In response to pressing climate issues, Europe is transitioning towards more sustainable transportation. Despite the potential environmental benefits, train travel often loses out to other modes of transportation in long-distance journeys (300+ km). Based on previous research, transfers seem to have a great impact on the attractiveness of train journeys. This study investigates the effects of different transfer attributes on travel choice behaviour in long-distance leisure trips. Through a combination of a literature review and focus groups, key attributes influencing travellers' itinerary choices were identified and incorporated in a stated choice experiment. The experiment, involving 431 respondents, used a Latent Class Choice Model (LCCM) to analyze preferences and willingness to pay for improvements in transfer attributes. The study finds that travel cost and travel time are of the most importance. The number of transfers and transfer time are less critical but still impactful. The service level has a small impact. When the transfer occurs in the journey is insignificant. Four distinct classes of travellers were identified: Time-Valuing Eco-Advocates (44\%), Cost-Sensitive Train Travellers (25\%), Train Dislikers (18\%), and Transfer-Averse Train Sceptics (13\%). The results indicate that reducing travel costs and time should be prioritized over improving transfers to maximize the attractiveness of train travel. However, optimizing transfer time and minimizing the number of transfers will still enhance the appeal for specific traveller classes. By analysing the results, it was found that although direct train services are generally preferred, well-designed transfer options can improve market potential. This does however require significant improvements in the European railway network design and regulations.

The Value of Travel Time (VTT) is perhaps the most important number in Cost Benefit Analysis as the travel time savings are often the largest benefit. The VTT is often derived from discrete choice experiments (DCEs), where respondents trade off time, money, and other attributes. However, respondents who are reimbursed for their travel costs might approach these trade-offs differently since they usually don’t bear the full cost of travel. This raises the research question: To what extent does travel cost reimbursement (TCR) affect the value of travel time?

We investigate the impact of TCR on the VTT using datasets from the Netherlands (NL2022) and the United Kingdom (UK2014). We hypothesize that TCR might influence the VTT directly, lead to less consistent choices among respondents, and result in a higher willingness to accept (WTA) among those with TCR compared to those without. Using Multinomial Logit (MNL) and Mixed Logit (ML) models, we analyze the effect of TCR on the VTT, whilst allowing for heteroskedasticity. We also separate TCR interactions into WTA and willingness to pay (WTP) components, while accounting for socio-economic interactions to ensure robust findings across different experimental setups.

Our results show that commuter respondents in the NL2022 dataset with full TCR have a VTT approximately 29% to 45% higher than those without reimbursement. In the UK2014 dataset, the increase is even more pronounced, ranging from 58% to 143%. This effect is significant and consistent in experiments where only time and cost are considered. However, as more attributes are introduced, the difference in VTT becomes insignificant. We found no consistent patterns of scale heterogeneity in the VTT for respondents with and without TCR, leading us to reject the hypothesis that TCR recipients make less consistent choices. Moreover, while the interaction with VTT is more pronounced in the WTA domain than in the WTP domain, the model accounting for this does not outperform a model assuming equal TCR interaction in both domains.

In conclusion, VTT is significantly different for commuters depending on whether they receive travel cost reimbursement. However, these differences cannot be explained by scale heterogeneity or differences in WTA and WTP. We argue that national VTT values should consider travel cost reimbursement, particularly as these values are often based on 2-attribute time-cost experiments.

Disruptions in urban public transport networks significantly impact urban mobility, causing inconveniences for passengers, economic losses, and vulnerabilities in transit systems. Major disruptions may lead some passengers to permanently switch to alternative modes like cars, even after the disruptions resolve, potentially worsening congestion and environmental issues. While the immediate impact of disruptions on behavior has been extensively studied, there is limited research on the long-term effects, often restricted to qualitative methods. This study aims to address this gap by proposing a framework to investigate the prolonged effects of public transport disruptions on passenger travel behavior using smart card data. A mixture latent Markov model is used to track passenger behavior from the pre-disruption to the post-disruption period. This framework identifies travel patterns and tracks how passengers transition between these patterns over time, thus inferring the impact of disruptions on behavior. Each passenger is assigned a mobility style that reflects their general travel attitude, such as those who do not change their behavior. The results from our case study reveal that the impact of the disruption was not as substantial as anticipated, with a high proportion of passengers maintaining their behavior.

