The transportation sector significantly impacts the environment, contributing to 29.4% of the European Union’s greenhouse gas (GHG) emissions in 2021. International efforts have been made to mitigate this impact, such as the implementation of the European Green Deal, aiming for a 90% reduction in transport-related emissions by 2050. Despite these efforts, long-distance travel, particularly by air and road, continues to contribute heavily to GHG emissions, driven by less sustainable modes like planes and cars. Rail transport, though a more sustainable option, remains underutilized, accounting for a small share of long-distance travel in Europe. Transfers in railway journeys have been identified as a major barrier to the wider adoption of rail travel. These transfers can increase travel time, induce stress, and reduce comfort, making rail less attractive compared to other modes. This study seeks to understand how railway transfers affect traveller behaviour and mode choice in long-distance leisure travel across Europe...

Urban areas have been facing the challenge of increased demand for urban transportation due to the expansion of opportunities for activities and the growth of e-commerce. These areas constantly face congestion, leading to more emissions and noise/air pollution. These circumstances have led to a strong interest in sustainability in mobility and logistics in urban areas. In particular, integrating mobility and logistics in a multimodal transport network instead of conventional transportation in which mobility and logistics are handled separately has caught interest. It is expected that the utilisation of water transit as a major means of urban transportation is important for the realisation of a sustainable multimodal urban transport network where abundant waterways are available, and combining mobility and logistics in the urban ferry system has the potential to provide more efficient urban transportation. To investigate the potential of such a system, a dynamic centralised fleet management model that optimises the operation of waterborne vessels is developed in this thesis, considering an electric waterborne vessel system for heterogeneous on-demand service that serves stochastic passenger travel and parcel delivery requests. The model dynamically optimises the operation of vessels by applying a rolling horizon and updates the operation plan every time a new request is inserted. Two solving algorithms, the exact method and the insertion heuristic, are proposed. The computational experiments are conducted by taking the city of Fredrikstad in Norway as the case location to evaluate the solving capability of the proposed solving algorithms and to assess the efficiency and service level of the transport system by comparing the performance with the conventional fixed purpose vessels under different demand scenarios. The demand scenarios are generated using a stochastic approach, which applies a non-homogeneous Poisson process for passenger requests and a probabilistic approach for parcel requests. The results suggest that the proposed insertion heuristic is capable of being applied to this model by providing good solutions in a significantly shorter computational time. Also, combining mobility and logistics results in higher efficiency and service levels for all demand scenarios than those of the fixed purpose vessels. To conclude, a model that determines the operation of electric waterborne vessels for on-demand heterogeneous services considering the stochasticity of the demand is developed in this thesis. The results show the capability of the proposed model to dynamically optimise the operation and the benefits of combining mobility and logistics in vessels for heterogeneous on-demand service.

While the romantic appeal of historic, centrally-located railway stations is undeniable, their efficiency in accommodating high-speed rail (HSR) services for passengers is questionable. The allocation of centrally-located stations benefits passengers who start or end their journeys in major cities, but imposes longer travel times for traversing passengers because trains generally reduce their speeds in urban environments. The majority of Europe’s HSR-stations are located in these urban centers, where expansion options are severely restricted. Therefore, this study investigates the passenger welfare effects of relocating HSR-services from city centers to city outskirts, which could be an alternative to capacity expansion in existing stations. It does so by utilising a combined route-choice model, with the Trans-European Transport Network (TEN-T) policy for 2050 as input. This study forecasts the station bottlenecks for 2050 and examines various HSR-service relocation scenarios. A logsum-based economic appraisal method evaluates the impacts on passenger welfare of these relocations. The findings reveal substantial potential welfare benefits when HSR services are relocated to peripheral stations. The order of magnitude of these benefits to the European economy ranges from tens of millions of euros per year to a couple of hundred million euros per year. This range depends on the traversing passenger volumes and the comparative in-vehicle time savings and distance savings between peripheral and urban routes. Both of these savings originate from the most important predictor of passenger welfare effects: the network design. It is recommended to policymakers to not instantly dismiss the possibility of peripheral HSR stations from a protectionist point of view, but to consider the option for its relatively low construction costs and externalities, and especially the potential for substantial passenger welfare effects.

