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Despite existing debates on the maritime cluster's institutional composition, geographical boundary, or evolutionary patterns, few studies have empirically scrutinized the relatedness of industrial sectors inside a maritime cluster. This study aims to develop an industry-relatedness model to quantitatively measure the paired industry relatedness among three maritime sectors: port-related, shipping-related, and maritime business services. Maritime sectors can be labour-intensive or capital-intensive, and thereby we constructed a compound ratio of capital per capita to address the heterogeneity issue in evaluating the industry relatedness. Instead of focusing on a specific maritime cluster, we applied the model to three representative maritime clusters, namely Western Europe, Guangdong-Hong Kong-Macao Greater Bay Area (GBA), and the Yangtze River Delta (YRD), to enhance the generalizability of our findings and implications. Findings suggest that the maritime cluster in Western Europe is characterized by more inter-sector connections. In Western Europe, Ship owning and operating , and Ship management and agency are found to operate with the highest relatedness; GBA has a polarized maritime industry structure with Hong Kong specializing in Ship finance and Shipbroking ; cities in YRD operate with a speciality in Shipbuilding and repairing. This study found that differences in industry relatedness are mainly attributable to the focal region's evolutionary history and governance structure. We also provided a valuable reference for policymakers to plan and improve local and regional maritime cluster development. • Modify the co-occurrence model by considering the heterogeneity of different maritime sectors. • Quantify the industry relatedness between different maritime sectors. • Compare three typical maritime clusters in terms of maritime industry-relatedness. [ABSTRACT FROM AUTHOR]

Maritime transportation is the backbone of global trade, as ships carry over 80% of trading goods worldwide. As the shipping industry is mainly powered by heavy fuel oil, it has an adverse environmental footprint due to the emissions of greenhouse gases and polluting substances. To comply with IMO emission regulations and optimally save on fuel costs (which can account up for 50% to 60% of the total cost of operating a ship), shipping companies are motivated to optimize energy consumption. In this study, we first develop am innovative and tailored artificial neural network-based fuel consumption prediction model. This model innovates in that it explicitly considers shipping domain knowledge by modifying and optimizing its structure and parameters, where such properties have rigorously been proven. Moreover, it considers a broad range of influence factors based on data fusion technology. Next, we optimize the ship sailing speed profile for a bulk carrier in two application scenarios using the predicted fuel consumption rates by the proposed neural network-based model as the input: one is a bi-objective model, and the other considers market-based measures. Numerical experiments show that the proposed fuel consumption prediction model outperforms other models and that the model we propose can help to improve ship energy efficiency by a considerable extent. The proposed model conforms more closely to common sense than existing models; thus, it will likely have a better potential for use in the maritime industry and other problems with similar domain knowledge possessed. • Use data fusion to combine a wide scope of data sources into a single dataset for artificial neural network (ANN) model construction • Develop a tailored ANN model for ship energy consumption prediction considering monotonicity and convexity • The mean absolute percentage error of the developed ANN model is 7.5% • Develop ship sailing speed optimization models in various application scenarios • The proposed ANN model improves ship energy efficiency by 2% to 2.5% [ABSTRACT FROM AUTHOR]

• A comprehensive comparative empirical analysis on the container shipping network. • Supported by extensive datasets, incorporates the latest development on river basin. • Combine descriptive and explanatory analysis from interdisciplinary approaches. • A large diversity exists in how inland port systems development. The formation of barge networks on rivers and associated inland port systems is subject to a complex set of influencing factors and mechanisms. This paper aims to present a comprehensive comparative empirical analysis focusing on the container shipping (barge) network in the Yangtze and the Rhine. This analysis is supported by extensive datasets on both river basins, incorporates the latest development on both rivers and is grounded on concepts and methods coming from transport geography and economic geography. We find that a large diversity might exist in how inland port systems and related gateway seaports are dealing with cargo flows and supply chains. In view of explaining this diversity, we make a distinction between geographical/nautical aspects, macro-economic factors and institutional/governance factors. In particular, we discuss the role of institutional and governance factors in barge network development by using the concepts of selection, retention and variation. [ABSTRACT FROM AUTHOR]

