SEASONAL SURGE. Railways passenger demand is strongly influenced by
holidays, regional events and weather patterns
| Photo Credit:
RAJU V
Indian Railways is often described as the backbone of the country’s economy. But it would be more accurate to describe it as one of the most complex operating systems in the world.
Passenger and freight trains share the same network. Demand is never static. Seasonal passenger surges, fluctuating freight requirements, defence movements, disaster relief operations, port evacuation pressures and energy security commitments all compete for finite network capacity — often simultaneously.
This complexity is managed through two large digital platforms: the Freight Operations Information System (FOIS); and the Control Office Information System (COIS). Together, they generate enormous volumes of operational data each day. Yet, much of the decision-making still relies on human judgment under deadline pressure, supported by rule-based prioritisation and experience.
This is where artificial intelligence has the potential to fundamentally change outcomes.
Why is the current model under strain?
The existing operating model is largely reactive. When congestion builds up, when terminals choke or when coal stocks fall to critical levels, corrective actions are taken. While this has worked reasonably well for decades, the system is increasingly stretched due to several factors: The rapid growth in passenger volumes, especially during festival and exam seasons; rising coal demand for thermal power plants; growth in containerised and express freight; interface complexities between dedicated freight corridors (DFCC) and the conventional network; and climate-linked disruptions, such as floods and heat-related infrastructure stress.
In such circumstances, manual prioritisation becomes both risky and inefficient.
AI’s role in the Railways is not to replace controllers or operating officers but augmenting them — much like an invisible conductor guiding a large orchestra.
Dynamic traffic prioritisation: AI models can continuously evaluate thousands of variables — train type, commodity criticality, passenger impact, power plant stock levels, defence requirements — and recommend optimal path allocations in real time.
Predictive congestion management: By analysing historical FOIS and COIS data, AI can forecast yard congestion, siding detentions, crew shortages and terminal overloads hours or even days in advance, enabling preventive interventions instead of crisis management.
Festival and peak-season demand forecasting: Passenger demand is strongly influenced by holidays, regional events and weather patterns. AI models trained on datasets can predict demand surges well in advance, enabling proactive rake planning and timetable adjustments.
Coal and power sector assurance: AI can continuously match power plant coal stocks with generation demand and network health, dynamically reallocating rakes to avoid critical shortages and last-minute firefighting.
Real-time disruption response: In the event of floods, accidents or signalling and/or overhead electrification (OHE) failures, AI can instantly simulate multiple recovery scenarios — re-routing options, crew redeployment and restoration sequences — helping decision-makers choose the least disruptive path.
DFCC and conventional network synchronisation: The full benefit of DFCCs depends on smooth handovers. AI can optimise interchange points to prevent spill-back congestion and maximise corridor throughput.
Real transformation
The most important shift is not technological but cultural — from manual prioritisation to data-driven orchestration, from reactive firefighting to predictive operations, and from experience-only decisions to AI-augmented judgment.
The Indian Railways already possesses the data, scale and operational depth needed to deploy AI meaningfully. The challenge is to integrate intelligence across silos and empower frontline decision-makers with predictive tools.
If implemented thoughtfully, AI can help transform the Indian Railways from a system that copes with complexity into one that anticipates and masters it — quietly, continuously and at national scale.
Lalit Chandra Trivedi, former General Manager, Indian Railway
(The writer is a former General Manager of the Indian Railways)
More Like This
Published on February 23, 2026