Artificial intelligence for data collection and application of the probabilistic-logistic method in ship traffic control systems of seaports

Abstract

An increase in the number of ships, their sizes, speeds and displacement increases the number of accidents, especially in places with intensive shipping. Control over the positions of vessels, their movement, identification of intentions is exercised by the vessel traffic control system (VTCS). A new generation VTCS that can predict optimal and safest traffic patterns in water areas requires artificial intelligence and forecasting elements. Currently, the fifth generation VTCSs are being implemented. They can plan optimal and safe traffic patterns in the water areas, depending on various external factors controlled by artificial intelligence. The VTCS is a traffic control body. Due to the intensive ship traffic, the control over water traffic is becoming more and more urgent. The “probabilistic” model and the target-object can stop moving when the signal is lost, and it is impossible to continue moving along the same route with the same speed. This must be taken into account when using software [4,5]. The knowledge base for developing a logistic-probabilistic method is available, but there is no real application, due to the lack of massive implementation of artificial intelligence in the software.