Reducing transport poverty can improve well-being and expand employment opportunities. This study investigates the relevance of socio-demographic and urban density predictors in relation to transport poverty contributor metrics for neighborhoods in the Amsterdam metropolitan area. Utilizing a spatial econometric framework, we assess the relevance of these predictors across three dimensions of transport poverty: mobility, accessibility, and affordability. Contrary to existing literature, our findings indicate that the demographic factors of gender and younger age are not significant predictors at the neighborhood level. Furthermore, the research identified a correlation between higher urban density and transport poverty. While higher urban density is associated with decreased car ownership rates and increased accessibility, it simultaneously correlates with higher public transport costs relative to income. Additionally, the method revealed a high cumulative spatial effect of income in connection with transport affordability, indicating spatially extensive income-related transport affordability disparities. Our research offers new insights into factors related to neighborhood-level transport poverty. The observed spatial dynamics call for targeted strategies that address the unique challenges for implementing equitable transport policies in both densely populated urban areas and less urbanized regions.

Passenger railway demand fluctuates daily, peaking at the start and end of the workday due to commuting to school and work. During the off-peak the volumes drop and most people travel for other purposes, like leisure and social visits, which results in different travel destinations. Despite this, many European Railways use fixed line plans and cyclic timetables that remain constant throughout the day. While this approach makes schedules easy to remember and provides ample off-peak travel options, it is primarily designed for peak-hour demand, making it less efficient. Furthermore, due to the different mix of travel purposes, a schedule based on peak-hour demand is not necessarily optimal for off-peak demand. This paper aims to combine the benefits of a cyclic timetable with the flexibility of an acyclic timetable in order to follow the time-dependent demand more closely. We propose a mixed-integer linear programming model that finds a timetable for a day consisting of several periods which each have its own line plan. The resulting timetable is required to be cyclic within each period and provide a good transition between the periods. The model is successfully tested on a case study with changing stopping patterns using data from the Dutch railway network, for which an optimal timetable is found. In this timetable, the transition between cyclic schedules can be done without cancelling trains or shifting trains from the new cyclic times.

Mobility is vital for societal wellbeing, economic growth, social inclusion, and access to essential amenities. However, the current system faces significant challenges, including environmental impact, unequal access, and safety concerns. […]

Regional non-electrified railway networks require replacement of diesel traction to meet increasingly stringent emission reduction targets. Since full electrification of these networks is often not economically viable due to their low utilization, battery-electric multiple units (BEMUs) are recognized as a potentially suitable long-term solution, offering zero-emission train operation while requiring only partial tracks electrification. One of the main challenges when introducing BEMUs is determining an optimal electrification layout, i.e. the location and the length of electrified track sections while taking into account the vehicles’ and infrastructure technical characteristics and constraints alongside the requirements related to maintaining current timetable and quality of service. This paper formulates this as an intermittent partial electrification network design problem and develops an optimization framework that integrates high-fidelity BEMU simulation model in deriving a cost-optimized network electrification configuration. The proposed method is demonstrated using the existing non-electrified regional railway network in the Netherlands with the rolling stock and transport services of Arriva as a case. The obtained solution provides about 30% lower capital costs compared to the conventional continuous partial electrification approach, and about 3.5 times cut in these costs compared to the fully electrified network. Additionally, further costs reduction is observed by increasing the maximum current absorption limits at standstill and by introducing flexibility in terms of operational margins.

Integrating bike-sharing programs with public transport enhances accessibility and car-independent mobility, yet a comprehensive societal cost-benefit analysis of this integration remains scarce. This study addresses this gap by conducting an ex-durante analysis of the OV-fiets program in the Netherlands, a station-based round-trip bike-sharing system designed to improve last-mile connectivity for train commuters. The analysis reveals that in the average (balanced) scenario, the net present value (NPV) of the OV-fiets scheme is positive, with a benefit-cost ratio (BCR) of 1.5. This indicates that the scheme has benefited the Dutch society over the 20-year period (2004–2023). In the pessimistic scenario, the NPV remains slightly positive with a BCR of 1.1. This implies that even under the least favourable conditions, where high costs and low benefits are assumed, the scheme still slightly exceeds the break-even point. Conversely, in the optimistic scenario, the BCR rises significantly to 2.4. Primary benefits include enhanced accessibility, reduced road congestion, and improved health outcomes. This research underscores the considerable societal value of the OV-fiets program in the Netherlands, warranting continued investment in the program and emphasising the need for ongoing bicycle safety measures and infrastructure improvements. However, OV-fiets might be successful in the Netherlands; our analysis also shows that copying it into other contexts is not straightforward. The seamless integration of bikes with trains is crucial, and the operators should be able and willing to accept operational losses.

