Identifying London’s near-miss clusters to predict crashes (Phase 1)

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Transport for London (TfL) wanted to understand whether near-miss events could act as early indicators of crashes, intervening in high-risk areas before fatalities occurred.

This approach is informed by Kent County Council’s Crash Remedial Programme (CRM) and near-miss analysis.

Specifically, TfL wanted to:

Identify clusters where near-misses coincide with crash occurrences.
Evaluate the extent to which Compass near-miss data overlaps with TfL crash records.
Test whether historical near-miss patterns can be predictive of future crash hotspots.
Create a data-driven dashboard to support proactive safety interventions.

Compass' Data Science team developed a custom dashboard to pinpoint clusters across the entire network (macro-analysis). Then, Compass' Road Intelligence platform was used to analyse risky road segments (micro-analysis).

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Use cases

How our customers are using Connected Vehicle data

Applications of vehicle-generated data for use cases across state-wide freight modelling, origin-destination studies, VMS signage effectiveness, road safety, and local area traffic management.

Identifying London’s near-miss clusters to predict crashes (Phase 1)

How our customers are using Connected Vehicle data

Optimising London’s bus services and network

Identifying London’s near-miss clusters to predict crashes (Phase 1)

Investigating freight rest stops across an entire network

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