Port of Rotterdam

Port of Rotterdam: Self-Learning Computers Predicting Vessel Arrival Times

The Port of Rotterdam is investing in the development of Pronto, an application for standardized data exchange on port calls.

Illustration. Image Courtesy: Pixabay under CC0 Creative Commons license

Almost half of the shipping companies, agents, terminals and other nautical service providers in the port use the system to plan, implement and monitor their activities during a port call, as explained by the Port of Rotterdam.

Pronto uses artificial intelligence to predict vessel arrival times in the port. Artificial intelligence and big data are enabling the arrival times of vessels in sea and inland ports to be predicted earlier and with increased precision.

“Various factors influence a vessel’s arrival time. This includes the vessel type and cargo type, as well as the location, route, sailing speed and movements of other vessels in the vicinity,” Arjen Leege, Senior Data Scientist at the Port of Rotterdam Authority, said.

“We have mapped out the most crucial parameters. During this process, we sometimes dropped parameters or added new ones. For instance, it emerged that the number of times a vessel has already entered the Port of Rotterdam is also relevant,” he added.

Data sources include AIS and the port authority databases, including vessel arrival times at the loading platform. Port authority data scientists used the parameters to develop a self-learning computer model. Initially, this was fed with some 12,000 items of historical data. The computer recognised patterns in these, enabling it to learn to predict how much time a vessel needs to move from the loading platform to the berth.

“Computers can make complex connections must faster than people. That is actually the power of artificial intelligence. A computer’s predictive capacity increases when it is fed continuously with up-to-date data. We can now predict with 20-minute precision when arriving vessels will reach the berth,” Leege explained.

“The computer can also look further into the future and calculate the arrival times of vessels that are still some seven days away from the Port of Rotterdam. By looking further ahead, we will ultimately be able to predict a vessel’s entire route. Perhaps even some 30 days in advance, including multiple ports.” 

“The more details we know at an earlier stage, the better we can plan our resources. If you know it will be busy in the port you can, for instance, increase the towage capacity in advance by requesting tugboats from another port to call at Rotterdam. Pronto can now also identify which vessels are bunkered, piloted or towed in the port. Possibly there will be new applications in the future that we’ve not considered as yet,” he continued.

As informed, using artificial intelligence has already reduced vessel waiting times in the Port of Rotterdam by 20 percent.

Robbert Engels, Product Lead Port Call Optimisation, sees further optimization potential:

“The more parties share data and work actively with the information they receive from the system, the more transparent the chain will become, the better we will be at taking decisions and the better we will be able to manage planning deviations.”

“It is still up to users themselves to interpret all the various times contained in Pronto, but in future, the computer may be able to help with this. The higher the data volumes, the more you can do. It goes without saying that there has been a strong focus on data security. Cyber security has been integrated into the system. We do not use any privacy-sensitive data,” according to Engels.

As well as optimism, there is skepticism regarding artificial intelligence. In practice, self-learning robots sometimes cause problems.

“Predictions are always complex and a computer can get it wrong. However, with Pronto we’ve not gone for a black box approach. We gave a lot of thought to the factors that determine the distance a vessel covers. We’ve provided the computer with reliable parameters for its predictions. In theory, we can even show per prediction how this came about,” Leege noted.

Image Courtesy: Pixabay