Autonomous Rate
0.0%
Closed-loop
Delay Reduction
0.0%
Predictive
Disruptions
0
All time
Escalations
0
Human-in-Loop
Railway Grid
Station
Junction
Track
Disrupted
Train
Fault Simulator
Learning Curve
Live Feed
All operations normal. No disruptions detected.
Active Fleet
Diagnostic Console
Bogie Section
Locomotive (Front)
Kalman Vibration
0.02
mm/s²
Bogie Track Stress
0.05
kN
Brake Pad Temp
42.8
°C
Catenary Voltage
25.1
kV
Continuous Diagnostic Agent Feedback
No active anomaly detected in this bogie section. Wheel diameter and suspension resonance are within LSTM normal baseline limits.
Network Grid Inventory
Active Trains
| ID | Name | Position | Destination | Speed | Load | Status |
|---|
Station Vertices
| Code | Name | Zone | Coord | Platforms | Junction |
|---|
Historical Incidents
| Incident ID | Timestamp | Cascade Accuracy | Intervention Accuracy | Details |
|---|
Continuous Learning Curve
Accuracy history — model adaptation to disruption cascades.
Self-Healing Architecture
Dynamic Bayesian Network Calibration
The post-incident learning agent monitors cascades in real time. When prediction error exceeds 20%, it auto-adjusts transition weights in the DBN to adapt to seasonal and mechanical railway variations.
LSTM Threshold
0.10
DBN Dampening
0.40
MCTS Simulations
50 / node