This thesis aims to contribute to the policy debate on the complexity of passenger rail markets and to provide recommendations for the Dutch market. An extensive literature review has been conducted to identify the policy issues that must be discussed when dealing with passenger rail markets. These issues are structured in a theoretical framework to describe market structures and understand market complexity. The framework classifies the policy issues into three levels of decision-making, influenced by causal factors and resulting in system performance measured by three categories of indicators. The approach to passenger rail market analysis proposed in this thesis can be useful for researchers and policymakers to assess passenger rail markets in a structured way. The relevance of the policy issues was identified through a Delphi study involving 13 experts and stakeholders. As a result of the study, the role of different levels of government in market coordination, coordination of investments, timetables and incidents, and the level of transition costs were identified as key policy issues. An analysis of the Dutch market resulted in a recommendation to continue direct award to Nederlandse Spoorwegen on the main rail network. However, it is also recommended that Dutch policymakers prepare for the introduction of competition to strengthen their coordinating role and be ready for the eventuality of competition.

Efficiently matching passengers with drivers is crucial for the performance of both ride-hailing and ride-pooling services, particularly when it comes to optimizing the time intervals for matching. Traditional methods often use fixed or real-time matching strategies, which do not fully consider the dynamic optimization of these intervals. This study introduces a dynamic optimization strategy based on reinforcement learning, applying the Proximal Policy Optimization (PPO) algorithm to flexibly adjust the matching intervals under various supply and demand conditions, aiming to reduce passenger wait times and improve vehicle utilization.

An efficient simulator is developed in this research to model the spatiotemporal matching processes for ride-hailing and ride-pooling services. Through experiments, the reinforcement learning-based matching strategy is compared against fixed interval and real-time matching strategies. Results indicate that the RL-based strategy outperforms in key metrics such as total waiting time and detour delay. Additionally, Potential-based Reward Shaping effectively addresses the sparse reward problem, further enhancing the model’s learning efficiency. The findings validate that dynamically optimizing matching intervals through reinforcement learning can improve the overall efficiency of ride-hailing services in dynamic supply-demand environments and is scalable and adaptable.

The transportation system in Israel is facing a crisis due to its heavy reliance on private vehicles. To shift towards sustainable mobility, improving public transport quality in the Tel Aviv metropolitan area is crucial. This research investigates the preferences of Tel Aviv citizens regarding transport projects aimed at enhancing public transport and encouraging alternative mobility methods. The study utilizes the Participatory Value Evaluation (PVE) method, where citizens curate their preferred project portfolio within a limited budget, assuming decision-making authority. The research also explores the face validity of PVE in Israel, considering the distinct governance perception in the country. This research provides valuable insights into Tel Aviv citizens' preferences for transport projects that enhance public transport quality and promote sustainable mobility. Through the application of PVE, the study reveals project preferences, face validity perceptions, and potential avenues for future research, contributing to the advancement of sustainable transport policies and decision-making processes in Israel.

To analyze latent multiple specific patterns in the line-based public transport daily delay occurrence, a data-driven explorative analysis of public transport daily delay spatial-temporal distribution pattern is performed based on the k-means clustering algorithm. Firstly, we used aggregated daily delay profile to visualize how the delay is distributed in space and time. And the pattern of daily delay distribution is represented by the image features. Secondly, the image features are extracted by the pre-trained neural network ResNet50, and the output image feature vector are used for implementing unsupervised k-means clustering algorithm. Finally, the k-means clustering results reveal five different daily delay patterns. The distinctive characteristics of these five delay patterns are analyzed and lead to some significant results, which could provide public transport operators with a better understanding of how delays occur on a specific line.

