Ports are actively pursuing greater operational efficiency to handle the increasing global flow of goods, while simultaneously improving the energy efficiency of their operations to comply with new environmental regulations. As a result, innovation-leading ports have begun to recognize the potential of digital twins to monitor, coordinate, and optimize port processes, enabling energy savings and reductions in both costs and CO2 emissions. Although digital twins have gained significant momentum in other domains, such as smart manufacturing and aerospace, their adoption in ports remains challenging. This can be explained by the multi-stakeholder nature of ports and the high complexity of their interconnected processes, requiring decision-making across organizational boundaries.

Grounded in the port context, this thesis examines what constitutes a digital twin, proposes a framework to assess the maturity of existing port digital twins, and develops modeling and explainable AI-enabled decision support components for port and maritime operations. These components span seaside, quay, yard, and gate processes and can serve as building blocks of future port digital twin implementations. The thesis consists of six papers:

Paper 1 provides an in-depth literature review of digital twins across multiple domains and transfers insights from these to the port domain. The paper outlines how digital twins can enhance operational efficiency and support energy savings in ports. It also identifies the characteristics and design requirements that a port-specific digital twin must fulfill. Based on these findings, the paper proposes a tailored definition of a digital twin for the port domain.

Paper 2 discusses how digital twins’ maturity can be assessed within six maturity levels and presents milestones for their implementation. Notably, Interoperability is identified as the highest maturity level,as the numerous stakeholders and their respective digital twins must work together to reach a coordinated system of systems performance. Using this assessment demonstrates that only a few innovation-leading ports have developed sophisticated digital twinning solutions so far.

Paper 3 focuses on container retrieval, balancing two competing objectives: minimizing yard crane moves and adhering to tight truck scheduling. This reflects the conflicting perspectives of different stakeholders in the port context. The provided optimization model and heuristic algorithm demonstrate that addressing both problems simultaneously may result in reduced efficiency of the individual objectives. However, from a systems perspective, this approach leads to higher overall port efficiency.

Paper 4 examines quay cranes at the system level by developing an explainable AI framework to predict whether a quay crane will experience a breakdown during vessel operations. Using monitoring data, operational data, and weather observations, the study identifies how operational intensity, hoist-related warning patterns, and environmental conditions jointly influence the likelihood of a breakdown. This system-level predictive capability enhances situational awareness and enables early identification of disruptions.

Paper 5 builds on Paper 4 by focusing on the prediction of individual critical error events. Rather than assessing the overall likelihood of a breakdown, the model identifies which error type is likely to occur next and estimates its timing. Using eXtreme Gradient Boosting with lagged error sequences, operational data, and weather conditions, the study offers component-level insights that complement the systemlevel prediction in Paper 4 and support more targeted maintenance interventions.

Paper 6 expands the perspective beyond ports by analyzing fuel consumption in inland ferry operations using GPS-derived trip legs and journeys enriched with environmental data. Combining unsupervised clustering to uncover operational patterns with supervised learning and SHAP-based explainability, the study identifies operational speed as the dominant driver of fuel consumption and links consumption patterns to individual captains’ driving behavior. This contributes to maritime decision-making by enabling targeted interventions such as eco-driving strategies.

Together, these six papers contribute a conceptual grounding of port digital twins, provide a tool for their assessment, and provide modeling components to aid in port and maritime decision-making.

Hamnar strävar aktivt efter ökad operativ effektivitet för att hantera det ökande globala varuflödet, samtidigt som de strävar efter att förbättra energieffektiviteten. Som ett resultat har ledande hamnar börjat se potentialen hos digitala tvillingar för att skapa överblick samt koordinera och optimera processer i hamnen. Målet med användningen av digitala tvillingar är energibesparingar samt minskning av kostnader och CO2-utsläpp. Medan digitala tvillingar har använts inom andra områden såsom tillverknings-, flyg- och rymdindustrin, har införandet i hamnar varit jämförelsevist långsamt. Detta kan förklaras, bland annat, av hamnens många olika involverade aktörer och den höga komplexiteten i de ofta sammanlänkade hamnprocesserna.

