01
Open Road Data
OpenStreetMap-derived ways, nodes, and tags provide public road geometry and context where available.
Method
How Road Risk Converts Geometry into a Comparative Output
This page explains how the model moves from public road data to geometry, physics checks, assumptions, graphs, reliability notes, and key terms.
Data Pipeline
Observed, Derived, Assumed, Interpreted
Short Method Summary: the model is built from retrieved public data, derived physical quantities, user-selected assumptions, fallback values where public data is incomplete, and comparative statistics.
02
Selected Road Geometry
The app analyses a selected segment rather than treating an entire road uniformly.
03
Curvature and Radius
Coordinate sequences are converted into heading change, radius, curvature, and bend context.
04
Physics Checks
Safe speed, slip ratio, centripetal demand, reaction distance, and braking distance are estimated.
05
Scenario Assumptions
Rain, fog, fatigue, overspeed, vehicle type, lighting, and surface quality are explicit assumptions.
06
Comparative Context
Outputs are interpreted using sampled distributions and percentiles under the same assumptions.
07
Reviewed Case Evidence
Case-evidence JSON is exported explicitly by the user. Local browser cases can appear immediately on that device, while public committed cases require review before they become part of the static Results dataset.
Input Categories
What Is Retrieved, Calculated, Assumed, and Interpreted
| Category | Examples | Role in Output | Boundary |
|---|---|---|---|
| Direct / Retrieved | OSM geometry, highway tag, surface, lighting, maxspeed where available. | Provides the public-data basis for the selected segment. | Missing public tags do not prove features are absent. |
| Derived | Heading change, radius, curvature, safe-speed estimate, stopping distance. | Turns road form and scenario values into physical checks. | Simplified calculations, not field measurements. |
| User-Selected | Weather, vehicle, fatigue, overspeed, visibility, surface assumptions. | Supports controlled scenario sensitivity tests. | Assumptions are not evidence of real conditions. |
| Fallback | Default friction, speed/context values, missing infrastructure context. | Keeps the model transparent when public data is incomplete. | Fallback reliance should reduce confidence. |
| Interpreted Output | Annualised Comparative Model Output, percentile context, confidence notes. | Reports the model result in a comparable form. | Not an observed crash rate or official rating. |
Output Assembly
How the Comparative Output Is Assembled
A
Geometry Demand
Curvature, radius, turn angle, and bend frequency.
B
Friction and Speed Checks
Safe speed, lateral demand, slip-style indicators, and stopping distance.
C
Scenario Multipliers
Weather, visibility, driver behaviour, vehicle profile, surface, and overspeed.
D
Context Factors
Road class, infrastructure context, traffic proxy, and data-confidence flags.
E
Statistics
Distribution context, percentile position, route peaks, and saved-case comparison.
Model Features
What the Live App Does
Road Data
Selection and Geometry Extraction
Click a road, load public road data, preserve relevant tags, and calculate segment geometry.
Physics
Safe Speed and Stopping Distance
Translate speed, radius, friction, and reaction assumptions into readable physical outputs.
Scenarios
Controlled Sensitivity Testing
Change conditions deliberately and inspect how the same road responds under different assumptions.
Statistics
Sampled-Network Comparison
Place one selected road within a sampled comparison set using percentiles and distribution views.
Spatial Tools
Routes and Isochrones
Analyse routes, route peaks, hotspots, and reachability contexts where supported by app data.
Evidence
Exports and Transparency
Use CSV, GeoJSON, JSON, case-evidence JSON, distribution data, and image-style exports for review.
Formula Chain
Core Geometry and Vehicle-Dynamics Relationships
Radius
Road Bend Geometry
\[ r = \frac{L}{\Delta\theta} \]
Segment length and heading change produce an estimated bend radius. Very small heading changes must be handled carefully.
Centripetal Demand
Curvature and Lateral Acceleration
\[ a_c = \frac{v^2}{r} \]
Higher speed or tighter radius increases the lateral acceleration needed to follow the curve.
