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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]