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It is important to study the risk posed by heavy shipping traffic to a subsea pipeline located near an industrial port area. In this context, it is essential to estimate the accident frequency in an attempt to eliminate subjectivity in the analysis process. This study proposes a model for estimating the ship sinking frequency over the subsea pipeline in the Madura Strait area. The Madura Strait is one of the busiest shipping lanes in Indonesia. Many ships pass through the fairway in the strait, and many industrial ports have been built in this area. The proposed model is developed based on Fujii's Model, and it uses Automatic Identification System (AIS) data as a ship traffic survey. Ship sinking accidents are considered based on ship-ship collisions over the critical subsea pipeline area. The ship-ship collision locations around the subsea pipeline and the ship traffic distribution models are estimated using AIS data. The causation probability Pc is determined based on a synthetics approach using a Bayesian network modified from Det Norske Veritas' and Hänninen's models. The causation probability is estimated by considering factors such as human performance, weather, technical problems, and support. The proposed model is validated by comparing its result with actual accident records for the Madura Strait area. The ratio value of 0.33 is considered to be reasonably agreement (ratio value ≥0.2).

The Madura Strait area is one of the busiest marine traffic regions in Indonesia. Many ships arrive, depart and travel through that area, thus influencing the air quality of the port environment. Furthermore, the Indonesian government has not yet ratified Marine Pollution (MARPOL) 73/78 Annex VI regarding the prevention of air pollution from ships and the absence of restricting regulations could have an influence on the level of air pollution. In this study, an Automatic Identification System (AIS) receiver is used to obtain ship data. AIS recognizes a vessel's Maritime Mobil Maritime Identify (MMSI), speed of ship, initial position of ship and ship type. This data is used to evaluate the marine traffic density in the Madura Strait area. Information from ship databases and AIS data are combined for retrieving gross tonnage (GT) information, which is then used to estimate the ship's air pollution emissions. Air pollution estimates also consider the ship's operation modes such as berthing, maneuvering and hotelling. The basic aims of this study are to evaluate marine traffic contributions to the nitrogen oxides (NO x ), sulfur oxides (SO x ), particulate matter (PM), carbon monoxide (CO) and carbon dioxide (CO 2 ) levels in the Madura Strait area, and to evaluate the possibility of using AIS data when estimating air pollution levels. The emission quantities of NO x , SO x , PM, CO (as indicators of air pollution) and CO 2 (as a greenhouse gas) are shown in this paper. The process for estimating emissions using AIS is a potential decision-making tool when considering issues such as the effects of ship emissions on health is also evaluated.