Ensuring safety and preventing accidents in waterway channels are critical challenges for networked marine surface vessel systems (NMSVs). This study introduces a regulation-aware decision-making system designed to minimize traffic conflicts and enhance navigational safety in inland waterway traffic separation schemes. The proposed framework integrates a hierarchical conditional state machine with chance-constrained model predictive control, allowing NMSVs to handle complex traffic situations while complying with safety regulations. The hierarchical conditional state machine effectively identifies vessel maneuver states, implementing safety constraints that proactively avoid collisions. Meanwhile, the chance-constrained model predictive control optimizes vessel trajectories, factoring in uncertainties and potential risks, while simultaneously enhancing operational efficiency. Simulation and experimental results demonstrate that the proposed system significantly reduces the likelihood of accidents and improves overall safety by efficiently managing vessel interactions. Compared to traditional methods, the regulation-aware approach ensures better collision avoidance, greater regulation compliance, and superior safety performance. This study confirms that the proposed decision-making system can be effectively implemented in real time, offering practical benefits for improving waterway safety and mitigating accident risks.

The advancement of smart shipping and autonomous navigation relies on Automatic Identification System (AIS) data, which provides essential ship trajectory information. However, raw AIS data lacks semantic context, making behavior annotation crucial for understanding navigation tasks and processes. Existing research faces two challenges: (1) a lack of clarity on which semantics should be abstracted for effective behavior annotation, and (2) insufficient consideration of spatial interactions between ship maneuvering and the navigational environment, particularly topological interactions. These issues complicate data extraction and hinder machine learning-based applications such as explainable trajectory prediction. This paper proposes a comprehensive framework for semantic annotation and indexing of ship behavior. The framework deconstructs ship behavior into a unified data structure using a relational database, where three types of behavior semantics are defined, including atomic, topological, and traffic behavior. Atomic behaviors (e.g., move and stop) are extracted to annotate raw trajectories, while topological behaviors, describing interactions between trajectories and the environment, are modelled using an improved Dimensionally Extended 9-Intersection Model (DE-9IM). The combination of these semantics enables the annotation of higher-level traffic behavior. The model is further evaluated via behavior annotation statistics, demonstrating its effectiveness in annotation and indexing high-level ship behavior.

The development of advanced ship positioning and intelligent sensing technologies has transformed navigation at sea, moving beyond reliance on captains’ experience and standard routes. The trajectories traversed by ships at sea contain valuable data that can be mined to map maritime transportation networks and inform intelligent navigation systems. Ship trajectory data at scale enables discovery of the underlying network of maritime routes, providing key insights for applications like intelligent navigation, abnormal behavior detection, trajectory prediction, and maritime traffic pattern analysis. This study reviews the development of research on maritime route networks (MRNs) derived from ship trajectory data. It summarizes the technical process to construct a MRN, contrasting approaches for identifying waypoints, extracting routes, and representing the overall maritime traffic network structure. Finally, this study explores potential applications of MRNs and anticipates promising future research directions in this domain.

The theoretical analysis of maritime accidents is a hot topic, but the time characteristics and dynamics of maritime accidents time series are still unclear. It is difficult to draw a clear conclusion from the cause analysis, so the accident is difficult to be predicted. To bridge this gap, this research analyzes the characteristics and evolution mechanism of maritime accidents time series from the perspective of complex network theory. The visual graph algorithm is used to model the complex network of maritime accidents data in 22 jurisdictions of the Yangtze River, map the time series into a complex network, and reveal the time characteristics and dynamics of maritime accidents time series based on the complex system theory. In the empirical analysis, degree distribution, clustering coefficient and network diameter are used to analyze the characteristics of time series. The results show that the degree distribution of maritime accidents time series network presents power-law characteristics in the macro and micro levels, which shows that the maritime accidents time series is scale-free. In addition, according to the clustering coefficient and network diameter, maritime accidents time series in the Yangtze River has the characteristics of small-world and hierarchical structure. The research of this manuscript shows that the occurrence of maritime accidents is not random events and does not follow specific patterns but presents the characteristics of complex systems, and this phenomenon is common. The analysis of maritime accidents time series by complex network theory can provide theoretical support for maritime traffic safety management.

Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method to detect fishing vessels’ fishing activities based on the defined local dynamic parameters and global statistical characteristics of vessel trajectories. On a local scale, the stop points and points of interest (POIs) in the vessel trajectory are extracted. Voyage extraction can then be conducted on this basis. After that, multiple characteristics based on motion and morphology on a global scale are defined to construct a logistic regression model for fishing behavior detection. To verify the effectiveness and feasibility of the method, vessel trajectory data, and fishing log data collected from Chinese ocean squid fishing vessels in Argentine waters in 2020 are integrated for fishing operation detection. Multiple evaluation metrics show that the proposed method can provide robust and accurate recognition results. Moreover, further analysis of the temporal and spatial distribution and seasonal changes in squid fishing activities in Argentine waters has been performed. A more refined assessment of the fishing activities of individual fishing vessels can also be provided quantitatively. All the results above can benefit the regulation of fishing activities.

The construction of maritime route networks holds significant importance for autonomous navigation of vessels. In this study, a two-layer maritime route network modeling method based on huge amounts of ship trajectory data was proposed. Firstly, we introduce a novel method for extracting nodes of the marine route network, which identifies feature points in ship trajectories through clustering. Secondly, we use a spatial computing method to transform ship trajectory data into a sequence of waypoint regions and establish a node connection matrix to realize the nodes' connection of the topological layer route network. And routes are extracted between waypoint regions to characterize the connection relationship of the geometric layer network. Finally, by connecting nodes of the topological layer with the support of the connection matrix and waypoint regions of the geometric layer with the route, the two-layer maritime route network that combines topological and geometric layers is constructed. The proposed method was applied to the waters of Vancouver, successfully constructing a topological-geometric two-layer maritime route network. Overall, the proposed method is beneficial for improving the safety and efficiency of autonomous navigation of ships, and has a positive impact on the development of smart shipping industry.

Vessels sailing in a single platoon could reduce resistance from the perspective of the whole platoon and the individual vessel, and contribute to improving energy benefits. Moreover, transportation energy costs and traffic efficiency are essential indicators for measuring waterborne transportation systems. We attempt to minimize transportation energy costs by coordinating platoon formation using a distributed framework of controllers. A large-scale coordinated vessel platooning program is proposed to minimize transportation energy costs and optimize traffic efficiency while guaranteeing safety. The control framework covers routing, energy consumption-dependent cooperative platooning decision and speed optimization based on graph search algorithm, cluster analysis, optimal control approach and model predictive control. Firstly, a local scheduling strategy combined with the leader vessel selection algorithm is adopted. Furthermore, we used cluster analysis to create a series of mergeable vessel platooning sets. Then, we used the mathematical planning method and a two-step hybrid optimal control approach to calculate the improvement and optimization of each vessel platoon's path and speed. Finally, the scalability of the scheduling strategy is elucidated. In a simulation of large scale inland waterborne network, savings surpassed 3.5% when six hundreds vessels participated in the system. These simulation results reveal that the scheduling strategy coordinating vessels into vessel platooning, which improves transportation efficiency as well as descends cost, comparing to a fixed origin route in the waterway network.

Ship behavior is the semantic expression of corresponding trajectory in spatial-temporal space. The intelligent identification of ship behavior is critical for safety supervision in the waterborne transport. In particular, the complicated behavior reflects the long-term intentions of a ship, but it is challenging to recognize it automatically for computers without a proper understanding. For this purpose, this study provides a method to model the behavior for computers from the perspective of knowledge modeling that is explainable. Based on our previous work, a semantic model for ship behavior representation is given considering the multi-scale features of ship behavior in cognitive space. Firstly, the multi-scale features of ship behavior are analyzed in spatial-temporal dimension and semantic dimension individually. Then, a method for multi-scale behaviors modeling from the perspective of semantics is determined, which divides the behavior scale into four sub-scales in cognitive space, considering spatial and temporal dimensions: action, activity, process, and event. Furthermore, an ontology model is introduced to construct the multi-scale semantic model for ship behavior, where behaviors with different semantic scales are expressed using the functions of ontology from a microscopic perspective to a macroscopic perspective consecutively. To validate the model, a case study is conducted in which ship behavior with different scales occurred in port water areas. Typical behaviors, which include leveraging the axioms expression and semantic web rule language (SWRL) of the ontology, are then deduced using a reasoner, such as Pellet. The results show that the model is reasonable and feasible to represent multi-scale ship behavior in various scenarios and provides the potential to construct a smart supervision network for maritime authorities.