Därför fokuserar denna avhandling, med utgångspunkt i hamnkontexten, vad som utgör en digital tvilling, presenterar egenskaper för olika mognadsnivåer hos befintliga digitala tvillingar, och introducerar modellerings- och beslutsstödskomponenter baserade på förklarbar AI för hamn- och maritima operationer. Dessa komponenter omfattar kustnära processer, kajoperationer, yard-processer och gate-funktioner, och kan fungera som byggstenar i framtida digitala tvillingar för hamnar. Avhandlingen består av sex artiklar:

Artikel 1 bygger på en omfattande litteraturöversikt, inom vilken digitala tvillingar för olika områden studeras ingående för att överföra insikter från dessa till hamndomänen. Detta resulterar i en presentation av vad som utgör en hamns digitala tvilling och de krav som en hamns digitala tvilling måste uppfylla, tillsammans med en diskussion om hur digitala tvillingar i hamnar kan bidra till energibesparingar.

Artikel 2 presenterar ett ramverk för hur mognaden hos digitala tvillingar kan bedömas baserat på sex mognadsnivåer och presenterar milstolpar för deras implementering. Noterbart är att interoperabilitet identifieras som den högsta mognadsnivån, eftersom de många intressenterna och deras respektive digitala tvillingar måste koordineras för att nå en fungerande system-av-systemnivå. Genom att använda denna bedömning visar det sig att endast några få innovationsledande hamnar hittills har utvecklat sofistikerade digitala tvillinglösningar.

Artikel 3 fokuserar på containerupphämtning med hänsyn till två konkurrerande mål: att minimera energikrävande kranrörelser och att hålla planerade tider för lastbilar. Detta speglar de potentiellt motstridiga perspektiven hos olika intressenter i hamnkontexten. Den utvecklade optimeringsmodellen och algoritmen visar att gemensam hantering av båda dessa mål kan leda till minskad effektivitet för de respektive individuella målen, men ökad effektivitet från ett systemperspektiv för hamnen som helhet.

Artikel 4 studerar kajkranar på systemnivå genom att utveckla ett förklarbart AI-ramverk för att förutsäga om en kajkran kommer att drabbas av ett driftstopp under ett fartygsanlöp. Genom att använda övervakningsdata från kranarna, operativa data från terminalen och meteorologiska observationer identifierar studien hur operativ belastning, hoist-relaterade varningar och väderförhållanden gemensamt påverkar sannolikheten för driftstopp. Modellen förbättrar situationsmedvetenheten och möjliggör tidigare identifiering av störningar.

Artikel 5 bygger vidare på Artikel 4 genom att fokusera på prediktion av enskilda kritiska felhändelser. I stället för att uppskatta sannolikheten för ett övergripande driftstopp förutser modellen vilken feltyp som sannolikt inträffar härnäst och när detta sker. Med hjälp av eXtreme Gradient Boosting i kombination med sekvenser av tidigare fel, aktuella operativa data och väderförhållanden tillhandahåller studien komponentnivåinsikter som kompletterar systemnivåanalysen i Artikel 4 och möjliggör mer riktade och tidskritiska underhållsåtgärder.

Artikel 6 breddar avhandlingens fokus till maritima operationer genom att analysera bränsleförbrukning i färjetrafik baserat på GPS- data och kompletterande miljödata. Genom att kombinera oövervakad klustring för att identifiera återkommande operativa mönster med övervakade prediktionsmodeller och SHAP-baserad förklarbarhet visar studien att fartygshastighet är den dominerande faktorn bakom bränsleförbrukning. Analysen kopplar också bränsleförbrukningsmönster till individuella befälhavares beteenden och möjliggör riktade åtgärder, såsom eco-driving.

Tillsammans bidrar dessa sex artiklar med en konceptuell grund för digitala tvillingar i hamnar, ett verktyg för att bedöma mognaden hos befintliga lösningar samt ett antal modelleringskomponenter som kan stödja datadrivet och förklarbart beslutsfattande i både hamn- och maritima verksamheter.

Efficient port operations depend on the disruption free operation of quay cranes (QCs), which transfer containers between vessels and internal trucks. As global container through put rises, QCs face increased pressure, resulting in accelerated wear and tear. This can lead to QC downtime, which could interrupt the entire chain of port operations. Therefore, timely identification and prediction of critical errors is essential to enable timely maintenance to lower the risk of downtime. This study utilizes two years of QC monitoring data, enriched with weather conditions and terminal operational context, alongside twenty critical error events identified by the terminal operator. The goal is to predict the occurrence and timing of these critical errors through a three-stage machine learning model. The first stage predicts the type of the next critical event based on historical error patterns, warnings, and contextual data. The second stage estimates a time window in which the event will occur. The third stage refines timing predictions when more than one hour remains. The first two stages are formulated as multiclass classification problems, and the third as a regression task. All stages utilize eXtreme Gradient Boosting (XGBoost). SHapley Additive exPlanations (SHAP) are used to identify influential features. Results show that the model predicts the next critical error type with 83% accuracy and its immediacy with 71% accuracy. However, approximating the timing of events anticipated to occur beyond one hour remains challenging. These findings support proactive maintenance planning and operational adjustments, helping port operators mitigate disruptions and enhance QC reliability.

