ABSTRACT
This paper focuses on the analysis of the COVID-19 effects on passenger shipping in Danish waters as an example and aims to analyse the differences in passenger vessel activities and emissions before and after the COVID-19 outbreak. Two sets of Automatic Identification System (AIS) data for the passenger ships sailing in Danish waters associated with the whole year respectively for 2020 and 2019 are used for a comprehensive evaluation of the passenger shipping activities in the region by means of the analysis of variance and bottom-up emission models. A comparison of those results based on the two datasets shows that the COVID-19 pandemic has a major impact on cruise ships, with a significant reduction in the number of ships, average speed, and average draught. In contrast, the pandemic has a smaller impact on ferry-pax only and ferry-ro pax vessels. The effects can also be seen from the fact that, after the COVID-19 outbreak, SOx emissions from cruise ships, ferry-pax only and ferry-ro pax vessels were reduced by 50.71%, 0.51% and 0.82%, respectively. This investigation provides an important reference for policy makers in the marine environment sector.
ABSTRACT
To achieve environmental sustainability on ships, stakeholders should make efforts to reduce emissions. Port authorities are crucial to attain this goal by introducing new policies. This study takes the Port of Long Beach as an example to assess port-wide ship emissions and explain the significance of shore power policy. Additionally, the study considers the impact of disruptions, such as the COVID pandemic, on ship emissions. The analysis compares data from three years before and after the pandemic to examine the relationship between ship waiting times, quantities, and emissions. The findings indicate that the majority of port-wide ship emissions are generated by berthing or anchoring vessels, from ship auxiliary engines and boilers. Furthermore, ship congestion due to reduced port productivity during the pandemic significantly increased emissions from berthing and anchoring vessels, with the emission proportion increasing from 68% to 86%. Adopting the shore power policy has effectively reduced ship emissions in port areas, and increasing the number of ships utilising shore power will be instrumental in tackling excessive ship emissions.
ABSTRACT
It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process. © 2022 Elsevier Ltd
ABSTRACT
Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.
ABSTRACT
Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.
ABSTRACT
Fighting against climate change and global warming is one of the biggest challenges faced by the Maritime Industry nowadays to make the supply chain greener and environmentally sustainable. Cutting greenhouse gases (GHG) emissions and decarbonizing the international shipping has been a paramount activity for the International Maritime Organization (IMO) since the first set of international mandatory measures to improve ships' energy efficiency and reduce CO2 emissions per transport work, as part of the International Convention for the Prevention of Pollution from Ships (MARPOL) released in 2011. Besides that, changes in consumption habits around the globe (i.e., digitalization and growth of e-commerce) plus disruptive events like the COVID-19 or the blocking of the Suez Canal, to name only a few, have also highlighted the need for building more resilient maritime transport networks. In this work, a pragmatical analysis of the principal machine learning algorithms has been carried out to provide a qualitative prediction of the Estimate Time of Arrival (ETA) of container vessels applied to short-sea shipping where the distance between ports is reduced. By exploiting both, the Automatic Identification System (AIS) and meteorological data gathered over a desired area of interest, the developed approach delivers a model capable of predicting the ETA of ships where the reaction time of the stakeholders involved in the management of the Port Call is very reduced (i.e., less than two hours of sailing between ports) and therefore, tolerance for error is low. Very positive results were obtained for the training dataset collected under real conditions for more than a year. The best results were obtained by the RF model with a Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 11.31 and 19.56 minutes respectively. © 2003-2012 IEEE.