Train passenger demand fluctuates throughout the day. In order to let train services, such as the line plan and timetable, match this fluctuating demand, insights are needed into how the demand is changing and for which periods the demand is relatively stable. Hierarchical clustering on both regular and normalized origin–destination (OD) data is used to determine for each workday continuous time-of-day periods in which the passenger demand is homogeneous. The periods found for each workday are subsequently used as input in a clustering algorithm to look for similarities and differences between workdays. The methods for finding homogeneous periods during the day and week are applied to a case study covering a large part of the railway network in the Netherlands. We find large differences between the periods based on regular OD matrices and those based on normalized OD matrices. The periods based on regular OD matrices are more compact in terms of passenger volumes and average kms travelled and therefore more suitable to use as input for designing a service plan. Comparison of different workdays shows that mainly the peak periods on Friday are far away from Monday to Thursday, and hence could benefit from an altered service plan.

Mobility patterns and transport systems have been heavily impacted due to the COVID-19 pandemic. Public transport is impacted heavily, as governments worldwide advised against using it. This paper presents the data collection effort initiated by NS (Dutch Railways) and Delft University of Technology to capture changes in travel behavior, attitudes and intentions related to the COVID-19 pandemic among Dutch train travelers. The survey set-up, data collection process, data validation and potential of the dataset are discussed. The data collection effort proves to be a valuable longitudinal data set that is ground for many research opportunities and policy insights.

In light of growing environmental challenges, the need to reconsider how we approach personal transportation is becoming increasingly evident. A shift from a private car-focused mobility system towards a more sustainable and equitable transportation system is needed. Car sharing is considered a means to achieve this, however, its usage and its impact are not entirely understood, as many studies do not consider the motives of individuals to use this alternative, treating the population of users as a homogeneous group. This study aims to reveal distinct car sharing usage profiles to gain a thorough understanding of the various motivates behind car sharing and its relation with travel behaviour. Six user profiles are uncovered using a Latent Class Cluster Analysis (LCCA) based on station-based carsharing data of one company operating in the Netherlands gathered through an online survey (N = 1281). The results show significant diversity in car sharing motives. The identified user groups have different effects on travel behaviour. Environmentally motivated car sharers use the shared car as a complete replacement for their private car, causing a substantial decrease in car ownership and usage. For utilitarian car sharers, and especially formerly carless individuals, the decrease in car ownership is less substantial and even an increase in car use can be observed. Finally, it was found that car sharing is mostly complementary to public transport use. Ways to promote the use of both modes could be explored.

Latent Class Analysis was used on the 2018/19 Dutch National Travel dataset to assess travel behaviour profiles and its connection with sociodemographic and spatial factors to grasp travel patterns in a more holistic context than previous research

Public transport systems offer significant potential for mitigation of transport sector’s greenhouse gas (GHG) emissions - the second largest contributor in the European Union (EU) accounting for 23.2% of the total GHG emissions in 2020. The Sustainable and Smart Mobility Strategy, part of the European Green Deal, stipulates higher utilization of public transport systems together with the diffusion of zero-emission vehicles, renewable and low-carbon fuels, and related infrastructures as prerequisites in reaching the overall decarbonization targets.

As major fleet operators, and in some cases builders of extensive infrastructure systems, public transport operators (PTOs) will require effective management of GHG emissions and established procedures for their transparent reporting. Current practices include mainly voluntary carbon footprint reporting often limited to the well-to-wheel (WTW) scope, i.e., direct emissions from fuel combustion (tank-to-wheel, TTW) and upstream emissions linked to the fuel production (well-to-tank, WTT). Due to the implementation of the Corporate Sustainability Reporting Directive (CSRD) which stems from the European Green Deal, further new reporting obligations and standards will follow for many PTOs from the reporting year 2025 onwards. Reporting under the CSRD will follow the European Sustainability Reporting Standards (ESRS) which define new mandatory disclosures for climate reporting. In particular, in addition to Scope 1 and 2 emissions, the reporting of Scope 3 emissions will become mandatory in accordance with the GHG Protocol. This imposes significant challenges for PTOs in accounting the full life cycle emissions which were traditionally omitted and considered as out of influence, with these emissions assumed a responsibility of vehicle suppliers.