Urban transportation planning faces increasing complexity as cities seek to optimize mobility systems without extensive historical data. This paper presents CI-SGNN (City-Invariant Spatial Graph Neural Network), a novel framework for cross-city bike-sharing demand prediction that leverages Point of Interest (POI) distributions and spatial attention mechanisms. Our approach addresses the critical challenge of predicting categorical mobility demand in new urban environments by learning transferable relationships between urban amenities and travel patterns from source cities. The framework integrates OpenStreetMap POI features with GNNs, enabling zero-shot transfer learning across diverse metropolitan areas. We formulate demand prediction as a multi-class classification problem, categorizing origin-destination pairs into five demand levels. Experimental validation using real CitiBike data from Manhattan and Washington DC demonstrates superior performance, achieving 72.4% accuracy, which overperforms state-of-the-art base-lines. The attention-based spatial aggregation mechanism effectively captures inter-zone dependencies. Our results demonstrate successful zero-shot adaptation capabilities, enabling practical deployment for bike-sharing infrastructure planning in cities lacking historical mobility data using only publicly available urban features. 

Ports are actively pursuing greater operational efficiency to effectively handle the increasing global flow of goods, while striving to improve the energy efficiency of their operations to comply with new environmental regulations. As a result, innovation-leading ports have begun to recognize the potential of digital twins to overview, coordinate and optimize port processes, resulting in energy savings, and reductions of costs and of CO2 emissions. While digital twins have gained momentum in other domains such as smart manufacturing and aerospace, their adoption in ports has been comparatively slow. This can be explained, among other things, by the multi-stakeholder nature of the port and the high complexity of the often interconnected port processes. Thus, this thesis, grounded in the context of ports, discusses what constitutes a digital twin, proposes characteristics to assess the maturity of existing digital twins, and introduces and evaluates mathematical models to support a key port process, which can be used as components of a digital twin for the port. The thesis is composed of three papers: 

Paper 1 is based on an extensive literature review, through which digital twins among different domains are studied in depth in order to transfer insights from these to the port domain. The resulting discussion of what constitutes a port’s digital twin and the requirements that a port’s digital twin must fulfil, together with a discussion of use cases of how port digital twins can contribute to energy savings, form the basis of Paper 1. 

Paper 2 discusses how digital twins’ maturity can be assessed within six maturity levels and presents milestones for their implementation. Notably, Interoperability is identified as the highest maturity level, as the numerous stakeholders and their respective digital twins must work together to reach a coordinated system of systems performance. Using this assessment demonstrates that only a few innovation-leading ports have developed sophisticated digital twinning solutions so far. 

Paper 3 is dedicated to coordinating container retrieval with stacking, combining two key port operations. Thus, it can present a key modeling component of a port digital twin, considering jointly the goals of reducing the energy demanding crane movements, as well as keeping schedules tight to avoid port congestion issues. This is directly reflecting the potentially conflicting perspectives of different stakeholders in the port context. The provided optimization model and algorithm show that jointly addressing both problems may lead to a reduced efficiency of both individual objectives, but from a systems perspective, leads to a higher overall port efficiency. 

Hamnar strävar aktivt efter ökad operativ effektivitet för att hantera den ökande globala varuflödet, samtidigt som de strävar efter att förbättra energieffektiviteten. Som ett resultat har ledande hamnar börjat se potentialen hos digitala tvillingar för att skapa överblick samt koordinera och optimera processer i hamnen. Målet med användningen av digitala tvillingar är energibesparingar samt minskning av kostnader och CO2-utsläpp. Medan digitala tvillingar har använts inom andra områden såsom tillverknings-, flyg- och rymdindustrin, har införandet i hamnar varit jämförelsevist långsamt. Detta kan förklaras, bland annat, av hamnens många olika involverade aktörer och den höga komplexiteten i de ofta sammanlänkade hamnprocesserna. Därför fokuserar denna avhandling, med utgångspunkt i hamnkontexten, vad som utgör en digital tvilling, presenterar egenskaper för olika mognadsnivåer hos befintliga digitala tvillingar, och introducerar samt utvärderar matematiska modeller som kan bli delkomponenter i en digital tvilling för hamnen. Avhandlingen består av tre artiklar:

Artikel 1 bygger på en omfattande litteraturöversikt, inom vilken digitala tvillingar för olika områden studeras ingående för att överföra insikter från dessa till hamndomänen. Detta resulterar i en presentation av vad som utgör en hamns digitala tvilling och de krav som en hamns digitala tvilling måste uppfylla, tillsammans med en diskussion om möjliga sett på vilka hur hamnens digitala tvillingar kan bidra till energibesparingar.

Artikel 2 presenterar ett ramverk för hur mognaden hos digitala tvillingar kan bedömas baserat på sex mognadsnivåer och presenterar milstolpar för deras implementering. Noterbart är att interoperabilitet identifieras som den högsta mognadsnivån, eftersom de många intressenterna och deras respektive digitala tvillingar måste koordineras för att nå en fungerande system-av-systemnviå. Genom att använda denna bedömning visar det sig att endast några få innovationsledande hamnar hittills har utvecklat sofistikerade digitala tvillinglösningar.

Artikel 3 fokuserar på koordinering av containerupphämtning koordinerat med staplings effektivitet, två viktiga hamnaktivieter. Därför representerar dessa en viktig modelleringskomponent i en hamns digitala tvilling, med beaktande av målen att minska de energikrävande kranrörelse, samt behovet av att hålla planerade tider för att undvika trängsel och väntan. Detta speglar direkt de potentiellt konfliktfyllda perspektiven hos olika intressenter i hamnkontexten. Den utvecklade optimeringsmodellen och algoritmen visar att gemensam hantering av båda dessa problemen kan leda till en minskad effektivitet för de respektive individuella målen, men en ökad effektivitet från ett systemperspektiv för hamnen som helhet.  

Digital twins have gained tremendous momentum since their conceptualization over 20 years ago, as more and more domains discover their value in driving efficiencies and reducing costs, while enabling technologies continue to advance. Originally aimed at product optimization and intelligent manufacturing, the range of applications for digital twins now spans entire complex, often highly interconnected systems such as ports, cities, and supply chains. Despite the increasing demand for sophisticated digital twinning solutions across all domains and scopes, their development is often still constrained by differing definitions, different understandings of their functional scope and design, and a lack of concrete methodology toward implementing a comprehensive digital twinning solution. Although there are already papers that evaluate the capabilities of existing digital twinning solutions on the basis of maturity levels, these usually consider the object to be twinned in isolation and are often domain-specific. With this article we address exactly this gap discussing how interoperability of digital twins can break physical boundaries of an isolated system, enabling system of systems joint optimization. We therefore consider interoperable digital twins to be the most mature twinning platforms, thus, we discuss in detail six digital twin maturity levels, departing from the interrelated contexts of ports, cities, and supply chains. Examples drawn from these domains demonstrate the need for interoperability toward optimizing processes and systems in realistic contexts, rather than in assumed isolation.

Policymakers must find efficient public transport solutions to promote sustainability and provide efficient urban mobility in the course of urban growth. A growing number of research papers are applying Geographically weighted regression (GWR) to model the relationship between public transport demand and its influential factors. However, few studies have considered the rapid development of journey inference from ticket transaction data. Similarly, the potential of GWR to analyze spatio-temporal changes that reflect changes in transportation supply and thus provide a measure for evaluating the local success of transport supply changes has yet to be exploited. In this paper, we use inferred journeys from smart card inferences as the dependent variable and analyze how public transport demand responds to a set of explanatory variables, emphasizing transport supply. Consequently, GWR and its successor Multiscale Geographically Weighted Regression (MGWR) are applied to analyze the spatially varying impact of transport supply changes for seven consecutive time frames between autumn 2017 and spring 2020, allowing conclusions about local changes in transport demand, as well as the benchmarking of transport supply changes. The (M)GWR frameworks predictive power is evaluated by training the model with past transport supply data and testing the model with data from the following consecutive years. The conducted analyses reveal that the (M)GWR model, using inferred journeys and transport supply data, can retrospectively predict the impact of transport supply changes on travel behavior and thus provides conclusions about the success of transport policies.