ABSTRACT
Seaports play an important role in the global shipping network. Shipping participants often attach great importance to the measurement of container port connectivity, as it reflects countries' access to world markets. As a result, various port connectivity index systems have been proposed by members of the shipping industry and scholars. In recent years, technological developments especially the advancement of high coverage and real-time Automatic Identification System (AIS) data, have provided a chance to improve the scope and frequency of the existing index systems. An improved system is expected to reflect the dynamic changes in a port's connectivity which may be induced by either local disruptions or shocks in the wider economy. This study builds a monthly container port connectivity index system by applying big data mining techniques, graph theory, and principal component analysis (PCA) to AIS data, taking both port factors and shipping network factors into consideration. AIS records from 2020 are used to calculate the connectivity score of 25 major container ports. We also compare our system with the connectivity index commonly used in the shipping industry, the Liner Shipping Connectivity Index (LSCI). Our results show that the measurement of connectivity can be improved over indices that depend primarily on indicators of traffic volume. Ports like Antwerp and Tanjung Pelepas rank high in the proposed system due to their sound performance on their accessibility and strategic position in the local region instead of their traffic volume. The monthly index system is also proven to reflect timely changes in the shipping industry through its accurate portrayal of changes in port connectivity during the COVID-19 outbreak. © 2022 Elsevier Ltd
ABSTRACT
Using AIS data to mine the dynamic characteristics of fishery resource exploitation helps to carry out scientific management of fishery and realize the sustainable development of marine resources. We proposed a framework that integrates multiple AIS data processing and analysis modules, which can efficiently divide fishing voyages, determine the fishing activities and identify fishing types, and provide near real-time analysis results on the number of fishing vessels, fishing duration, voyages and so on. The framework was applied to 1.68 billion AIS trajectory data points of approximately 588,000 fishing vessels. We selected China's sea areas overall and six fishing grounds as the research area, explored the characteristics of fishing vessel activities in winter and spring of 2019, and analyzed the impact of COVID-19 on winter-spring fishing in China in 2020. In 2019, our results showed that the number of fishing vessels in China's sea areas gradually increased over time, with the Chinese New Year holiday affecting fishing activities at the corresponding time but having little impact on the entire month. We found that the changing laws of the fishing duration and voyages in the inshore fishing grounds were similar to those of the number of fishing vessels, which increased to varying degrees over time. Gillnetters were the most numerous fishing vessel type operating in the inshore fishing grounds with increased in spring, while seiners had an absolute advantage in the Xisha-Zhongsha fishing ground. In 2020, during the occurrence period of COVID-19, the fishing activities in China's sea areas was almost unaffected. During the outbreak period, the number, distribution range, activity intensity, and fishing duration of fishing vessels all experienced a relatively large decline. After the epidemic was effectively controlled, they were rapidly increased. In addition, we found that compared with the Government Response Stringency Index, the number of fishing vessels and the number of new confirmed cases showed a more obvious negative correlation. By processing, mining and analyzing AIS data with high spatial-temporal granularity, this study can provide data support for the reasonable development of fishery resources, and help fishery practitioners make wise decisions when responding to unexpected emergencies (e.g. pandemics).
ABSTRACT
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.
ABSTRACT
This paper focuses on the analysis of the COVID-19 effects on passenger shipping in Danish waters as an example and aims to analyse the differences in passenger vessel activities and emissions before and after the COVID-19 outbreak. Two sets of Automatic Identification System (AIS) data for the passenger ships sailing in Danish waters associated with the whole year respectively for 2020 and 2019 are used for a comprehensive evaluation of the passenger shipping activities in the region by means of the analysis of variance and bottom-up emission models. A comparison of those results based on the two datasets shows that the COVID-19 pandemic has a major impact on cruise ships, with a significant reduction in the number of ships, average speed, and average draught. In contrast, the pandemic has a smaller impact on ferry-pax only and ferry-ro pax vessels. The effects can also be seen from the fact that, after the COVID-19 outbreak, SOx emissions from cruise ships, ferry-pax only and ferry-ro pax vessels were reduced by 50.71%, 0.51% and 0.82%, respectively. This investigation provides an important reference for policy makers in the marine environment sector. [ FROM AUTHOR] Copyright of Maritime Policy & Management is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
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.
Subject(s)
COVID-19 , Carbon Dioxide , Carbon Dioxide/analysis , Humans , SARS-CoV-2 , Ships , SingaporeABSTRACT
The movement of cruise ships has the potential to be a major trigger of coronavirus disease (COVID-19) outbreaks. In Australia, the cruise ship Ruby Princess became the largest COVID-19 epicenter. When the Ruby Princess arrived at the Port of Sydney in New South Wales on March 19, 2020, approximately 2700 passengers disembarked. By March 24, about 130 had tested positive for COVID-19, and by March 27, the number had increased to 162. The purpose of this study is to analyze the relationship between the cruise industry and the COVID-19 outbreak. We take two perspectives: the first analysis focuses on the relationship between the estimated number of cruise passengers landing and the number of COVID-19 cases. We tracked the movement of all ocean cruise ships around the world using automatic identification system data from January to March 2020. We found that countries with arrival and departure ports and with ports that continued to accept cruise ships until March have a higher COVID-19 infection rate than countries that did not. The second analysis focuses on the characteristics of cruise ships infected with COVID-19. For this purpose, we utilize the list named "Cruise ships affected by COVID-19" released by the Centers for Disease Control and Prevention. As a result, cruise ships infected with COVID-19 were large in size and operated regular cruises that sailed from the same port of arrival and departure to the same ports of call on a weekly basis.