A real time simulation optimization framework for vessel collision avoidance and the case of singapore strait

Safety is a primary concern for the various transport means. For sea transport, this includes various aspects like human safety at sea and at port, and also environmental safety and sustainability. In heavy-traffic regions where the waters are congested and vessels sail very closely together, ensuring these safety needs can be challenging. In this paper, we leverage on the rich information transmitted through the automatic identification system (AIS) and propose, for the first time, an integrated simulation-optimization approach for real time collision avoidance. This enables capturing of stochastic dynamic behavior of vessels for better prediction and fast trajectory optimization for application in real time. Specifically, a realistic agent-based model is developed based on behavioral learning in a real-environment, and incorporated into a fast collision avoidance optimization model in real time to provide robust collision avoidance that is able to account for future stochastic consequences of the actions taken. To achieve this, we develop: 1) a vessel pattern recognition method that mines the rich AIS data to produce realistic trajectory models; 2) an agent-based simulation model to enhance future trajectory prediction; and 3) a fast surrogate-based sampling technique to generate collision avoidance maneuvers for vessel captains in real time. To illustrate the feasibility of the approach, we use the case of the Singapore strait, one of the busiest straits in the world.