Another reason for neglecting emissions associated with upstream (e.g., vehicles and equipment production) and downstream (end-of-life) processes in the past is the absence of a globally-recognized and accepted standard for the calculation of the carbon footprint that adopts a life cycle perspective and covers the entire transport supply chain. As the only international and transport-specific standard, ISO 14083 (previously EN 16258) provides general principles and guidelines, while limiting the scope to WTW emissions. PTOs and transport sector in general are faced by the existence of a diverse mix of state-supported standards, standards self-developed by associations, recommendations by research bodies, regional approaches, methods and tools for individual modes of transport, mainly focusing on the freight transport and logistics sector.

This paper aims to provide a thorough systematic review of the existing standards, guidelines, methods and tools developed for transport-related GHG emissions calculation and reporting, and to synthesize a methodological framework for the assessment of life cycle emissions from public transport services. The review begins with a juxtaposition of outputs provided by each standard or method with PTO’s reporting needs. The paper then discusses the analysis scopes of existing standards/methods in regard to the types of GHG emissions reported, life cycle analysis boundaries, modal coverage, and fuel/energy coverage. Further, an explanation of the calculation processes used by the standards/methods, including a discussion of data needs and limitations is provided. Based on the results presented, a methodological framework for the calculation of life cycle GHG emissions is presented and showcased for the railway and bus transport services of Arriva in Limburg region in the Netherlands. The case study considers heterogenous train and bus fleet, including both electrical and diesel powertrains, allowing for the comparative assessment of various propulsion systems. It also provides deeper understanding of the environmental impacts of emerging technology such as Lithium-ion batteries, often regarded as a critical component in novel powertrain solutions in the transport sector associated with a high degree of uncertainty in their overall carbon footprint. Finally, a discussion of the need for a consistent accounting and calculation standard for PTO’s life cycle GHG emissions management concludes the paper.

The outcomes of this study can be leveraged by PTOs in determining their overall emissions and identifying the main contributors to their overall carbon footprint. In addition to the corporate responsibility and the compliance with the new regulation, applying appropriate measures and good reporting practices can help PTOs to improve their market share, company image, and value. For instance, the Dutch “CO2 Performance Ladder” management tool provides a concrete award advantage during the tendering process for certified companies in the Netherlands and Belgium. This system managed by the Foundation for Climate Friendly Procurement and Business (in Dutch: SKAO) is being adopted in Ireland, France, the United Kingdom (UK), and Germany. Furthermore, consistent calculation and declaration procedures can help PTOs in their preparation for different trading schemes, such as the European Trading System (ETS), the world’s first carbon market established within the EU in 2005.

Making the mobility sector sustainable is a key part in the transition to a climate- and carbon-neutral society (European Parliament, 2023). For trips too long to be performed by active modes (walking, cycling), public transport is the most sustainable alternative available to travellers. With a high capacity and efficient use of resources, it is ideal for transporting larger numbers of people over longer distances. (Brons et al., 2009).
In the Netherlands, 39% of all train passengers arrive to the station by bicycle, with over half a million bicycle parking spaces offered around the country (Dutch Railways, 2023). And although some travellers have their own bicycle on both the home and activity side of the train trip, most only have a bicycle on their home-end, meaning they have to rely on other forms of transportation on the activity-end, such as walking or local public transport (buses, trams, metros). A bicycle-sharing scheme is also available at almost 300 stations, with over 21 thousand bicycles, resulting in 5.4 million total trips in 2022 (Dutch Railways, 2023).

In recent years, the rise of digitalisation and increased use of smartphones have brought along with them many new shared (electric) (micro)mobility alternatives to the mobility ecosystem, such as car-sharing, (electric) bicycle-sharing, e-scooters, e-steps to name but a few. Currently mainly present in larger cities and used predominantly for shorter trips within urban areas, they do hold the potential to improve the accessibility of public transport stations, especially for more distant access/egress trips due to the assistance of electric motors.