• Develop a novel data-driven framework for port-skipping identification. • Empirically explore decision preferences of shipping lines for port-skipping. • Reveal various strategic decisions of different shipping lines to heal service disruptions. • The proposed methodology can be cross-applied to identify other disruption recovery behaviors. Service disruption hurts the reliability of liner shipping schedules and the lack of high-quality data impedes research on schedule recovery in liner shipping. With the support of the Automatic Identification System (AIS), a satellite-based tracking system acquiring real-time records of vessels' navigation trajectories worldwide, this study develops a novel data-driven framework to uncover the vessel schedule disruption recovery behavior (i.e., port-skipping). The framework consists of a series of independent yet closely related algorithms, which are in turn tasked with screening port calls, estimating closed-loop routes, measuring similarity between different routes, and identifying port-skipping behavior, respectively. Compared with partial proprietary data from the shipping industry and port authorities, the identification results can provide transparent datasets with wide applications and much easier access publicly. The developed data-driven framework is implemented with vessel trajectory information of around 2,000 container vessels worldwide from January 2016 to December 2020, and the results prove its validity and practical value for liner shipping scheduling management. [ABSTRACT FROM AUTHOR]

Abstract This study aims to develop a maximum likelihood regression tree-based (MLRT) ZINB (zero-inflated negative binomial) model to predict shipping accident mortality, and also to examine the factors which affect the loss of human life in shipping accidents. Based upon 23,029 sets of shipping accidents observations collected from 2001 and 2011 in global water areas, a tree comprising 7 terminal nodes is built, each of which is assigned by a separate ZINB model. Model results indicate that the large number of shipping accident casualties are closely related to collision, fire/explosion, sinking, contact, grounding, operating time, capsizing, docking condition, hull/machinery damage, and miscellaneous causes. In addition, it is found that there is a larger casualty count for the accidents occurring under adverse weather conditions or far away from coastal/port areas. In addition, sinking is recognized as the accident type which causes the largest number of casualties. This study can help the decision makers to propose effective strategies to reduce shipping accident casualties. Highlights • Develop a maximum likelihood regression tree-based ZINB model. • The large number of casualties relates to collision operating time, capsizing and docking condition. • Sinking is the accident type causing the largest number of casualties. [ABSTRACT FROM AUTHOR]

Highlights • A new, significant, and timely topic and consider inland New Eurasia Land Bridge. • New bi-level programming model and exact reformulation and solution approach. • Utilize real data in the study and thus provide more practical managerial insights. Abstract This paper aims to reconstruct the shipping service network between Asia and Europe by considering the improvement of New Eurasian Land Bridge rail services and Budapest-Piraeus railway. In particular, to reflect the decision making process in reality, a bi-level programming model is established to maximize the total profit of the liner shipping company in the upper level and meanwhile to minimize the total cost of the shippers in the lower level. Computational experiments considering different scenarios are conducted to obtain the new optimal networks under different cases. Several insightful findings are observed, further leading to useful managerial insights. [ABSTRACT FROM AUTHOR]

China's steel output has maintained rapid growth over the past twenty years. Due to this, a large number of iron ore ports/terminals have been built along the Yangtze River, and the Yangtze River bulk port system has experienced a unique development in its structure. This paper aims to understand the evolution of this bulk port system. 1 1 In this paper, bulk cargo mainly refers to iron ore bulk cargo. along the Yangtze River. To achieve this objective, first the development phases of the Yangtze River bulk port system are reviewed, taking the theoretical (container) port evolution model as a benchmark. Then several hypotheses addressing certain features of bulk port system development are proposed, followed by using panel data analysis to test these hypotheses. Based on this discussion and analysis, the major driving forces that are reshaping bulk port development along the Yangtze River are then summarized. It is found that evolution of the Yangtze River bulk port system in general follows the port development models in previous literature. However, the trend toward regionalization and an offshore hub have not appeared. Besides this, iron ore transshipment is moving outward both for sea ports and river ports, and few iron ore transshipment gateway hubs are occurring. Furthermore, the transshipment function of a bulk port plays a significant role in port traffic changes, but this role is affecting sea ports differently to river ports. The container throughput of transshipment sea ports has a significant negative effect on bulk traffic, whereas that of transshipment river ports has a positive effect. Geographical conditions, institutional factors and national policy, industry agglomeration, changes in market supply and demand, and technology updates are major factors driving changes to the port system structure. These factors are observed to function either individually or collectively at different development stages. [ABSTRACT FROM AUTHOR]

