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.

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.

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.

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. @en

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.

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.

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.

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.

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%.

Following the emergence of new mobility trends, in which shared mobility plays an essential role, Public transport operators face the challenge of reacting in a convenient way to the changes brought to the transportation scene. Even though different collaborations are being put in place between public operators and shared mobility providers, the effects they may have in mode choice are still uncertain, and to the best of the author’s knowledge still to be studied. Accordingly, a stated choice experiment was conducted in the city of Rotterdam, in which preferences towards shared bicycles and shared mopeds were analysed under the assumption of a perfect integration between transit and shared mobility. The results suggest that under said conditions, shared modes have the potential to be interesting egress alternatives for trips by metro, which could imrprove preferences towards the metro. Furthermore, considering choice determinants exhibited during the experiment, it is noticed that egress cost and total travel time are key aspects for traveller’s choices. Finally, young people, respondents that claimed having used shared modes before, and frequent transit users showed a better perception of shared modes.

In this thesis an investigation is performed into the effect of different speed limits on freeway capacity. From literature, much is known about the variety of factors that affect capacity, but the exact effect of the speed limit on capacity is not yet clear. In recent years, several speed limits changes have taken place at multiple two-lane freeway bottlenecks throughout The Netherlands, which makes it possible to compare effects of different speed limits at the same location. To evaluate the effect of the speed limit on capacity, the Product Limit Method has been applied to identify breakdown flows and generate capacity distributions, which could subsequently be compared for different limits. In the comparison of capacity distributions under different speed limits, it was found that significant changes in capacity had occurred, but that no uniform direction of the effect could be found. Subsequently, to control for location specific factors and other variables, Fixed Effects regression has been used to determine the effect of the speed limit on the breakdown flow. It was found that the breakdown flow under the 120 km/h limit was significantly higher than under the 130 km/h limit (in the range of 60 to 190 vehicles per hour) and that the breakdown flow under the 100 km/h limit was, in some cases, also higher than under the 130 km/h limit. In addition to this, it was found that a significant positive relation exists between the height of the speed limit and the fraction of flow in the passing lane. Moreover, it was shown that the relation between the fraction of flow in the passing lane and the level of breakdown flow was best represented by a quadratic relation, which could indicate that an “optimal” distribution of flows may exist. Given the results of this thesis, it is posed that a change in the speed limit is likely to affect capacity primarily through altering the lane flow distribution and that it will depend on the layout of a freeway location what the optimal lane flow distribution is and which speed limit leads to this optimal lane flow distribution.

The increasing urbanisation leads to more traffic movements in cities. Since car traffic in particular has a negative impact on the livability in the city, an alternative for car travel should be investigated to discourage car use. A possible alternative for car use could be the integrated bicycle-transit mode. Although more and more research is being conducted on shared bicycle systems and the combination with public transport, there is still no clear approach to design such an integrated system. The aim of this research was to develop a design approach for bicycle-transit, and subsequently this approach was applied to a case study. First, a spatial multi-criteria analysis was used to find suitable locations for shared bicycle docking stations. Secondly, an assessment framework was drawn up to determine the indicators on the basis of which different network designs were assessed. Finally, traveller flows have been investigated to analyse several alternative network designs. From this research, it was concluded that the optimal network for the case study Binckhorst in The Hague consists of four shared bicycle stations and a tram line between the train stations Den Haag CS and Voorburg.

Recently many bicycle sharing programs have been introduced in cities worldwide. The combined use of shared bicycles and public transport could offer an attractive alternative for private motorised vehicles and could contribute to making cities better accessible and liveable. However, it is unsure whether this combination, especially with urban public transport (bus/tram), is often used or that shared bicycles function more as a substitute of urban public transport. Therefore, this study explores to what extent people use the shared bicycle in combination with urban public transport within a mid-size city, The Hague, in the Netherlands and how the integration between these modalities could be improved. This is examined by analysing the operational trip data of the bicycle sharing program called HTM-fiets, conducting a survey among the users of this bicycle sharing program and performing an expert meeting. The results indicate that in this case the shared bicycle does not complement urban public transport to a large extent in a single ride and seems more used as a substitute of urban public transport. Therefore, it is probably more valuable to focus on integrating the shared bicycle with urban public transport as a whole than to assure a good integration in a single ride. To realise the integrated public transport and shared bicycle system, the shared bicycle could be marketed as a form of public transport and the shared bicycle could be made available in areas with weak urban public transport connections to increase the total network and with that the accessibility in cities.