This paper investigates cycling route preferences, with a focus on the cycling environment. To represent the cycling environment street-level images were used. A recently proposed model incorporates computer vision into a traditional discrete choice model to accommodate choice tasks involving numerical attributes and images. This computer vision-enriched discrete choice model (cv-dcm) was applied using a stated choice experiment, where respondents had to choose between two cycling routes. Each route was defined by three attributes, including commute time, number of traffic lights, and the cycling environment, the latter visualised using street-level images. While the cv-dcm relies on a neural network, making interpretability challenging, this study addressed this by collecting detailed cycling environment attributes. Results showed that the cycling environment was the most influential factor, with cyclists preferring green areas and separated cycling lanes. On average, cyclists were willing to take a 1.5-minute detour for a cycle trip of 11 minutes to use a separated cycling lane instead of a mixed-traffic road. These insights offer valuable insights for policymakers aiming to design cycling environments that align with cyclists’ preferences.

Despite being a long-cherished EU ambition and a crucial key to achieving climate goals, there is still no European High-Speed Rail (HSR) network, with the few completed projects often facing disappointing demand resulting in unprofitability. In order to gain insights to profitable network design, this study develops a new formulation to the “Transport Network Design & Frequency Setting Problem” (TNDFSP), as current literature lacks one that can optimally solve the problem for instances of this size while also accounting for demand elasticity. Our model works with elastic and dynamic HSR demand calculated from optimal fare settings and observed travel mode characteristics from competing alternatives. The optimal solution is largely insensitive to fare changes, and in some measures outperform current state-of-the-art. Our model considers the 111 most populous European cities, along with all their origin-destination (OD) pairs, and finds the most profitable network design within a reasonable solution time frame. The results show HSR can be very profitable in Europe, but only when concentrated around a selected group of the largest cities in the western part of the continent. Despite being profitable and contributing significantly to set Green Deal goals, the viability of our network heavily depends on the willingness of several countries to invest significantly in infrastructure construction.

This PhD dissertation examines the societal implications of newly emerged, two-sided ridesourcing platforms that connect travellers with self-employed drivers. It uses an agent-based approach to model the dynamic interaction between ridesourcing supply and demand, exploring how market outcomes are influenced by travel demand, labour market conditions, and platform strategies. The findings inform policymakers on how to enhance the welfare effects of these markets for travellers, drivers, and the broader public.@en

High-speed rail (HSR) is gaining increasing attention due to its sustainability and transport capacity, aligning with ambitious European transport goals for 2030 and 2050. Despite its potential, the creation of a unified European HSR network faces challenges rooted in poor coordination and national interests. This study addresses the absence of a comprehensive, long-term strategy for establishing such a network, with a specific focus on critical infrastructure development. It introduces an iterative network growth model to determine where, when, and at what cost HSR infrastructure should be built under centralized decision-making processes given current budget allocations. The approach analyses the dynamic interaction between infrastructure expansion and the long-distance transport market demand distribution. Results emphasize the benefits of adopting centralized decision-making and appraisal processes, highlighting that achieving these goals requires a comprehensive, collaborative effort, as well as a proper European institutional investment management with more spending power.

Hierarchy is a network property that identifies the organisation and importance of network elements. Public transport systems consist of stops organised by connections, which can be regarded as networks. In a Public Transport Network (PTN) with a high hierarchy, the number of elements gradually decreases as their importance increases, where the majority of elements have low importance and a few high-important elements. The hierarchical organisations in PTNs contribute to the network performance by efficiently allocating resources based on the elements' importance. The hierarchy has been studied in transport with different methods and data sources. However, the topology-related quantification and comparison methodologies of PTN hierarchy and its mode-wise and continent-wise effects are scarce. A unified PTN hierarchy definition and the quantification methodology are necessary to enable PTN comparisons in terms of the network organisations and reflect the relative PTN performance.

