Route Navigation Research Articles

A ship navigation information service system for the Arctic Northeast Passage using 3D GIS based on big Earth data

ABSTRACT Research on Arctic passages has mainly focused on navigation policies, sea ice extraction models, and navigation of Arctic sea routes. It is difficult to quantitatively address the specific problems encountered by ships sailing in the Arctic in real time through traditional manual approaches. Additionally, existing sea ice information service systems focus on data sharing and lack online calculation and analysis capabilities, making it difficult for decision-makers to derive valuable information from massive amounts of data. To improve navigation analysis through intelligent information service, we built an advanced Ship Navigation Information Service System (SNISS) using a 3D geographic information system (GIS) based on big Earth data. The SNISS includes two main features: (1) heuristic algorithms were developed to identify the optimal navigation route of the Arctic Northeast Passage (NEP) from a macroscale perspective for the past 10 years to the next 100 years, and (2) for key sea straits along the NEP, online local sea-ice images can be retrieved to provide a fully automatic sea ice data processing workflow, solving the problems of poor flexibility and low availability of real sea ice remote sensing data extraction. This work can potentially enhance the safety of shipping navigation along the NEP.

Modeling the hydrodynamic interactions between the river morphology and navigation channel operations

The Nile River is the main route for inland navigation in Egypt. The vessels navigating through inland waterways generate complex physical forces that need to be studied extensively. Quantifying the effects of vessels sailing along a waterway is a complex problem because the river flow is unsteady and the river bathymetry is irregular. This paper aims to investigate the hydrodynamic effects resulting from the movement of vessels such as return currents around the vessel, the draw down of the water surface, under keel clearance, and the shear stress induced by vessels operating in the Nile River. Modeling such effects has been performed by applied the two-dimensional ADH (adaptive hydraulics) model to a river reach for different navigation channel operation scenarios. The obtained results show that the draw down heights, the water fluctuation, and the shear stress magnitude are larger when the river cross sectionals are narrow and the shallow water depths. These river sections are considered more disposed to bed erosion and it is morphologically unsafe. The section having the narrowest width and the lowest depth was associated with the largest drawdown percentages of 98.3% and 87.3% in one-way and two-way scenarios. While the section having the widest width and the largest depth was associated with the least drawdown percentages of 48.5% and 51.9% in one-way and two-way scenarios. The section having the narrowest width and the lowest depth was associated with the largest fluctuations of 22.0 cm and 41.9 cm in one-way and two-way scenarios. While the section having the widest width and the largest depth was associated with the least fluctuations of 0.6 cm and 1.8 cm in one-way and two-way scenarios. The section having the narrowest width and the lowest depth was the worst section for under keel clearance of 5.0 cm and 33.3 cm in one-way and two-way scenarios. While the section having the widest width and the largest depth was the best section, where its clearance values were 183.2 cm and 155.0 cm in one-way and two-way scenarios. It is concluded that a numerical model is a valuable tool for predicting and quantifying the hydrodynamic effects of vessels moving through a two-dimensional flow field and can be used to evaluate different scenarios that are difficult to measure in the field or a physical model. Also, it provides visualization products that help us understand the complicated forces produced by vessels moving in a navigation channel.

Extracting the Maritime Traffic Route in Korea Based on Probabilistic Approach Using Automatic Identification System Big Data

To protect the environment around the world, we are actively developing ecofriendly energy. Offshore wind farm generation installed in the sea is extremely large among various energies, and friction with ships occurs regularly. Other than the traffic designated area and the traffic separate scheme, traffic routes in other sea areas are not protected in Korea. Furthermore, due to increased cargo volume and ship size, there is a risk of collisions with marine facilities and marine pollution. In this study, maritime safety traffic routes that must be preserved are created to ensure the safety of maritime traffic and to prevent accidents with ecofriendly energy projects. To construct maritime traffic routes, the analysis area is divided, and ships are classified using big data. These data are used to estimate density, and 50% maritime traffic is chosen. This result is obtained by categorizing the main route, inner branch route, and outer branch route. The Korean maritime traffic route is constructed, and the width of the route is indicated. Furthermore, this route can be applied as a navigation route for maritime autonomous surface ships.

A framework for quantifying fatigue deterioration of ship structures under changing climate conditions

ABSTRACT The randomness of sea conditions and loading sequences are among the key contributing factors that affect our ability to accurately predict the fatigue crack growth in ships. Climate change may alter the long-term characteristics of these factors along the service life of existing or newly constructed ships. This paper presents a framework for quantifying the impact of climate change on crack propagation in ship hulls. A probabilistic fatigue crack propagation approach is developed to account for uncertainties associated with material properties and loading conditions; specifically, those affected by climate change. Global Climate Models (GCMs) are used to quantify the long-term effects of climate change on the sea conditions and the resulting ship loading time histories. The proposed approach is applied to a tanker ship operating within predefined routes in the Atlantic Ocean. The results show that the effect of climate change on the crack propagation depends on the navigation route.