To get a crucial understanding of train travel, micromobility and how they can interact and complement each other, we investigated the perceptions and preferences of individuals; how they perceive these emerging modes, how likely they are to use them and what they find important when making their travel decisions. We looked into the joint access-mode and train station choice, analysing how the quality of access modes and train service at a specific station affect each other; in other words, how do people trade-off attributes from different legs of the same trip. Secondly, we investigated the potential to use various shared mobility services, included a comparison when pitted against the car and bicycle.
Lastly, we evaluated the impact of introducing shared e-mopeds on public transport, considering it as both an egress mode at the activity-end of the trip, as well as a potential competing mode for the main leg of the journey.

We show that more positive perceptions of micromobility and a higher intention to use such services is often linked with past experience using such services, digital savviness (knowing how to use a smartphone), a more multimodal travel behaviour portfolio (particularly frequent use of public transport) and a higher achieved level of education. We analysed latent market segments and uncovered similar patterns, with a somewhat large share of those ready to use (shared) micromobility, with around a quarter of the population being more sceptical, but then also residing in rural areas, having a lower level of education and being less skilled with digital technology.
These results provide valuable insights for policymakers on how to proceed with introducing such services. Selecting the correct policy is vital to achieve a desired modal shift, as introducing new modes can also result in shifts from modes which are already at a satisfactory level (e-mopeds attracting cyclists for example). We show that shared e-mopeds can be both a competitor and ally to public transport, meaning that the service implementation strategy is key to secure the desired outcomes and mitigate the negative side-effects.
Future research should also compare how the various new micromobility services compete with each other for new and existing travellers. Additionally, checking for potential induced demand of such services, both as a the main mode or simply as an access/egress mode, would be valuable for policymakers.

Recent literature has given increasing attention to the different ways in which people can be excluded from transportation systems, with a reduction in economic, social, and recreational opportunities as a result. These theoretical insights into how transport poverty and transport accessibility can be measured and understood do not always relate directly to actual planning processes. Different government agencies have authority over different policy and infrastructure elements and within a transportation authority, different departments have different actors, goals, and approaches. An important first step in making advances in social inclusion is for transportation authorities to be able to evaluate their current approach as a starting point for policy improvements. Presently, however, no systematic method exists for conducting that evaluation. This article refines an existing Transportation Related Social Exclusion (TRSE) framework and develops a method for comprehensively evaluating existing TRSE reduction efforts in transportation policy. It applies this method to the operating agreements for the three public transportation concession areas in the Amsterdam Transport Region, using textual analysis to develop a policy overview for ten dimensions of TRSE. The application of the method reveals that different forms of TRSE receive substantially different levels of attention within the Amsterdam Transport Region. The evaluation serves as a tool for systematically understanding how different dimensions of social exclusion are being addressed, allowing for a starting point for discussing whether or not existing policy is adequate and creating opportunities for incorporating new theoretical concepts into practice.

As the offer of digital services in transport expands, understanding users’ digital engagement and how it developed over time is important to make informed policy decisions. In particular, we lack an understanding of how both PT (public transport) and car users access and engage with digital technologies and perceive them to be necessary to travel. This article aims at bridging this gap, using a 2022 survey of representative samples from both populations in the Netherlands. There is clear evidence of travellers getting more used to digital technologies over time. In 2022, at most 80% of car and PT users relied at least from time to time on their smartphone to look for travel information. As expected, higher digital skills correlate positively with the likelihood of using smartphone-based travel information. It is worth noting that PT users report higher digital skills than car users, while these samples do not differ significantly in terms of age and education levels. As such, low (perceived) digital skills might be a barrier to switching from the car to public transport. Almost 75% of car and PT users think that travelling is more difficult nowadays without a smartphone, demonstrating a radical shift in societal expectations within a decade and a half. Alternatives like public information displays exist and are still used by the majority, but traditional communication channels are not deemed sufficient anymore to travel worry-free. These perceptions can contribute to shaping reality and may put those with a lower digital access at a disadvantage.

