Search results

Ship route prediction (SRP) is a quite complicated task, which enables the determination of the next position of a ship after a given period of time, given its current position. This paper aims to describe a study, which compares five families of multiclass classification algorithms to perform SRP.

Tested algorithm families include: Naive Bayes (NB), nearest neighbors, decision trees, linear algorithms and extension from binary. A common structure for all the algorithm families was implemented and adapted to the specific case, according to the test to be done. The tests were done on one month of real data extracted from automatic identification system messages, collected around the island of Malta.

Experiments show that K-nearest neighbors and decision trees algorithms outperform all the other algorithms. Experiments also demonstrate that linear algorithms and NB have a very poor performance.

This study is limited to the area surrounding Malta. Thus, findings cannot be generalized to every context. However, the methodology presented is general and can help other researchers in this area to choose appropriate methods for their problems.

The results of this study can be exploited by applications for maritime surveillance to build decision support systems to monitor and predict ship routes in a given area. For example, to protect the marine environment, the use of SRP techniques could be used to protect areas at risk such as marine protected areas, from illegal fishing.

The paper proposes a solid methodology to perform tests on SRP, based on a series of important machine learning algorithms for the prediction.

This research proposes a framework to conceptualise the potential realm of data regarding shipping connectivity for application of data analytics which can be used to generate deeper insights with respect to the state of such linkages and potential areas for practical application.

The study method involved comprehensive presentation of different perspectives of assessing shipping connectivity and levels of data contained within container shipping services and proposed potential application to analyse profitability, performance, competitiveness, risk and environmental impact.

Advances in capabilities to handle large volumes of data offer scope for an integrated approach which utilises all available data from various stakeholders in analyses of liner shipping connectivity. Research shows how different types of data contained in container shipping services are related and can be organised for application of data analytics.

Research implications are offered to shipping lines, port managers and operators and policymakers.

This research presented a conceptual framework that captures the range of data involved in container shipping services and how data analytics can be practically applied in an integrated manner.

This paper is the first in literature to discuss in detail the different levels of data that reside within shipping services that constitute liner shipping connectivity for application of data analytics.

The Northern Sea Route (NSR) could become viable in the near future. If this happens, it will radically reduce sailing times and distances on routes from Asia to Northern Europe. However, although much has been written about the feasibility of the NSR, about the issues involved and about the possible opening of the route, the views of key stakeholders from companies who would potentially benefit from the route have been little explored. The purpose of this paper is to complement the existing literature on the feasibility of and issues related to the NSR by presenting and discussing the results from in-depth qualitative interviews with nine key stakeholders based in Shanghai and Taiwan who have extensive research, knowledge and practical experience of NSR.

Based on a grounded theory analysis, a total of nine key stakeholders knowledgeable about NSR and the majority with sailing experience of NSR are interviewed, including one government official, two professors, shipping experts in six liner and one bulk shipping companies.

The authors present interviewees’ thoughts regarding the feasibility of NSR at the current time in terms of practicalities, ships, costs, information and wider issues.

These thoughts show that whilst the potential of NSR is huge in theory, in practice the overall perception of it in terms of current feasibility from a company perspective is one of challenges and unknown issues. Shipping companies can benefit from the authors findings when considering the feasibility of NSR as a shipping route. Ultimately, the picture emerges that without one country, probably Russia, taking the lead on the route, it will remain only a theoretical one.

In-depth interviews with grounded theory are used to investigate current and actual thoughts on NSR. This paper highlights correlations and additions to show a fuller picture of current knowledge and adds views from Shanghai and Taiwan.

The objective of this research is to examine the performance of weather routing service in the North Pacific Ocean based on a global container shipping company.

The data comprise two passages: one that departs from the port of Taipei to the port of Los Angeles (TPE-LAX) and another that departs from the port of Tacoma to the port of Kaohsiung (TCM-KSG). A weather routing service was utilized to compare the differences of the distance, sailing time and fuel consumed among different voyages.

Results indicated that the average speed of vessel in winter is faster than in summer. The vessels consumed much more fuel in the winter than they did in the summer. In terms of the distance of the passage, the results show that the ships' sailing distance across the North Pacific Ocean in the summer was shorter than it was in the winter.

Due to the difficultly of practical data collection, relatively few sailing records were employed in this study. It is suggested that additional sailing records should be collected, which adopt weather routing recommendations, to more comprehensively analyze sailing performance in future research.

The study's findings offer valuable guidance to different stakeholders in the maritime industry (e.g. seafarers, marine hull and machinery companies, Protection and Indemnity Club (P&I), ocean container carriers and freight forwarders) to clarify their responsibilities in order to achieve desired sailing outcomes.

To the best of the authors' knowledge, the current study is the first research to utilize practical sailing data to provide objective evidence of sailing performance based on a weather routing service, which can assist various stakeholders to make optimal decisions.

