In the coming decades, the shipping sector is facing various challenges, requiring adaptations for achieving sustainable shipping, against climate change consequences, for facilitating alternative activities at sea, and for transitioning towards more autonomous shipping. Several incidents related to these challenges force us to take a good look at how the system can keep performing its function conditional to these changes. Scientific studies hereby regard the collective of (interacting) shipping activities as a system. Outcomes of data analyses and models are intended to support decision makers in designing effective improvement measures. However, the usefulness of the outcomes to the decision makers can be better, amongst others due to poor communication between science and decision makers, due to analysis objectives not being achieved, and due to unrealistic data requirements.

At the foundation of the analysis is often a disciplinary approach, or \textit{way of thinking}, which determines which solution space is considered, and which input sources are accepted. Looking from multiple \emph{perspectives} can broaden this, and thereby improve the formulation of analysis objectives and the identification of relevant input data. Besides determining which perspectives are relevant for a specific problem, the remaining challenge is related to how these alternative perspectives can be merged into an integrated whole. The aim of this thesis is to design a framework for an early integration of multiple perspectives in the analysis of shipping systems to improve their usefulness in the decision-making process. The first ambition for the framework is to provide a formulation of analysis objectives and data requirements in view of multiple perspectives, and the second ambition is to develop a data-structure concept to merge the perspectives.

For the first ambition, a literature study into systems with similar characteristics as a shipping system revealed that the analyses of these systems are mostly performed from one or several of the perspectives regarding its objectives, that we refer to as: (1) \textbf{scales}, addressing the ``where'' and ``when'' of system performance, uncovering spatial patterns and temporal variations, (2) \textbf{conditions}, considering the connection between system performance and its underlying physical processes and environment, (3) \textbf{behaviour}, considering the influence of individual or collective behaviour on the system performance and (4) \textbf{dependencies}, identifying causal relationships and sensitivities within the system. For each of the distinguished perspectives, based on the data sources and analysis types of the relevant studies, specifications could be formulated about the highest detail level on one hand, and the information required to aggregate to higher levels, up to the system level, on the other.

The second ambition, regarding a concept for merging these multi-perspective requirements, was obtained by introducing a new data structure referred to as an \emph{event table}. In this data structure, inspired by the existing concepts of moving features and event logs, each row represents a distinct event, and each column indicates a characteristic of the event. A single event is defined by the highest-detail-level specifications for each perspective. Besides some columns that form the unique event definition, the \emph{attributes} provide additional information about each event. Filtering and aggregation operations on the event table allow zooming in and zooming out, offering flexibility to investigate global patterns in detail, or to assess the impact of detail level processes, thereby fulfilling the second ambition for the framework.

The framework outlines the relationship between the availability of input materials and the ambition of the analysis goals. Hence, developments in the field of data science, analysis techniques, and computational facilities increase the scope, detail level, and modeling complexity captured in the analysis goals. By parallelising and scaling-up computations, the scope and detail level of analyses can be increased. By joining multiple spatially and temporally varying data sources, environmental influences can be determined. By applying dimension-reduction and outlier detection techniques, many characteristics of vessel behaviour can be assessed to determine anomalous behaviour. By labelling known behaviour, cause and effect can be coupled to improve the predictive capabilities. Applying these developments to the monitoring activities regarding nautical safety demonstrated how these developments can extend the ambition level of the analysis.

The framework was applied to two shipping-related cases. The first case considered nautical safety risks at the North Sea imposed by the potential event that vessels get adrift while being surrounded by offshore infrastructure, like wind parks. Based on the formulated multi-perspective objectives, the event table was constructed, whereby each event was defined by combination of a vessel of particular type and size (indicated by a category), to be present at a particular location at sea (indicated by a cell, part of a grid), under particular environmental conditions (a combination of wind direction, wind speed, wave height-period combination, wave direction, and current profile). For each event, the probability of occurrence could be determined, and conditional to this, using a drift path prediction tool, the probability that the vessel would drift into a wind park after $n$ hours in case of technical problems. Filtering and aggregation operations on the table revealed how a single analysis can support location specific design of barriers between wind parks and shipping lanes, as well as evaluation of strategies for emergency response vessels.

The second case considered shipping emissions on Dutch inland waterways. Based on the framework, analysis objectives were formulated for three perspectives; scales, conditions and behaviour. This resulted in an event table whereby each event corresponded with a single vessel, sailing a single waterway section on the Dutch fairway network. For each event, based on the sailed trajectory, the vessel properties, and the environmental characteristics, the energy use as well as the associated emissions could be estimated. The entire collection of events in the table represented all vessels travelling on the Dutch inland waterway network over the course of four months. Filtering and aggregation operations on the table revealed how emissions are impacted by river currents, and that a large share of the emissions is caused by waiting, idling, and manoeuvring vessels.

Both cases demonstrated how application of the framework can lead to an improved understanding of how the shipping system performs and responds to varying conditions and external changes. More importantly, they showed that the event table concept was capable of supporting formulation of promising improvement measures. This offers policy makers better support when making decisions. Owing to the versatility of the event-table concept, it is possible to anticipate on unseen or unforeseen perspectives in the future.

The increasing amount of activities at sea, including the development of offshore wind parks, result in a more confined space for shipping, requiring the assessment of risk changes regarding nautical safety and the design of potential mitigation measures. The main contribution of this paper is the transparent evaluation of allision probabilities, based on an event-based approach. This enables a structural consideration of conditional probabilities, and supports uniting quantitative and qualitative analyses. The event-based approach allows evaluating the outcomes from various perspectives: scales, conditions, behaviour and dependencies. The analysis outcomes are represented in a concept called “event table”, from which these perspectives can be extracted. Consequently, from this single data structure, insights can be gained ranging from spatial variations of the risk (highly detailed or global patterns), to detailed distinction between the most important influencing factors (varying from vessel type to environmental condition). It is furthermore possible to switch between wind-park specific risks and assessment of operational and strategic risk-mitigating measures for the entire area. The core feature of incorporating multiple perspectives not only allows various views on the safety risks, providing a better understanding of the most important contributing factors, as well as effectiveness of intervention measures. Our analysis shows the added value of additional distance between shipping lanes and wind parks in the spatial design, and we demonstrate how our multi-perspective approach supports the strategic and operational decisions around the availability and deployment of emergency response vessels.

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.

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.

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.

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.

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.