A new initiative to boost vessel traffic management is underway in the Port of Singapore.
Fujitsu Limited, Singapore Management University (SMU), the Agency for Science, Technology and Research (A*STAR) and the Maritime and Port Authority of Singapore (MPA) are collaborating on predictive technologies using artificial intelligence and big data analytics to optimize the management of Singapore's port and surrounding waters. The initial research and development was conducted under the guidance of the Urban Computing and Engineering Centre of Excellence (UCE CoE), a public-private partnership that was established in 2014.
The Straits of Singapore and Malacca comprise one of the world’s busiest sea lanes. According to the MPA, at any given moment there are about 1,000 vessels in the Singapore port, with a ship arriving to or leaving Singapore once every two to three minutes.
Several key technologies are now being developed for improving the management of maritime vessel traffic. These include:
Prediction models, such as:
• A short-term trajectory prediction model that accurately predicts the trajectory of a vessel using machine learning and motion physics
• A long-term traffic model that can forecast the traffic situation based on the traffic patterns of a large number of vessel types, derived from historical data
Risk and hotspot calculation models, such as:
• A risk calculation model that can reliably quantify the near-miss risk of a pair of vessels, by integrating various risk models (ensemble risk model)
• A hotspot model that dynamically reveals changing risk hotspots through spatio-temporal data analysis
Intelligent coordination models, such as:
• A spatial coordination model that seeks to re-route vessels to avoid near-miss and collision incidents
• A temporal coordination model that coordinates the passage timing of vessels to reduce hotspots
These technologies will eventually be able to recognize a near-miss risk prior to the event (e.g. 10 minutes beforehand) by combining short-term trajectory prediction with risk calculation. Another target is to forecast and mitigate a dynamically changing hotspot before it is generated (e.g. 30 minutes beforehand) by integrating long-term traffic forecasts, hotspot calculations and intelligent coordination models.
The project aims to implement maritime traffic coordination technology that is akin to air traffic control. “With the advent of autonomous ships, this technology can potentially disrupt vessel traffic management to reduce human errors and improve navigational safety," said Professor Lau Hoong Chuin, SMU's Lab Director and Lead Investigator for the UCE CoE. "Enhancing navigational safety is an enormous challenge, as there is no single right path for how to achieve it.”