With the continual development of modern transportation technology and artificial intelligence technology, how to recognize the complex phenomenon of ship behavior existing in maritime traffic has become a hot topic. Maritime traffic is a complex system, the emergence of ship behavior is a leading cause of traffic complexity, and make up the core ideas of this research. This research studies ship behavior from three aspects: ship individual behavior, ship-ship interaction and multi-ship behavior. According to the movement state attribute, the improved Social Force Model has been developed by considering of the interactive effects between ships. On that foundation, the complex network model has been built to analyze the emergence of multi-ship behavior in a macroscopic view. Through experimental analysis of ship behavior in different scenarios, the results show that the repulsive force between ships changes in the ship behavior dynamic model can express the dynamic characteristics of the ship. And structural entropy in marine traffic situation complex network has been proved to describe the maritime traffic system. As such, the framework proposed in this paper can provide a new perspective for further understanding and research of ship behavior.

Aiming at the cognition problem of systematic ship behavior in harbor, a behavior recognition model based on semantic reasoning based on discrete event system modeling theory was proposed. Firstly, the hierarchical modeling of the behavior of ships in port waters is divided into data layer, event layer, activity layer and process layer. Based on a certain theoretical understanding of ship behavior on different time scales and space scales, build a ship behavior cognitive model; Secondly, in the data layer, the trajectory key point detection and segmentation extraction of the motion trajectory of port ship AIS data are performed by integrating port navigation rules to realize the labeling of ship trajectories. Finally, based on the labeling results of the data layer, the ontology is used to make inferences to discover the implicit ship behavior, and realize the trajectory of the ship from the data layer to the semantic layer. Experiments were performed using Xiamen Port data. The experimental results show that the behavioral cognitive ontology based on discrete system modeling can realize the cognitive and semantic reasoning of ship behavior at different time and space scales.

The essence of the path planning problems is multi-modality constraint. However, most of the current literature has not mentioned this issue. This paper introduces the research progress of path planning based on the multi-modality constraint. The path planning of multi-modality constraint research can be classified into three stages in terms of its basic ingredients (such as shape, kinematics and dynamics et al.): Route Planning, Trajectory Planning and Motion Planning. It then reviews the research methods and classical algorithms, especially those applied to the Unmanned Surface Vehicle (USV) in every stage. Finally, the paper points out some existing problems in every stage and suggestions for future research.

This paper investigates the motion-planning problem for an unmanned surface vehicle (USV), in which the goal is to find the shortest search time, the shortest path in navigational waters, all subject to collision avoidance and USV dynamics constraints. A new motion-planning method is proposed, based on topological position relationships (TPR), to achieve this solution. Firstly, the TPR of the obstacles and the USV are constructed, based on the spatial distribution of the obstacles. This gives an overall topological navigation map, which is different from the usual grid-based map. Secondly, a numerical model of unit decomposition is built to constrain the dynamics of the USV, so that the motion of the USV better fits the exact situation. Motion planning in this study is achieved by combining the topological navigation map and a numerical model of the USV. Finally, Numerical simulations and field tests verify the effectiveness of our formulated model and proposed algorithm.