This paper investigates the kilometer charge and the potential effects of emission-based differentiation, how this policy is supported among Dutch citizens and how it affects different groups. It investigates the behavioural change caused by the new policy using Dutch stated preference discrete choice data. Two stated choice experiments are performed, one exploring the demand for privately used alternative fuel vehicles and one on the modal shift for one-person trips with varying distances. A mixed logit model was applied to determine to what extent specific groups were price-sensitive to the kilometer charge. We find that well-educated,high-income people, who either frequently use their car or own a business car, are more incentivised by the kilometer charge to purchase alternative fuel vehicles. In general, people value purchase prices over the kilometer charge and range. Moreover, it turns out that these groups are willing to purchase a substantially more expensive car in exchange for a lower variable kilometer charge. By applying a multinomial logit model for all distances in the mode choice experiment, we find that the least sensitive group towards the kilometer charge for a modal shift are high-educated, older, commuting car users that live relatively far away from the nearest train station. Furthermore, the larger the travel distance, the higher The value of travel time savings ranges from 4.44 - 14.00 euro for distances including train and car whilst small city trips with bike and car as mode alternatives have a value of 6.86 euro. The found behavioural effects were used to evaluate the impact of constructed scenarios with varying tariffs for the kilometer charge, ultimately showing that an emission-based differentiation can positively affect tax income and emission savings. Besides that, this implementation form is the most supported by the Dutch population. In addition to these substantial insights, this paper makes a contribution by showing how stated preference discrete choice data can enhance the decision-making of such pricing policies.

COVID-19 had (and still has) a huge impact on travel behaviour. Most research on the effects of COVID-19 on travel behaviour is based on daily travel behaviour. Long-distance travel is most of the time not included. Therefore, this paper studies the impact of perceived COVID-19 risk on mode-choice for long-distance travel in Europe. This paper uses a Stated-Preference approach which consists of two parts, the perceived risk rating experiment and the main mode choice experiment. In total
1147 responses were collected. In the rating experiment, respondents rated their perceived risk based on eight attributes. In the main choice experiment, respondents chose between train, plane and care based on travel time, travel cost, travel comfort and perceived risk. With this,
the Value of Risk (VoR) for a decrease in perceived risk is derived, both expressed in travel cost and travel comfort. Moreover, the Willingness to Pay (WtP) for comfort and Value of Time (VoT) are derived. With the combination of both models, the WtP for risk attributes given a perceived risk level is derived. The combination of both models shows the Willingness to Pay values for the different risk attributes, given a certain perceived risk level. To test the influence of perceived risk on modal split, three routes with real-world values are used. The results implicate a maximum of 5% market share difference possible. This shows that perceived COVID-19 has a moderate effect on modal split.

Increased urbanisation has led to significant challenges for public transport operators. Inconsistent demand leads to peaks in passenger activity on the network. Moreover, the COVID-19 pandemic has introduced a need for social distancing as well, limiting the desired capacity of vehicles. To combat this, intelligent real-time and data-driven decision making is required. In many cases, the data required is lacking or not available in real-time. Our research addresses these challenges by providing means to gain insight into the passenger load of public transport vehicles. The focus of this research is to investigate how using in-vehicle sensor data can help in constructing an estimate of the passenger load and evaluate its contribution.

By combining in-vehicle sensor signals with historical passenger flow patterns, a novel fusion model
based on gradient boosting machines is constructed that can make real-time predictions of the passenger load using this data as input features. The evaluation shows that its estimates have a mean absolute error (MAE) score of 7.83, outperforming a random forest model baseline by 37%. Moreover, a crowding indicator analysis demonstrated that when predicting crowding indicators, the model achieves a weighted F1 score of 0.828. An ablation study found that excluding the in-vehicle features from the model reduces the model’s performance significantly, it could reduce the performance by up to 42%. In fact, the same experiment showed that having only the in-vehicle features is preferable to historical passenger flow features. Therefore, we conclude that using in-vehicle sensor data can be a feasible alternative to historical AFC data for predicting the passenger load.

