Study on Probabilistic Risk Assessment Model for Crossing Situation in Sunda Strait

The new routeing measures in Sunda Strait, that consists of traffic separation scheme (TSS), precautionary area (PA), and inshore traffic zones (ITZ), have been implemented since July 2020. The Automatic Identification System (AIS) data is utilized to understand the traffic pattern in this area. One dataset before and one dataset after the implementation of TSS is then converted into a vessel trip database. Eight vessel courses, two positions, and seven crossing zones are determined from the density plot to categorise the vessel trips. This study introduced a different perspective to assess the crossing situation by using three point-of-views to calculate the probability, namely crossing zone basis, course basis, and vessel type basis. The analysis result shows that the crossing situation is mostly occurred in the precautionary area of the new routeing measure.

The increasing maritime traffic density in the Makassar Strait, particularly due to the development of the Hub Port at Indonesia’s new capital (IKN), necessitates a structured Traffic Separation Scheme (TSS) to ensure navigational safety. This study aims to design a preliminary TSS by integrating hydro-oceanographic data, vessel traffic analysis, and maritime risk assessment. A qualitative research methodology employing case studies and literature reviews was applied. Vessel movement patterns were analyzed using data from the Indonesian Maritime Security Agency (BAKAMLA RI) and vessel tracking applications. Safety assessments were conducted based on historical collision records and probabilistic modeling of grounding, stranding, and vessel collisions. Hydro-oceanographic data, including wind speed, wave height, and tidal currents, were sourced from secondary data and validated using numerical modeling. The findings suggest that the Makassar Strait TSS should consist of two separate lanes, each 3 km wide, with dedicated northbound and southbound routes. The design considers navigational obstacles such as subsea pipelines, offshore platforms, and coral reefs, ensuring safe passage. Hydro-oceanographic analysis confirms that prevailing conditions, including wind speeds below 15 knots and maximum wave heights of 1.25 meters, comply with recommended maritime safety standards. Additionally, statistical evaluations of vessel movement and collision probability indicate that a 3 km-wide channel effectively mitigates accident risks. This study provides an initial framework for optimizing TSS implementation in one of Indonesia’s most strategic waterways. The results highlight the importance of structured traffic management in reducing navigational hazards and improving maritime efficiency. The proposed TSS design aligns with international safety regulations and aims to support the long-term development of the IKN Hub Port as a critical maritime node. However, this study relies on secondary data and does not incorporate real-time traffic monitoring or high-resolution hydrographic surveys. Future research should integrate real-time Automatic Identification System (AIS) data and in-situ hydrographic measurements to refine the TSS structure. Additionally, further hydrodynamic simulations should be conducted to enhance route optimization under extreme weather conditions.

Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination

Maritime traffic pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime traffic model. In this paper, we propose a framework of maritime traffic pattern extraction from vessel AIS information, which learns a traffic pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, traffic pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts traffic pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective traffic pattern. The proposed method strongly supports the further research on maritime traffic pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.

Recreational boating is an increasing activity in coastal areas and its spatiotemporal overlap with key habitats of marine species pose a risk for negative noise impacts. Yet, recreational vessels are currently unaccounted for in vessel noise models using Automatic Identification System (AIS) data. Here we conduct a case study investigating noise contributions from vessels with and without AIS (non-AIS) in a shallow coastal area within the Inner Danish waters. By tracking vessels with theodolite and AIS, while recording ambient noise levels, we find that non-AIS vessels have a higher occurrence (83%) than AIS vessels, and that motorised recreational vessels can elevate third-octave band noise centred at 0.125, 2 and 16 kHz by 47–51 dB. Accordingly, these vessels dominated the soundscape in the study site due to their high numbers, high speeds and proximity to the coast. Furthermore, recreational vessels caused 49–85% of noise events potentially eliciting behavioural responses in harbour porpoises (AIS vessels caused 5–24%). We therefore conclude that AIS data would poorly predict vessel noise pollution and its impacts in this and other similar marine environments. We suggest to improve vessel noise models and impact assessments by requiring that faster and more powerful recreational vessels carry AIS-transmitters.

In order to study the ship traffic flow in LaoTieshan channel, this paper rewrites the rules of NS (Nagel-Schreckenberg) model of Cellular Automata (CA) based on AIS（Automatic Identification System） data. By referring to the idea of synchronous flow in three-phase traffic flow theory and introducing the speed adaptation mechanism into the simulation, the new model reproduces the traffic flow of LaoTieshan channel and gets the relevant fundamental diagrams. Compared with the actual flow-density diagram drawn from AIS data, it is found that the simulated flow generated by the improved model is more in line with the actual condition, and also conforms to the phase transition of traffic flow in LaoTieshan channel. The method and idea of the simulation are helpful to explain and investigate some complex situations in LaoTieshan channel.

