Modelling of VTS supervisor by algorithm based on Petri net: case study of Dover incident


The paper deals with collision prevention problem in maritime transport in the area of the narrow canals with predefined routes. The Dover incident, which is analysed and described in the paper, has shown that the control of the passage of ships through the critical areas must be upgraded with an automatic supervising system, which warns the human operator of incorrect ship motion and help the operator to make the right and timely decision. The general idea is to improve the safety of navigation by introduction of automatic collision prevention based on automated supervisor helping to human operator in Vessel Traffic System (VTS) control centre. The VTS supervisor automatically monitors marine traffic by using data from Automatic Radar Plotting Aid (ARPA) radar and others sensors. Such supervisor detects real time and Course Over Ground (COG) of the vessel entering a particular sector, and then estimates the required time for vessel’s passage into another sector. VTS supervisor compares the real time and estimated time of passage of the specific ship through particular sector as a part of surveillance area. In addition, it compares and monitors the deviation of the course during transition of zones (sectors). If significant difference for both values are occurred VTS supervisor triggers a time alarm or a course alarm respectively. In the paper authors have modelled and simulated collision prevention with performed by the alarm actions of VTS supervisor improved with algorithm module based on hybrid Petri net formalism and Visual Object Net ++ tool.

Keyword : discrete event systems in maritime, Dover incident, hybrid Petri net, maritime sector supervisor, maritime traffic control centre, maritime collision prevention, Visual Object Net

How to Cite
Bošnjak, R., Kezić, D., Belamarić, G., & Krile, S. (2021). Modelling of VTS supervisor by algorithm based on Petri net: case study of Dover incident. Transport, 36(4), 305-316.
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Nov 11, 2021
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