Crowd simulation is a well-developed research field, but its outputs are not structurally used in police event preparation. Police decision-making during large-scale events is strongly experience-driven. Professionals rely on practical knowledge, prior experience, local context, and coordination with other actors to assess risks and prepare measures. This is necessary in practice, but it can also make assumptions about crowd movement, bottlenecks, emergency accessibility, and possible measures difficult to test or explain.

The main challenge is therefore not the absence of simulation models, but the absence of a method that translates simulation outputs into usable decision-support information. This paper presents the Crowd Simulation Decision Framework, developed using Design Science Research at the Rotterdam Police. The framework is a method-type artefact that structures when and how crowd simulation can be used in the risk analysis and advisory phase. It links police risks, simulation questions, scenarios, outputs, interpretation, and possible measures to decision points in the event process.

The framework was demonstrated in the Meent case during a large-scale running event in Rotterdam and evaluated with police professionals. The evaluation focused on interpretability, usefulness, and usability. The findings suggest that the framework can support risk analysis by making assumptions explicit, comparing scenarios, and helping to substantiate police advice. At the same time, simulation outputs require clear explanation, transparent assumptions, a model check with practitioners, feedback loops, and a simulation expert who can translate between police practice and technical modelling. Without these conditions, outputs may be misinterpreted or may suggest more certainty than the model can provide.

The paper contributes design knowledge on how existing crowd simulation outputs can be integrated into experience-driven police decision-making. Simulation-based decision support should start with the decision-making problem, not with the model. The framework should therefore be seen as structured support for professional judgement, not as proof that simulation automatically improves decision-making. ... Read more

With increasing awareness of the environmental impacts of motorized trans-port. low-carbon mobility modes such as cycling are gaining importance. This dissertation investigates bicycle traffic dynamics and develops data-driven spatial-temporal prediction models under varying environmental and operational conditions. The findings aim to support policymakers, mobility providers, and system developers in making more efficient and evidence-based decisions for bicycle transportation systems.
... Read more

Human driving behaviour is inherently heterogeneous, shaping traffic dynamics and affecting traffic safety, efficiency and sustainbility. This dissertation develops an interpretable, AI-driven framework to identify, model, and simulate heterogeneous driving behaviour using naturalistic data. By analysing action phases, patterns, and behavioural sequences, it reveals how behavioural variability influences traffic performance and supports improved traffic management, personalised driver assistance, and human-aware autonomous vehicle design. ... Read more

Technological advancements have transformed how travellers access and navigate transport systems. This thesis analyses how such developments impact (potential) public transport users, especially those struggling with digital technologies. It also explores ways to mitigate potentially exclusionary effects of digitalisation in transport, helping transport operators, authorities and policymakers ensure that digitalisation does not disadvantage vulnerable users. ... Read more

The deployment of moving sensor platforms (e.g., self-driving cars, drones, and other instances) with advanced sensing is rapidly increasing the capture of human features at unprecedented temporal and spatial scales, especially in cities. This thesis advances knowledge on extracting information from this novel data source for traffic research and practice, while highlighting implications for privacy and beyond. Findings provide insights for traffic control and management, policy development, and anyone involved in responsible urban innovation. ... Read more

As automated vehicles (AVs) become more common, their influence on human-driven vehicles (HDVs) in mixed traffic is increasingly relevant. This dissertation explores how AVs affect HDV driving behavior—specifically car-following, overtaking, and gap acceptance—and how these behavioral adaptations influence traffic efficiency. Combining driving simulator experiments, field tests, and traffic microsimulation, the research provides insights into the dynamics of mixed traffic and their implications for infrastructure and AV deployment. ... Read more

In the face of accelerating urbanization and the consequent surge in housing demands, particularly in European capitals, Copenhagen is at the forefront of adopting innovative urban development strategies. One such strategy is the conceptualization and eventual realization of Lynetteholm, an artificial island designed to mitigate housing shortages while fostering sustainable urban growth. This initiative reflects a broader trend towards exploring new urban spaces that cater to the burgeoning population, leveraging the potential of reclaimed land and waterfront development.

This thesis situates the Lynetteholm project within the broader context of Copenhagen's historical commitment to sustainable urban development and efficient public transportation systems. It explores the unique challenges and opportunities presented by this ambitious project, particularly in the realm of mobility and transportation. By drawing on comparative analyses with cities like Amsterdam and Venice, which share Copenhagen's characteristic of being intertwined with waterways, the research aims to distill valuable insights into managing urban mobility in water-centric urban environments.

