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 developed a dashboard, watch the video to learn more.

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.

Optimising London’s bus services and network

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

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Investigating freight rest stops across an entire network

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