Improving Near Miss Detection in Maritime Traffic in the Northern Baltic Sea from AIS Data

Abstract

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.

A probabilistic analytics method to identify striking ship of ship-buoy contact at coastal waters

“…Review papers by Szlapczynski and Szlapczynska (2017) present the rationale of the most influential models/methods. Du et al (2021) summarized the typical ship domains and its applications as shown in Fig. 1.…”

“…The development and application of innovative statistical and analytics methods to provide novel insights into collision risk assessment at sea are essential and meaningful (e.g., Zhang et al, 2020;Mannering et al, 2020;Zeng et al, 2016;Antão and Soares, 2019). The research over the last two decades mainly focuses on (a) safety domain quantification (e.g., Lei et al, 2021;Szlapczynski et al, 2018a), (b) conflict scenarios detection (e.g., Zhang et al, 2015a;Zhang et al, 2021a;Zhang et al, 2021b;Du et al, 2021), and (c) collision risk evaluation (e.g., Rawson and Brito, 2021;Zhang et al, 2020;Huang and Van Gelder, 2020a). Mostly these approaches qualify risks by evaluating the geographical positioning of ships and the difficulty of avoiding collisions to present spatial-temporal maps to demonstrate collision risk levels, which meet the requirements for the evaluation of ship-ship collision risk or ship-to-offshore structures contact risk (Goerlandt and Montewka, 2015).…”

A Domain-Based Model for Identifying Regional Collision Risk and Depicting Its Geographical Distribution

“…With the rapid development of the global economy, maritime trade has become more frequent in recent years, and maritime traffic demand is accordingly increasing, causing significant changes in ship traffic [1]. The changes to ship traffic are mainly reflected in the increasing volume and complexity of ship traffic, leading to an obvious increase in the probability of collisions, which have a serious impact on maritime traffic safety and cause serious loss of life, property, and environment [2,3].…”

Randomly Testing an Autonomous Collision Avoidance System with Real-World Ship Encounter Scenario from AIS Data

“…An Automatic Identification System (AIS) provides rich information including ship type, position, course over ground (COG) speed over ground (SOG) [25], etc. AIS has been applied in a series of studies on data fusion, abnormal detection, and risk detection [26][27][28]. Inspired by this, a few studies are attempting to mine AIS data for MASS designing testing scenarios.…”