While previous studies focused mainly on East Asia to Europe or United States trade routes, in recent years, trade among South-East Asian countries has increased notably. The price of transporting a container is not fixed and can fluctuate heavily over the course of a week. Besides, extant literature only identified seasonality patterns in the container freight market, but did not explore route-varying seasonality patterns. Hence, this study analyses container freight seasonality patterns of the six South-East Asian routes of the South-East Asian Freight Index (SEAFI) and the index itself and forecasts them.

Data of the composite SEAFI and six routes are collected from the Shanghai Shipping Exchange (SSE) including 167 weekly observations from 2016 to 2019. The SEAFI and individual route data reflect spot rates from the Shanghai Port to South-East Asia base ports. The authors analyse seasonality patterns using polar plots. For forecasting, the study utilize two univariate models, autoregressive integrated moving average (ARIMA) and seasonal autoregressive neural network (SNNAR). For both models, the authors compare forecasting results of original level and log-transformed data.

This study finds that the seasonality patterns of the six South-East Asian container trade routes are identical in an overall but exhibits unique characteristics. ARIMA models perform better than SNNAR models for one-week ahead test-sample forecasting. The SNNAR models offer better performance for 4-week ahead forecasting for two selected routes only.

Major industry players such as shipping lines, shippers, ship-owners and others should take into account the route-level seasonality patterns in their decision-making. Forecast analysts can consider using the original level data without log transformation in their analysis. The authors suggest using ARIMA models in one-step and four-step ahead forecasting for majority of the routes. The SNNAR models are recommended for multi-step forecasting for Shanghai to Vietnam and Shanghai to Thailand routes only.

This study analyses a new shipping index, that is, the SEAFI and its underlying six routes. The authors analyze the seasonality pattern of container freight rate data using polar plot and perform forecasting using ARIMA and SNNAR models. Moreover, the authors experiment forecasting performance of log-transformed and non-transformed series.

This paper applied grey wave forecasting in a decomposition–ensemble forecasting method for modelling the complex and non-linear features in time series data. This application aims to test the advantages of grey wave forecasting method in predicting time series with periodic fluctuations.

The decomposition–ensemble method combines empirical mode decomposition (EMD), component reconstruction technology and grey wave forecasting. More specifically, EMD is used to decompose time series data into different intrinsic mode function (IMF) components in the first step. Permutation entropy and the average of each IMF are checked for component reconstruction. Then the grey wave forecasting model or ARMA is used to predict each IMF according to the characters of each IMF.

In the empirical analysis, the China container freight index (CCFI) is applied in checking prediction performance. Using two different time periods, the results show that the proposed method performs better than random walk and ARMA in multi-step-ahead prediction.

The decomposition–ensemble method based on EMD and grey wave forecasting model expands the application area of the grey system theory and graphic forecasting method. Grey wave forecasting performs better for data set with periodic fluctuations. Forecasting CCFI assists practitioners in the shipping industry in decision-making.

This study aims to analyze information on vessel traffic between the two Koreas with a probability distribution for each route/vessel type. The study will then conduct an estimate for maritime transport patterns of inter-Korean trade in the future. To analyze the flow of inter-Korean coastal shipping, this study conducted visualization analysis of shipping status between North and South Korea by year, ship type, and port using navigation data of three years from Port Logistics Information System (Port-MIS) sources during 2006 to 2008, which saw the most active exchanges between the two governments. Also, this study analyzes shipping status between the two governments as a probability distribution for each port and provides the prospects for future maritime transport for inter-Korean trade by means of Bayesian Networks and simulation. The results of the analysis are as follows: i) when North-South routes are reopened, the import volume for sand from North Korea will be increased; ii) investment in the modernization of ports in North Korea is required so that shipping companies can generate profit through economies of scale; iii) the number of the operating vessels including container ships between the two governments is expected to increase like when the tensions and conflict on the Korean Peninsula was release, especially between Busan port in South Korea and Nampo port in North Korea; and iv) among container ships, transshipment containers imported and exported through Busan Port will be shipped to North Korea by feeder transportation.

A trend in modern supply chain management has been to substitute information for inventory. In this chapter, an approach to how information and communication technology can be used to achieve this in a maritime logistics context is outlined and described based upon a bulk shipping case.

The approach used is based on data-driven modeling and analysis, in which current logistics and commodity storage costs are benchmarked against a “best possible solution.”

To make a new solution operative, a change should be made based upon an analytical decision-making approach, ICT infrastructure development, and inter-organizational development. Thus, the proper use of analytical and transactional information and communication technology in maritime logistics would enable logistics chain stakeholders to track stock levels and ultimately allocate vessels to move cargo when that is logistically most cost effective. Further, this could support a development in the contractual relationships between producer and shipping line changing from a Contract of Affreightment to a Service Level Agreement relationship.

There is room for enhanced use of information and communication technology to provide decision and operational support at strategic, tactical, and operational levels within maritime logistics. This chapter explains some of the driving forces for this, together with a tested approach and method for this, given into a specific, practical case.

This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.

This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.

The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.

The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.

The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.