Ports are living ecosystems where boats, crews, passengers, visitors, cyclists, delivery workers and other users interact with industrial activities every day.

At the same time, ports already generate and collect large amounts of information — from access data and port calls to dock occupancy, infrastructure use and sensors. However, this information is not always used to understand how people and activities move through the port or to anticipate potentially risky situations.

The challenge is to use this information to create a safer, smarter and more integrated relationship between the port and the city.

Develop a solution that:

• Helps visualise, understand or anticipate movements and activities in the port
• Improves safety for people and port operations
• Identifies potential risks such as overcrowding, unsafe interactions or unauthorised access
• Supports better management of port spaces, infrastructure and services
• Takes into account the needs of different port and city users

Expected Outcome

A functional prototype or dashboard of a solution that supports safety and operational decision-making, together with a clear concept for its implementation in a real port environment.

Siltation is a natural process that affects port accessibility and operations. Dredging helps maintain navigable areas, but it is costly, and can affect marine and coastal ecosystems.

With more than 20 years of historical data, the port Port de Plaisance and Port Atlantique La Rochelle have an opportunity to better understand sedimentation patterns, anticipate future changes and explore ways to reduce the need for dredging.

Develop a solution or approach that:

• Helps understand and predict sedimentation patterns
• Identifies ways to reduce or optimise dredging needs
• Takes into account climate change and changing coastal conditions
• Considers the long-term technical, economic and ecological feasibility of the proposed approach

Expected Outcome

A practical, data-driven solution demonstrating how the port can reduce sedimentation and optimise dredging operations while minimising environmental impacts and risks to biodiversity.

Klaipėda FEZ is an industrial ecosystem in which companies depend on shared infrastructure and services: electricity, water, gas, telecommunications, internal roads, drainage systems, logistics routes and emergency resources.

A disruption affecting one element of this infrastructure may create cascading consequences across several companies. A power outage may stop production and loading operations. Heavy rainfall may block a critical road or overload drainage infrastructure. A telecommunications or cyber incident may disrupt access control and operational communication. An unavailable logistics route may affect several companies simultaneously.

Today, individual companies may have their own emergency and business continuity procedures. However, there is no shared operational tool that allows the FEZ management company to:

• understand dependencies between companies and common infrastructure;
• simulate how a disruption may spread across the territory;
• identify the companies, people and operations facing the highest risk;
• compare possible response actions;
• coordinate information and shared resources during the first critical hours.

We are looking for a prototype of an interactive Klaipėda FEZ resilience and emergency coordination platform which allow users to activate different disruption scenarios, predict which infrastructure objects and companies may be affected directly and indirectly, estimate the severity, likely duration and operational consequences of a disruption, prioritise the most critical risks and affected operations, recommend coordinated actions for the first 15, 60 and 180 minutes, support the allocation of limited shared resources such as backup generators, alternative routes, emergency transport, technical teams or temporary water supply, allow an authorised operator to modify or override the recommendations, and present a clear comparison between different response strategies — with the solution providing understandable reasons for its recommendations, since a simple and explainable decision-support model is preferred over a technically complex but non-transparent AI system.

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