Ever increasing traffic load on freeways calls for smart solutions to improve throughput. With the uprise and standardization of C-ITS, accuracy and latency of traffic state measurements are immensely improved, while the span of control is increased. In this paper, a traffic control algorithm that aims to prevent breakdown at recurring bottlenecks on freeways is presented. By using C-ITS to control connected vehicles and manipulate the traffic state upstream of bottlenecks, a breakdown and capacity drop can be prevented. To evaluate the developed control concept, a microscopic simulation study was performed. The controller was evaluated on both reductions in penetration rate and compliance to control signals. The results are promising, but further development is still required before real-world application is possible.

In order to reduce congestion numbers on the highways, ramp metering can be used. The currently existing ramp metering control strategies are of a macroscopic nature. This means that the average occupancy on the main lane or average flow values on the main lane determine the temporarily fixed cycle times for the traffic light located near the on-ramp to control the inflow from the on-ramp. In this thesis, it is investigated to what extent a microscopic approach to ramp metering would lead to less travel time delays. This approach shows a green light for the on-ramp vehicles when a gap in the main lane flow on the right lane has been detected instead of using the temporarily fixed cycle times. Computations on the location of the gap measurement loop detectors is required. Therefore, an acceleration distribution has been composed by means of an experiment as well. Concludingly, it was found that the proposed microscopic algorithm leads to additional travel time savings in the base case scenario compared to the currently used Rijkswaterstaat algorithm. Furthermore, adjustments to the developed microscopic control structure, such as a combined Rijkswaterstaat and microscopic algorithm, are proposed and it is recommended for further research to implement these adjustment and investigate their effect on the travel time delays. (PS. The paper was included at the end)

The almost ceaselessly growing Air Transportation System has led to concerns across airports on how to deal with this growth. Also Amsterdam Airport Schiphol (AAS) experienced large increases in number of passengers and movements over the last decades: the current limit of 500,000 annual movements of the airport is (nearly) reached. Before stretching the current limit, AAS shall demonstrate that its growth is safely possible. In the past, both Hillestad et al. (1993) and the Dutch Safety Board (2017) investigated the safety at AAS and concluded that, despite vulnerabilities, the airport was safe.
This research explores challenges and opportunities towards improving airport taxiway systems. Therefore, a currently lacking definition for taxiway system at systems level is defined first. Besides, Key Performance Indicators (KPIs) for taxiway systems are defined in order to identify parameters for improving the system based on stakeholders' point of views: safety, capacity, robustness, and environmental impact. Also, methods for assessing taxiway systems on these identified KPIs are defined. Next, AAS is used as a case study to analyze the performance of the taxiway system at the airport on the defined safety KPI. As part of the safety analysis of AAS, for the first time a taxiway system was analyzed on the Sustainable Safety vision as developed for road traffic - and showed to be valuable. Based on the safety analysis, challenges and opportunities within the taxiway system are identified and recommendations provided.

Dynamic traffic management (DTM) plays an important role from Dutch policy perspective to prevent road congestion and has been developed from control strategies to services. Five traffic control centers, 22 different DTM systems with 35 functions and over 50,000 DTM components make up the national traffic management network in the Netherlands. The malfunctioning of the DTM systems is expected to create negative impacts to the traffic, proper maintenance planning is necessary to ensure their availabilities. However, there is less knowledge about the DTM malfunctions, which makes it difficult to monetize the malfunction effects and therefore to optimally deploy the maintenance budget. In this research, a macroscopic dynamic traffic assignment model “MARPLE” is used to evaluate the social costs of the DTM malfunctions according to the failure function, failure duration, and failure location.
The motorway network around Amsterdam is chosen as the study area in this research, and four DTM systems and measures were evaluated, including the rush hour lane (RHL), the motorway traffic management (MTM) system, the dynamic route information panels (DRIPs) and the ramp metering (RM) system. By conversing the DTM malfunctions into the motorway network, the introduced impacts to the traffic both in local and network levels are identified.
This research made the first attempt to modify DTM malfunctions in a macroscopic dynamic traffic assignment model, and a methodology was developed to calculate the malfunction costs both in traffic flow and safety aspects. The outcome of this research answered what-if questions with regarding to DTM malfunctions, it also proved the feasibility of the ambition to translate the DTM malfunction impacts at a network level into its social costs, according to which the maintenance strategy for the DTM systems can be better deployed. Overall, the initial goal of calculating the malfunction costs of the DTM systems with a newly developed methodology is met. Through the identified limitations and improvement strategies, the framework developed in this study could offer the possibility to refine the analysis, and/or easily be applied to other DTM systems and road parts.

