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In the digital era, major shipping lines are developing instant quote and online booking platforms. In one of the first attempts to investigate the post-event effects of this trend, we evaluate how a shipping line's online quote platform impacts its shipper portfolio and the ordered container volume. In order to control for unobserved, time-varying effects that could be correlated with the platform's implementation, we apply the regression discontinuity design (RDD) method to the import trade data from 2016 to 2019 of a top shipping line that released its platform in August 2018. We also adopt global polynomial regression and local liner regression so as to control for the effects of different polynomial time trends. We find the container volume assigned to the shipping line declined slightly after the online platform was launched. The container orders of small shippers increase by 3.97 TEU on average after online platform adoption. The volume of assigned containers from other shippersdeclines. These findings hold fruitful implications for shipping lines. A significant increase in the number of small shippers and their container volumes demonstrate administrative cost saving and risk mitigation for a shipping line. However, it also leads to possible loss of large customers. [ABSTRACT FROM AUTHOR]

This article presents an analysis of the competitiveness of Brazilian container port terminals and discusses the implications for the sustainable development of this sector in Brazil in the context of container ship size maximization. A cluster analysis was developed. Thirteen criteria were selected in the analysis taking into consideration both the previous literature and the characteristic of Brazilian ports. The classification shows that the third group of terminals (Santos Brasil, BTP and Paranaguá) has the highest average values in eleven out of the thirteen criteria, standing out mainly in annual capacity, number of plugs, and GDP hinterland. This is the most competitive group with the highest frequency of ship visits. It is also interesting to find that this group of terminals has a limitation regarding the operational draft that limits the port sector when receiving large ships. Considering the expected inclusion of New Panamax-class ships operating on the Brazilian coast, container terminals have restrictions due to the limited operational draft. Conclusions are drawn regarding the potential for implementing strategies and policy interventions. The study represents an application inspired by the current debate in the Brazilian port sector, which presents the country's urgent problem of receiving large capacity ships. [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]

To address cruise companies' revenue loss caused by over-reliance on indirect channels for ticket distribution, we design a dual-channel pricing policy that includes dynamic horizontal and vertical pricing regimes aiming to maximizing cruise companies' revenue. In the horizontal pricing regime, cruise companies and travel agencies offer uniform prices to consumers dynamically. In the vertical pricing regime, cruise companies provide dynamic commissions to travel agencies based on their sales performances. In view of the dynamic customer arrivals and uncertain demand, we model the dynamic dual-channel pricing decisions as a Markov decision process, and solve the model by means of a reinforcement learning method. Our computational results show that dual-channel pricing policies generated by our method outperform the classic Expected Marginal Seat Revenue method, which has been widely used in similar industries such as airlines for revenue management. In addition to the various features of dual-channel in ticket distribution, our model also takes into account customers' onboard consumptions, which is a critical source of revenue in cruise industry but has never been considered in the literature. As a result, our model and computational results provide insightful implications for cruise revenue management. [ABSTRACT FROM AUTHOR]

Maritime transportation is important, not only for its contribution to global trade, but also for its potential role in our common effort to curb the global environmental problems and make the world's economic development sustainable. An interesting finding is that shipping firms are more likely to adopt proactive GSCM strategies, which contradicts the findings of most management studies that reactive firms outnumber proactive and leading companies. All sample firms were clustered into "the leaders", "proactive firms", and "reactive firms" according to their level of GSCM strategy adoption. [Extracted from the article]

To reduce CO2 emissions from shipping activities to, from, and within the European Union (EU) area, a system of monitoring, reporting, and verification (MRV) of CO2 emissions from ships are implemented in 2015 by the EU. Although the MRV records in 2018 and 2019 have been published, there are scarce studies on the MRV system especially from a quantitative perspective, which restrains the potential of the MRV. To bridge this gap, this paper first analyzes and compares MRV records in 2018 and 2019, and then develops machine learning models for annual average fuel consumption prediction for each ship type combining ship features from an external database. The performance of the prediction models is accurate, with the mean absolute percentage error (MAPE) on the test set no more than 12% and the average R-squared of all the models at 0.78. Based on the analysis and prediction results, model meanings, implications, and extensions are thoroughly discussed. This study is a pioneer to analyze the emission reports in the MRV system from a quantitative perspective. It also develops the first fuel consumption prediction models from a macro perspective using the MRV data. It can contribute to the promotion of green shipping strategies. [ABSTRACT FROM AUTHOR]