Central to the thesis is the exploration of smart mobility solutions, specifically Autonomous Navigation Systems (ANS) and Demand-Responsive Transportation (DRT), framed within the increasingly popular concept of Mobility as a Service (MaaS). The investigation delves into the potential synergy between these technologies and the existing urban transport framework in Copenhagen, with a particular focus on enhancing the "Harbor Bus" service. The envisioned autonomous demand-responsive ferry service (ADRT) is posited as a sustainable, efficient, and user-centered mobility solution that seamlessly integrates with the city's transport network, thereby facilitating the smooth incorporation of Lynetteholm into Copenhagen's urban tapestry.

The proposed ADRT system, characterized by its autonomous operation and demand-responsive nature, is designed to double the capacity of the current Harbor Bus fleet, addressing both the anticipated residential influx in Lynetteholm and the broader transportation needs of Copenhagen's residents. This system not only promises enhanced operational efficiency and reduced environmental impact but also aligns with the city's long-term vision of achieving CO2 neutrality and fostering a "green wave" of commuting practices.

Furthermore, the concept of "Ferry Oriented Development" (FOD) is introduced as a strategic urban planning approach that capitalizes on the untapped potential of waterways. By developing ferry terminals as focal points of urban activity, FOD encourages the formation of vibrant, interconnected communities that prioritize sustainable transport modes, thereby reinforcing Copenhagen's commitment to environmental sustainability and efficient urban mobility.

In sum, this thesis offers a comprehensive examination of the interplay between urban expansion, sustainable development, and innovative transport solutions in the context of Copenhagen's Lynetteholm project. It presents the design of a conceptual framework for an ADRT system that embodies the principles of autonomy, responsiveness, and integration, thereby contributing to the discourse on future urban mobility. This research provides a nuanced, evidence-based perspective on the deployment of smart mobility solutions in the face of rapid urban growth, offering valuable insights and recommendations for urban planners, policymakers, and stakeholders engaged in shaping the future of urban living in Copenhagen and beyond. ... Read more

A comprehensive understanding of shippers’ preferences can help transport freight forwarders create targeted transport services and enhance long-term business relationships. Nevertheless, limited research examined the benefit of considering shippers’ preferences in the decision-making of synchromodal transport planning and the collection of relevant data is still not straightforward.
This research proposes an innovative framework to learn shippers’ preferences in synchromodal transport operations and optimize transport services accordingly. A preference learning method is developed to capture shippers' preferences through pairwise comparisons of transport plans. In order to model the underlying complex nonlinear relationships and detect heterogeneity in preferences, artificial neural networks are employed to approximate shippers' utility for a specific plan. Based on the learned preference information, a synchromodal transport planning model with shippers’ preferences (STPM-SP) is proposed, with the objectives of minimizing the total transportation cost and maximizing shippers’ satisfaction. An Adaptive Large Neighborhood Search algorithm is developed for solving this optimization problem. This algorithm takes into account the two different objective functions and searches for Pareto solutions to the planning problem.
A case study is conducted based on the European Rhine-Alpine corridor to demonstrate the feasibility and effectiveness of the proposed methodological framework. Basic discrete choice models, binary logit models, are used as benchmarks for preference learning and the synchromodal transport planning model without preferences (STPM) is used as the benchmark for planning. The results show that the proposed preference learning method has better predictive power than the baseline model, achieving higher accuracy and lower variation. With the consideration of shippers’ preferences, STPM-SP can significantly increase shippers' satisfaction with transport services. Scenarios with different types of preferences are tested and results show that the average of maximum improvements in satisfaction reached 37.76%. This research contributes to learning shippers' preferences in the transport operation process and highlights the importance of incorporating these preferences into the decision-making process of synchromodal transport planning. ... Read more

During the influx to mass events in the past, situations have regularly arisen that people experienced as unpleasant or even unsafe. Nowadays, many researchers focus on regulating this influx by influencing travel choices, where only little attention is paid to travellers’ preferences. This research identifies the preferences of different type of visitors towards receiving information, and the extent to which visitors with different information profiles act on the information messages they receive. A survey was distributed (N=378), and the number of variables was reduced by performing an Exploratory Factor Analysis. The Latent Class Cluster Analysis distinguished three clusters based on their information message preferences. The results show that people are most interested in receiving information via email a few days before departure and that it is challenging to inform them at a later time. The most impressionable cluster mainly consists of young people, unfamiliar with the event area. Event organisations can apply the results by better-aligning communication with the preferences of their visitors. The differences in behavioural intentions between the clusters turned out to be limited. This must be further investigated since it is not in line with expectations. ... Read more

