Gridlock Disruption Intelligence

Live Disruption Map

Stratified sample of critical events across Bengaluru.

Corridor Risk Index

Ranked by severity & high priority concentration.

Corridor	Risk Score	High Priority	Total Events
No corridor data available

Response Recommendation Engine

Simulate an event to calculate required resources based on historical corridor precedence and current traffic patterns.

Simulate Event

Run a simulation to generate resource deployment recommendations and severity forecasts.

Post-Event Learning Replay

Chronological replay of historical events to measure the system's predictive calibration over time.

1/1/1970Jan 1, 19701/1/1970

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Showing live state (0 active events). Click any marker to view its corridor's calibration.Dataset restricted to 0 incidents with confirmed resolution outcomes
(~39% of all logged incidents — the remainder lack duration data due to a chronic officer logging gap, not a recency issue).

Calibration Trend

Click a corridor marker to view its calibration trend.

Post-Event Learning Loop

This replay engine demonstrates system calibration. By replaying historical events chronologically, we measure the gap between the Report-Time Predicted Severity (what we knew at dispatch) and the Resolved Severity (how bad it actually was).

If the rolling error narrows as the sequence increases, the corridor is exhibiting count-based learning. If it remains flat, corridor-level structural factors are dominating the prediction gap.

GRIDLOCK DISRUPTION INTELLIGENCE

Live Disruption Map

Corridor Risk Index

Response Recommendation Engine

Simulate Event

Incident Profile

Severity & Impact

Temporal Context

Post-Event Learning Replay

Calibration Trend

Post-Event Learning Loop

Calibration Trend

Post-Event Learning Loop