The maritime industry plays a key role in reducing greenhouse gas (GHG) emissions, as an effort to combat the global issue of climate change. The International Maritime Organization (IMO) is ...targeting a 50% reduction in GHG emissions by 2050 compared to 2008. To measure Singapore's progress towards this target, we have conducted a comprehensive analysis of carbon dioxide (CO2) emissions from the Western Singapore Straits based on the voyage data from Automatic Identification System (AIS) and static information from Singapore Maritime Data Hub (SG-MDH). Two methodologies, the MEET and TRENDS frameworks were applied to estimate the emission volume per vessel per hour. The data analysis results were next aggregated and visualised to answer key questions such as: How did the carbon emission level change from 2019 to 2020, in general, and for specific vessel types? What are the top vessel types and flags that had the highest carbon emissions? Did the traffic volume and emission level decrease during the Circuit Breaker period in 2020? The results of this study can be used to review Singapore's emission control measures and will be of value to the Maritime and Port Authority (MPA) of Singapore responsible for managing CO2 emissions at the Singapore Port.
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•The MEET and TRENDS framework was used to estimate the CO2 emission per vessel.•Singapore flag vessel CO2 emissions reduced by 10.68% from 2019 to 2020.•Emission level dropped significantly in August 2020 and remained low until end of the year.•Carbon emissions for ferries reduced considerably during the Circuit Breaker.•This study contributes to the management of CO2 emissions at the Singapore Port.
Predicting the likelihood of maritime accidents is hindered by the relative sparsity of collisions on which to develop risk models. Therefore, significant research has investigated the capability of ...non-accident situations, near misses and encounters between vessels as a surrogate indicator of collision risk. Whilst many studies have developed ship domain concepts, few have considered the practical considerations of implementing this method to characterise navigational risk between waterways and scenarios. In order to address this, within this paper we implement and evaluate the capability and validity of domain analysis to characterise and predict the likelihood of ship collisions. Our results suggest that the strength of the relationship between collisions and encounters is varied both between vessel types and the spatial scale of assessment. In addition, we demonstrate some key practical considerations in utilising domain analysis to predict the change in collision risk, through a hypothetical wind farm. The outcomes of this study provide research direction for practical applications of domain analysis on collision risk assessments.
•The suitability of domain analysis for collision risk assessment is discussed.•A national picture of encounter frequency is used to inform collision risk.•The relationship between encounters and historical collisions is investigated.•A practical method for predicting the change in collision risk is proposed.
Vessel operations at port play a particular role in port-related air emissions. Hotelling, manoeuvring and cruising operations in the harbour areas generate a large share of local and global ...pollution, external costs and public health issues. Emission abatement demands effective regulation for vessel compliance and enforcement adequacy in despite of geographic differences in jurisdiction. A connecting relation between regulatory frameworks and atmospheric pollution from vessels operations at port is so far, missing in literature. This paper aims at filling in this gap by addressing exhaust gasses (NOx, SOx, CO, CO2) and particles (PM2.5) released from operative vessels in port with differing regulatory frameworks (Las Palmas, St. Petersburg, and Hong Kong). Estimations are based on the Ship Traffic Emission Assessment Model (STEAM) and AIS traffic information over a twelve-month timeframe. Contribution of this paper relates to revealing emission patterns of vessel operations in port and the assessment of current regulatory frameworks. Results and lower emission profiles shed light to sulphur regulation differences and the potential benefits in new policy measures (polluter pays principle, cold ironing and others) of accounting operative modes and shipping sub-sectors.
•Shipping emission in ports under diverse geographical and regulatory framework.•Emissions patterns at port from general and passenger vessel traffic.•Results show how disaggregation of emission inventory can provide policy support.•Port emission patterns cannot be solely explained by regulatory differences.•Policy recommendations based on regulation, port governance and emission results.