Based on the current research gaps, this study develops topology-based PTN hierarchy quantification and comparison methodologies. First, six topological characteristics of PTN hierarchy are identified from the element scale (vertex accessibility, element intermediacy and vertex cluster importance) and network scales (scale-free structures, high-clustering structures and vertex connection pattern). Second, six element-based or network-based topological indicators are selected to quantify the topological characteristics. For element-based indicators (vertex degree centrality, closeness centrality, betweenness centrality and eigenvector centrality), the coefficient of determination (R square) of the indicator's probability density distribution fitting the skewed normal distribution represents the PTN hierarchy. For network-based indicators (modularity coefficient and assortativity coefficient), the quantified indicator values represent the PTN hierarchy. Next, the radar chart is developed for comprehensively assessing the normalised six-dimension PTN hierarchy.

To evaluate the performance of the methodology, a database based on the GTFS data containing topological information of 63 high-capacity unimodal PTNs worldwide is applied with the hierarchy quantification methodology as a case study. In the PTN hierarchy comparison, the hierarchy in the closeness centrality and betweenness centrality are prone to be high and have higher importance for PTN operation, reflecting the hierarchical organisations of stops' accessibility and the traffic intermediacy on infrastructures. PTN hierarchy in the eigenvector centrality and network modularity dimensions reflect the mono-centric or multi-centric network structures. In the vertex degree centrality and network assortativity dimensions, the identified PTN hierarchy topological characteristics are less significant. In mode-wise effects analysis, the order of modes having PTN hierarchy from high to low is metro, tram and BRT networks. The continent-wise effects show that the European PTNs usually have a higher hierarchy than North American PTNs.

Overall, the research offers a unified topology-based PTN hierarchy as a network property. This study's quantification and comparison methodologies bring a comprehensive multi-dimension and intuitive perspective for PTN hierarchy with the radar chart representation. With the case study, the six-dimension PTN hierarchy, the mode-wise and continent-wise effects are analysed. The methodology benefits the universal PTN performance comparison with basic topological information.

If people repeat choices in daily travel behaviour under stable conditions, over time this can form habitual travel behaviour and lead to inertia. If travellers face a new metro line, to what extent does their habitual travel behaviour affect their willingness to use this new transport alternative. This study investigate the inertia effects in a multimodal public transport network using a fully revealed preference data set. Habitual route choice behaviour is quantified prior to the opening of the new metro line. We then use the quantitative metric for habitual behaviour as an indicator in the situation after the metro line opens to reflect the inertia effects of individuals. The outcome of the route discrete choice model reveal that the inertia effects for the new metro line are significant. The interaction effect of habitual travellers with the new transport alternative show that there is an additional effect for the new alternative if travellers are classified as habitual. Furthermore, not accounting for inertia effects leads to bias in travel demand estimations of the new transport alternative. The model with the inertia effect improves model fit and is able to more accurately.

Short-haul flights, known for their high carbon intensity relative to distance, are increasingly challenged by high-speed rail (HSR), a viable and environmentally friendly alternative. This study develops a model adapting the “Transit Network Design and Frequency Setting Problem” to high-speed rail, focusing on optimizing existing networks rather than designing new ones. Two sequential algorithms are developed and integrated in the model, which initially generate the optimal paths in the network, and subsequently, set the appropriate frequencies to the existing and new lines. The model is applied to two scenarios within the European rail network, to assess the impact of network modifications on demand distribution, operational costs, and infrastructure development. The findings reveal that the impact of network modifications varies depending on the size and development level of the area. In smaller, well-developed networks, modifications mainly involve adjusting frequencies of existing services, while larger areas with diverse infrastructure levels necessitate new lines and modified configurations. The model offers varying implementation options, balancing capacity utilization and cost minimization, aiding stakeholders in making informed decisions. The potential of utilizing existing infrastructure for sustainable mobility is highlighted, suggesting that future endeavors should include diverse case studies and incorporate user and social cost perspectives, along with timetabling and operational factors for a more comprehensive evaluation