Shipping movements are operated in a complex and high-risk environment. Fatal shipping accidents are the nightmares of seafarers. With ten years’ worldwide ship accident data, this study develops a binary logistic regression model and a zero-truncated binomial regression model to predict the probability of fatal shipping accidents and corresponding mortalities. The model results show that both the probability of fatal accidents and mortalities are greater for collision, fire/explosion, contact, grounding, sinking accidents occurred in adverse weather conditions and darkness conditions. Sinking has the largest effects on the increment of fatal accident probability and mortalities. The results also show that the bigger number of mortalities is associated with shipping accidents occurred far away from the coastal area/harbor/port. In addition, cruise ships are found to have more mortalities than non-cruise ships. The results of this study are beneficial for policy-makers in proposing efficient strategies to prevent fatal shipping accidents. [ABSTRACT FROM AUTHOR]

The recent experience of lockdowns during COVID-19 highlights the prolonged impact a pandemic could have on ports and the shipping industry. This paper uses port call data derived from the Automatic Identification System (AIS) reports from the world's 30 largest container ports to quantify both the immediate and longer-term impact of national COVID-19 lockdown policies on global shipping flows. The analysis uses the Difference-in-Difference (DID) and combined regression discontinuity design (RDD)-DID models to represent the effects of lockdown policies. The combination of RDD and DID models is particularly effective because it can mitigate time trends in the data, e.g., the Chinese New Year effect on Chinese ports. This study further examines the potential shock propagation effects, namely, how lockdown policy in one country (i.e., China) can affect the number of port calls in other countries. We categorize ports in other countries into a high-connectivity (with Chinese ports) group and a low-connectivity group, using a proposed connectivity index with China derived from individual vessel trajectories obtained from the AIS data. The results provide a clearly measurable picture of the kinds of trade shocks and consequent pattern changes in port calls over time caused by responses to lockdown policies of varying levels of stringency. We further document the existence of significant shock propagation effects. As the risk of pandemics rises in the twenty-first century, these results can be used by policy makers to assess the potential impact of different levels of lockdown policy on the maritime industry and trade flows more broadly. Maritime players can also use findings such as these to manage their capacity during lockdowns more effectively and to respond more flexibly to changing demand in seaborne transportation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Ship navigation information derived from the Automatic Identification System (AIS) is widely used in the shipping industry. As AIS reports are featured with huge volume and noises, preprocessing of AIS data is essential before its further application. This study aims to develop effective algorithms to denoise and compress raw trajectories derived from AIS reports. Specifically, an effective noise detection method based on statistical theory and sliding window is first proposed to identify glitches in a given trajectory. Linear interpolation is further used to rectify the glitches detected. Then, two modes of algorithms are proposed for trajectory compression: static mode with preset threshold for compression and dynamic mode considering the distance between trajectory points and the coastline in a real-time manner. Numerical experiments show that the noise detection and rectification algorithms and the trajectory compression algorithms are accurate and highly efficient considering compression rate, information loss, and computation time. The main innovation of this research includes developing an accurate and robust trajectory glitch detection and rectification algorithm, proposing two modes of trajectory compression algorithms, and combining the two tasks in a holistic robust framework. Especially, the dynamic compression mode can overcome a major problem encountered in static compression where the compressed trajectories may go across land. It can also deal with more flexible compression requirements and thus should be more applicable in practice. • Developed a noise detection algorithm based on statistical theory to identify glitches in a trajectory from raw AIS data. • Used linear interpolation method to rectify the glitches detected in a trajectory. • Proposed effective online trajectory compression algorithm in static and dynamic mode (considering point-coastline distance). • Combined noise detection and rectification with trajectory compression algorithm in a holistic and robust framework. • Evaluated trajectory compression performance using various metrics under varied scenarios. [ABSTRACT FROM AUTHOR]

