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AI consultant in Rotterdam

We first test whether a model adds value using your own ship, sensor or planning data, as we did for Rijkswaterstaat at the Botlekbrug and Spijkenisserbrug.

Discuss your challenge Read the Rijkswaterstaat case study

We simulated traffic congestion caused by a bridge opening for Rijkswaterstaat

Together with the NGinfra partners, we investigated whether data could improve the operation of the Botlekbrug and Spijkenisserbrug for shipping, freight trains and road traffic; the first proof of concept, using AIS data, a congestion model and an LSTM, showed that it could.

Read the case study

Which decisions about bridges, quays or berths are still based on gut feeling?

A planner can see from AIS data that a seagoing vessel will arrive later than expected, but still adjusts crane schedules, driver assignments and train paths by hand. A model that combines vessel movements, schedules and traffic levels can calculate in advance how a decision will affect the rest of the chain.

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Kraan aan de kade in de Rotterdamse Europoort bij schemering

Advice based on data from ships, sensors and camera footage

Discuss your challenge

A proof of concept first

Your own historical data shows whether a model adds value. For Rijkswaterstaat, we used it to simulate the effect of opening a bridge on traffic congestion.

Figures to inform port policy

For Port of Amsterdam, we made historical berth use clear, so policymakers can use the figures to decide whether more or fewer berths are needed.

Cameras where nothing is measured

At the Floating Farm in Rotterdam, we installed cameras ourselves and used computer vision to capture the feeding process.

Someone to take ownership after the pilot

A model for bridge openings or activity along the quay becomes outdated when shipping patterns change. We agree in advance who will monitor, retrain and manage it.

The bottleneck between waterways, roads and rail determines where we start

  1. Bottlenecks and partners across the chain

    Which decision creates a bottleneck at a bridge, quay or in planning, and which parties are affected or provide the data?

  2. Testing against historical data

    A proof of concept using historical AIS, sensor or planning data shows whether a model adds value.

  3. A proposal in the existing system

    The model makes a proposal in the system the operator or planner already uses. They make the decision themselves.

Is your challenge in ports, water or food ready for AI?

Assess it in a conversation

A good fit if

You have historical data on vessel movements, sensors, schedules or images, and a recurring decision that currently relies on experience, such as opening a bridge, allocating a berth or deciding the order of operations at a terminal.

Not a good fit if

The data is held by a partner in the supply chain who does not share it, or the decision comes up so rarely that a fixed rule or a good dashboard is enough. If so, we’ll recommend that instead.

What the scope depends on

The number of parties in the port or supply chain providing data, whether data needs to arrive in real time, how much historical data is available, and whether the project remains a proof of concept or goes into production.

Who takes ownership across the chain?

The uncertainty is rarely about the model. More often, it is about who shares the data and who manages it when more organisations benefit from it. We agree on this before development begins.

Sensors, schedules or camera footage: which data should you put to work first?

Sensors

Choose this if measurements are already being collected over time, such as AIS positions, water levels or bridge monitoring. Our advice for port and infrastructure questions: that historical data usually already exists.

Schedules

Choose this if the opportunity lies in sequence and timing: which bridge, crane or driver is needed when. You’ll also need data on what actually happened; see optimising schedules with AI.

Camera footage

Choose this if what you want to know isn’t measured anywhere, such as feed left uneaten. Allow for labelling work and GDPR agreements; read more about computer vision.

Experience in waterways, ports and urban farming, based in Amsterdam

Data Science Lab has worked with data and AI since 2016 and is a Databricks and Microsoft partner. Through our Data for Good initiative, we worked for Floating Farm. We can meet at your place, at ours or online.

Book an introductory meeting

One decision on the water, on the road or at the quay is enough for an initial conversation

Stef Stoelwinder

Stef Stoelwinder

Business Development

Your first point of contact. In our first conversation, we’ll discuss one decision in your chain, the data involved and who manages it.

E-mail info@datasciencelab.nl

Phone +31 (0)20 3636 724

Which bottleneck between ship, quay and customer will you tackle first?

Describe the bottleneck and the data you already have. We’ll reply by email or arrange an initial conversation.

Emailinfo@datasciencelab.nl Phone+31 (0)20 3636 724 Book an appointment

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Let us know how we can help you.

Frequently asked questions

AIS (Automatic Identification System) is the system ships use to automatically transmit their position, speed and voyage details. According to Rijkswaterstaat, it has been mandatory since 2016 for commercial vessels and recreational vessels longer than 20 metres on waterways covered by the Inland Navigation Police Regulations. Historical AIS data lets you determine when ships reach a bridge, lock or quay.

For the Botlekbrug and Spijkenisserbrug, AIS data was one of the sources. To learn how data helps planners spot disruptions in the logistics chain earlier, read about data and AI for logistics.

With a congestion model, which calculates how a traffic jam forms and clears when a road temporarily closes. In the Botlek case study, we also used AIS data and an LSTM, a neural network that learns patterns in time series.

The proof of concept showed that you can simulate the effect of a bridge opening on traffic congestion. The long-term goal is a system that suggests an opening time to the bridge operator that causes the least disruption overall. The operator makes the final decision.

Start by agreeing who provides which data, how often and under what terms. Decisions by infrastructure managers across the region affect one another: the province of Zuid-Holland and Rijkswaterstaat agreed that the Hartelbrug and Spijkenisserbrug must never open at the same time.

In the Botlek case study, we worked for Rijkswaterstaat with the partners of NGinfra, a knowledge platform that brings together organisations including Havenbedrijf Rotterdam, ProRail and Rijkswaterstaat. A shared data and AI platform only makes sense once those agreements are in place.

Through an IoT platform, Havenbedrijf Rotterdam collects real-time sensor data on infrastructure, water and air in the port. Rijkswaterstaat continuously monitors the operation of the Botlekbrug. These measurement series provide a basis for predictive maintenance and for forecasting water levels or congestion.

A sensor series only becomes useful when you can link it to failures, maintenance and events. Making those connections often takes more work than building the model itself.

Yes, if what you want to know is visible and is not measured elsewhere. At the Floating Farm in Rotterdam, the cows’ feed included by-products from the city, such as spent grain from Rotterdam breweries. We installed cameras and built a computer vision model to make the feeding process easier to understand and reduce feed waste.

If visitors or employees can be identified in the footage, the GDPR applies. Decide in advance which images you will keep and who can see them.

For the Botlekbrug, the goal is a system that suggests an opening time to the operator; the operator decides. Where a system acts on its own, it is still supervised: the Maeslantkering closes fully automatically, but when high water is forecast, a Rijkswaterstaat team monitors the process and intervenes if necessary.

Annex III, the AI Act’s list of high-risk applications, includes AI used as a safety component in critical digital infrastructure, road traffic and the supply of water, gas, heating or electricity. Your legal or compliance team should assess whether your application for a bridge, flood barrier or road falls into this category. The obligations and when they apply are set out under AI Act and timeline.

No. Data Science Lab has one office, on Westerdoksdijk in Amsterdam. We can meet at your office, at ours or online.

For the Botlek case study, we worked with the NGinfra partners on two bridges in the region, and at the Floating Farm we installed the cameras ourselves.

When a fixed rule in an operating plan or schedule already solves the problem, or when insight is enough. For Port of Amsterdam, the task was to measure and present historical berth usage so policymakers could make decisions based on figures.

A model will not help if partners in the logistics chain do not share their data. Start with agreements instead, or with a Power BI dashboard built on the data you already have. If that is enough, we will say so.

What will you read first: the approach, logistics or computer vision?

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