The methodology has been extended by constructing a short-term forecasting model based on Seasonal ARIMA and GARCH that uses real-time signals of the passenger load to update its forecasts. The results show that while the forecasts lack accuracy initially, once the model is updated the forecasts improve up to almost a negligible error. When model predictions are used as update signals, the forecasting model is still able to improve and the results are competitive, despite the error contained in the signals.

Overall, we conclude that the proposed real-time model offers a suitable method for passenger load prediction and clearly demonstrates the effectiveness of using in-vehicle sensor data as input features. Moreover, we have presented a feasible method for using these features in a forecasting setting with a real-time model as an intermediary

Public transport (PT) plays a vital role in commuting billions of travellers in cities all over the world, providing a mode that is both sustainable and accessible. Metro networks are especially apt at this considering their high-capacity and high-speed operation in urban environments. Comparing different metro networks to one another is a suitable manner for transport planners to gain insights into the characteristics of their networks and which areas of improvement exist. In the field of network science, metro networks have been studied extensively in recent decades. While this provided many new insights in the field of network science, the practical relevance for the field of transport science often remained limited. This limited relevance is primarily caused by the lack of realism of the network representations used, not incorporating the actual operation and service that the network provides. As such, this study proposes a comprehensive comparison of metro networks worldwide including service information. This comparison study includes service characteristics in the form of the total travel time indicator for shortest path calculations, which is a combination of the in-vehicle time, waiting time and number of transfers. The median of this total travel time is taken for each network and compared to that of other networks. This metric in turn is contrasted with network- and city-related characteristics in order to explore relations between these factors and to explain the patterns discovered. From this analysis, it is revealed that the travel time increases with network size. The indicator that is especially apt at explaining the differences in total travel time between networks is the number of stations combined with the average direct station distance. The total travel time methodology applied in this study shows significantly different results to other commonly used methods that rely only on in-vehicle time or hops to calculate shortest path travel times. The waiting time turns out to be the main contributor to these significant differences. Future studies can expand on this by considering other network science indicators and looking further into local indicators. In addition, the methodology could be expanded with more detailed transfer information and other PT modes.

The provision of good accessibility is the main function of a well-functioning public transport (PT) system, the quality of which affects the ability of people to access basic needs such as employment, education, and healthcare. It is therefore important that PT accessibility is distributed in an equitable, or fair, manner. This thesis evaluates the equity of a PT network according to three distinct distribution principles, defined as alternative ideas of what constitutes a fair distribution of resources. Egalitarianism, proportionality, and sufficientarianism are applied in separate equity evaluations of the same area, resulting in the identification of zones of surplus and deficit accessibility according to each principle. When comparing the results of the three equity evaluations, it is found that both the locations and magnitudes of accessibility surpluses and deficits differ between principles. Some principles have deficit zones in common at the periphery of the study area, indicating that the borders of concessions may have been somewhat neglected in the service planning process. However, overall, the main finding is that a PT network that is equitable according to one principle may not be equitable according to another. It is also attempted to use the results of each equity evaluation to make frequency modifications to achieve a more equitable PT network according to each principle. Equity evaluation according to egalitarianism and proportionality were found to not be possible due to the circular calculations inherent in the methodology. It was possible to use the results of the sufficientarianism equity evaluation in network design, therefore this principle can be used as an input in the network planning process. Although further research is still required, these findings can help to inform the incorporation of equity in future PT planning policy, for example through inclusion in concession requirements.

