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.@en

Spatiotemporal companion pattern (STCP) mining is one of the means to identify and detect group behavioral activities. To detect the spatiotemporal traveling pattern of ships from massive spatiotemporal trajectory data and to understand the movement law of group ships, this article proposes a feature-driven approach for STCP mining that consists of (1) generating the grid index via the rasterizing of geospace and characterizing trajectory points via the spatiotemporal trajectory grid sequences (STTGSs) of ships; (2) designing filtering rules with the constraints of range, time and distance to construct a candidate set for ship STCP mining; and (3) measuring the STTGS similarity of the associated ships and setting the confidence threshold to realize spatiotemporal companion mining. The effectiveness of the proposed method is practically validated on a real trajectory dataset which is collected from the Taiwan Strait waters. The experimental results are as follows: 825 pairs of associated ships and 225 pairs of accompanying ships are mined when the grid size is 0.05° and the confidence is 0.5. Larger grid sizes can increase the inclusiveness of the associated ship trajectory similarity measurement, which can result in an increase in confidence of pattern. A large number of pseudo-accompaniment ships are extracted to the result set, resulting in a more dispersed distribution of pattern confidence. By verifying the proposed method, accompanying behavioral activities such as ship cooperative operation, companion navigation method, and so on, can be detected. These results can provide a reference for the research of ship group behavior identification and have an important application value for water transportation management.@en

The remote monitoring method based on air-quality monitoring sensors is a common way to monitor the FSC (fuel sulfur content) of oils for ships. Considering the influences of geographical environments, atmospheric conditions, regional ship traffic flow, emission characteristics of ships, and height of monitoring sensors on the monitor station chosen, a new method was proposed to optimize the site selection for monitoring the FSC of fuel oils used by ships in waters of the port. SO2 numeric simulation and observation from sensors were integrated to estimate the FSCs. The proposed method was utilized to recommend the sites of the fixed sniffing monitoring stations in Yantian port, Shenzhen, China from June and July 2018. The results showed that the experimental stations could monitor FSCs, and the relative difference between the estimated and actual FSCs of ships was 16.34%. The proposed method for recommending sites of FSC monitoring sensors contributed to intelligently supervising air pollutants emitted from ships and fuel oils of ships in the emission control areas of China.@en

To simplify the micro-level CO2 (carbon dioxide) emission calculation model, reduce the dataset quality requirement of the model, and cut down the volume of calculation, a meso-level voyage-based emission model (MeVEM) for inland ships is proposed with their navigation characteristics considered. The navigation characteristics and the main influencing factors of inland ship emissions are analyzed. The main engine power and average speed of the ships are selected as the model inputs. Accurate CO2 emissions are calculated by the use of the micro-level emission model. With that, first-order and second-order polynomial regression models are employed to establish the fitting formula to estimate the emissions per kilometer. To validate the proposed model, the Junshan segment in the middle reaches of the Yangtze River is selected as the study area, and the model parameters are determined to estimate the CO2 emissions. It is found that the model of emission per kilometer (ej, k) established by second-order polynomial regression is more accurate. The results show that the percentage error in the total amount (PETA) of the results estimated by the four proposed models (CO2 emission estimation model for the upstream cargo ships, the downstream cargo ships, the upstream oil tankers, and the downstream oil tankers) are all within ±5%, which verifies the feasibility and applicability of the model. The proposed meso-level model allows us to use a smaller input dataset which is easier to obtain, and estimate CO2 emissions from ships simply and accurately.@en

Future emission reductions can be guided by a timely evaluation of the impact of emission reduction initiatives in the marine sector. To forecast carbon emission reductions and assess the consequences of various emission reduction techniques, we offer a generic framework for a ship carbon emission prediction model based on several emission reduction scenarios. Three factors—port and ship electrification (PSE), clean fuel substitution (CFS), and ship speed optimization (SSO)—are combined to create emission reduction scenarios with various intensities (lenient, intensive, and aggressive). In the case of Wuhan Port, the effect of carbon reduction was evaluated by comparing various measurements with the baseline scenario. The results indicate that the aggressive PSE and CFS scenarios will reach the carbon peak in 2029 and 2025, respectively. Finally, the possibility of reducing emissions offered by various solutions is examined, and recommendations for port ships' carbon footprint reduction are provided.@en