Safe Speed
Friction-Limited Speed
\[ v_{\text{safe}} = \sqrt{\mu_{\text{eff}} g r} \]
Effective friction, gravity, and radius create a simplified physical speed check.
Stopping Distance
Reaction and Braking Demand
\[ d_{\text{stop}} = v t_r + \frac{v^2}{2 \mu g} \]
Speed affects stopping demand strongly because braking distance scales with velocity squared.
Formula boundary
These equations explain the public model chain. The live app includes additional implementation detail for fallback assumptions, route sampling, exports, and UI state.
Interpretation Boundary
How This Differs from Crash Prediction
What It Does
Comparative Model Output
Road Risk compares selected roads and scenarios using public geometry, physics-informed checks, assumptions, and sampled context.
What It Does Not Do
No Observed Crash Probability
The model does not estimate the probability of a real crash on a named road and does not replace collision-history analysis, audits, or professional review.
Scenario Assumptions
What Is Adjustable, Assumed, or Fallback
Affects stopping distance and handling interpretation; user selected; not a full vehicle simulation.
Affects safe speed, slip ratio, and braking distance; observed if tagged, otherwise fallback.
Affects friction, reaction context, and scenario multipliers; not live environmental measurement.
Fatigue, distraction, BAC, and overspeed are controlled assumptions, not claims about real drivers.
Road class, lanes, lighting, surface, and active-travel context depend on public data completeness.
| Input | Affects | Type | Limitation |
|---|---|---|---|
| Vehicle profile | Stopping distance and handling interpretation. | User-selected assumption | Not a full vehicle simulation. |
| Surface / friction | Safe speed, slip ratio, and braking distance. | Observed if tagged; fallback otherwise | Live friction is not measured. |
| Reaction time | Reaction distance and total stopping distance. | Assumed | No individual driver is observed. |
| Weather / visibility | Friction, visibility, reaction context, and scenario multiplier. | Scenario assumption | Not a live environmental measurement. |
| Road class / traffic proxy | Exposure and baseline context. | OSM-derived proxy | Road class is not a direct traffic count. |
Deep dive: Assumptions Register
Graphs and Comparative Statistics
Raw Output Needs Context
Distribution Shape
Shows whether a selected road sits in a common or unusual region of sampled outputs.
Percentile Position
Shows how much of the sampled set lies below the selected output.
Spread and Tails
Shows median, quartiles, spread, and whether upper-tail values are common or rare.
Scenario Shifts
Shows how rain, fog, speed, fatigue, or vehicle profile changes the sampled context.
Percentile boundary
Percentiles depend on the sampled area, road mix, active assumptions, and public-data completeness. They are not universal rankings.
Data Quality
Confidence Depends on Public Data Completeness
More coordinate nodes can describe bends more clearly; sparse geometry can reduce precision.
Missing surface, lighting, lane, speed, or sidewalk tags do not prove those features are absent.
Fallbacks keep the model inspectable but should lower interpretation confidence.
Validation and Reliability
Internal Checks, Not Operational Certification
- Sharper curvature should generally increase comparative output under the same scenario.
- Smaller radius should reduce the friction-limited safe-speed estimate.
- Higher speed should increase stopping distance non-linearly.
- Lower effective friction should reduce safe speed and increase braking distance.
- Further calibration against collision-history datasets and professional review is future work.
Deep dive: Validation and Reliability
Key Terms
Vocabulary Used Across the Site
- Prospective Model
- Examines possible risk signals before collision-history data is introduced.
- Relative Model Output
- A comparative indicator under defined assumptions, not an official safety rating.
- Percentile
- A selected road's position within a sampled comparison set under the same assumptions.
- Data Confidence
- A caution level based on geometry quality, missing tags, and fallback assumptions.
Deep dive: Full Glossary
Next Step
Apply the Method to a Road Segment
Use the live app to select a road, change one scenario assumption, inspect the output, then compare it with graphs and exports.