Public transport systems have been and continue to be shaped by disruptive forces, impacting individuals’ travel behaviour and how they interact with public transport. This thesis analyses the impact of disruptors on travel behaviour, the perception and use of public transport, enabling operators and policymakers to design appropriate measures and policies in order to improve the quality of service, the sustainability of transport and the liveability of our environment. ... Read more

Understanding economic decision-making is essential for impactful policy design. In the literature, two main modelling paradigms exist: compensatory and non-compensatory. In this work, we advance the field of decision theory by developing a flexible choice model capable of approximating two modelling paradigms without imposing any a-priori assumptions. Furthermore, through the use of the proposed model, we empirically identify the decision strategy involved in the choice of Mobility-on-demand (MoD) services. Finally, independent of the modelling paradigm, we propose and empirically validate a framework to model the effect of interpersonal network on choice behaviour. ... Read more

Dedicated lanes have been proposed as a potential scenario for the deployment of connected and automated vehicles (CAVs) on the road network. However, knowledge on the design and operation of DLs and their impacts on the behaviour of drivers of CAVs and manual vehicles is lacking in the literature. This dissertation provides a research agenda on design and operation of dedicated lanes and investigates the impacts of such lanes on the behaviour of human drivers. ... Read more

Although many agree that the use of bicycles improves mobility and quality of life in a city, much less clear is how to assess the progress being made in this direction and how to plan bikeable cities. The bikeability of a city depends on many diverse and interrelated factors such as the land use and transport system, culture and social norms, as well as individuals’ perceptions. Among the many factors influencing bikeability the infrastructure network, made of streets and intersections, is a fundamental component to allow safe and convenient cycling in a city. For this reason, this thesis focuses on infrastructure-related bikeability aspects and how to assess them. Planning for bicycle infrastructure has been piece-wise and location-specific resulting in every city developing its own best practices without contributing to a more general theoretical guidance on how to assess and develop attractive and
convenient bicycle networks. Since a systematic approach to bicycle infrastructure evaluation and planning is lacking we formulate the following research goal:

To gain empirical knowledge on bicycle infrastructure networks and develop methodological tools to assess infrastructure-related bikeability. ... Read more

This thesis constitutes a first step towards measuring the passenger capacity of station platforms. First, it defines platform capacity on the basis of the locations of queues at exit escalators from platforms, the presence of passengers in the platform-edge danger zone and the duration of stops. It then renders capacity measurable using real-life data covering train stops and passenger behaviour on platforms. ... Read more

As one of the most important freight transportation modes, maritime transport has been the backbone of international trade and global economy. From the cargo flow point of view, seaports and inland shipping link the individual countries and the global waterborne transportation networks. To analyze the current ship traffic and port performance or predict future scenarios, understanding ship behavior in ports and waterways is necessary. However, the depicted sailing environment is in the current studies far simpler than the real-life ports and waterways. To this end, we formulate the following research objective:
to gain empirical knowledge of ship behavior in real-life sailing environments and to empirically investigate the influencing mechanisms of intrinsic and external factors. ... Read more

On July 22nd 2018 the Noord/Zuidlijn, a metro line crossing the city center of Amsterdam, became operative. This entailed a transformation from a direct bus and tram network to a network consisting of a metro trunk line and bus and tram feeder lines. This study explores and uses interoperable smart card data from before and after the Noord/Zuidlijn became operative to measure the regional travel time and transfer impacts of this network overhaul. On a working day on average 1,350 hours of travel time are saved. The travel time savings and losses are 2,350 and 1,000 hours per day respectively. Additionally, 2,500 extra transfers are made per working day. The transfers gains are 4,500 transfers and the transfer losses are 7,000 transfers per working day. 20% of the travelers experiences a decrease in travel time of more than 1 minute, 10% experiences an increase in travel time of more than 1 minute. Furthermore, interoperable smart card data showed to be promising as it captures travel behavior across multiple operators. However, the fact that the number of travelers is given in bins complicates analysis. This study develops a methodology to work with interoperable smart card data. Additionally, as only few studies have evaluated the transportation impacts of a large-scale public transport network overhaul ex-post, the findings of this research could improve public transport planning and assessment. ... Read more

With the increasing demand for public transport systems worldwide and also a lot of these systems running at their maximum capacity, there is a strong need for finding ways for these systems to operate in a more efficient way. In recent transportation research there is an increasing attention for operational conditions and the impact of passengervehicles interaction on the timetables of urban rail networks. Passengervehicle interaction can have a strong impact on the operational conditions of an urban rail line as a large part of the dwell time of a vehicle can be explained by the number of boarding and alighting passengers. ... Read more