The international shipping industry has prioritized emissions reduction and invested in green shipping technologies. Shipping alliances have also significantly transformed market structure of container shipping industry, resulting in increased market power for major alliances in recent years. These strategic alliances have substantial implications for the economic incentives to invest in green shipping technologies. This study examines the impact of shipping alliances on green shipping investment by constructing an economic model comprising a port, two shipping lines, and government authority. The analysis demonstrates that shipping alliances promotes green shipping investments, even with increased shipping traffic, leading to an overall reduction in emissions. While these alliances improve shipper surplus, the enhancement of social welfare depends on whether cost savings from reduced emissions outweigh higher investment costs associated with green shipping. Endogenizing emission tax results in lower social-optimal emission tax, which drives more shipping traffic, offsetting emissions reduction from increased green shipping investment. [ABSTRACT FROM AUTHOR]

Highlights • Consider a stochastic bulk ship scheduling problem in industrial shipping that has never been addressed in the literature but has real impact in the shipping market. • Develop a two-step solution scheme consisting of a dynamic programming algorithm and a tailored Benders decomposition method to solve the problem effectively. • Conduct extensive numerical experiments to demonstrate that proposed solution method can well solve the considered problem in various and practical sizes. Abstract This paper studies a ship scheduling problem for an industrial corporation that manages a fleet of bulk ships under stochastic environments. The considered problem is an integration of three interconnected sub-problems from different planning levels: the strategic fleet sizing and mix problem, the tactical voyage planning problem, and the operational stochastic backhaul cargo canvassing problem. To obtain the optimal solution of the problem, this paper provides a two-step algorithmic scheme. In the first step, the stochastic backhaul cargo canvassing problem is solved by a dynamic programming (DP) algorithm, leading to optimal canvassing strategies for all feasible voyages of all ships. In the second step, a mixed-integer programming (MIP) model that jointly solves the fleet sizing and mix problem and the voyage planning problem is formulated using the results from the first step. To efficiently solve the proposed MIP model, this paper develops a tailored Benders decomposition method. Finally, extensive numerical experiments are conducted to demonstrate the applicability and efficiency of the proposed models and solution methods for practical instances. [ABSTRACT FROM AUTHOR]

Historical time-series data of container traffic in ports are characterised by strong randomness that causes the time-series prediction performance to vary significantly. In this sense, the prediction accuracy differs as a function of exogenous variables related to port characteristics and the maritime shipping context. Thus, the measuring problem of a priori container traffic predictability (i.e., the ability of methods to predict port traffic variables) arises. Academia suggests that this predictability may be related to entropy as a measure of the information provided. This paper analyses the Sample Entropy (SampEn), as an indicator of the complexity represented by the container traffic time-series data, comparing it with the error metrics provided by a statistical prediction and an artificial intelligent method. The Autoregressive Integrated Moving Average (ARIMA) and Back Propagation (BP) Neural Network are used to obtain the prediction performance of container port traffic in a data set composed of 20 Chinese ports and 5 selected international ports. Unexpectedly, the results indicate that the correlation between the SampEn and the prediction performance of the time series is weak. The complex ecosystem of the container shipping sector and the different port characteristics are suggested as the cause of this poor correspondence. Our findings help to understand the relationship between entropy and the predictability of time-series data in a sector (i.e. port traffic), which has not been explored before. • We use the Sample Entropy as an indicator to measure the complexity of the container traffic time-series data. • We use ARIMA and BP Neural Network to obtain the prediction performance of the container port traffic dataset composed of 20 Chinese ports and 5 international ports. • We find the correlation between the SampEn and the prediction performance of the time series is weak. • We propose the complex ecosystem of the container shipping sector and the different port characteristics are the cause of this poor correspondence. [ABSTRACT FROM AUTHOR]