Charging Stack Podcast: PATTERN and Why Digital-twin Road Infrastructure Matters in 2026

PATTERN is an EU-funded project building a SaaS platform that creates a live, data-driven digital twin of road infrastructure, so road agencies and cities can understand what’s happening on the street in real time and act earlier on safety, congestion, maintenance, and emissions.

In this episode, we sit down with Manos Papoutsakis (FORTH) and Ivana Maršić (Myrio) to break down what “digital twin for roads” actually means, why Malta was chosen as the first proving ground, and how the next pilots in Greece and Croatia are being planned.

This episode is for you if:

• You work in road infrastructure, transport planning, or traffic operations
• You’re in a city or road agency trying to move from reactive fixes to proactive decisions
• You manage safety hotspots, congestion zones, or maintenance planning and need stronger evidence
• You build smart city / infrastructure tech and care about interoperability and open standards
• You’re involved in EU projects and want to understand what “SaaS digital twin” looks like in practice

In this episode, you’ll learn:

⚡ What PATTERN means by a “digital twin” for roads: a live digital copy that reflects what’s happening now, not last month
⚡ What data goes in today (video/audio analytics) and what’s planned next (weather + maintenance data) to explain not just what happened, but why
⚡ Why safety is a core use case: spotting risky behaviour patterns (like informal crossings) before crashes happen
⚡ How incident detection can reduce queues and pollution by triggering faster mitigation actions
⚡ Why Malta was a strong first pilot: concentrated traffic patterns + motivated local partners (including Infrastructure Malta)
⚡ How Greece and Croatia fit into the rollout, and what it takes to build stakeholder alignment (workshops, governance, funding paths)
⚡ How anonymisation works in practice (blur faces/plates, keep only what’s needed, discard the rest) and how the team thinks about GDPR
⚡ Standalone vs integration: using existing cameras and plugging into current city workflows instead of forcing a clean-slate setup
⚡ What “open data standards” unlock for infrastructure platforms when multiple systems need to talk to each other

Topics covered include

• Why PATTERN exists in the first place: roads behave differently hour-to-hour (school streets, event zones, commuter peaks), and static planning misses that.
• What “digital twin for roads” means here: a live digital copy of the road plus the live behaviour on it, so operators can see “what’s happening now,” not just historical averages.
• Safety as the core driver: how the platform flags behaviour that signals infrastructure mismatch (people crossing where they “shouldn’t,” conflict-prone spots), so fixes can happen before crashes.
• The data inputs today vs next: video/audio analytics now, then layering weather + maintenance to move from “what happened” to “why it happened.”
• What PATTERN can detect in practice: lane blockages, minor incidents, unusual patterns, and the knock-on impact these have on queues, travel time, and emissions.
• Why “traffic counters” don’t cut it: counting vehicles tells you volume, but not the cause (incident, unsafe crossings, unexpected obstruction) or what to do next.
• Why Malta was a good first pilot: a dense, concentrated mobility environment + partners ready to collaborate closely, which makes iteration and learning faster.
• How the next pilots differ: Greece and Croatia require mapping local pain points, evaluating existing camera coverage/tech maturity, and building a credible business case per stakeholder group.
• Integration reality: PATTERN can use existing cameras as inputs and aims to fit into city workflows as either a standalone dashboard or an add-on to current systems.
• Cloud vs edge decisions: most processing runs in the cloud for scale and updates, but anonymisation can run closer to the camera to reduce privacy risk.
• What anonymisation means (and why it matters): blurring faces and license plates, keeping only what’s needed for analytics, and discarding the rest so it can’t become a surveillance archive.
• The hardest part isn’t only technical: aligning institutions on data sharing, privacy procedures, and cross-city differences is often the real bottleneck.
• What “being proactive” looks like by 2030: fewer surprises, earlier interventions, and decisions that happen before congestion and safety issues spiral.
• The two “meta” themes the guests call out: open data standards (so systems can interoperate) and security/access control (because cities can’t deploy tools they don’t trust).

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