Car dominance in urban landscapes poses environmental, health, and congestion challenges. This comprehensive study examines the potential of shared mobility in car-free areas. Specifically, it investigates the mobility behaviour of inner-city older adult residents (50 + ), traditionally heavy car users through a case study of small-medium-sized Dutch cities and a stated preference experiment. This study applies a Latent Class model to analyse the heterogeneity in passengers’ preferences, identifying four distinct groups: Price Sensitive & Private Car Enthusiasts, Time-Conscious Travellers, Pro-Cycling & Conventional travellers, and Micromobility Enthusiasts. The model predicts class membership based on travel behaviour data from the stated choice experiment and examines the role of key factors such as travel cost, travel time, and walking distance in shaping mode choices across five transport options: bike, e-bike, e-scooter, e-Brommobiel, and e-car. The findings reveal that a significant portion of travellers recognise the value of shared mobility options in reducing private car dependency, underscoring the need for targeted interventions to address barriers and enhance accessibility to promote shared mobility adoption. Based on these distinct passenger segments, the study proposes specific policy measures that not only enhance transport planning but also address existing challenges and user concerns in sustainable urban mobility.

Shared micromobility may enhance accessibility as part of a comprehensive public transport network. A qualitative stakeholder perspective is needed to reflect the practical opportunities and challenges for integrating shared micromobility and public transport. This research synthesizes literature alongside 25 interviews with professionals across North America and Europe to answer: 1) what challenges and opportunities do different integration approaches offer? And 2) what should be prioritized when integrating shared micromobility and public transport? We frame integration efforts in three pillars: 1) institutional, 2) physical, and 3) digital integration. Findings show that integration efforts are effective only when foundational preconditions are met including high-quality public transport, dedicated micromobility infrastructure, supportive land uses, and issue framing. Institutional and physical integration are both necessary to facilitate multimodal travel and create cohesive regional travel networks. Digital tools can support integration but often requires substantial resources, and faces data sharing and privacy, feasibility, and scaling challenges.

Offering shared mobility options at transit stops can potentially increase the service area of a stop and consequently, possible detours in transit lines can be eliminated to decrease in-vehicle travel times for through-passengers and reduce operational costs. However, current research mostly focusses on shared mobility options and expected behaviour only, whilst not looking at this integrated transit network design problem. Additionally, most focus of current studies is on the integration of shared mobility in urban areas and/or around train stations, leaving a gap on suburban areas and transit lines with lower demand. In order to answer our research question “what the effects are of increased route directness for low-demand transit lines in conjunction with offering shared mobility at transit stops”, we developed a mesoscopic model extension for the aggregated four-step transport model to model changes in travel behaviour as a result of straightened transit lines and the simultaneous integration of shared modes. Discrete choice models are used to accurately model first and last mile preferences of people, based on the access and egress distance, demographics and available (shared) modes. Finally, the probability of passengers cancelling their complete trip as a result of increased first and last mile distances is also explored. This model framework was applied to nine case studies in the Netherlands. The synthesis of the case studies resulted in key factors contributing to a promising redesign of the transit network. The main factor is that through-passengers should significantly outnumber local passengers, by at least 75%-25%. Additionally, the increase in access and egress times should not be significantly larger than in-vehicle time savings of through-passengers. Moreover, it is found that the mode share of micromobility in the first and last mile is approximately 15% across the different cases, whereby the highest usage can be seen for people under the age of 25 and for distances greater than 1 km. Finally, it is concluded that the additional costs of shared mobility are on average only 10% of the savings in operational costs.

Shared micromobility (SMM), including bicycles, e-bikes, scooters, etc., is often cited as a solution to the first and especially the last mile problem of public transport (PT), yet when implemented, they often do not get adopted by a broader travelling public. As behavioural adaption is largely related to peoples’ attitudes and perceptions, we develop a behavioural framework based on the UTAUT2 framework to gain better understanding why individuals may (not) be willing to use SMM. Through an exploratory factor analysis (EFA) and a latent class cluster analysis (LCCA), we study the adoption potential of SMM and assess drivers and barriers as perceived by different user groups. Our findings uncover six user groups; Shared mobility positives, Car-oriented sharing neutrals, Older apprehensive sharers, Young eager adopters, (Shared) Mobility avoiders and Skilled sharing sceptics. The Young eager adopters and Shared mobility positives tend to be the most open to adopting SMM and able to do so. Older apprehensive sharers would like to, but find it difficult or dangerous to use, while Skilled sharing sceptics are capable and confident, but have limited intention of using it. Car-oriented sharing neutrals and (Shared) Mobility avoiders are most negative about SMM, finding it difficult to use and dangerous. Factors relating to technological savviness, ease-of-use, physical safety and societal perception seem to be the strongest adoption predictors. Younger, high-educated males are the group most likely and open to using SMM, while older individuals with lower incomes and a lower level of education tend to be the least likely.

