Participated in C-MobILE (2017–2021), one of Europe's flagship projects for real-world C-ITS deployment across multiple European cities.
GERTRUDE
Bordeaux-based ITS company with European C-ITS deployment experience and growing expertise in distributed AI traffic management.
Their core work
GERTRUDE is a Bordeaux-based French private company working in intelligent transport systems (ITS) and connected urban mobility. Their H2020 participation covers two complementary tracks: the large-scale European deployment of Cooperative ITS (C-ITS) — vehicle-to-infrastructure communication systems — and the development of distributed algorithmic systems for real-time traffic and mobility management. Their recent project keywords (distributed control, machine learning, demand management) suggest the company contributes software, systems architecture, or technical consultancy to transport technology consortia rather than pure research. They operate exclusively as a consortium partner, bringing specialist ITS or mobility management capabilities to pan-European deployment and research initiatives.
What they specialise in
DIT4TraM (2021–2024) explicitly targets distributed control architectures for traffic flow and multimodal mobility management.
DIT4TraM keywords include machine learning and demand management, signalling GERTRUDE's growing engagement with data-driven mobility optimisation.
Both projects address the practical deployment of mobility services at city or corridor scale, spanning Innovation Actions and Research Actions.
How they've shifted over time
In their first project (C-MobILE, 2017–2021), no specific technical keywords were captured, but the project context points firmly at infrastructure-facing C-ITS deployment — roadside units, vehicle communication protocols, and multi-site pilot operations. By their second project (DIT4TraM, 2021–2024), the vocabulary shifts decisively toward algorithmic intelligence: distributed control, machine learning, and demand management. This trajectory suggests GERTRUDE is moving from deployment execution toward the software and intelligence layer of transport systems — the brains rather than just the wiring.
GERTRUDE appears to be transitioning from connected-infrastructure deployment toward AI-driven, distributed mobility management — a direction that aligns with the next wave of EU transport digitalisation funding.
How they like to work
GERTRUDE has never led an H2020 project, always joining as a participant — a consistent specialist-contributor posture. Both projects were large pan-European consortia (C-MobILE alone involved dozens of cities and organisations), which explains the disproportionately high partner count of 63 unique organisations from just two projects. This suggests GERTRUDE is comfortable operating within complex multi-stakeholder transport programmes rather than driving agenda-setting from the coordinator seat.
GERTRUDE has built a network of 63 unique consortium partners across 12 countries through just two projects, reflecting the large-scale, multi-site nature of European ITS deployment initiatives. Their reach is broadly European, with no evidence of a tight bilateral or national cluster.
What sets them apart
GERTRUDE occupies an uncommon position for a non-SME French private company: active participation in both a large-scale C-ITS deployment action and a distributed-intelligence research action, giving them hands-on experience across the full ITS maturity spectrum from pilot deployment to algorithmic R&D. For a consortium builder in the transport or smart-city space, they offer a French industrial partner with real-world European ITS deployment credentials and emerging ML-for-mobility capabilities — without the overhead of a large systems integrator.
Highlights from their portfolio
- C-MobILEOne of Europe's largest C-ITS deployment projects (IA scheme), giving GERTRUDE direct experience in live multi-city connected mobility rollouts — rare for a Bordeaux-based private company.
- DIT4TraMMarks GERTRUDE's pivot toward algorithmic transport management, combining distributed control systems and machine learning in a research-grade RIA — broadening their profile beyond pure deployment.