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Alstom

Managing uncertainty in rail networks

Making critical decisions in seconds to eliminate delays and maximise network capacity.

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The challenge

Managing modern rail traffic means handling extreme complexity, where every disruption triggers a domino effect across entire regional or national networks. When a delay or fault occurs, traditional systems struggle to recalculate priorities instantly.

For a global giant like Alstom, the challenge isn’t just monitoring trains, but having real-time Decision Intelligence capability: resolving complex traffic conflicts in a fraction of a second by adjusting departures, routes and speeds to prevent gridlock. In high-density contexts, any delay in decision-making translates immediately into financial losses, penalties and disruption for thousands of passengers. What’s needed, then, is a shift from reactive emergency management to dynamic, mathematically optimised planning that can maximise the use of existing infrastructure without ever compromising safety.

How we create value

Since 2013, Optit has worked with Alstom, embedding Decision Science directly at the heart of the company’s traffic management system, developed in Bologna.

We designed and built an advanced algorithmic component based on operational research, capable of processing billions of logistical combinations instantly. When an operational disruption occurs, Optit’s algorithm analyses the entire network in seconds, identifying the optimal strategy for rescheduling timetables, diversions and train speeds. This goes beyond simply supplying technology: it’s an engineering extension that brings applied mathematics and data science into mission-critical systems.

The value lies in replacing empirical rules of thumb with rigorous mathematical optimisation, supporting human decision-makers in split seconds when precision and speed are non-negotiable.

Impact

Integrating Decision Intelligence into Alstom’s systems has delivered:

  • Immediate resolution of traffic conflicts, in real time and within seconds.
  • Maximised capacity of existing infrastructure, avoiding congestion.
  • Service reliability, with a drastic reduction in delays even under maximum operational stress.
Managing uncertainty in rail networks