Portfolio-wide risk view around critical infrastructure points
SAMRoute helps transport-infrastructure teams build a shared, portfolio-wide view around critical points, by making surrounding context visible and dependencies clearer.
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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 (LX) 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. But what’s needed, it is also 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.
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. But what’s needed, it is also 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.
Point-only inventories often remain scattered and incomplete, so much of the surrounding context stays unseen. A systematic scan brings that context into view, adding colour and consistency across the set.
SAMRoute scans your network and makes critical points comparable.
It reveals context and models dependencies, then turns both into portfolio indicators.
We scan the portfolio, integrate traffic-at-risk emitters, and look beyond the usual radius to add colour and reveal wider context. Combined with modelled dependencies, this creates a more consistent, comparable basis to prioritise focus areas and investment programmes.
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.
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. It will support machine-to-machine hooks and keep outputs up to date as data changes.
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. It will support machine-to-machine hooks and keep outputs up to date as data changes.
A 5-step overview of the chain: (1) data inputs—generic geospatial layers plus task-specific/customer data; (2) the geospatial engine—context discovery at wider extent plus routing calculations; (3) backend processing and the portfolio UI; (4) access sharing for internal/external review; (5) upcoming capabilities—notifications (system-to-system) and continuous refresh as data evolves (near-live re-analysis).
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.
— To see it in action, request a walkthrough.
Explore SAMRoute in a live demo
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