In this thesis, the functioning of passenger delay-based refund schemes in urban public transportation settings is evaluated, such as metros or light-rails. These are used when a passenger encounters a delay on their trip, in order to grant a form of refund to them. To the best of knowledge, no research has been carried out yet on several aspects of these refund schemes, such as which design considerations can be taken into account upon establishing one, or how their performance can be evaluated. To clarify these issues, the state of the practice is reviewed by surveying the internet and the information that is being provided by the individual public transport operators. By categorising this information, it can be established what the design aspects of these refund schemes are. These are the delay type such as departure or arrival delay, the delay threshold, i.e., the minimum delay from which a trip taken is elegible for a refund, the refund type, which is the form of refund that the passenger receives, and the possibility for alternative transport to be refunded. In order to determine whether there could be a form of linearity between delays and refunds, and to facilitate the evaluation of different refund schemes, a regression analysis was carried out on approximately a year of passenger trip data and vehicle data from the Washington Metro. This enabled acquiring more reliable, i.e., excluding individual passenger’s possibly erroneous behaviour, observations of the departure delay and arrival delay of every trip taken during this study period. Using this data, a possible link between the total amounts of delays encountered and refunds granted per day was analysed. It could be concluded that there is indeed a strong linear effect between these two. Furthermore, a number of key performance indicators (KPI’s) was established which allowed to assess the performance of a set of synthetic refund schemes, in which the parameters were varied that resulted from the aforementioned survey. Furthermore, a refund scheme was recreated that uses the general punctuality on a line and subsequently refunds all travelers that can have been expected to be effected by a possible drop in this punctuality. Using these KPI’s, conclusions could be drawn on the performance of different refund schemes, in the sense of how many refunds per trip are granted, how the ratio of the average delay of all refunded trips vs. the average delay of all trips is, how many revenue losses the refund schemes yield for the operator, and how much of the time losses (using a certain Value of Time) are compensated for.

Following the European Green Deal, transport- related emissions must be reduced by 90% in 2050. While aviation contributes to a significant share of these emissions, the substitution of flights for rail travel is often considered to achieve a reduction in emissions of greenhouse gasses. Mostly, travelers consider this option when the full trip can be substituted directly. However, possibilities also lie in intermodal travel, where specific journey parts are substituted. To promote this way of travel, the two modes should have a strong level of service, and there should be a significant level of integration between the two modes.
In this thesis, a method is presented to assess the position of airports’ in a European intermodal network. This tool helps identify airports with the potential to facilitate intermodal travel in Europe. The service levels in both the air and the rail network, the physical integration between the modes, and the geographical location of airports are considered in different variables to assess the network. A Principle Component Analysis identifies the shared information between the different variables. The Principle Components are then clustered using an Agglomerative Hierarchical Clustering. With this clustering, airports are grouped based on their characteristics of intermodal factors, and different roles in an intermodal network can be identified. Comparing the descriptive statistics of each cluster helps assign qualitative labels to the cluster to classify the airports’ roles. After creating the benchmark with data from the current network situation, scenarios are constructed to assess the effects of implementing different policies regarding intermodality. Extending rail services and/or reducing short-haul flights are policies formed into scenarios in which the underlying data is modified. The change of clustering and classifications caused by the data modification helps identify potentially interesting airports in the network.
The results show that 20 to 30 of the 113 considered airports show a substantial role in intermodality. Also, the findings identify airports that change their position due to the scenario implementations and offer the potential for a significant role in an intermodal network. Finally, comparing the classifications in the different scenarios shows that the constructed scenarios can enable up to 14.3% extra passengers to travel intermodal.