ОБЪЕДИНЯЯ ЕВРАЗИЮ: ПЕРСПЕКТИВЫ РАЗВИТИЯ СЕВЕРНОГО МОРСКОГО ПУТИ

The article considers the history of the formation of the Northern Sea Route, reveals its importance as a transit artery, including in such key historical events of the history of Russia as the civil war of 1917–1922 and the Great Patriotic War of 1941–1945. The article also analyzes the current state of the Northern Sea Route, identifies opportunities, issues, and prospects for using that navigation route. The measures taken by the government of the Russian Federation for the further development of the modern icebreaker fleet are revealed. The importance of the Arctic zone of the Russian Federation as a strategically important region due to its geopolitical potential and mineral reserves is noted. The article specifies the key role of the Arctic in ensuring Russia’s energy and economic security. It also discloses the main export-transit direction of Arctic communications. The author considers issues of the development of international cooperation in the Arctic and emphasizes the increasing relevance of the Northern Sea Route in the context of changing climatic conditions. In his opinion that navigation route is not only an effective alternative to the Suez Canal, but also an additional factor in Russia’s political and economic rapprochement with key Asian players, in particular, China

Neural basis of topographical disorientation in the primate posterior cingulate gyrus based on a labeled graph.

Patients with lesions in the posterior cingulate gyrus (PCG), including the retrosplenial cortex (RSC) and posterior cingulate cortex (PCC), cannot navigate in familiar environments, nor draw routes on a 2D map of the familiar environments. This suggests that the topographical knowledge of the environments (i.e., cognitive map) to find the right route to a goal is represented in the PCG, and the patients lack such knowledge. However, theoretical backgrounds in neuronal levels for these symptoms in primates are unclear. Recent behavioral studies suggest that human spatial knowledge is constructed based on a labeled graph that consists of topological connections (edges) between places (nodes), where local metric information, such as distances between nodes (edge weights) and angles between edges (node labels), are incorporated. We hypothesize that the population neural activity in the PCG may represent such knowledge based on a labeled graph to encode routes in both 3D environments and 2D maps. Since no previous data are available to test the hypothesis, we recorded PCG neuronal activity from a monkey during performance of virtual navigation and map drawing-like tasks. The results indicated that most PCG neurons responded differentially to spatial parameters of the environments, including the place, head direction, and reward delivery at specific reward areas. The labeled graph-based analyses of the data suggest that the population activity of the PCG neurons represents the distance traveled, locations, movement direction, and navigation routes in the 3D and 2D virtual environments. These results support the hypothesis and provide a neuronal basis for the labeled graph-based representation of a familiar environment, consistent with PCG functions inferred from the human clinicopathological studies.

An algorithm for comparative analysis of power and storage systems for maritime applications

This paper presents an innovative algorithm to compare traditional and innovative energy systems onboard for maritime applications. The solutions are compared adopting a multi-criteria method, considering four parameters (weight, volume, cost, emissions) and their relevance according to the kind of ship and navigation route. The algorithm, which includes a large and updated database of market solutions, leads to the implementation of HELM (Helper for Energy Layouts in Maritime applications) tool. HELM was conceived to support the design of maritime systems: it chooses the best technology comparing traditional marine diesel engines, propulsion systems with alternative fuels (methanol, ammonia, LNG) and innovative low-emission technologies (fuel cell and batteries). Two case studies are investigated: (i) a small passenger ship for short routes (ii) and a large size ro-ro cargo ship. For case (i), fuel cells represent a competitive solution, in particular considering navigation in emission control areas. For case study (ii) Internal Combustion Engines shows are the best solution. The evaluation of alternative fuels is performed, considering a sensitivity analysis on emissions’ importance: methanol, LNG, and ammonia are promising solutions. For case (i), the installation of electrical batteries is also evaluated to analyse potential advantages to reduce the amount of H2 stored onboard.

Fault-Tolerant Partition Resolvability in Mesh Related Networks and Applications

Fault-tolerance of a system measures its working capability in the presence of faulty components in the system. The fault-tolerant partition dimension of a network computes the least number of subcomponents of network required to distinctively identify each node in the presence of faults, having promising applications in telecommunication, robot navigation and geographical routing protocols. In this paper, certain triangular mesh networks including, triangular ladder (<i>Tl_s</i>), triangular mesh (<i>T_s</i>), reflection triangular mesh (<i>rl</i>(<i>T_s</i>)), tower triangular mesh (<i>Tr_s</i>) and reflection tower triangular mesh (<i>rl</i>(<i>Tr_s</i>)) networks are discussed for their partition and fault-tolerant partition resolvability. In this regard, it is shown that the partition dimension of these networks is 3, whereas their fault-tolerant partition dimension is 4.