Flooding events not only cause physical damage to the road infrastructure and roadside systems, yet they potentially lead to significant traffic disruptions over the network which have a large impact on the sustainability of urban cities. According to several climate change scenarios, flooding events as a result of intense precipitation are expected to continue into the future becoming more frequent and severe. Therefore, it is essential that engineers design and manage transport networks as efficiently as possible under adverse weather conditions in order to build more resilient transport systems. To do so, professionals need to understand and analyse the impacts of flooding on transport networks. Modelling the impacts of flooding events on the road network is nowadays possible by the use of integrated simulation techniques that combine a flood simulation model with a microscopic traffic model. With these modelling techniques, the geographic location, severity and other impacts of flooding on the road network can be predicted and studied. Nevertheless, in order to perform a consistent traffic simulation under flooding conditions, a thorough analysis of driving behaviour under such conditions needs first to be accomplished, which is a research field that has been scarcely investigated thus far. Therefore, the main contribution of this thesis is to provide a systematic methodology for studying driving adaptation effects under flooding conditions at two complementary levels, namely microscopic and macroscopic level. This consists on a multilevel approach that uses microscopic traffic data of vehicles driving through flooding to extend the study to a macroscopic level. A video analysis procedure called the `The 3-Step Video Analysis Approach´ (3SVAA) is developed by the author to extract microscopic traffic parameters from video recording of vehicles driving under different flood depths. This methodology is implemented through an empirical study that analyses two videos of vehicles crossing waterlogged stretches part of the A94 and A93 major roads in Scotland (UK). First, the 3SVAA is introduced as a suitable data collection technique to extract microscopic traffic variables (i.e. vehicle's speed, time headways and spacing) from video recording of vehicles driving through flooding. More specifically, these parameters are obtained from the vehicle's trajectories in the space (m)-time(s) diagram, which is the main outcome of this video analysis approach. Subsequently, by applying a multilevel analysis, the study can be extended to a macroscopic level by correlating macroscopic variables (i.e. average speed, flow and density) with their microscopic counterparts through the fundamental relationship of traffic flow. This allows to estimate part of the fundamental diagrams of vehicles travelling under different flood depths, and therefore to study flood impacts on free flow speed and capacity.

With the increasing popularity of active modes (mainly pedestrians and bicycles), the spaces shared amongst these modes are also rising. Such spaces are often referred to as shared spaces and can often be seen in public areas near train stations, shopping streets and educational institutions. Such spaces are said to enhance safety and resolve spatial limitations of the space while the users are seen to exhibit interesting and complex behaviours as they freely interact with different modes approaching from various directions. This creates a need to collect data and understand the behaviour of people within such spaces.
Many studies focusing on shared space interactions are limited by the current methods of data collection and data extraction. Other studies on people movement also face similar issues. Current data extraction approaches using cameras include repetitive and labour-intensive tasks which makes them costly and inefficient. Thus, this study focuses on automating the data extraction approach to obtain the ground-plane trajectories of people using the data recorded from a 3D-stereo vision camera. The data processing framework was divided into three main stages, (i) agent detection, (ii) ground-plane representation and (iii) agent tracking. The agent detection uses a neural-network-based detection model to detect people on the visual video which is then paired with the depth data to represent people on the ground plane. Each person is represented as a single point on the ground plane for every frame of the video. These points were then inputted into a tracking model to provides trajectories of people on the ground plane. Overall, this research integrates the recent state-of-the-art technologies into a single framework to automate the data extraction process and tests this framework in a real-world setting (behind the Amsterdam central station’s shared space area). For each stage, the advantages, challenge and recommendation for further improvements have been made. By automating the data processing of real-world datasets (including pedestrians and cyclists), This study reduces the cost and provides an easy way to collect and process data on real-world movements of pedestrians and cyclists. This will encourage urban planners to better understand people’s movement within their spaces and facilitate data-driven design approaches in the future.