Enabling cycling at the home side or at the activity side of transit trips has been recognized as a promising solution to address transit network discrepancies and enhance connectivity between residents and employment opportunities. However, this multimodal solution is conditional to bicycle parking and cycle lanes, and urban planners need tools to identify relevant locations for these infrastructures. This research presents a novel method to quantify the impact of potential cycling infrastructures on job accessibility. Using a logsum-based indicator, we assess the spatial distribution of accessibility improvements across neighborhoods when residents have the option to cycle from and to transit stops. Then, we quantify the individual contribution of every potential bicycle parking location and cycle lane to the overall accessibility improvements. The proposed approach offers valuable support to urban planners in identifying the best locations for (1) multimodal bicycle parking and (2) cycle lanes to foster synergies between cycling and transit. To demonstrate the efficacy of our method, we apply it to the case study of Amsterdam. The findings reveal that bicycle parking at metro stops and cycle lanes connecting these stops to dense and remote locations contribute the most to accessibility improvements, as they effectively connect these areas to high-frequency and high-speed transit lines. Additionally, we observe that few strategic infrastructures account for most of the accessibility improvements in Amsterdam.

The COVID-19 pandemic poses an unprecedented challenge for public transport systems. The capacity of transport systems has been significantly reduced because of the social distancing measures. Therefore, new avenues to increase the resilience of public urban mobility need to be explored. In this work, we investigate the integration of bike sharing and public transport systems to compensate for limited public transport capacity under the disruptive impacts of the COVID-19 pandemic. As a first step, we develop a data analysis model to integrate the demand of the two underlying systems. Next, we build an optimization model for the design and operation of hybrid mixed-fleet bike sharing systems. We analyze the case of the subway and public bike sharing systems in Milan to assess this approach. We find that the bike sharing system (in its current state) can only compensate for a minor share of the public transport capacity, as the needs in fleet and station capacity are very high. However, the resilience of public urban mobility further increases when new design concepts for the bike sharing system are considered. An extension to a hybrid free-floating bike and docked e-bike system doubled the covered demand of the system. An extension of the station capacity of about 37% yields an additional increase of the covered demand by 6.5%–7.5%. On the other hand, such a hybrid mixed-fleet bike sharing system requires many stations and a relatively large fleet to provide the required mobility capacity, even at low demand requirements.

This paper presents a method for estimating Well-to-Wheel (WTW) energy use and greenhouse gas (GHG) emissions attributed to the advanced railway propulsion systems implemented in conjunction with different energy carriers and their production pathways. The analysis encompasses diesel-electric multiple unit vehicles converted to their hybrid-electric, plug-in hybrid-electric, fuel cell hybrid-electric or battery-electric counterparts, combined with biodiesel or hydrotreated vegetable oil (HVO) as the first and second generation biofuels, liquefied natural gas (LNG), hydrogen and/or electricity. The method is demonstrated using non-electrified regional railway network with heterogeneous vehicle fleet in the Netherlands as a case. Battery-electric system utilizing green electricity is identified as the only configuration leading to emission-free transport while offering the highest energy use reduction by 65–71% compared to the current diesel-powered hybrid-electric system. When using grey electricity based on the EU2030 production mix, these savings are reduced to about 27–39% in WTW energy use and around 68–73% in WTW GHG emissions. Significant reductions in overall energy use and emissions are obtained for the plug-in hybrid-electric concept when combining diesel, LNG, or waste cooking oil-based HVO with electricity. The remaining configurations that reduce energy use and GHG emissions are hybrid-electric systems running on LNG or HVO from waste cooking oil. The latter led to approximately 88% lower WTW emissions than the baseline for each vehicle type. When produced from natural gas or EU2030-mix-based electrolysis, hydrogen negatively affected both aspects, irrespective of the prime mover technology. However, when produced via green electricity, it offers a GHG reduction of approximately 90% for hybrid-electric and fuel cell hybrid-electric configurations, with a further reduction of up to 92–93% if combined with green electricity in plug-in hybrid-electric systems. The results indicate that HVO from waste cooking oil could be an effective and instantly implementable transition solution towards carbon–neutral regional trains, allowing for a smooth transition and development of supporting infrastructure required for more energy-efficient and environment-friendly technologies.