The maneuvering characteristics of the unmanned surface vehicle itself are very important to motion planning due to the limited water scale area. If the size, motion state, and maneuvering characteristics of the unmanned surface vehicle are not considered, the shortest path obtained is actually not feasible in the restricted waters. In this article, the widely used A* algorithm is improved by accounting for the maneuvering characteristics of the unmanned surface vehicle, named as the Label-A* Algorithm, which is further employed to fix the problem related to the motion planning for the unmanned surface vehicle in restricted waters. The solution to the motion planning mainly contains three stages. First, the unmanned surface vehicle trajectory unit library is established based on its maneuvering characteristics; second, an improved label-A* Algorithm is constructed, and the unmanned surface vehicle motion planning method is proposed with the trajectory unit, which is suitable for the restricted waters; Finally, numerical simulations and filed tests are designed to verify the formulated model and proposed algorithm. The motion planning method can simultaneously meet the state constraints, maneuvering characteristics constraints, and water scale constraints of unmanned surface vehicle.

Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.

With the booming of marine industry, occurrence of maritime oil spills will not only loss of life and property, but also has brought about a long-term and bad influence to ocean environment and ecosystem system. This paper introduces a platform architecture for marine oil spill response capacity visualization, and elaborates the visualization of oil spill emergency elements, emergency resources dynamic interconnection and regional oil spill response capability with marine oil spill accidents. The platform is based on the B/S (browser/server) framework along with the tools of Baidu map API and MapV visualization system to realize the oil spill emergency resources visualization, oil spill emergency capability visualization and oil spill emergency response. The successful running of the platform provides scientific support for the decision-making in handling the oil spill emergency.

Traffic flow theory has been applied to the study of marine traffic flow and underpins the phenomena being studied. This paper is intended to demonstrate the uses of classical fundamental diagrams, which are developed previously on the basis of highway traffic, by conducting empirical tests on marine traffic data. Marine traffic data are extracted from the Automatic Identification System (AIS) from Shanghai port in 2010 and 2018. Interpolation methods are used to estimate the three primary variables of fundamental diagrams, namely, flow rate, traffic density, and local speed. The relationships among these three variables are discussed and marine traffic data are further compared and contrasted with classical traffic flow models. The importance of the finding is to create consistency, predictability and uniformity of uses of fundamental diagrams on marine traffic flow.

The special SOX Emission Control Areas (ECAs) had been established to reduce pollution in coastal region. Real-time ship emissions monitoring should be strengthened in SECA. The mainstream remote measurements of gas concentrations are the optical or sniffer monitoring methods. Exhaust emissions location and intensity could not be acquired by methods of remote measurements. Based on monitoring multiple sensors, this paper presents a new inversion method for characteristics of single ship emissions. The Gaussian plume diffusion model (GPDM) and Genetic Algorithm (GA) to calculate the position and concentration of single ship emissions. Simultaneous measurements of CO2, SO2 and NOX concentrations can be achieved by several monitoring stations on the wharf. Multiple solutions of the ship emissions location and intensity have been calculated by the selection, crossover and mutation of GA. As parameters of Gaussian plume diffusion model, those solutions are employed to calculate the gas concentrations on the location of the monitoring stations. The optimal one can be acquired by comparing the measurement and the calculation. The presented algorithm is verified by the real practice for Yantian harbor in Shenzhen.

It is of critical importance to prevent collisions at sea for navigation safety. Some popular techniques have been proposed and been used in practice, e.g. closest point of approach, collision threat parameters area, etc. However, most of these techniques assume that the target ship keeps a constant velocity which is unrealistic and may easily lead to a false alarm. In this article, Velocity Obstacle (VO) algorithms are applied to support collision avoidance with target ships whose trajectories are non-linear (with time-dependent velocities) and (probabilistically) predictable. In particular, Linear-VO, Non-Linear VO, and Probabilistic VO algorithms are used for this purpose. Compared to traditional approaches, these algorithms are capable of detecting collision dangers with target ships sailing non-linearly and (probabilistically) predictably, finding collision-free velocities in multi-ship scenarios, and preventing collisions. Two scenarios are designed to demonstrate the performance of VO algorithms. The results show that the proposed VO algorithms can facilitate collision detection and offer proper collision-free solutions for ships.