Travel time is an integral part of all human activities. In transport planning, travel time is one of the most widely used and significant parameters in the design of transport systems. However, people's ability to estimate travel time and time, in general, is limited. The aim of this thesis is to broaden the knowledge on the perception of travel time of active modes. More specifically, this thesis's objective is to provide insight into the deviation of perceived travel time to the actual trip duration of cycling and walking trips and to identify the factors that influence it. Individual characteristics and emotional state, trip characteristics, and external conditions such as the weather are considered. To reach the research aim, data are collected on the subjective and objective travel time and on the possible determinants. A mobile application is developed to serve as the single data collection tool. The app incorporates a location (GPS) data source and multiple surveys. The trip duration, which is reported by the survey respondents right after the completion of a trip, is considered as the subjective travel time. Data are collected for two weeks in the Netherlands in June 2020. An analysis is performed on the obtained dataset in terms of descriptive statistics and regression analysis. The former reveals that, on average, people underestimate the duration of trips conducted by bike or on feet. Via linear and logistic regression models, the decisive factors of travel time misperception are pointed out. Disappointing weather, high physical effort demand, and compulsory trip purposes prolong the perceived trip duration. On the contrary, the usage of an information source for planning a trip leads to a shorter subjective travel time. The overall underestimation of travel time by active modes indicates that the perceived disutility due to travelling could also be lower than the objectively measured one when travelling by these modes. The findings of the study are discussed in regards to recommendations to practice and future research. Furthermore, the thesis provides suggestions for further usages of mobile applications for the collection of data on travel time distortion and on travel experience, in general.

An inclusive and sustainable transport network allows people the opportunity to access their daily needs. When there is a discrepancy between someone’s transport needs and their supplied network, they are at the risk of experiencing transport poverty. This study looks to define the transport needs for clusters of neighbourhoods of different socioeconomic and demographic characteristics in Amsterdam, The Netherlands by evaluating the relationship between the network’s supply and demand. Neighbourhoods are grouped through their characteristics using a Latent Class Clustering Analysis into likewise clusters. Supply and demand indicators are designed for each neighbourhood using GTFS and smart card ridership data. The effect of a neighbourhood’s cluster on the relationship is analyzed through an interaction effects linear regression. It was found that improvements to the supply indicators had a positive relationship with the overall demand of the network. However, this is consistent for all defined clusters of Amsterdam which is contrary to the expectations of the analysis. It was assumed that the supply indicators, reflecting convenience and efficiency of the network, would be needed variably for different socioeconomic groups of Amsterdam. These findings provide valuable insights into the current needs of Amsterdam residents, the state of the public transit network, and how transport planners should focus future infrastructure improvements. Insights are also gathered on how transport poverty research may be improved through statistical clustering techniques and using data in order to measure the use of the network.

Limiting climate change forces us to switch to a more sustainable mobility. Travelling by train is more sustainable. Night trains might be a solution, they over several advantages such as a higher comfort level and can travel long-distances during the night. However, there currently is no knowledge about the Willingness to Use night trains. This study is the first into this Willingness to Use night trains, as an alternative for flying, for long-distance European travel. To do so, this paper makes use of two Stated-Preference experiments. A comfort rating experiment, in which the comfort rating is the dependent variable. In this experiment, it is explored to which extent night train characteristics influence the ’perceived comfort’ rating. In the mode choice experiment, it is in turn investigated how this comfort rating is traded-off against more traditional mode choice attributes such as trip time and trip costs. The study presents the results of linear regression and a Panel Mixed Logit model, estimated on 804 collected responses in the Netherlands. The results of both models are combined to derive Willingness-to-Pay values for an improvement in one of the comfort attributes, given an initial comfort level. Furthermore, several segments were identified using a latent class choice model. At last, the Willingness to Use the night train is explored for several scenarios. It is shown that when the night train is introduced as-is, it is predicted to have a market share of about 60%. Positioning the night train as a low-cost competitor results in a significant drop in market share.

This research investigates the travel behavior of users of an integrated mobility platform. Passive revealed preference data is collected from a smartphone application for integrated mobility. This data, consisting of the choices in a multimodal journey planner, allows evaluating travel behavior in integrated environments with high ecological validity. As such data has not been used yet for conventional travel behavior researches, this research also experiments with the application of conventional methods to this type of data. It is found that for the specific integrated platform, mostly public transport users engage with the platform. Public transport is dominant within the planned trips of users, and extreme situations are required for users to deviate in the mode choice between PT and non-PT alternatives. Furthermore, it is shown that discrete choice modeling is a suitable method for analyzing for this type of data.