Aiming at solving the problem that existing automatic route-planning algorithms fail to consider the navigation rules in traffic separation scheme (TSS) zones, this paper proposes a ship automatic route-planning algorithm that fully considers TSS-zone navigation constraints. First, a formalized TSS-zone automatic planning module with a quadrilateral decomposition mechanism is designed, which realizes standardized processing of regular TSS zones and completes TSS-compliant route replanning through three core steps: invalid waypoint deletion, TSS-zone-traversal-order determination, and constrained route replanning. Second, a particle swarm optimization (PSO) algorithm is selected as the base global route-planning algorithm via a multi-algorithm comparative framework, considering the requirements of optimality, stability and real-time performance for ship-navigation. The TSS module is deeply integrated with the PSO algorithm, forming a unified global route-planning algorithm that balances TSS compliance and route optimality. Comparative experiments with four mainstream swarm intelligence algorithms (PSO/SSA/IVY/GOA) show that the PSO algorithm outperforms the others in terms of route length, stability and comprehensive efficiency, with an optimal route length of 57.71 and a low standard deviation of 3.42. Furthermore, the proposed algorithm is validated by real nautical chart data of Bohai Bay under single- and double-TSS-zone scenarios. The results indicate that the algorithm can stably generate TSS-compliant routes, with only a small increase in route length (0.6% and 4.4% for a single TSS zone, 1.1% and 1.8% for two TSS zones) and computational time and can automatically adjust the traversal strategy according to the start–end point settings. The designed TSS module has good scalability and can be integrated with other optimization algorithms, providing a feasible technical solution for an intelligent ship navigation system to realize automatic and compliant route planning in TSS zones with dense traffic.

Data mining approach to shipping route characterization and anomaly detection based on AIS data

Automatic Identification System (AIS) provides abundant near real-time information of moving vessels in the sea of the whole world and has been widely used in the fields of vessel collision avoidance, Maritime Situation Awareness (MSA) and ocean surveillance. The development of satellite-based AIS further expands the range of AIS and enables a wide converge of AIS data collection, which solves the problem of lacking AIS data in high-seas. At the same time, AIS data provides a rich source for data mining for maritime traffic analysis. In this paper, a typical clustering algorithm called K-means is applied to deal with the Space-based AIS(S-AIS) data received by “TianTuo-3” satellite developed by National University of Defense Technology. We use Elbow Rule to determine the optimal number of clusters and calculate the normalized standard deviation of COG(Course Over Ground) and SOG(Speed Over Ground) of vessels in south Africa area as their features to conduct clustering. This method is supposed to evaluate vessels' sailing stability and used in detection of low-likelihood behaviors or anomalies of vessels.

Identifying illegal fishing activities from Automatic Identification System (AIS) data is difficult since AIS messages are broadcast cooperatively, the ship’s master controls the timing, and the content of the transmission and the activities of interest usually occur far away from the shore. This paper presents our work to predict ship types using AIS data from satellites: in such data, there is a pronounced imbalance between the data for different types of ships, the refresh rate is relatively low, and there is a misreporting of information. To mitigate these issues, our prediction algorithm only uses the sequence of ports the ships visited, as inferred from the positions reported in AIS messages. Experiments involving multiple machine learning algorithms showed that such port visits are informative features when inferring ship type. In particular, this was shown to be the case for the fishing vessels, which is the focus of this paper. We then applied a KD-tree to efficiently identify pairs of ships that are close to one another. As this activity is usually dangerous, multiple occurrences of such encounters that are linked to one ship sensibly motivate extra attention. As a result of applying the analysis approach to a month of AIS data related to a large area in Southeast Asia, we identified 17 cases of potentially illegal behaviours.

Path planning for large ships in inland waterways considering risk assessment of AIS data

The development of China’s digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data.

Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities

Artificial Force Fields for Multi-agent Simulations of Maritime Traffic: A Case Study of Chinese Waterway

The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high interest. Accurate matching of ships detected in SAR images with AIS data requires compensation of the azimuth offset, which depends on the ship’s velocity. The existing procedures interpolate the route information gathered by AIS to estimate the ship’s velocity at the epoch of the SAR data, to remove the offset. Matching accuracy is limited by interpolation errors and AIS route information unavailability or uncertainties. This paper proposes the use of SAR-based ship velocity estimations to improve the integration of AIS and SAR data. A case study has been analyzed, in which the method has been tested on TerraSAR-X images collected over the Gulf of Naples, Italy. Presented results show that the matching is improved with respect to standard procedures. The proposed method limits the distance between the AIS report and the SAR-based detection to less than 150 m, which is in line with maritime surveillance needs.