React

Measure and predict the impact of road safety interventions

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Designed for evidence-led safety decisions.

React turns data from road safety interventions into actionable insights. It helps local authorities, traffic management and road safety providers to prove impact and decide where to act next.

  • Find matched pairs using similar regions and road features.
  • Evaluate impact from similar interventions on matched roads.
  • Forecast improvement on target corridors.
  • Identify future candidates for optimal intervention planning.
Intervention Risk index Predicted without treatment Observed with treatment Expected improvement

Once an intervention is applied, we compare the divergence between these matched corridors to estimate impact.

Forecast intervention impact with matched corridors

React works for any road safety intervention, provided sufficient data to support it. It combines predictive baselines with matched-corridor outcomes to estimate post-intervention impact.

  1. Predict baseline: Use DriveFactor Predict to forecast future crash risk on the target corridor (no treatment).
  2. Match + observe: Find similar treated corridors and measure their safety improvement after intervention.
  3. Apply: Calculate the expected impact on the target corridor to estimate post-intervention impact.

Stage 1 — Target corridor (untreated)

Example: a 30→20mph proposal on Stokesley Road, Tees Valley.

Road classA - 2 Lane
Length (km)3.2
Daily Traffic Flow (Average)20,116
Average gradient1.3%
Population density2,228
Junction density (per km)8.86
Crossing and Signal density (per km)4

Stage 2 — Similar treated corridors

Example: Wales 30→20mph corridors with strong similarity scores.

Treated corridor Similarity index Crash frequency change (per-year)
Bridgend Road0.62−41.7%
Park Road0.58−11.1%
A400.53+6.7%
Hereford Road0.51+33.3%
Poolstock Lane0.500.0%

Higher similarity gives more confidence that the observed crash change is transferable to the untreated corridor.

Stage 3 — Expected change (per-year)

Derived from 12 matched treated corridors.

Control average change: +8.3%
Treated average change: −18.7%
Expected improvement: -27.0% Crash frequency
Without intervention With intervention Predicted crashes Baseline After

Bars compare predicted crash frequency with and without treatment.

What to learn more about how DriveFactor can help your organisation?

Contact hello@drivefactor.ai to schedule a demo and pilot plan.