Navigational Risk Research Articles

A Novel Fuzzy Multi-Factor Navigational Risk Assessment Method for Ship Route Optimization in Costal Offshore Wind Farm Waters

A Novel Fuzzy Multi-Factor Navigational Risk Assessment Method for Ship Route Optimization in Costal Offshore Wind Farm Waters

A decision support model for ship navigation in Arctic waters based on dynamic risk assessment

As countries increasingly operate commercially on Arctic routes, safe and efficient navigation in the complex Arctic waters becomes a key issue. For this reason, proposed here is a route planning method for ships based on a risk map formed by the dynamic correlation of multiple factors. Specifically, a dynamic Bayesian network is used to predict the spatial and temporal risks of the gridded Arctic waters, thereby constructing a navigation risk map of those waters. On the decision-making basis of avoiding high-risk waters while achieving the shortest voyage distance, the A* algorithm is used to plan a ship's route. A case study shows that the proposed method can (i) reflect objectively the changes in the temporal and spatial risk distributions of different types of ship navigating in Arctic waters and (ii) provide a direct basis for ship navigation route planning. In the entire Arctic waters, July to October is the period with the lowest risk for the cargo ship the Yong Sheng and the liquefied-natural-gas carrier the Boris Davydov to carry out commercial voyages. The navigable period for the Boris Davydov is longer than that for the Yong Sheng with five months.

Dynamic anti-collision A-star algorithm for multi-ship encounter situations

For the complex multi-ship encounter scenarios, this article proposes a dynamic collision avoidance path planning algorithm based on the A-star algorithm and ship navigation rules, namely Dynamic Anti-collision A-star (DAA-star) algorithm. A dynamic search mechanism of the DAA-star algorithm considering time factors is designed to enable the collision avoidance for situations with known moving obstacles. A quaternion ship domain is generated based on Automatic Identification System (AIS) data, and the navigation risk cost is calculated with the combination of the quaternion ship domain and potential field. The searching constraints conforming with the Regulations for Preventing Collision at Sea (COLREGS) rules are set for the DAA-star algorithm to guarantee the safety of collision avoidance. Meanwhile, the individual ship maneuverability constraints and maneuverability differences from ship to ship are both considered in the proposed DAA-star algorithm, which can solve the path planning problem with dynamic obstacles in multi-ship encounter scenarios. The simulation results show that, compared with the traditional A-star algorithm and dynamic A-star algorithm, the DAA-star algorithm can generate more reasonable dynamic and static obstacle avoidance paths in complex navigation scenarios in the trade-off between the navigation risk and economical efficiency.

OPTIMAL NAVAL PATH PLANNING IN ICE-COVERED WATERS

The Northern Sea Route (NSR) links the Atlantic and Pacific oceans through the Arctic and it is critical for global trade as it provides a route between Asia and Europe that is significantly shorter than the alternatives. NSR is soon expected to open for intercontinental shipping due to global warming and thus presents tremendous opportunities for reductions in shipping time, cost, and environmental impacts. On the other hand, facilitating this route requires innovative approaches due to the navigation risks associated with its ice-covered waters. This study presents a graph-theoretical approach for optimal naval navigation in ice-covered sea routes with flexible turn angles based on the idea of large-adjacency grid graphs. Our model allows for asymmetric left and right turn radii as well as turn speeds that vary as a function of the turn angle and it offers natural-looking navigation paths.

RISK ASSESSMENT OF ARCTIC NAVIGATION BY USING IMPROVED FUZZY-AHP APPROACH

In this study, navigational risk factors of the Arctic Ocean are defined and numerical weights of each risk are obtained by using Improved Fuzzy Analytical Hierarchy Process (IF-AHP) method after conducting expert consultations. The Northern Sea Route shortens the maritime distance approximately 7000 nautical miles comparing to the conventional Suez Canal route. Therefore, it takes a significant role of being economic and time advantage for global logistics. Its geographical position, presence of ice, heavy weather conditions, strong currents and winds are some risks for Arctic transportation. There always have the possibility of unpredictable catastrophes such as a collision, grounding, hull damage and etc. in this region. Reflections of such unwanted incidents might be very costly for economic, political, environmental and safety concerns. Due to there are limited academic studies regarding to analytical and systematical risk identification and determination of risk levels, this study contributes to complete this academic gap.

