Policymakers in the maritime sector face the challenge of designing and implementing decarbonization policies while maintaining safe navigation. Herein, the inland sector serves as a promising stepping stone due to the possibility of creating a dense energy supply infrastructure and shorter distances compared to marine shipping. A key challenge is to consider the totality of all operational profiles as a result of the range of vessels and routes encountering varying local circumstances. In this study, we use a new scheme called “event table” to transform big data on vessel trajectories (AIS data) combined with energy-estimating algorithms into shipping-emission outcomes that can be evaluated from multiple perspectives. We can subsequently tie observations in one perspective (for example, large-scale spatial patterns on a map) to supporting explanations based on another perspective (for example, water currents, vessel speeds, or engine ages and their contributions to emissions). Hence, combining these outcomes from multiple perspectives and evaluation scales provides an essential understanding of how the system works and what the most effective improvement measures will be. With our approach, we can translate large quantities of data from multiple sources into multiple linked perspectives on the shipping system.@en

Purpose: Maintenance dredging can often hinder port operations resulting in waiting times for seagoing vessels. The purpose of this paper is to investigate the dynamics between maintenance dredging activities and seagoing vessels, specifically focusing on how waiting times can be reduced. Then, the role of selecting different maintenance dredging strategies in reducing these waiting times is outlined. Methods: The study analyzes historical automatic identification system (AIS) data to identify the interaction between maintenance dredging and seagoing vessels and quantify the hindrance periods for the Mississippihaven case study in the Port of Rotterdam, the Netherlands. The trajectories of the vessels are analyzed in a simple case to show how the vessels interact and how the waiting times are quantified. The interactions are checked with the Port of Rotterdam for different port calls to ensure that maintenance dredging was the reason for these delays. Results: By analyzing the AIS data analysis of vessels in a given time window, the dredgers for maintenance work can be identified and their activities within or near the terminal can be determined. In addition, the waiting time of the seagoing vessel caused by the maintenance dredging is quantified at the terminal entrance. Conclusion: The study discusses how the maintenance dredging operations could be improved by adjusting the loading and sailing phases of maintenance dredging and provides some theoretical and managerial insights. Alternative port maintenance strategies to minimize the waiting time caused by the hindrance are also discussed.

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Reducing waiting times is crucial for ports to be efficient and competitive. Important causes of waiting times are cascading interactions between realistic hydrodynamics, accessibility policies, vessel-priority rules, and detailed berth availability. The main challenges are determining the cause of waiting and finding rational solutions to reduce waiting time. In this study, we focus on the role of the design depth of a channel on the waiting times. We quantify the performance of channel depth for a representative fleet rather than the common approach of a single normative design vessel. The study relies on a mesoscale agent-based discrete-event model that can take processed Automatic Identification System and hydrodynamic data as its main input. The presented method’s validity is assessed by hindcasting one year of observed anchorage area laytimes for a liquid bulk terminal in the Port of Rotterdam. The hindcast demonstrates that the method predicts the causes of 73.4% of the non-excessive laytimes of vessels, thereby correctly modelling 60.7% of the vessels-of-call. Following a recent deepening of the access channel, cascading waiting times due to tidal restrictions were found to be limited. Nonetheless, the importance of our approach is demonstrated by testing alternative maintained bed level designs, revealing the method’s potential to support rational decision-making in coastal zones.@en

PIANC Task Group 234 concludes that the “path to decarbonization of inland waterway transport is different for different corridors and in different countries”. This calls for an approach that can consider largescale differences as well as local influences when evaluating the emissions of inland vessels. This paper demonstrates the use of a so-called “event table” that allows corridor scale estimations of inland shipping emissions, while retaining the ability to identify the most important source mechanisms that produce these emissions. We considered three corridors in the Netherlands: Antwerp-Rotterdam, Antwerp-Duisburg and Rotterdam-Duisburg. Using the event table and four “pivoting perspectives”, we quantify large-scale emission patterns and investigate underlying mechanisms for these three corridors. Our study shows that despite their close vicinity, different mechanisms are responsible for observed emission peaks on these corridors. On the Antwerp-Rotterdam corridor, the most important contributions to emissions are slowly sailing vessels near the two locks that are on this route. It is furthermore shown that deeper fairway sections contribute to significantly lower emissions locally. On the Rotterdam-Duisburg corridor, we show that river currents significantly influence the emissions of vessels per travelled distance unit. The Antwerp-Duisburg corridor contains a combination of these factors.
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The planning and construction of offshore wind parks reduces the margin for error of nearby shipping activities. During the last couple of years, several incidents on the North Sea have raised the attention for the risk of ship-ship and ship-infrastructure collisions. Although often not the primary cause, environmental conditions play an important role in these incidents, and prevention and intervention measures, like the placement of emergency response vessels, are often deployed considering metocean conditions. To improve the design of risk-reducing measures, we need to understand better how vessels behave under varying environmental conditions. It is important to gain insight into risk patterns at system scale while retaining the ability to explain how these patterns are linked to underlying mechanisms. For this purpose we propose creating a so-called ‘event’ table, that couples ship behaviour data from Automatic Identification System (AIS) data to environmental data. The structure of the ‘event’ table, whereby events are defined as vessels sailing within a specific section of the system, allows appending analysis results and other data sources to the events. We show how the ‘event’ table adds important new perspectives to the analysis of nautical safety at sea.@en