Cities are growing worldwide, which leads to an increase in trips in urban areas. In Europe, more than half of the trips are made by car, while car takes most space of all modalities. Of all modalities car has the highest CO2 emissions. Thehigh number of trips by car within cities lead to challenges related to the accessibility, livability, and sustainability of cities. Sustainable mode alternatives in this research are the bicycle, shared bicycle, and urban public transport whichhave potential for being an attractive alternative on short trips (<5 kilometer). Therefore, this study researches factors that influence car users’ mode choice towards those three sustainable modes, for stand-alone trips with trip purpose shopping. Through a stated preference survey amongst car drivers in a Dutch mid-sized city (N=360), preferences are gathered with respect to the mode choice. A panel mixed logit model with error component and interaction variables is used for the analysis of the stated preference data. The factors with most impact on mode choice are (shared) bicycle travel time, bicycle parking costs, shared bicycle availability, public transport travel costs, and public transport in-vehicle crowdedness. The non-mode factor with most impact is attitude towards tram, which is negatively correlated to car frequency. Car users tend to switch towards public transport, so a guaranteed seat in public transport, a more positive attitude towards public transport and higher car parking costs can achieve the switch. The switch towards bicycle can be made if the bicycle travel time is lower than currently. ... Read more

Over the past few decades, transport authorities globally have resorted to transport models for testing policy interventions and simulating the results as part of the ex-ante analysis. Within the domains of traffic assignment, there is a greater focus on the dynamic representation of traffic, which has proved to be more accurate when compared to their static counterparts. This has put Dynamic Traffic Assignment (DTA) Models at the forefront of development. Departing from the classical traffic flow theories Macroscopic DTA’s simulates aggregated traffic analogous to the flow of fluids or gases. This aggregation enables high-speed computation with the ability to achieve a stable equilibrium state within feasible model run times. Due to the large number of Macroscopic DTA models developed worldwide, the model user is posed with the problem of using the correct model for the correct application. The current research aims to provide an answer to this problem through the design, development, and validation of an evaluation framework for Macroscopic DTA’s. The objective evaluation of the DTA’s is performed through certain Measures of Performances (MoPs). The subjective side of evaluation showcases the differences in importance associated with model features which vary from model users to application domains. Three macroscopic DTA models popular in the Netherlands are used for the application of the framework: the MARPLE (Model for Assignment and Regional Policy Evaluation), StreamLine: MaDAM (Macroscopic Dynamic Assignment Model), and StreamLine: eGLTM (event-based Generalized Link Transmission Model). From the results, it is observed that For a Strategic Planning application, both MARPLE and StreamLine: eGLTM proved to be better alternatives, as they performed exceedingly better in achieving a stable state of convergence. However, as the time horizons of application became smaller as is the case with Tactical and Operational planning, the final score for StreamLine: MaDAM improved substantially due to its accuracy involved in link-level propagation and queuing. The evaluation scores also showcase the fundamental trade-off between model complexity and computational speed was visible from the results. We can observe variations across model users, which validates our original hypothesis that the right choice of a model primary depends on the person using it and the application it is deployed for. ... Read more

E-bike sharing has gradually gained popularity in recent years, while the research in this field is still quite limited. This study applies data-driven methods, mainly demand pattern analysis, to facilitate the development of operational strategies in a cost-effective and operator-friendly way. Demand pattern is analysed in an innovative spatial analytical unit, overlapping circle, which is proven to achieve more beneficial effects than the traditional units (i.e., the administrative units), and hourly clustering is conducted to derive the reallocation strategies by mitigations of imbalance in supply and demand in recurrent hourly clusters. Additionally, this work constructs several indicators to evaluate the strategies in a real-life context, taking both the operator and the users into account. The proposed methodology is applied in a case study, bondi’s e-bike sharing in The Hague with a 4-month time frame from 19-06-2021. There are 5 hourly clusters emerging via agglomerative hierarchical clustering: 1) the first peak hour (16:00-16:59); 2) the second peak hour (17:00-17:59); 3) the first transition hour (18:00-18:59); 4) the second transition hour (19:00-19:59); 5) the off peak (20:00-15:59). The corresponding reallocation strategies are then proposed to alleviate the imbalance in different periods. Additionally, adjustment in the operational areas is suggested by the supply efficiency and trip duration/distance analyses. The results prove that the operational strategies obtained from demand patterns indeed improve the service, with almost 1.5 times ridership, circa 20% decrease in vehicle idle time, compared to the baseline. and a decent monthly net retention rate at around 60%. ... Read more