AI Case Study
The Port of Rotterdam Authority reduces vessel waiting times by 20% with machine learning
The Port of Rotterdam has leveraged artificial intelligence to predict vessel arrival times in the port. The port's system, Pronto, utilises historical data on past arrival times as well as vessel and cargo type, location, route, sailing speed and other. Using a self-learning model predictions are made earlier and with greater accuracy. As a result, vessel waiting times in the port have dropped by 20%.
Freight And Logistics
"For instance, this technology is enabling the arrival times of vessels in sea and inland ports to be predicted earlier and with increased precision.
"Computers use the sea of data that we produce these days for self-training. The Port of Rotterdam is also investing in this development with Pronto, an application for standardised data exchange on port calls," the port authority said.
Almost half of 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. Pronto uses artificial intelligence to predict vessel arrival times in the port.
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."
"We can now predict with 20-minute precision when arriving vessels will reach the berth. 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". Using artificial intelligence has already reduced vessel waiting times in the Port of Rotterdam by 20 per cent."
"Significant efficiency steps can also be taken in the maritime sector regarding big data and artificial intelligence, said Port of Rotterdam.
"Various factors influence a vessel’s arrival time", stated Arjen Leege, Senior Data Scientist at the Port of Rotterdam Authority. "This includes the vessel type and cargo type, as well as the location, route, sailing speed and movements of other vessels in the vicinity. 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"."
Historical data on vessel arrival times and other parameters