Product Documentation · Road Safety Analytics Platform
User Groups
Question Framework
This document defines the structured question framework used across the road safety analytics platform. Questions are organised by user group and spatial level, following a progressive analytical flow: Overview → Type → Factors → Anomalies.
01 · Introduction
Objective
The question framework structures how different user groups interrogate road safety data. Each question is mapped to a user group, a spatial level, and a stage — ensuring that insights flow progressively from macro city-wide awareness down to granular intersection-level root causes.
Core Flow: Every question set follows the same analytical progression — Overview → Type → Factors → Anomalies — applied at each spatial level and tailored to the needs of each user group.
02 · Architecture
Spatial Drill-Down Model
The system connects three levels of analysis, each building on the previous. Users move from identifying where problems exist to understanding exactly why they occur.
City
Identifies where problems exist
Macro-level awareness of crash density, hotspots, and systemic trends across the city. Entry point for all investigations.
Corridor
Explains how problems behave
Segment-level patterns, road-specific behaviours, and corridor ranking by risk contribution and severity.
Intersection
Reveals why they occur
Granular diagnosis of individual locations — collision patterns, causal factors, and outlier behaviour relative to nearby points.
03 · User Groups
Users
Three distinct user groups interact with the platform, each with a different analytical purpose and decision-making remit.
Operations
Police · Corridor Director
Monitoring and immediate action. Focused on fast identification of problem areas, behaviour-driven risk awareness, and prioritisation of response.
Analysis
Safety Analyst · Engineer
Diagnosis and solution design. Deep root-cause identification, reliable correlation across variables, and confidence in analytical conclusions.
Strategy
City Director · Elected Official
Prioritisation and investment decisions. Clear justification for funding, alignment between safety data and city planning.
04 · Framework
Question Stage Legend
Each spatial level is interrogated through four progressive stages. The same four stages apply to every user group and every level, ensuring consistency across the entire framework.
Stage 01
Overview
What is happening?
Stage 02
Type
What kind of problem is this?
Stage 03
Factors
Why is this happening?
Stage 04
Anomalies
Where should attention go?
05 · User Group
Operations — Monitoring → Immediate Action
Value Enabled
Fast identification of problem areas · Behaviour-driven risk awareness · Immediate prioritisation of action
C
City Level
Macro awareness · Hotspot identification
Overview
- Which areas have the highest crash density?
- Which zones are emerging vs. persistent hotspots?
- How has risk shifted over time?
- Which areas require continuous monitoring?
Type
- What collision types dominate these hotspots?
- Which types are increasing fastest?
- Are pedestrian crashes concentrated in specific areas?
- Do specific types repeat across locations?
Factors
- What behaviours are driving crashes (speed, alcohol, violations)?
- Are these behaviours time-dependent?
- Do the same factors repeat across hotspots?
- Are factors geographically clustered?
Anomalies
- Which areas show sudden spikes?
- Which locations transitioned from low to high risk?
- Where do patterns behave abnormally?
- Which hotspots persist despite attention?
R
Corridor Level
Segment risk · Behavioural patterns
Overview
- Which corridors contribute most to crashes?
- Which corridors are worsening over time?
- Is risk concentrated or evenly distributed?
Type
- Which crash types dominate each segment?
- Do patterns shift along the corridor?
Factors
- Do behaviours vary across segments?
- Are crashes linked to infrastructure gaps?
Anomalies
- Which segments deviate from corridor behaviour?
- Where are localised spikes occurring?
I
Intersection Level
Point-level monitoring · Immediate response
Overview
- How frequently do crashes occur here?
- Is this location improving or worsening?
Type
- What exact collision patterns exist at this point?
Factors
- What behaviours consistently cause crashes here?
Anomalies
- Is this location an outlier vs. nearby intersections?
- Is this part of a larger cluster?
06 · User Group
Analysis — Diagnosis → Root-Cause Explanation
Value Enabled
Deep root-cause identification · Reliable correlation across variables · Confidence in analytical conclusions
C
City Level
Long-term trends · Statistical distribution
Overview
- What long-term trends exist across the city?
- Which areas consistently show high risk?
- How does the overall safety picture compare year-on-year?
Type
- What collision types dominate spatially?
- How are crash patterns distributed across districts?
- Which types are statistically over-represented?
Factors
- What factors correlate strongly with KSI outcomes?
- How do multiple variables interact to elevate risk?
- Which environmental or temporal factors amplify severity?
Anomalies
- Which areas deviate statistically from expected patterns?
- Are anomalies consistent over time or episodic?
- Do outliers share common underlying causes?
R
Corridor Level
Comparative analysis · Infrastructure correlation
Overview
- How does this corridor compare to similar corridors?
- Which segments contribute most to overall risk?
Type
- Are there hidden temporal patterns in crash types?
- Which segment-type combinations are most diagnostic?
Factors
- What infrastructure or operational factors drive risk?
- How do behaviours interact with physical road design?
Anomalies
- Which segments behave unexpectedly relative to corridor norms?
- Are anomalies explainable by a single causal factor?
I
Intersection Level
Statistical significance · Causal stability
Overview
- What is the statistical significance of this location's crash rate?
- How does it compare to similar intersections citywide?
Type
- What combination of collision types defines this intersection?
- Does this match known high-risk patterns?
Factors
- What combination of factors causes crashes at this point?
- Are causal factors stable or shifting over time?
Anomalies
- Is this intersection's behaviour an artefact or a genuine signal?
- Does it cluster with nearby high-risk points?
07 · User Group
Strategy — Prioritisation → Investment Decisions
Value Enabled
Clear prioritisation of high-impact areas · Strong justification for investment · Alignment between data and planning
C
City Level
KPI tracking · Systemic risk · Investment allocation
Overview
- Is the city improving on key safety indicators over time?
- Which areas contribute most to overall risk burden?
- Where are the largest gaps between current and target performance?
Type
- Which crash types drive the most fatalities and serious injuries?
- Where should type-specific interventions be focused?
Factors
- What systemic issues are driving risk at scale?
- Which factor clusters are most amenable to intervention?
- Where will behaviour-change programmes have the greatest impact?
Anomalies
- Which areas require urgent intervention vs. planned investment?
- Where will investment yield the highest safety return?
- Which persistent hotspots have resisted previous efforts?
R
Corridor Level
Return on investment · Capital justification
Overview
- Which corridors offer the highest return on safety investment?
- Where can improvements be implemented quickly and cost-effectively?
Type
- Which crash types on this corridor are most preventable?
- What intervention types are best matched to the dominant pattern?
Factors
- What systemic corridor-level issues justify capital investment?
- How do infrastructure gaps compare in cost vs. impact?
Anomalies
- Which corridor segments are underperforming relative to investment already made?
- Where are the highest-leverage quick wins?
I
Intersection Level
Point prioritisation · Engineering vs. enforcement
Overview
- Why is this location a priority relative to others?
- What is the expected impact of intervention here?
Type
- What intervention type is best matched to the collision pattern?
Factors
- What causal factors are addressable through engineering or enforcement?
- How does cost of intervention compare to expected benefit?
Anomalies
- How does this compare to similar investments made elsewhere?
- Is the risk profile stable enough to justify long-term investment?