Live Demo
Dynamic Prediction Engine
12951 Mumbai Rajdhani
The Challenge
Why conventional ETA estimation is insufficient for modern railway operations.
Schedules cannot fully account for real-time operational conditions, relying on fixed recovery times rather than dynamic realities.
A delay at one point in a long journey creates cascading effects that affect downstream stations and intersecting routes.
Congestion, signal halts, speed restrictions, unscheduled stoppages, and weather can drastically change running time.
Uncertain arrival times affect passengers, station planning, crew scheduling, platform allocation, and feeder transport.
How It Works
From raw data to actionable intelligence in four steps.
Live Data
Ingests real-time train locations, GPS updates, and current schedules.
Context & History
Incorporates historical delay patterns, sectional running times, congestion, and weather data.
Prediction Engine
ML models forecast dynamic ETAs for all upcoming intermediate and destination stations.
Continuous Updates
Recalculates predictions seamlessly as train conditions and operational factors change.
What Makes It Predictive?
YatriGo does not simply add the current delay to the scheduled arrival time. Instead, the prediction model forecasts the expected remaining delay and arrival behavior.
By understanding how trains actually run in the real world, the system can predict whether a train is likely to make up for lost time or fall further behind schedule.
The model evaluates:
- Distance remaining to the destination or intermediate station
- Current and average delay across the journey
- Temporal patterns (time of day, day of week)
- Historical train behavior and past performance
- Sectional running-time patterns and bottlenecks
The Operations Dashboard
A clean, functional interface surfacing the intelligence of the prediction engine.
Who Benefits
Accurate, dynamic ETAs benefit everyone in the transit ecosystem.
Better understanding of expected arrival times. Passengers spend less time waiting on platforms and can plan journeys with confidence.
Better preparation for changing train arrivals. Staff can manage platform crowding effectively and provide accurate announcements.
Improved planning and coordination. Operators can identify bottlenecks in real-time and optimize network flow to minimize cascading delays.
Better synchronization with predicted train arrival. Bus and taxi operators can adjust their schedules, improving last-mile connectivity.