Efficient repositioning strategies for idle vehicles in ride-sourcing systems help reduce passengers' waiting time and drivers' operational costs, which help platforms attract more passengers and drivers. In this paper, we propose a decentralized repositioning strategy for drivers, in which drivers make individual decisions on where they reposition themselves, based on their own experiences. In comparison to an existing centralized repositioning strategy, in which drivers comply with reposition instructions provided by the platform, we examine the effects of the decentralized strategy on service rate, passengers' waiting time and drivers' net income. We compare the reposition strategies under different supply and demand levels and different demand spatial distribution dispersion rates. We also explore the influence of platform information on drivers' decision-making process.
We found that decentralized repositioning strategies have better performance in reducing waiting time, while the centralized strategy is better at increasing driver income and service rate. We also found that when platform information is accessible, the system has the best performance when 20% to 60% proportion of drivers utilize platform information when making decisions.

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

Around the world, authorities try to increase the attractiveness of multimodal public transport (PT)-related trips to reduce car usage. To achieve this, a seamless combination between the different modes is necessary. The Dutch train station operator NS tries to enhance the combination of the bike and train by providing a train station-based round-trip bikesharing (SBRT) scheme located at train stations throughout the country. This scheme allows users to rent a bike to connect the train station and their destination. The round-trip characteristic SBRT makes it unique in comparison to widely applied one-way bikesharing schemes. While on the latter a wide range of research exists, little research has been conducted on round-trip bikesharing, especially when being integrated into an existing public transport scheme. This paper aims to fill this gap by identifying potential temporal and weather-related determinants for SBRT-rentals of the Dutch SBRT-system OV-fiets using multiple linear regression (MLR). The results are compared with findings on one-way bikesharing schemes. The results are then used as an input to forecast short-term demand. To identify a best performing forecasting method, the statistical methods MLR and Prophet are compared with the neural-network based method Long Short-Term Memory (LSTM).
It is found that for hourly rentals in an SBRT-system, the highest explanatory power achieved with the number of train travelers leaving the corresponding train station, followed by temporal and weather-related determinants. Further, the magnitude of the correlation between the determinants and the hourly demand differs across the stations in the system. For forecasting, the performance of the methods differs across the stations and forecasted periods due to the stations' distinct characteristics. But, especially in times of uncertainty, LSTM is likely to outperform the others due to it's capability of adapting to short-term changes in the demand.

Compared to traditional public transport, ride-hailing makes it possible for people to get a more comfortable and faster riding experience with a higher fare. Ride-sharing fall in between the two, offering a discount at the price level of ride-hailing, yet operates with more detours and less comfortable experience. In this study, with different price levels for ride-hailing and discount rates for ride-sharing, we would like to examine the system performance of co-existence of ride-hailing, ride-sharing and public transport services. We would also like to search for an optimal solution for the ride-hailing & ride-sharing company to maximize its profit. We apply ExMAS, an open-source agent-based model for ride-sharing simulation, to simulate passengers' and vehicles' behavior on a microscopic level, and acquire numbers of results. Based on our model, in the case of Amsterdam, when price level is 1.1 euro/km and discount rate is 0.4, the company could enjoy maximum profit and market share. It is also found that, when price level gets higher more people opt for the competitive mode instead, resulting in the overall profit falling significantly.

The Ministry of Infrastructure and Water Management looks into the possibility of raising the awareness of Dutch citizens as policy measure to encourage travellers to travel in a more conscious and more climate-friendly manner. Scientific literature, however, shows contradictory results on the effectiveness of this measure. An ongoing academic discussion is present on the extent to which awareness about the consequences of behaviour results in a desirable change of that behaviour. This study applies structural equation modelling on Dutch Mobility Panel data to examine if and via which causal pathway of socio-psychological factors information and awareness about the relative impact of flying on climate change run to the intention to fly. We find that raising awareness of Dutch citizens about the relative impact of flying on climate change is not enough to change people’s intended flying behaviour. The current awareness only changes people’s attitudes and reasons used to justify their flying behaviour, not the intention to fly. Further research, however, is necessary to find out if raising awareness on other consequences or other alternatives result in the same findings. Only then it could be recommended to look into other measures to encourage travellers to travel in a more conscious and more climate-friendly manner.