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7/6/2026
Engineering a Real-Time Rail Network Visualization System

Engineering a Real-Time Rail Network Visualization System

Original: Real-time map of Great Britain's rail network!

Short summary

Engineering real-time rail visualization requires normalizing heterogeneous data feeds (FUSION/TRUST), interpolating discrete timing events into continuous movement, and rendering thousands of dynamic objects via GPU acceleration (Deck.gl, Mapbox GL). The architecture uses Kafka for distributed ingestion, WebSockets for low-latency distribution, and Kalman filters to smooth data anomalies like train ghosting. Success hinges on handling legacy protocols, topological accuracy in rail graphs, and spatial partitioning to reduce client load.

  • •Data normalization: FUSION/TRUST feeds require conversion to unified schemas via Protocol Buffers
  • •Real-time rendering: WebGL libraries (Deck.gl, Mapbox) handle thousands of moving objects at 60fps
  • •Anomaly handling: Kalman filters smooth train disappearance/reappearance (ghosting) events from signalling faults

Generated with AI, which can make mistakes.

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