Portfolio-wide risk view around critical infrastructure
SAMRoute helps transport-infrastructure teams build a shared, portfolio-wide view around critical infrastructure, by making surrounding context visible and dependencies clearer.
Coverage remains uneven across critical points.
Local reviews are essential, but don't provide a consistent portfolio-wide view
Today, critical points of infrastructure like level crossings are understood point by point. Studies, reviews, and mandated traffic counts exist, but coverage is uneven. The operational view is a patchwork.
What's missing is a portfolio-wide view that is systematic, homogeneous, and easier to maintain—one that adds colour to the picture by making the surrounding context visible. What's also needed is a systematic way to reveal functional dependencies, so it becomes clearer which key flows rely on which points.
Turning that context and dependency view into indicators would create a shared basis to compare points across the portfolio and help prioritise focus areas and investment programmes.
SAMRoute scans your network and makes critical points comparable.
It reveals context and models dependencies, then turns both into portfolio indicators.
SAMRoute helps turn scattered, uneven information into a shared, comparable picture, so teams spend less time reconciling and more time deciding what to do first across a full network.
First, it expands situational context by scanning well beyond the usual radius, so the surrounding environment becomes easier to see in a consistent way at scale.
Then, it clarifies functional dependencies by surfacing which key flows are likely linked to which points (under explicit assumptions), so portfolio discussions can rely on a more consistent basis alongside existing studies, reviews, and local expertise.
SAMRoute runs a traceable chain from data to results.
We scan context and model dependencies, then aggregate per critical point.
SAMRoute integrates general-purpose geospatial data (network topology, reference layers, context datasets) and adds task-specific inputs such as inventories of emitters and critical points (and, when available, customer datasets). Modelling choices rely on explicit assumptions that can be reviewed and adjusted.
It generates origin–destination (OD) pairs and computes modelled routes, with a primary route and an alternative that avoids a given critical point. It keeps only OD pairs that actually traverse the point, aggregates results per critical point, and stores them for live use in the UI.
The chain stays traceable end-to-end (inputs → assumptions → results). It supports sharing, and will support machine-to-machine hooks and keep outputs up to date as data changes.
See the level-crossing demo in action
Make local context visible. Clarify at-risk road-flow dependencies. Align stakeholders on one shared referential.
This demo is built for rail-infrastructure teams. It shows how local context around a level crossing—and the dependencies of at-risk road flows—can be described on a consistent basis, then consolidated into a portfolio view you can sort and compare to support prioritisation.
— To see it in action, request a walkthrough.
Explore SAMRoute in a live demo
Request sandbox access, or request a live exchange to tour the demo and map the next steps
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Rue des Iles Kerguelen
35760 Saint-Grégoire, France
