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The information-rich vessel movement data provided by the Automatic Identification System (AIS) has gained much popularity over the past decade, during which the employment of satellite-based receivers has enabled wide coverage and improved data quality. The application of AIS data has developed from simply navigation-oriented research to now include trade flow estimation, emission accounting, and vessel performance monitoring. The AIS now provides high frequency, real-time positioning and sailing patterns for almost the whole world's commercial fleet, and therefore, in combination with supplementary databases and analyses, AIS data has arguably kickstarted the era of digitisation in the shipping industry. In this study, we conduct a comprehensive review of the literature regarding AIS applications by dividing it into three development stages, namely, basic application, extended application, and advanced application. Each stage contains two to three application fields, and in total we identified seven application fields, including (1) AIS data mining, (2) navigation safety, (3) ship behaviour analysis, (4) environmental evaluation, (5) trade analysis, (6) ship and port performance, and (7) Arctic shipping. We found that the original application of AIS data to navigation safety has, with the improvement of data accessibility, evolved into diverse applications in various directions. Moreover, we summarised the major methodologies in the literature into four categories, these being (1) data processing and mining, (2) index measurement, (3) causality analysis, and (4) operational research. Undoubtedly, the applications of AIS data will be further expanded in the foreseeable future. This will not only provide a more comprehensive understanding of voyage performance and allow researchers to examine shipping market dynamics from the micro level, but also the abundance of AIS data may also open up the rather opaque aspect of how shipping companies release information to external authorities, including the International Maritime Organization, port states, scientists and researchers. It is expected that more multi-disciplinary AIS studies will emerge in the coming years. We believe that this study will shed further light on the future development of AIS studies. [ABSTRACT FROM AUTHOR]

This study proposes high-frequency container port congestion measures based on Automatic Identification System (AIS) data. Vessel movement information of 3,957 container ships from March 2017 to April 2017 is included. The world top 20 container ports' berth and anchorage areas are identified through Density Based Spatial Clustering of Applications with Noise (DBSCAN) and convex hull methods, and their hourly port congestion statuses are depicted in terms of the traffic volume and turnaround time. The constructed congestion measures overcome the disadvantages of the traditionally used port or industry data, which is heterogenous, behind the time and not easy to obtain publicly. The higher frequency (hourly) of the proposed measures can effectively monitor any slight change in port performance. A Long Short-Term Memory (LSTM) neural network model is then proposed for congestion prediction using constructed congestion measures. Point prediction and sequence prediction are both performed. We innovatively introduce congestion propagation effects into the prediction model as input features. Using Shanghai, Singapore and Ningbo ports as case studies, results show that the inclusion of congestion propagation effect can improve the prediction performance especially for sequence prediction. This study provides significant implications and decision support for container shipping market participants. [ABSTRACT FROM AUTHOR]

• We explore the reasons behind the wrong destination reports in AIS. • We use data mining techniques to extract information from AIS. • We build a discrete choice model to analyse the reliability of observed reports. • Our model has an 84.1% accuracy rate in ascertaining the correctness of destination reports. • We provide managerial and policy suggestions to ship operators, port authorities, and regulators. The Automatic Identification System (AIS) is a shipping database that includes the physical characteristics and real-time dynamics of ships. It has attracted great attention from academia recently and has been broadly applied in solving various problems in different fields. The voyage destination report is a piece of information recorded in AIS that indicates the heading port in a ship's voyage. This information is widely referred to by port operators for traffic estimation, and by shipping traders for supply forecasting, etc. However, we find that a considerable proportion (nearly 40%) of this information has been erroneously entered, both intentionally and unintentionally. In this paper, we aim to propose targeted policies to correct the inaccurate reports based on assessing the probability of observing wrong destination port reports of ships in AIS. To this end, we first of all conduct extensive interviews with relevant shipping stakeholders to understand the reasons behind the wrong destination reports. Second, based on the interviews and relevant literature we propose the influence factors. Third, we generate a data sample set based on the voyages performed by Capesize and Panamax bulk ships around the globe in a year. To generate this sample set, we leverage data mining techniques to extract the information from an AIS database and other databases. Finally, a discrete choice model is built to achieve the proposed objective. The results demonstrate that our model has an 84.1% accuracy rate in ascertaining the correctness of destination reports observed in AIS. We also find that, for a voyage, the speed of the ship, the historical accuracy rate of destination reports made by the ship, and the distance between the recognized origin and the reported destination of the voyage, have the most significant impacts on the accuracy of the destination report. Based on the findings, we provide managerial and policy suggestions to ship operators, port authorities, and regulators. [ABSTRACT FROM AUTHOR]

• Empirically explore the factors that affect shipowners' energy choices. • Apply various data mining methods and a threshold discrete choice model. • Address the heterogeneity of the carriers towards environmental awareness. • Provide important practical implications for responding to the new emissions regulations. On January 1st, 2020, the International Maritime Organization (IMO) implemented a new regulation for a 0.50% global sulphur cap for marine fuels, which was a dramatic decrease from the previous emissions cap of 3.5%. The new regulation will have an enormous impact on the shipping market. At present, there are different feasible schemes for reducing sulphur emissions from ships. Shipowners need to consider the economic cost, energy feasibility, and other relevant factors of different schemes before making decisions. This paper empirically explores the factors that affect shipowners' energy choices. Based on the new emerging individual ship dynamic data, Automatic Identification System (AIS), and other relevant databases, we apply various data mining methods and a threshold discrete choice model combined with an oversampling technique to conduct quantitative measurements and statistical analyses of factors for each ship type that affect the shipowners' choices. Three groups of indicators, including ship characteristics, shipowner characteristics, and market conditions, are considered in our analysis. In the model, we also address the heterogeneity of the carriers towards environmental awareness. This study provides important practical implications for responding to the new emissions regulations among maritime and maritime-related industries and policymakers. [ABSTRACT FROM AUTHOR]