Collision risk assessment in coastal waters

• Main Author: Grossmann, Martin (author)
• Other Authors: Kana, Austin (graduation committee), Hopman, Hans (mentor), Hassel, Martin (mentor), Delft University of Technology (degree granting institution)
• Format: Master Thesis
• Language: English
• Published: 2019
• Subjects: risk assessment; Collision detection; Automatic Identification System; coastal areas; near-collision; Vestfjorden
• Online Access: http://resolver.tudelft.nl/uuid:30c436c1-7727-4068-9315-383c040ba6a0

The amount of international shipped cargo grows steadily, and seas are exploited for seabed mining and energy production more than ever. As a result, there is an increase in traffic density and decrease in free navigational space, potentially causing a higher incidence of dangerous navigation situations that may lead to ship collisions. This thesis establishes a hypothesis that there are coastal areas where the risk of collision is unexplored and abnormally high and has not been analysed yet. Therefore, the thesis aims to develop a method suitable to assess the risk of collision in coastal waters. A thesis literature review primarily focuses on the current collision risk assessment methods, circumstances of collisions, and sources of navigational information for coastal waters. The literature review concludes that a promising and novel approach is to detect near-collision situations based on the data from the Automatic Identification System (AIS). The near-collision exists when a ship's safety domain is violated and, simultaneously, the ship performed a last-moment evasive manoeuvre, which is identified by an abnormal ship's rate of turn. Building on a previous basic version of the near-collision detection method and AIS data provided by Safetec Nordic AS, this thesis develops a collision risk assessing tool that significantly outperforms the original method. The performance of the designed method was evaluated using AIS data from the Vestfjorden area in Norwegian coast during 2013-2015. The case study shows that this approach effectively detects near-collision situations but identifies a considerable number of new false near-collisions. The details and spatial distribution of detected near-collisions provide valuable insight into navigational areas vulnerable to collisions, collision circumstances, and frequency of collisions.