Count the journeys as well as the collisions

Bodily injury: count people as well as collisions

In 2025, 127,883 casualties were recorded. 34.8% were in collisions injuring more than one person. The Lab separates casualty role, age, road conditions and adjusted KSI, with every person counted once per breakdown.

Multi-claimant exposure can matter for bodily-injury accumulation, but police injury counts do not measure compensation or developed insurer losses.

Comparable EV / ICE outcomes

The Lab standardises conditional collision severity across shared vehicle-age, driver-age, road, urban/rural and light strata, separating cars from taxis. It reports how much of each fuel population remains in common support. The frozen 2025 file has no vehicle-age values, so that year’s adjusted comparison is unavailable.

These controls improve descriptive comparability. They do not establish accident frequency per mile, fault or a causal powertrain effect. Sparse taxi support and unmeasured vehicle/driver differences remain important.

Residence is not road exposure

Recorded home-area agreement and published home-to-collision distance bands now sit alongside explicit missingness in the Lab. Collision geography describes where events occurred; pricing geography usually describes where a policyholder lives. Residential mileage or policy exposures are needed before estimating a postcode relativity.

Frequency per 100,000 miles

In 2025, the national matched-road comparison records 0.0296 injury collisions per 100,000 motor vehicle miles. Road categories use the corresponding official annual mileage, and vehicle comparisons count involvements against same-class mileage.

Region and joint annual road/vehicle comparisons are available. Month, weekday and hourly rates use explicitly modelled allocations from DfT profiles; they do not establish a causal time-of-day effect.

Vehicle mileage: what can the fuel comparison support?

The 2025 broad car-and-taxi rate is 0.0483 involvements per 100,000 matching miles. It includes all fuels and ages.

Fuel-specific mileage and age-adjusted rates remain withheld: the MOT sample includes non-car Class 4 vehicles, grouped use does not identify calendar mileage, and the fleet expansion is incomplete. The Lab reports the observed MOT age profiles, missing intervals, calibration gaps and required sensitivities. Stock-based indicators remain separate.

Local rainfall: measured traffic and a small event sample

The 2024 M4 winter pilot contains 200 complete road-days, 8.26 million estimated vehicle miles and 2 included injury collisions. Wet/drier rates retain zero-collision days and use the same daily rainfall and traffic windows. Such sparse counts cannot establish a stable weather effect.

The Lab maps station and counter coverage, separates observed from imputed traffic and compares alternative gauges and prior traffic on common days. Temperature and ice effects remain unavailable. This sampled network does not justify a causal or insurer pricing multiplier.

Driver age and journey purpose

The 2025 commuting indicator is 0.02994 known-England, known-purpose car/van involvements per 100,000 estimated household driver miles. Missing purpose and residence make this a coverage-limited recorded-subset indicator, not a complete population rate. The Lab combines survey and count uncertainty and shows separate missing-record assignment scenarios.

Age exposure profiles include survey intervals. Age-specific rates, commercial business comparisons and joint age-purpose rates remain withheld until population and driving scopes can be reconciled.

Does geographic risk survive road-mix adjustment?

The common regional panel contains 8 places. With discovery groups fixed in 2022–2023, the higher group remains above the lower in 2 of 2 later years. The comparison uses observed versus expected collisions for the same motorway, A-road and minor-road mileage.

Only 2025 authority road-mix estimates pass the exposure checks. Earlier authority comparisons and M25 section frequencies remain withheld. Broad regional persistence and two later years do not establish a causal or insurer pricing effect.

Frequency: separate volume from rate

The comparable panel moves from 0.04 to 0.03 injury collisions per 100,000 motor vehicle miles between 2016 and 2025. Changes in how much, when and where vehicles travel can affect the result.

For an insurer, this supports testing road exposure alongside annual mileage. It does not establish a change in insured claim frequency: damage-only events and many unreported injuries are outside this dataset.

Persistence: useful structure, with competing explanations

The highest-risk discovery group remains above the lowest in 4 of 4 eligible later years. Earlier-versus-later area rank correlation is 0.954. These comparisons retain the same areas and freeze the original groups.

Stable road mix, exposure patterns and reporting differences can also generate persistence. A pricing study should test decomposed geographic features alongside the existing postcode factor, using features available at quote and later claims held out from model construction.

Severity: injury outcomes and financial loss differ

Adjusted serious-injury measures help compare road outcomes through changes in police reporting. They do not estimate compensation, repair costs, credit hire or large-loss development. The Lab separates casualties per mile from the proportion of recorded injury collisions that are fatal or serious.

For UK motor insurance, these are candidate indicators for bodily-injury exposure. Financial severity requires an insurer’s developed claims experience.

Vehicle and weather comparisons need exposure context

The detailed 2021–2025 records support road, time and vehicle comparisons. Licensed-car stock joins provide a fuel-cohort involvement indicator. Raw stock comparisons do not control for vehicle age, mileage, driver selection or road use. The separate matched analysis reports its own controls and remaining limitations.

The separate annual context view joins country weather to a fixed geographic road panel. It describes association across years; it does not estimate the effect of rain or ice on an individual journey.

A road-exposure or telematics analysis should use measured driving exposure in each road and time environment before translating these patterns into pricing factors.