Research on the Application of Cold Energy of Largescale Lng-Powered Container Ships to Refrigerated Containers

Abstract With the aim of considering the problem of excess fuel cold energy and excessive power consumption of refrigerated containers on large LNG-powered container ships, a new utilisation method using LNG-fuelled cold energy to cool refrigerated containers in cargo holds is proposed in this study, and the main structure of the cold storage in the method is modelled in three dimensions. Then, combined with the different conditions, 15 different combination schemes of high temperature cold storage and low temperature cold storage are designed to utilise the cold energy of LNG fuel, the exergy efficiency and cold energy utilisation rate calculation model of the system is established. The simulation tool ‘Aspen HYSYS’ is used to simulate and calculate the exergy efficiency and cold energy utilisation rate of the system under 15 combinations, verifying the feasibility of the scheme. According to the characteristics of such a ship’s cross-seasonal navigation routes and the number of refrigerated containers loaded in different ports, the combination schemes of the number of low-temperature cold storage and high-temperature cold storage are selected. Thus, the average exergy efficiency and cold energy utilisation rate of the whole line is obtained, which proves that LNG-powered container ships could effectively utilise the cold energy of LNG. By calculating the total electric energy consumed by refrigerated containers on the whole sailing route, before and after the adoption of the LNG cold energy method, it is found that the adoption of this new method can promote the realisation of energy saving and emission reduction of ships.

Combinatorial properties of flat sections of the generalized Pascal's pyramid and construction of navigation routes

Combinatorial properties of flat sections of the generalized Pascal's pyramid and construction of navigation routes

AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM

Accurate vessel trajectory prediction is essential for maritime traffic control and management. In addition to collision avoidance, accurate vessel trajectory prediction can help in planning navigation routes, shortening the sailing distance, and increasing navigation efficiency. Vessel trajectory prediction with automatic identification system (AIS) data has thus attracted considerable attention in the maritime industry. Original AIS data may contain noise, which limits their application in real-world maritime traffic management. To overcome this problem, this study proposes a vessel trajectory prediction method that combines data denoising and a deep learning prediction model. In this method, data denoising is realized in three steps: trajectory separation, data denoising, and standardization. First, outliers from the original AIS data samples are removed, after which the moving average model is employed to further clean up the data; finally the denoised data are standardized into uniformly distributed time-series data. Bidirectional long short-term memory (Bi-LSTM) is then applied for vessel trajectory prediction. The performance of the proposed prediction model was verified using data on the trajectories of ten vessels and comparing the results obtained with those obtained using other prediction models (exponential smoothing, autoregressive integrated moving average, support vector regression, recurrent neural network, and LSTM models); the trajectory data were downloaded from a public AIS database. The experimental results revealed that model prediction accuracy increased after the data denoising process. Specifically, the Bi-LSTM model had the lowest mean absolute error, mean absolute percentage error, and root-mean-square error, demonstrating that the proposed method is highly efficient for trajectory prediction and can help vessel traffic controllers predict accurate vessel tracks; this would enable them to take early preventive measures to avoid collisions and thus improve the efficiency and safety of maritime traffic.

Secured Route Selection Using E-ACO in Underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) are promising, emerging technologies for the applications in oceanic research. UWSN contains high number of sensor nodes and autonomous underwater vehicles that are deployed to perform the data transmission in the sea. In UWSN networks, the sensors are placed in the buoyant which are highly vulnerable to selfish behavioural attack. In this paper, the major challenges in finding secure and optimal route navigation in UWSN are identified and in order to address them, Entropy based ACO algorithm (E-ACO) is proposed for secure route selection. Moreover, the Selfish Node Recovery (SNR) using the Grasshopper Optimisation Algorithm (GOA) is used to minimize the packet loss in the UWSN. The performance of the proposed E-ACO method is compared with existing routing methods such as Secure Authentication with Protected Data Aggregation (SAPDA), Secure Energy Efficient and Cooperative Routing (SEECR), Fault Resilient Routing using Moth Flame Optimization (FRR-MFO) and Improved ACO (IACO) method. The packet delivery ratio of the proposed E-ACO with 500 nodes is 0.89 which is higher than other existing methods such as SAPDA, SEECR, FRR-MFO and IACO.

Predicting the Quality of Spatial Learning via Virtual Global Landmarks.