A Novel Method for Navigational Risk Assessment in Wind Farm Waters Based on the Fuzzy Inference System

Offshore wind power is an effective way to solve the energy crisis problem and achieve sustainable economic development. Aiming at the problems that the navigational risk of ships in the waters of offshore wind farms is difficult to quantify due to complex factors, this paper proposes a method of navigational risk assessment in the waters of offshore wind farms based on a fuzzy inference system. Firstly, through the analysis of the factors affecting the navigation system of wind farm waters, it is found that the navigational risk is affected by natural factors and navigational environment factors. Then, the visibility, the number of traffic flows, the number of encounter areas, and the distance between the sailing route and the wind farm are extracted to evaluate the risk of natural factors and the risk of the sailing environment in the navigation system of the wind farm waters, respectively. Considering the mutual influence of the factors, the fuzzy inference rules of navigational risk influence are established according to the expert experience, and a method of navigational risk assessment based on the fuzzy inference system in offshore wind farm waters is developed. In order to verify the effectiveness of the proposed method, a comprehensive evaluation of the navigational risk of wind farm waters in Changle offshore sea of Fujian Province is carried out, and the evaluation results are consistent with the actual situation. The proposed method has important theoretical significance for the navigational safety supervision of offshore wind farm waters.

Near-Field Route Optimization-Supported Polar Ice Navigation via Maritime Radar Videos

The accurate design of ship routing plans in arctic areas is not easy, considering that navigation conditions (e.g., weather, visibility, and ice thickness) may change frequently. A ship’s crew identifies sea ice in arctic channels with the help of radar echoes, and ship maneuvering decisions are made to avoid navigation interference. Ship officials must manually and consistently change the ship’s route of travel, which is time-consuming and tedious. To address this issue, we propose a near-field route optimization model for the purpose of automatically selecting an optimal route with the help of radar echo images. The ship near-field route optimization model uses a multiobjective optimal strategy considering factors of minimum navigation risk and steaming distance. We verified the model’s performance with the support of the Xuelong voyage dataset. This research finding can help a ship’s crew to design more reasonable navigation routes in polar channels.

A Hybrid Iterated Greedy Algorithm for Hydrographic Survey Routing Problem

Hydrographic surveying is a necessary task in the maritime community, which can contribute to maritime security, economic development, scientific research, and environmental protection. Hydrographic surveying is a regular and costly activity; hence, careful hydrographic survey planning is required. Hydrographic survey routing is one of the main tasks in hydrographic survey planning, in which we not only need to find the most interesting maritime area(s) (usually evaluated through a risk measure, aggregating navigation risk, environmental impact, and/or data obsolescence), but also the shortest route to do the hydrographic surveying. In this article, first, we attempt to formulate the hydrographic survey routing problem and then develop an efficient hybrid iterated greedy algorithm to solve the problem. The proposed algorithm consists of three stages, that is, Stage 1 with a memetic algorithm to find a good starting point, Stage 2 with a global greedy algorithm to explore the global search space, and Stage 3 with a local greedy algorithm to exploit the local search space. Five real case studies in France are conducted to validate the performance of the developed algorithm.

