient holds within every driver age band — so it is not simply an artefact of who drives old cars.
descriptive Correction to our July 2026 report. That page said a 20-year-old vehicle's crash was “more than twice as likely to be fatal” as a near-new one's. Within driver age bands the ratio is 1.6×, 1.94× and 1.47×. The pooled figure was inflated by driver mix and is withdrawn.
Drivers with no recorded age band are excluded: their rates (2.5–8.3%) index record incompleteness, not survival. The level shift for drivers aged 60 and over reflects occupant frailty and is not a statement about the vehicle.
Share of light-vehicle involvements in fatal crashes, by the crash dataset's own vehicle-type field. A composition share, not a rate.
descriptive Between 2012 and 2024 the car share fell from 57.1% to 44.2% while station wagons rose from 21.6% to 29.8%, utilities from 15.2% to 17.9% and vans/LCVs from 6.1% to 8.2%.
Why there is no SUV line here. This dataset has no SUV category. Our July 2026 page identified SUVs with a fixed 34-nameplate list. Tested against the dataset's own vehicle-type field, only 34,709 of the 74,820 vehicles the dataset codes as station wagons were on that list — fewer than half, while 2,546 listed models were coded as cars, 664 as utilities, 219 as light commercials and 81 as panel vans. The two constructs disagree in both directions and the list has no external validation, so it is retired and no SUV-specific trend is published. “Station wagon” here contains both wagons and SUVs.
Deaths per year by road user type. A count, not a rate — no exposure denominator is available and none is implied.
descriptive Motorcyclist deaths rose from 38 in 2012 to 61 in 2024. Driver deaths fell from 146 to 130. Pedestrian deaths moved from 35 to 47.
This page previously carried brand and model comparisons. They are gone. Each had a specific defect that a caveat could not repair.
If you used the withdrawn figures to compare brands or models, please stop. They are not reliable for that use.
Cumulative counts are fleet share, exposure and era mixed together. The chart's own caption bridged readers into the severity table below it, which compounded the problem rather than qualifying it.
The share of a brand's involvements that were fatal is a composite quantity. It aggregates single-vehicle, multi-vehicle, multi-occupant and pedestrian events, which are not exchangeable crash roles across brands. The 95% intervals we published quantified sampling error around a number whose dominant error was structural, which made it look more precise than it was.
Not reproducible. No full Victorian registration denominator is joined into our pipeline, so the two-decimal figures could not be regenerated from the cited source. It was published as an “indicative band”, but a band you cannot reproduce is not a band.
Held pending a written death-attribution specification and sensitivity checks under alternate attribution rules, because attribution choices can move a model across the diagonal. No claim from that figure is in force on this page.
Superseded by the section at the top of this page. A pooled per-model fatality rate mixes single-vehicle, multi-vehicle and pedestrian events, so it could not identify the mass claim it was making
The assistant answers only from this report's tables: it drafts read-only SQL and a deterministic verifier checks every number against the query results before you see it. If it can't verify, it refuses. Use the button at the bottom-right, or start from a question:
Scope note, 2026-08-11. The assistant's tables were rebuilt against this version of the report, so it queries the figures shown above and nothing else. The brand, model and fleet-adjusted tables were removed along with their sections and it will decline questions about them rather than answer from withdrawn numbers. Its previously curated answer set was cleared for the same reason — every stored answer quoted a figure that is no longer published.
Report vic-vehicle-fatality-2026-08. Supersedes vic-vehicle-fatality-2026-07.
Victoria Road Crash Data (Department of Transport and Planning, CC-BY-4.0), ACCIDENT / VEHICLE / PERSON / NODE tables, crashes from 1 January 2012 to 31 October 2025, portal snapshot of 8 June 2026. 198,839 crashes, 362,687 vehicle records, 3,564 deaths. The dataset records police-reported casualty crashes; property-damage-only crashes are absent.
Victoria records injury crashes only. There is no uninjured denominator, so nothing on this page is a risk of crashing — every figure is severity conditional on an injury crash having occurred. MUARC's Used Car Safety Ratings exclude Victoria from risk calculation for exactly this reason. We do not publish per-model protection or harm-to-others ratings, because both require the component this dataset does not have.
Crash data does not attribute legal fault, and neither do we. There is no fault field. The vehicle-slot code in the DCA classification identifies which vehicle's movement defines the crash type; it is not fault, and no published figure on this page uses it. Where we needed to know which vehicle struck which, we used the recorded physical point of impact instead.
Make and model are free-text fields truncated to six characters. Our July page disclosed roughly 9% unknown make; for light vehicles the correct figure is 1.38% (3,989 of 289,896), and that figure needed re-deriving. Those records are not missing at random: they carry 54.8% missing year of manufacture against 1.5% for the rest, a mean tare weight of 835 kg against 1,515 kg, and a driver killed-or-hospitalised rate of 8.22% against 15.16%. Unknown make indexes incomplete recording in less severe crashes. That biases any brand statistic, and is one reason the brand sections are withdrawn rather than repaired.
Make strings are also fragmented, and our harmonisation was incomplete: Hyundai appears as HYNDAI (14,977) and HYUNDA (161); Volkswagen as VOLKS (9,477), VOLKSW (96) and VW (2); Mercedes-Benz as MERC B (7,102) and MERCED (49); BMW as “B M W” (5,552) and “B.M.W.” (32); Land Rover as L ROV (1,354), LAND R (14) and LANDRO (6). Between 0.6% and 1.6% of each marque sat outside its largest string. Small, real, and not previously disclosed.
Every section carries a written estimand — the quantity in symbols, the sampling frame, and the unit of analysis — before any chart. Every primary contrast is tested under at least one split that could reverse it; a sign flip holds publication. Every sentence is tagged descriptive, associational or external. Every number traces to a logged query.
external The VIAS Institute (Belgium) has published on mass difference and injury outcomes for the lighter party, and the European Transport Safety Council campaigns on large-vehicle risk in urban areas. Those are separate studies on other data, with controls this analysis does not have. They are context for the direction found here, not evidence for it. No external body has reviewed or endorsed this page.
Involvement rates do not indicate fault, vehicle defect, or manufacturer liability. Nothing here is engineering, legal or consumer advice. For controlled per-model safety ratings use MUARC UCSR and ANCAP.