As today's transportation systems have gradually developed into intelligent transportation systems, the fundamental elements of the system consist of vessels, harbors, and ship-shore ...information-communication technology applications. Safety is the priority for intelligent transportation, securing the transportation environment for vessels. Because there are more dangerous situations when sailing in the sea, our research deploys artificial intelligence of thing with the 5G network to ensure ship safety. To achieve the goal of intelligent ships for vessels to share information between groups; moreover, the geofencing technology can protect vessels from sailing into risky zones. The security mechanism of our system can detect malicious attacks from Dynamic Domain Name System and radio jamming attacks. Additionally, the network security mechanism proposed in this article can safeguard data reliability and safety, enabling vessels to detect collisions in the front and improve safety through the 5G network and the sensor. The performance analysis has proven that the network security approach of this article surpasses other studies; regarding the geofencing part, this article has also conducted a practical experiment to introduce it into the automatic-identification-system (AIS) and 5G system. The experimental results prove that the suggested approach can ensure the AIS network security in vessels; moreover, the system can precisely judge whether there are obstacles in front of the ship, making sure the vessel safety.
With the establishment of satellite constellations and terrestrial networks of Automatic Identification System (AIS) receivers, an increasing number of ship trajectories have become available, and ...the data size of trajectories that must be recorded is increasing. As a result, transmitting, processing and storing data have become important issues. At the same time, ship behaviour information is hidden in AIS data. Hence, an effective method is required to not only compress redundant information but also maintain the main characteristic elements included in the trajectory. In this paper, a novel algorithm considering the spatial and motion features of trajectories is designed, which can compress AIS trajectories based on ship behaviour characteristics. The proposed algorithm has two main parts: the Douglas-Peucker (DP) algorithm is employed to simplify trajectories according to spatial features, and a sliding window is adopted to simplify trajectories based on motion features. Furthermore, statistical theory is applied to help determine the thresholds of motion features in sliding window algorithms. The two results are merged to form a trajectory simplification algorithm that considers ship behaviours. To verify the effectiveness of the proposed algorithm, numerical experiments are performed. The results indicate that the proposed algorithm can efficiently simplify trajectories by considering ship behaviour as needed.
•This study proposes a simplification method that considers ship behaviours.•Statistical theory is applied to help determine the thresholds of the motion features.•Numerical experiments are performed to verify that the proposed algorithm performance outperforms other methods.
The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT). In maritime IoT, the spatio-temporal ...vessel trajectories, collected from the hybrid satellite-terrestrial automatic identification system (AIS) base stations, are of considerable importance for promoting traffic situation awareness and vessel traffic services, etc. To guarantee traffic safety and efficiency, it is essential to robustly and accurately predict the AIS-based vessel trajectories (i.e., the future positions of vessels) in maritime IoT. In this work, we propose a spatio-temporal multigraph convolutional network (STMGCN)-based trajectory prediction framework using the mobile edge computing (MEC) paradigm. Our STMGCN is mainly composed of three different graphs, which are, respectively, reconstructed according to the social force, the time to closest point of approach, and the size of surrounding vessels. These three graphs are then jointly embedded into the prediction framework by introducing the spatio-temporal multigraph convolutional layer. To further enhance the prediction performance, the self-attention temporal convolutional layer is proposed to further optimize STMGCN with fewer parameters. Owing to the high interpretability and powerful learning ability, STMGCN is able to achieve superior prediction performance in terms of both accuracy and robustness. The reliable prediction results are potentially beneficial for traffic safety management and intelligent vehicle navigation in MEC-enabled maritime IoT.
•A method of extracting maritime traffic routes from AIS data is proposed.•A ship's trajectory is defined as a “stop-waypoint-stop” trip semantic model.•The construction of ship maritime traffic map ...is based on graph theory.•The proposed method is applied to real-world AIS data.
Maritime traffic route is the basis for the analysis of ship traffic characteristics and navigation safety. However, the spatial freedom of ship navigation makes the extraction of maritime traffic route a challenging task. In order to realize the extraction of ship traffic routes at sea, a maritime route extraction method based on ship history automatic identification system (AIS) data is proposed. In this method, the ship trajectory with rich position information is transformed into a ship trip semantic object (STSO) with semantic information, and each ship trip is abstracted as a “stop-waypoint-stop” trip object. In addition, based on the graph theory, the STSO is further integrated into the nodes and edges of a directed maritime traffic graph to realize the extraction and expression of the shipping routes. In order to prove the effectiveness of this method, the real-world AIS historical data are used for testing, and the results show that this method can effectively extract maritime traffic routes.