Shipping emissions have been considered a significant source of air pollution in the cities along the Yangtze River, with severe impacts on the climate and human health. This study created a complete annual ship emission inventory for the middle reaches of the Yangtze River and assessed its impact on air quality on a regional scale. To estimate the complete emissions, 9 main engine power regression models for different ship types were created to handle those vessels with absent main power data, and a high spatial-temporal resolution annual emission inventory was developed with the activity-based method combined with Automatic Identification System (AIS) data of the full year of 2018. The total emissions of CO2, CO, SO2, NOX, PM2.5, PM10 and HC in middle reaches of the Yangtze River were 5.67 × 105, 1.02 × 103, 5.41 × 102, 1.06 × 104, 2.43 × 102, 2.45 × 102 and 3.52 × 102 tons respectively. Then, the Weather Research and Forecasting with Chemistry (WRF-Chem) model was used to study the dispersion of the ship pollutants in the atmosphere and quantize the impact on the urban area. This research will provide services for the maritime authorities to develop green shipping and emission supervision.@en

This study develops a system named Wuhan University of Technology Intelligent Tracer (WUT-iTracer) for identifying ships using high-fuel-sulfur-content (FSC) fuel oil in port waters. The system integrates a land-based sniffing monitoring module and a numerical simulation module. The land-based sniffing monitoring module collects and processes simultaneous several parameters from the automatic identification system (AIS) and micrometeorological monitoring system, which include the concentrations of gaseous pollutants emitted from ships, marine meteorological parameters, and dynamic and static information of ships. The numerical simulation module was developed based on a mobile ship emissions diffusion model, which estimates the concentration of SO2 at the location of the WUT-iTracer assuming the ship using compliant fuel. The relative difference between the measured and simulated concentration of SO2 is used to detect if the ship uses high-FSC fuel oil. The WUT-iTracer was installed on the shore of Yantian Port, Shenzhen, China, during June and August 2018. Ten ships were selected as samples to verify the efficacy of the WUT-iTracer. Based on these data, it was found that the ship can be treated as using high-FSC (>0.5%) fuel oil if the relative difference between the measured and simulated SO2 concentration is larger than 44%. The field test shows that the WUT-iTracer works well for identifying ships with high-FSC fuel oil. However, further tests and developments are needed for the system to work in different ports and weather conditions.@en

A method is proposed for remotely detecting sulfur content in fuel oil used by ships in emission control areas (ECAs) based on the direct collection of SO2 emission data from the plume of the ship and numerical simulation models. Assuming a ship in the ECA uses fuel oil with compliance fuel sulfur content (FSC), activity-based ship emission assessment model is firstly utilized to calculate the SO2 concentration emitted from the ship. Numerical simulation models are then used to estimate the theoretical SO2 diffusion concentration at the location of the monitoring equipment. The observed SO2 concentration at the same time can then be integrated to estimate the FSC of the ship. A sample of eleven ships was selected to verify the proposed method in Yantian port, Shenzhen, China in June 2018. The result illustrates that the method can remotely detect ships using fuel oil with the FSC greater than 0.146%m/m in the ECAs, the relative error is 46%.@en

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.@en

The purpose of this paper is to evaluate the effect of the seasonal flow field on inland ship emissions and to improve calculation accuracy. Firstly, the flow field model is built through numerical simulation to correct the sailing speed of the ship from the Automatic Identification System (AIS) information in real-time. Then, an optimal emission estimation model for inland ships considering flow field factors is proposed. Finally, the effectiveness of the optimization model is demonstrated by a case study, and the influence of the seasonal flow field on emission calculation is analyzed. It indicates that the calculation results of the model considering the influence of the flow field are more accurate. Without considering the flow field, the results of emission calculations are often underestimated, especially in summer, which shows the importance of incorporating the flow field factors into the calculation of inland ship emissions.@en

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.