Introduction: The world is facing multiple challenges, e.g. in the realm of health, urbanization, climate change and social inclusion. Mobility can be part of the problem yet has the potential to be part of the solution. Serious games can support decision-making in a complex environment. This paper describes a serious game that aims to stimulate individual learning of (future) professionals in transport and urban planning, and facilitate discussions on goals and arguments concerning different (new) mobility options. Methods: This paper presents the first version of the serious game: ‘the Optimal Mobility Mix’. This card game was developed and played in five sessions in the Netherlands, India and the USA with over 100 participants. Feedback was collected informally in a debrief, through a survey (N = 25) and through observation. Results: During the game, participants formed small groups and collaborated to select a mobility mix using playing cards that aligned with the five societal goals (effective mobility, efficient city, economy, environment & health and equity), of the 5E framework (Van Oort et al., 2017). The survey indicated that the game was highly interactive, fun and relevant. Although the game's ‘reflection of reality’ scored relatively low, it still remained positive. Conclusions: Overall, the game successfully facilitated discussion on societal goals and transportation modes and appears positive on its learning objectives. The effect of the game in policy and decision making practice will be tested in future research. Striking a balance between capturing the complexity of reality and creating an accessible and flexible model for professionals to interact with – in the form of a game, is an ongoing challenge. Future developments aim to involve key players and broader planning-related policies through a codesign process with game design iterations. To support this process, observations and recordings can be intensified to improve feedback processing.

Understanding user's perception of service variability is essential to discern their overall perception of any type of (transport) service. We study the perception of waiting time variability for ride-hailing services. We carried out a stated preference survey in August 2021, yielding 936 valid responses. The respondents were faced with static pre-trip information on the expected waiting time, followed by the actually experienced waiting time for their selected alternative. We analyse this data by means of an instance-based learning (IBL) approach to evaluate how individuals respond to service performance variation and how this impacts their future decisions. Different novel specifications of memory fading, captured by the IBL approach, are tested to uncover which describes the user behaviour best. Additionally, existing and new specification of inertia (habit) are tested. Our model outcomes reveal that the perception of unexpected waiting time is within the expected range of 2–3 times the value-of-time. Travellers seem to place a higher reward on an early departure compared to a penalty for a late departure of equal magnitude. A cancelled service, after having made a booking, results in significant disutility for the passenger and a strong motivation to shift to a different provider. Considering memory decay, our results show that the most recent experience is by far the most relevant for the next decision, with memories fading quickly in importance. The role of inertia seems to gain importance with each additional consecutive choice for the same option, but then resetting back to zero following a shift in behaviour.

Train passenger demand fluctuates throughout the day and week and these fluctuations are expected to increase due to the COVID-19 pandemic. In order to let train services, such as the line plan and timetable, match this fluctuating demand, insights are needed into how the demand is changing and for which periods the demand is relatively stable. Hierarchical clustering on origin-destination (OD) data is used to determine for each workday continuous time-of-day periods in which the passenger demand is homogeneous. The periods found for each workday are subsequently used as input in a clustering algorithm to look for similarities and differences between workdays. Both normalized and regular OD matrices are tested as input for the method. In normalized OD matrices, only the structure of the demand is captured, while in the regular OD matrices both the structure and the volume of the demand are included. The methods for finding homogeneous periods in demand during the day and week are applied to a case study covering a large part of the railway network in the Netherlands. We find large differences between the periods based on regular OD matrices and those based on normalized OD matrices. The periods based on regular OD matrices seem more appropriate to use as input for designing a service plan. Comparison of the periods over the week shows that mainly the peak periods on Friday are far away from Monday to Thursday, and hence could benefit from an altered service plan.

Neighbourhood mobility hubs may play an important role in mitigating the impact of passenger cars on climate change and urban public space. As a relatively new concept, academic research on the user potential of neighbourhood mobility hubs is so far limited. This research aims to identify which user groups are likely to adopt services offered by a neighbourhood mobility hub. A survey was distributed in the Netherlands (N=298), an Exploratory Factor Analysis (EFA) executed and a Latent Class Cluster Analysis (LCCA) estimated. Four distinctive groups of intended users are uncovered. Two of the clusters have intentions to use neighbourhood mobility hubs. Two other clusters do not (yet) intend to use neighbourhood mobility hubs. The clusters indicate that people who currently already travel more by sustainable modes (train or (e-)bicycle) are more likely to be adopters of neighbourhood mobility hubs than the traditional car users. In practice, this may limit the positive effect of hubs or even increase car use. However it could also facilitate those travelling sustainably to do so for longer as additional shared modes become available to them via hubs. Limitations and directions for further research are discussed.