As an essential sub-network of the global liner shipping network, China's international liner shipping network was the earliest to be affected by the COVID-19 and also had a significant impact on the ...global shipping network. This paper uses Automatic Identification System (AIS) data to analyze the impact of COVID-19 on the typical route networks and major ports of China's international liner shipping. On this basis, the changes in network efficiency and connectivity under the failure of important nodes is simulated. The research finds that, during the epidemic period, the scale of China's international liner shipping network increased, with more routes gathering at fewer hub ports. Still, the overall connectivity and connection strength declined. Meanwhile, the epidemic caused fluctuations in container volume and the mismatch of ship cargo capacity supply, in which China-U.S. routes was the most prominent. From the view of node, the competitiveness of China's mainland ports was significantly promoted during the epidemic. In addition, ports such as Busan, Singapore, and Hong Kong substantially impacted China's international liner shipping network. The current study might be helpful for the industry management departments and related companies to prepare contingency plans, thus enhancing the resilience of the logistics chain and ensuring the stability of the global supply chain.
•Empirical evidence of 141 incidences of past port disruptions across 27 events.•Ports disruptions have a median of 6 days with a 95th quantile of 22.2 days.•Ten day disruption in U.S.A. associated ...with a 35 m/s wind speed and/or 2.5 m storm surge.•All events cause simultaneous disruption at multiple ports.•Production recapture more likely than port substitution.
Ports are located in low-lying coastal and riverine areas making them prone to the physical impacts of natural disasters. The consequential disruptions can potentially propagate through supply chains, resulting in widespread economic losses. Previous studies to quantify the risks of port disruptions have adopted various modelling assumptions about the resilience of individual ports and marine network logistics. However, limited empirical evidence is available to validate these modelling assumptions or to provide deeper understanding of the ways in which operations are adapted during and after disruptions. Here, we use vessel tracking data to analyse past port disruptions due to natural disasters, evaluating 141 incidences of disruptions across 74 ports and 27 disasters. Results show a median disruption duration of six days with a 95th percentile of 22.2 days. All analysed events show multiple ports being affected simultaneously, challenging some of the studies that only focus on single port disruptions. Moreover, we find that the duration of the disruption scales with the severity of the event, with an increment of 1.0 m storm surge or 10 m/s wind speed associated with a two day increase in disruption duration. In contrast to commonplace assumptions in model studies, substitution between ports is rarely observed during short-term disruptions. On the other hand, production recapture happens in practice in many cases of port disruptions. In short, empirical vessel tracking data provides valuable insights for future modelling studies in order to better approximate the extent of the disruption and the potential resilience of the port and maritime network.
A growing concern about the depletion of marine resources due to fishing overexploitation and degradation of ecosystems has been demonstrated over the last decade. Monitoring the spatial and temporal ...distribution of fishing activities is an important tool for fisheries management which can also be used by other sectors such as fisheries science, public authorities, policy-makers and marine spatial planning. In this paper we introduce the first map of fishing activity at a Mediterranean scale of EU and non-EU fishing vessels, extracted using Automatic Identification System ship tracking data. Fishing activity maps were produced for three different years with a spatial resolution of 0.01° × 0.01°. As a main result, for the first time, changes of bottom trawl fishing activities between two consecutive years were map for the whole Mediterranean Sea. The results confirmed the suitability of this monitoring system to obtain reliable information on the extent of bottom trawl fishing activities.
•Marine Spatial Planning needs monitoring the impact of human activities at sea.•AIS can be used as an alternative/integrative source to VMS to map fishing effort.•This manuscript confirms the suitability of AIS to describe the impact on seabed in terms of fishing tracks.•A three-year assessment bottom trawl fishing activity is provided for the whole Mediterranean Sea.•Change in bottom trawl fishing activity between two consecutive years were mapped.
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