An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP

Deep Reinforcement Learning (DRL) is widely used in path planning with its powerful neural network fitting ability and learning ability. However, existing DRL-based methods use discrete action space and do not consider the impact of historical state information, resulting in the algorithm not being able to learn the optimal strategy to plan the path, and the planned path has arcs or too many corners, which does not meet the actual sailing requirements of the ship. In this paper, an optimized path planning method for coastal ships based on improved Deep Deterministic Policy Gradient (DDPG) and Douglas–Peucker (DP) algorithm is proposed. Firstly, Long Short-Term Memory (LSTM) is used to improve the network structure of DDPG, which uses the historical state information to approximate the current environmental state information, so that the predicted action is more accurate. On the other hand, the traditional reward function of DDPG may lead to low learning efficiency and convergence speed of the model. Hence, this paper improves the reward principle of traditional DDPG through the mainline reward function and auxiliary reward function, which not only helps to plan a better path for ship but also improves the convergence speed of the model. Secondly, aiming at the problem that too many turning points exist in the above-planned path which may increase the navigation risk, an improved DP algorithm is proposed to further optimize the planned path to make the final path more safe and economical. Finally, simulation experiments are carried out to verify the proposed method from the aspects of plan planning effect and convergence trend. Results show that the proposed method can plan safe and economic navigation paths and has good stability and convergence.

The Methodology for Assessing the Impact of Offshore Wind Farms on Navigation, Based on the Automatic Identification System Historical Data

Mounting offshore renewable energy installations often involves extra risk regarding the safety of navigation, especially for areas with high traffic intensity. The decision-makers planning such projects need to anticipate and plan appropriate solutions in order to manage navigation risks. This process is referred to as “environmental impact assessment”. In what way can these threats be reduced using the available Automatic Identification System (AIS) tool? This paper presents a study of the concept for the methodology of an a posteriori vessel traffic description in the form of quantitative and qualitative characteristics created based on a large set of historical AIS data (big data). The research was oriented primarily towards the practical application and verification of the methodology used when assessing the impact of the planned Offshore Wind Farm (OWF) Baltic II on the safety of ships in Polish Marine Areas, and on the effectiveness of navigation, taking into account the existing shipping routes and customary and traffic separation systems. The research results (e.g., a significant distance of the Baltic II from the nearest customary shipping route equal to 3 Nm, a small number of vessels in its area in 2017 amounting to only 930) obtained on the basis of the annual AIS data set allowed for an unambiguous and reliable assessment of the impact of OWFs on shipping, thus confirming the suitability of the methodology for MREI spatial planning.

ALGORITHM DESIGN FOR SHIP’S STEERING WITH SPECIFIED LIMITATIONS UNDER VARIOUS WEATHER CONDITIONS

The problems of developing the risk assessment methods to estimate the level of safety of the vessel under the current conditions on a planned route before staring the pilotage as well as to make the decision on the beginning or suspension of pilotage in order to increase the level of navigational safety are discussed in the paper. Moreover, the application of the research results will reduce the affect of the human factor in decision-making in tasks related to the sea-going vessel’s operation. The developed method for the quantitative assessment of navigational risks will improve the safety of ship’s pilotage. It can also be applied in the decision-making support systems for the navigator in case of collision avoidance actions. The research results presented in this paper can be used to create automatic control systems.

Harbour Traffic Hazard Map for real-time assessing waterway risk using Marine Traffic Hazard Index

The evaluation of maritime waterway risk is required to take further advance measure for reduction of ship collision accident. In general, a risk assessment of ship collision can be estimated by analyzing the level of risk for navigation to prevent collision. This paper proposes a framework of real-time multi-ship collision risk assessment, named Habour Traffic Hazard Map to identify encounter ships, using Marine Traffic Hazard Index from the large amounts of monitored vessels in a surveyed sea area. The methodology uses the influence of a ship to the neighborhood to detect high-risk area in busy waterway from a AIS data. The proposed index is validated by comparison with traffic congestion rate of surveyed area. To illustrate the effectiveness of the framework proposed, an experimental case study has been carried out within Busan Port, Korea. The result is shown as hazard map that is effective and efficient at showing the dynamic level of risk and predicting the potential collision locations. Therefore, this model can improve the safety and security of maritime waterway.

Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China

Sea fog is a disastrous marine phenomenon for ship navigation. Sea fog reduces visibility at sea and has a great impact on the safety of ship navigation, which may lead to catastrophic accidents. Geostationary orbit satellites such as Himawari-8 make it possible to monitor sea fog over large areas of the sea. In this paper, a framework for marine navigation risk evaluation in fog seasons is developed based on Himawari-8 satellite data, which includes: (1) a sea fog identification method for Himawari-8 satellite data based on multilayer perceptron; (2) a navigation risk evaluation model based on the CRITIC objective weighting method, which, along with the sea fog identification method, allows us to obtain historical sea fog data and marine environmental data, such as properties related to wind, waves, ocean currents, and water depth to evaluate navigation risks; and (3) a way to determine shipping routes based on the Delaunay triangulation method to carry out risk analyses of specific navigation areas. This paper uses global information system mapping technology to get navigation risk maps in different seasons in Bohai Sea and its surrounding waters. The proposed sea fog identification method is verified by CALIPSO vertical feature mask data, and the navigation risk evaluation model is verified by historical accident data. The probability of detection is 81.48% for sea fog identification, and the accident matching rate of the navigation risk evaluation model is 80% in fog seasons.

An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning

The paper proposes a probabilistic framework for assessing the risk of ships based on a hybrid approach and multiple data sources. A Bayes-based network learning approach uses data from the New Inspection Regime of the Paris MoU on Port State Control to characterise the relationships among risk parameters and uses these parameters to evaluate the ship static risk. Other data sources are used to develop a Bayesian Network model to assess the dynamic risk of the ship. The data is aggregated by Bayesian Network and Evidential Reasoning approaches to evaluate the overall risk of ships in coastal waters. The objective of the study is to develop a model to assess the risk of an individual ship by considering its static risk profile and the geographical-dependant risk factors related to the characteristics of the maritime traffic flow and other local characteristics that influence the navigational risk of the ship. The results show that the integrated approach is able to assess the overall risk of a ship based on multiple data sources, providing empirical evidence of using multiple data sources in risk analysis applications. Moreover, the developed model identifies the most critical circumstances and the key impact factors in the study waters, which can support decisions on risk prevention and mitigation measures and local maritime traffic management.

Vessel navigation risk and stern-swing index in sharp bend channels

Owing to the drastic change in flow conditions and river boundary of sharp bend channels, marine accidents occur frequently. A typical sharp bend channel was selected, whose flow velocity and navigation were studied by using a scaled hydrodynamic model and scaled vessel model maneuvering test. Based on the vessel speed, drift angle, and transverse flow velocity, the stern-swing index Δ was deduced, and a navigation risk model was established, which showed different navigation status and corresponding ranges of Δ. Transverse flow velocity is usually larger at the impact point of current on the river bank. The impact point moves downstream when flow rate increases. The hull undergoes swing, rotation, and drift under the combined action of current force and rudder force, the resultant moment caused by these two forces was represented by Δ. When the absolute value of Δ is in the range of 0–1.2, it can be used to judge the risk of a vessel passing through the sharp bend channel.

Navigational Risk of Ships Proceeding Through a Fairway

Purpose: Navigational risk is a criterion for navigational safety assessment on fairways. To estimate navigational risk, we must determine the probability of an accident and its consequences. Design/Methodology/Approach: The authors have developed methods of determining navigational risks caused by deterioration in navigational conditions during the passage of ships through the waterway system and for the estimation of risk caused by shipboard equipment failures. In a simulation experiment, full ships with 5.000 DWT to 100.000 DWT capacity performed emergency stopping after rudder jamming. Findings: The results of simulation tests developed in the form of mathematical models constitute data for the determination of ship's navigational risk due to rudder failure. Practical implications: The models can be used for defining simplified relationships allowing authors to determine the width of safe emergency manoeuvring areas. Originality value: The procedure has been developed and navigation algorithms for determining the risk associated with the failure of the steering of the vessel passing through the different types of the fairway.

