Search

• A novel framework of regional collision risk identification is proposed. • SDOI is proposed to represent the collision risk of relative bearing and distance. • DCPA, TCPA and SDOI are used to represent collision risk between vessels. • DBSCAN is used to simplify the computing process of the proposed framework. • An improved Shapley value method is used to estimate contributions. Identification of regional collision risk in water area is of significance for the safety of navigation. However, traditional risk identification models are subject to the limitations in accuracy, short-term identification and traffic characteristics. Herein, a framework was put forward to identify regional collision risk instantaneously based on AIS data. The vessels were clustered by using the spatial clustering method. Afterwards, the framework was divided into two steps. Firstly, collision risk of each cluster was obtained by collision risk and contribution of the vessels within the cluster. An analytical method was adopted to identify collision risk of each vessel from the perspective of vessel pairs. Contribution of each vessel was determined by using improved Shapley value method in game theory. Secondly, regional collision risk was obtained by collision risk and contribution of each cluster. Case studies were carried out based on the AIS data of Northern Yellow Sea in China to validate the validity of the proposed framework. The results show that the proposed framework can effectively identify collision risk in water area, presenting the potential for collision risk monitoring and collision risk analysis of water area. [ABSTRACT FROM AUTHOR]

Marine traffic survey is a fundamental part of marine traffic engineering. In the assessment of the safety or effectiveness in particular water areas, the distribution of ship tracks is always included as one of the most significant results of a marine traffic survey. In this paper, methods of marine traffic survey are discussed and an appropriate method is selected to obtain the traffic data considering the available research circumstances in China. A semiautomatic artificial way of plotting a distribution diagram of ship tracks is presented with illustrations. Details of the diagram plotted by using the collected traffic data are described for reference. [ABSTRACT FROM AUTHOR]

The identification of maritime traffic complexity is important to guide maritime surveillance operators towards the more critical areas for the real-time monitoring of the maritime traffic situation. To identify and map the traffic complexity effectively, this paper proposes a dynamic maritime traffic complexity model based on radial distribution functions. For modelling the traffic complexity, three sub complexity models are established first by addressing the speed, course and position of the ships. For each sub-model, the main variable is used for building the radial distribution function model, and a variation method is used to estimate the complexity. Finally, the overall traffic complexity can be identified and mapped by synthesizing the results from the three sub-models. The AIS data in the northern Yellow Sea of China is used for validating the proposed model. The results show that the proposed model can effectively identify the traffic complexity in the studied sea area, and have an advantage in some special sea area compared with the previous traffic complexity model. By applying the proposed model, maritime surveillance operators can better monitor the real-time traffic situation and improve their working efficiency, which are crucial for the improvement of navigational safety. • a dynamic maritime traffic complexity model based on radial distribution functions is proposed. • Three sub complexity models are established first by addressing the speed, course and position of the ships. • For each sub-model, the main variable is used for building the radial distribution function model. • The overall traffic complexity can be identified and mapped by synthesizing the results from the three sub-models. • The AIS data in the northern Yellow Sea of China is used for validating the proposed model. [ABSTRACT FROM AUTHOR]

In order to avoid collision, ships usually take collision avoidance manoeuvres, such as course alteration. However, the effect of such a manoeuvre is influenced by ship manoeuvrability. This paper proposed an improved danger sector model to identify the collision risk between two encountering ships. The proposed model is improved from the danger sector model, which is based on a course alteration manoeuvre by taking ship manoeuvrability into consideration. To involve ship manoeuvrability in the proposed model, the turning circle of a ship is determined when it calculates the size of the danger sector. For validating the proposed model, the Automatic Identification System (AIS) data in the northern Yellow Sea of China were used to carry out some experiments of different encountering scenarios. The results show that the proposed improved danger sector model can identify the collision risk between two encountering ships effectively and has advantages compared with the original danger sector model. The proposed model can help mariners and maritime surveillance operators have a more accurate and effective assessment of ship collision risk in the water area, which can contribute to the improvement of navigational safety. [ABSTRACT FROM AUTHOR]

• A novel model of anchorage collision risk identification is proposed. • Regional collision risk model was improved to fit the characteristics of vessels in anchorage. • Safe room is proposed to represent the anchorage collision risk from a global perspective. • Fuzzy logic was used to obtain the final anchorage collision risk index. • AIS data of vessels in the anchorages off the coast of Shandong peninsular was used for case study. The collision risk in the port area, especially in the anchorage area, increases gradually along with the progressive development of port traffic. This paper proposes a novel anchorage collision risk model in the identification of the collision risk of anchorage. First, the regional collision risk model was improved in order to fit the characteristics of vessels in anchorage. Second, a novel anchorage collision risk indicator was proposed based on ship domains to evaluate the collision risk of anchorage from a global perspective. Finally, the two indicators were synthesized to obtain a more accurate anchorage collision risk index. The AIS data of vessels in the anchorages off the coast of Shandong peninsula in the Northern Yellow Sea of China were used for the case studies. The results indicate that the proposed anchorage model may effectively represent the collision risk of anchorage. The proposed model may serve as reference for the safety analysis of port traffic and assist maritime surveillance operators in the collision risk monitoring of port traffic to enhance traffic safety. [ABSTRACT FROM AUTHOR]