Naslov SHIP COLLISION PROBABILITY MODEL BASED ON MONTE CARLO SIMULATIONS AND
ARTIFICIAL NEURAL NETWORK (Bi-LSTM) Naslov (engleski) SHIP COLLISION PROBABILITY MODEL BASED ON MONTE CARLO SIMULATIONS AND
ARTIFICIAL NEURAL NETWORK (Bi-LSTM) Autor Srđan Vukša Mentor Pero Vidan (mentor)Član povjerenstva Anita Gudelj (predsjednik povjerenstva)Ustanova koja je dodijelila Sveučilište u Splitu Datum i država obrane 2023-12-20, Hrvatska Znanstveno / umjetničko TEHNIČKE ZNANOSTI Univerzalna decimalna klasifikacijaUDC ) 62 - Inženjerstvo. Tehnika. Tehnologija Sažetak The safety of navigation is of global importance. Good organisation, surveillance and
management of maritime traffic contribute significantly to the safety of any maritime region.
Although great efforts have already been made to improve safety, maritime accidents still occur.
By analysing traffic and ship data, it is possible to identify areas where there is a higher risk
due to increased traffic density. This makes it possible to measure the efficiency and safety of
maritime traffic in a monitored region, considering the ship collision probability.
Maritime traffic density is the number of ships passing through the monitored area in a
given time period. The challenge for coastal maritime regions is the variation in traffic density
and mode characteristics. Another challenge is how this variation in shipping routes affects the
ship collisions probability or other accidents at sea.
Traffic density can be measured using data from Automatic Identification System – AIS.
The information provided by AIS is real-time data designed to improve maritime safety and
security. AIS data can also be used for scientific research purposes to improve maritime safety
by developing predictive models for collisions in a research area. The AIS data are received
and stored in "raw format" - National Marine Electronic Association – NMEA files. These files
must be processed, i.e., decoded. Only decoded NMEA files can be used in the model and to
determine collision probability.
The objective of the model in this research is to analyse the traffic density and determine
the collision probability based on the available AIS data for the research area. The proposed
model is applied to the Split maritime area. However, it can be applied to any navigational area
for which the AIS data set exists.
The proposed architecture of the collision probability model includes the following 5 steps:
1. AIS data collecting, processing and verification;
2. Decoding and cleaning-up of AIS data;
3. Route data set creation;
4. Monte Carlo simulations – MC;
5. Learning, validating and testing the proposed Bidirectional Long Short – Term Memory
Artificial Neural Network – Bi-LSTM.
Sažetak (engleski) The safety of navigation is of global importance. Good organisation, surveillance and
management of maritime traffic contribute significantly to the safety of any maritime region.
Although great efforts have already been made to improve safety, maritime accidents still occur.
By analysing traffic and ship data, it is possible to identify areas where there is a higher risk
due to increased traffic density. This makes it possible to measure the efficiency and safety of
maritime traffic in a monitored region, considering the ship collision probability.
Maritime traffic density is the number of ships passing through the monitored area in a
given time period. The challenge for coastal maritime regions is the variation in traffic density
and mode characteristics. Another challenge is how this variation in shipping routes affects the
ship collisions probability or other accidents at sea.
Traffic density can be measured using data from Automatic Identification System – AIS.
The information provided by AIS is real-time data designed to improve maritime safety and
security. AIS data can also be used for scientific research purposes to improve maritime safety
by developing predictive models for collisions in a research area. The AIS data are received
and stored in "raw format" - National Marine Electronic Association – NMEA files. These files
must be processed, i.e., decoded. Only decoded NMEA files can be used in the model and to
determine collision probability.
The objective of the model in this research is to analyse the traffic density and determine
the collision probability based on the available AIS data for the research area. The proposed
model is applied to the Split maritime area. However, it can be applied to any navigational area
for which the AIS data set exists.
The proposed architecture of the collision probability model includes the following 5 steps:
1. AIS data collecting, processing and verification;
2. Decoding and cleaning-up of AIS data;
3. Route data set creation;
4. Monte Carlo simulations – MC;
5. Learning, validating and testing the proposed Bidirectional Long Short – Term Memory
Artificial Neural Network – Bi-LSTM.
Ključne riječi
Automatic Identification System – AIS
AIS data processing
Safety of navigation
Collision probability
Python code
Monte Carlo simulations
Bidirectional Long Short – Term Memory Artificial Neural Network – Bi-LSTM.
Ključne riječi (engleski)
Automatic Identification System – AIS
AIS data processing
Safety of navigation
Collision probability
Python code
Monte Carlo simulations
Bidirectional Long Short – Term Memory Artificial Neural Network – Bi-LSTM.
Jezik engleski URN:NBN urn:nbn:hr:164:581940 Datum promocije 2024 Studijski program Naziv: Stjecanje doktorata znanosti izvan doktorskog studija Vrsta resursa Tekst Način izrade datoteke Izvorno digitalna Prava pristupa Zatvoreni pristup Uvjeti korištenja Datum i vrijeme pohrane 2024-01-19 08:06:59