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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.

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The activity-based methodology is becoming an increasing way to calculate exhaust emissions from ships in a port. Existing studies make great effort to build and analyze ship emission inventory in a variety of ports by applying this method to historical ship trajectory data. This kind of static emission inventory however, cannot meet the needs of real-time ship emission monitoring. This article proposes a method of dynamic calculation of ship exhaust emissions based on real-time ship trajectory data. Firstly, real-time ship AIS messages are partitioned into continuous data blocks and go through a series of pre-processing operations, including trajectory extraction, association and interpolation. Ship activity parameters are then determined by database querying and regression analysis based on ship attributes. Subsequently, an improved activity-based methodology is employed to estimate exhaust emissions from ships in a distributed way. Based on the grid model, regional ship exhaust emissions can be statistically and dynamically calculated by the spatial allocation of all ship emissions. In a case study, a real-time monitoring platform for ship exhaust emissions in Shenzhen port is developed to demonstrate the effectiveness of the proposed method.@en

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.

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A trajectory-cell based method was proposed for unmanned surface vehicle (USV) motion planning to combine the expression of the dynamic constraints and the discretization of the search space. The dynamic constraints were expressed by the USV trajectories produced by the mathematical model. The search space was performed by the discretization rules with the consideration of the path continuity, the search convenience and the maneuvering simplification. Therefore, the trajectory-cells were the discretized trajectories, which made the search space meet the USV dynamic constraints, and guaranteed the final spliced path continuous. After abstracting the characteristics of those cells, the available waypoints and headings were represented as the search indexes. Finally, a trajectory-cell based path searching strategy was proposed by determining the cost function of the A* algorithm. The results showed that the proposed algorithm can plan a practical motion path for the USV.@en

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.@en

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.@en

Stops along taxi trajectories, such as picking up and dropping off passengers, are spatially clustered and related to certain attributes of places where stops are made. To detect the hidden knowledge regarding these places, this article examines the semantics of massive taxi stops in a large city. Each taxi trajectory is modeled as a series of sequential semantic stops labeled by street names. All the trajectories can be examined as a document corpus, from which the hidden themes of the stops are identified through Latent Dirichlet Allocation model. Conventional GIS tools are coupled with topic modeling toolkit to visualize and analyze potential information of stop topics for understanding intra-city dynamics. The effectiveness of this approach is illustrated by a case study using a large dataset of taxi trajectories including approximately 4,000 taxis in Wuhan, China.@en

Aiming at fine motion control on a small scale area for unmanned surface vehicle (USV), an approach for motion planning based on Trajectory Unit has been proposed. By making use of USV's hydrodynamic model and combining corresponding rules, the method generates a Trajectory Unit set which contains different orbit segments. Through the location and direction that the set of tracks can reach, the route searching in research waters can be done. Finally, a practical motion route will be planned for an USV. The experimental results show that, the planning route can not only avoid obstacles, but also meet the movement characteristics of an USV. And the approach has realized the fine motion control for an USV in a small range of scenarios.@en

Regarding aiming at multiple mobile objects in a complex water traffic- navigation environment, the chapter puts forward a route-planning method based on the complexity map. First, a Complexity Map was established according to the theory of complexity measurement. Then, combined with the A* algorithm, the actual cost was modified by making use of the distribution of the complexity values. Meanwhile, to reduce the distance of the whole voyage and avoid the local minimum in the process of path-finding, the Euclidean distance from the current point to the target was set to estimate heuristic cost. In addition, by using the standardization method, the value of complexity and distance became dimensionless. Finally, considering the ship dimensions, a channel-boundary constraint was added. The experimental results show that in the case of satisfying the ship dimensions, the best path is close to the shortest route and avoids all the high complex areas.@en