This study investigated the potential of ex-post fare policy evaluation utilising smartcard data. It looked into how to identify and measure the actual outcomes of a policy, define the degree of its success, and even find some unexpected side-effects of high importance. The study is based on a case of the public transport fare policy introduced by the regional administration of Stockholm County in January, 2017. The policy focused on changing the fare structure basis, in particular switching from a zonal to a flat-fare scheme. The goal of the study is to determine how the policy affected the traveling behavior of different public transport user groups and what equity implications it brought. Altogether, these insights could be transformed into lessons for designing a more advanced evaluation approach and future policy-making.

The development of automated vehicles offers advantages for the transportation systems of the future. As a result, new and unknown challenges within the field of transportation arise. Moreover, there are uncertainties within the behavioural responses of travellers and amongst other things, the changes in the modal split within the transportation market. There is a lack of extensive knowledge of public transport user preferences regarding automated vehicles. In this study, the relative preferences for a trip with a self-driving bus were compared to a trip with a regular bus. To establish this, a stated preference experiment was conducted. Based on the responses of 282 respondents, a mixed logit model including latent variables was estimated. Based on the estimation results, it can be concluded that public transport users currently show a lower preference for the self-driving bus than for the regular bus. Moreover, travellers’ preferences to travel on the autonomous bus improve when no surveillance is present. Travellers with an increased level of trust are found to perceive more utility of a self-driving bus. This effect is stronger for women, which could explain the outcome that women are less likely to travel by autonomous bus than men. These relative preferences result in the self-driving bus being a competitive alternative for short urban trips, which allow for the increase of the travel costs for the self-driving bus compared to the travel costs for the regular bus. At last, the estimation and application outcomes of this study provide an increased understanding of the stated preferences of public transport users for self-driving vehicles operated as public transport services for urban trips.

On-demand transit has become a common mode of transport with ride-sourcing companies like Uber, Lyft, Didi transforming the way we move. With the increase in popularity for such services, the supply needs to adapt according to the demand. For this, the demand needs to analyzed to examine if there are recurrent patterns in them; making it predictable and easily manageable. The identified demand patterns can then be used for optimized fleet management. In this paper, we propose three steps for extracting such demand patterns from travel requests (1) constructing the origin-destination zones by spatial clustering (2) calculating the hourly origin-destination matrix for each day, and (3) temporal clustering to extract the dynamic demand patterns. We demonstrate the three step approach on the open-source Didi taxi data. The data is composed of 1 month (November 2016) of travel requests data from a small area in Chengdu, China with approximately 200 000 rides for a single day on average. It can provide insight into the day-to-day regularity and within-day regularity of the demand patterns in Chengdu.

The current rail infrastructure is used intensively in and around large cities, several functional network levels can be defined within the rail infrastructure. The combination of different network levels puts pressure on the available infrastructure capacity and investments are needed to increase the infrastructure capacity. The definition between different networks is fading and will become less important in the future and it can be classified in a different way. Interoperability is a term that deals with the sharing of rail infrastructure. This thesis describes the possibilities of interoperability between metro and train networks. The Noord/Zuidlijn metro-line in the city of Amsterdam is used as a case study and its Key Performance Indicators were assessed both before and after interoperability was experimentally applied and tested. The interoperability that is the subject of this paper entails the continuation of the Noord/Zuidlijn metro onto the ProRail network from station Amsterdam Zuid via station Amstelveenseweg and Schiphol to Hoofddorp. Currently nowhere in the Netherlands situations occur where a metro line continues its path on the mainline rail network as described in this thesis. This research outlines how interoperability influences the performance of a public rail transit system if the technical difficulties that obstruct this interoperability can be overcome.