Analyzing the effects landmarks have on spatial learning is an active area of research in the study of human navigation processes and one that is key to understanding the links between human brain dynamics, landmark encoding, and spatial learning outcomes. This article presents a study on whether electroencephalography (EEG) signals related to virtual global landmarks combined with deep learning can be used to predict the accuracy and efficacy of spatial learning. Virtual global landmarks are silhouettes of actual landmarks projected into the navigator's vision via a heads-up display. They serve as a notable frame of reference in addition to the local landmarks we all typically use for route navigation. From a mobile virtual reality scenario involving 55 participants, the results of the study suggest that the EEG data associated with those who were exposed to global landmarks shows a visibly better capacity for predicting the quality of spatial learning levels than those who were not. As such, the EEG features associated with processing VGLs have a greater functional relation to the quality of spatial learning. This finding opens up a future direction of enquiry into landmark encoding and navigational ability. It may also provide a potential avenue for the early diagnosis of Alzheimer's disease.

Sector search strategies for odor trail tracking

Ants, mice, and dogs often use surface-bound scent trails to establish navigation routes or to find food and mates, yet their tracking strategies remain poorly understood. Chemotaxis-based strategies cannot explain casting, a characteristic sequence of wide oscillations with increasing amplitude performed upon sustained loss of contact with the trail. We propose that tracking animals have an intrinsic, geometric notion of continuity, allowing them to exploit past contacts with the trail to form an estimate of where it is headed. This estimate and its uncertainty form an angular sector, and the emergent search patterns resemble a "sector search." Reinforcement learning agents trained to execute a sector search recapitulate the various phases of experimentally observed tracking behavior. We use ideas from polymer physics to formulate a statistical description of trails and show that search geometry imposes basic limits on how quickly animals can track trails. By formulating trail tracking as a Bellman-type sequential optimization problem, we quantify the geometric elements of optimal sector search strategy, effectively explaining why and when casting is necessary. We propose a set of experiments to infer how tracking animals acquire, integrate, and respond to past information on the tracked trail. More generally, we define navigational strategies relevant for animals and biomimetic robots and formulate trail tracking as a behavioral paradigm for learning, memory, and planning.

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.

INVESTIGATION OF GIS BASED MOBILE ROUTE PLANNING – NAVIGATION APPLICATIONS FOR TOURISM ACTIVITIES

Abstract. Mobile devices can be used in many areas such as shopping, entertainment and education. The number of mobile applications which provides information about a city or region, helps to find route by navigation and makes accommodation plans with food and beverage suggestions, is also increasing. Since these devices are portable and have access to internet, they emerges as a new and important potential for tourism sector. In particular, the increase in the use of route planning and navigation applications, which are location based services within the scope of Geographic Information Systems, has also affected the tourism sector. Which route planning and navigation applications are used extensively by tour operators, tourist guides and individual visitors ? What are the strengths and weaknesses about these applications? Answers to such questions were sought in this study.

A review of level ice and brash ice growth models

AbstractBrash ice forms in harbours and ship channels from frequent ship passages and the resulting freezing–breaking cycles create a unique ice formation. The brash ice accumulation over the winter season is a result of meteorological, thermodynamical and mechanical processes. A reliable brash ice growth model is an important asset when determining navigation routes through ice conditions and when establishing port ice management solutions. This review aims to describe the brash ice development and its modelling as well as the key parameters that influence the brash ice growth and its estimation. This paper summarises the brash ice growth models and the fundamental theories of level ice growth upon which these models are based, and outlines the main knowledge gaps. The results highlight the importance of porosity and piece size distribution and their effect on the consolidation process. The inclusion of the brash ice lateral movement and the side ridge formation would improve the accuracy of forecast models. Furthermore, the findings of the study identify the effect of omitting meteorological parameters such as snow and radiation, from the brash ice growth models. Their contribution to the level ice thickness suggests a significant influence on the brash ice consolidation process.

Personalized Route Planning System Based on Driver Preference.

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers’ needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers’ preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness.

NAVIGATION PATHS AND URBANISM IN THE BASIN OF MEXICO BEFORE THE CONQUEST

AbstractThe island nature of the Aztec capital, Tenochtitlan, is an under-studied aspect in our understanding of this unique urban space, located in the Mexican highlands of Mesoamerica. The island location induces cross-links from aquatic and terrestrial paths to create connectivity and continuity within the lacustrine cultural landscape of the Basin of Mexico during the Postclassic period (a.d. 900–1521). Although Cortés described this city as the “Venice of the New World,” no specific and systematic investigation of facilities related to water transport has been carried out. In this article, I fill this gap through a study of navigation routes which were conceived to facilitate the continuous movement of people and goods through the numerous canals crisscrossing the Aztec capital, and which are identifiable by means of anthropic markers that respond to functional needs. Transition zones (piers, quays, shoreline areas), coordination zones (ports), and activity zones (customs facilities, warehouses, bridges, sacred sites) are all related to the practice of water transport and intimately related to terrestrial roads. I identify and locate these areas using a multidisciplinary methodology based on archaeological data, ethnohistorical testimonies, and pictographic and iconographic documents.

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.