A Novel Adaptive Visual Analytics Framework for Multiship Encounter Identification

The automatic identification of multiship encounter is a vital criterion for ship collision avoidance and intelligent maritime safety surveillance. However, the parameters of ship encounter identification in the existing studies are fixed, and the methods are weak to give an automatic and visual performance in the multiship encounter identification. In order to fix the existed gap, this paper proposed a novel adaptive visual analytics framework for automatic multiship encounter identification based on density-based spatial clustering of applications with noise (DBSCAN) and visual analytics by adjusting the parameters of ship encounter adaptively. The DBSCAN clustering method was applied to detect the clusters of encounter ships and filter out the nonencounter ship, and the distribution and density of the encounter ship had been visualized on the nautical chart to give a better perception of ships’ behavior with a potentially high navigational risk. The framework had been designed and developed using DBSCAN and visual analytics, and the effectiveness was evaluated and validated by adjusting different parameters of multiship encounter within the Southwest waters of Zhoushan Island, China. The results showed that the proposed framework had a good performance in the visual identification of multiship encounter within confined waters, which could assist the ship collision avoidance and intelligent maritime surveillance system.

Research and Countermeasures on LNG Ship Port Area Navigation Risk Assessment Based on Fuzzy Comprehensive Evaluation Method

As a new energy source, LNG occupies an important position in the energy structure, and it is of great significance to carry out research on the navigation risks of LNG ships as carriers. Through the establishment of an LNG ship port area risk evaluation index system, the an exponential scale method in the analytic hierarchy process is used to calculate the weight value of each index, and the fuzzy comprehensive evaluation method is used to calculate the navigation risk level of LNG ships. An LNG ship in Lusi port area of Nantong Port is selected for risk assessment, and based on the results of the risk evaluation, a targeted risk control plan is proposed.

Risk Assessment of LNG Ship Navigation in Inland Water Based on FAHP-cloud Model

In order to understand the navigation safety risks of LNG ships in inland waterways, the paper combines the general accident risk assessment and the specific navigation environment of inland rivers[1], and analyzes the probability of collision accidents[9]. Combined with the characteristics of the specific navigation environment, to construct a hierarchical model of the risk factors for the navigation safety of inland LNG ships. Set the entry channel in the Dongsha operation area of Zhang jiagang Port area as the research scene, use the FAHP (fuzzy analytic hierarchy) process to determine the weight value of each navigation risk factor, use the cloud model to quantify the conversion of qualitative risk factors[13], quantitatively evaluate the risk level of risk factor indicators, and combine qualitative analysis to analyze the risk level of the approach channel in the Dongsha operation area from a quantitative perspective. The result objectively demonstrated the risk level of the channel, and verified that the evaluation result conforms to reality.

Research on Ship Meteorological Route Based on A-Star Algorithm

Ship meteorological navigation is based on hydrometeorological data of a certain time scale, considering the ship’s motion characteristics and its own characteristics. First, we provide the best route for the ship and then use real-time local weather information to correct the route during the ship’s navigation. It can also be expressed as follows: it is based on the hydrological and meteorological conditions of the ship during its voyage and the seakeeping characteristics of the ship itself, and the route planning method is used to select the best route for the ship. The best route is a balance between economy and safety, that is, based on ensuring the safety of ship navigation, the route that meets the shortest navigation time, the least fuel consumption, or the least navigation risk is obtained. Weather navigation includes the optimization of the initial route before sailing and the correction of the route after sailing. As there may be errors in hydrometeorological forecasts, especially in the accuracy and real-time performance of medium and long-term forecasts, the optimal initial route may not achieve the best results. Therefore, after the ship sails, it is necessary to adjust and correct the preferred initial route based on the meteorological information detected by the sensors or the continuously updated hydrometeorological forecast data to ensure the best effect of meteorological navigation. This paper proposes a weather route planning method based on the improved A-star algorithm. The convex shape of the concave obstacle and the expansion of the obstacle are carried out; according to the position of the target point relative to the starting point, the search direction of the A-star algorithm at each node is restricted, and an improved A-star algorithm is proposed. The simulation of global weather route planning shows that the improved A-star algorithm can not only find the optimal path but also effectively reduce the number of nodes that the algorithm needs to search during operation. Compared with the classic algorithm, the improved algorithm reduces the number of node searches by 29.25%.