Unplanned train disruptions are a source of passenger dissatisfaction because they are often accompanied by overcrowding and lack of information. To better accommodate passengers during disruptions and preventing travellers from switching to other less sustainable modes of transport, mitigating control strategies can be applied by railway operators. This however requires predicted passenger flows over all available travel options as an input. Due to the COVID-19 pandemic these passenger flows have becomes less predictable, as many travellers have gained an additional feasible alternative to cope with unplanned disruptions on outbound commuter trips − they may return home and start teleworking. Because this travel option is only available to teleworkers and now utilized more than before the COVID-19 pandemic, heterogeneity in route choice behaviour has increased. To fill this knowledge gap and provide predictions of passenger flows, an online survey containing a labelled stated choice experiment was carried out among Dutch train commuters. Consequently, a latent class choice model was estimated to investigate the influence of disruption characteristics, teleworking, COVID-19 risk perception and information provision on travel behaviour during train disruptions in the Netherlands and uncover heterogeneity in behaviour. Our results indicate that the strongest predictors of route choice behaviour are the moment of discovering the disruption, the disruption length and job characteristics. Uncovering four latent classes shows the different valuations of crowding, waiting times and additional travel times among commuters. Commuters with the option to telework are more likely to return back home during disruptions as well as commuters who are sceptic towards the provided information and those who are still conscious of COVID-19. Commuters who cannot telework and trust the provided information are more likely to reroute within the train network whereas commuters who cannot telework and do not trust the provided information are more likely to wait for the disrupted services to resume.

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 for the off-peak. 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.

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.

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.

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. […]

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.

Public transport companies (PTCs) are facing significant challenge in phasing out high-polluting diesel vehicles and selecting alternative propulsion solution for their city bus fleet renewal. To support PTCs in this complex multi-criteria decision making process, this paper presents an integrated method, that includes the improved Analytical Hierarchy Process (AHP) to define the weights of criteria, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess and rank considered solutions. The method is applied in a case study of Arriva, one of the Dutch major PTCs, with battery-electric system with opportunity charging identified as the optimal configuration.

Access/egress travel to train stations poses a significant barrier to increasing the number of train travellers. The last mile is challenging for travellers, given the lack of private modes to reach the destination, strongly limiting the egress range from the station. An often-cited solution is shared micromobility (SMM): bicycles, e-bikes, e-scooters and e-mopeds. Through a stated preference survey, we analyse activity-end mode-choice preferences for SMM, walking and public transport (PT) among the Dutch population. Using a latent class choice model, we uncover three user groups: Multimodal SMM enthusiast (58%), who choose based on the trade-offs between various travel characteristics, while not having strong modal preferences. They are the most open, ready and able to use SMM. SMM hesitant cyclists (16%) have a strong preference for cycling and while they are open to using SMM, they may not feel themselves ready, stating that use of SMM can be difficult and dangerous. SMM-averse PT users (27%) are most likely to use PT and avoid SMM as they find it too difficult and dangerous to use. For policymakers, the high preference to walking over short egress distances reaffirms the need for continued focus on transit-oriented development. For longer distances, policymakers should focus on improving PT service in high-density high-demand areas, as high frequencies and dense PT networks can be justified, while stations in low-demand areas are better served by SMM. Policymakers should also prioritise SMM modes that are cheaper and that travellers are familiar and comfortable with, such as bicycles.

Many cities have ambitious climate targets, like becoming climate-neutral by 2050, 2040 or 2030, but are uncertain about how mobility and land use should change in order to reduce GHG emissions to levels consistent with the cities’ aims. Finding answers on this, and also on the question of how to strike an effective balance between desired fundamental transformation and realistic expectations, was at the heart of the Interreg Europe project 2050 CliMobCity (2050 Climate-friendly Mobility in Cities). The project partners were four medium-sized but otherwise quite different cities (Bydgoszcz, Plymouth, Thessaloniki and Leipzig), along with the Potsdam Institute for Climate Impact Research (PIK) and TU Delft. Each partner city conducted a so-called demonstration study, which required defining ambitious packages of measures (mobility, land use, electrification) and forecasting changes in mobility by macroscopic transport modelling. PIK then analysed GHG emissions using its carbon model. This paper extends the demonstration studies into a case-study approach. This is preceded by an exercise in consistently structuring types of GHG and mobility performances and corresponding measures. The case-study approach is supplemented by a review of the literature on shared and micromobility. A conclusion drawn from the case study is that none of the partner cities is in fact sufficiently reducing GHG emissions. Even if electricity production was completely green, the remaining gap between GHG reduction aims and analysed GHG delivery lies between 30–81 %, dependent on the scenario and city. Shared and micromobility seem not to lead to strongly deviating conclusions. We discuss policies to close the gap. One major option discussed is tackling GHG emissions from urban freight, in forms such as organising public-private cooperation designed to accelerate the electrification of freight vehicles.

This literature review examines 58 studies on equity in transit fare policy, addressing developments since the last comprehensive review in 1990. It divides the literature into seven categories: (1) fare equity calculation methods; (2) flat versus distance based fares (3) equity impacts of fare changes; (4) differentiated fares; (5) creating equitable fare policy for people with low incomes; (6) fare capping; and (7) free fare public transport. For each category, the review outlines the key findings and suggests areas for future research. Overall, the current literature shows that fare equity outcomes are highly context-dependent and shaped by system design, demographic patterns, and policy implementation. The literature suggests that direct subsidies to low income people result in better equity outcomes than subsidies to groups that collectively have a lower than average income. Additionally, free fare public transport has received considerable attention even as the literature suggests its benefits may be quite limited. In contrast, the limited attention given to fare capping has suggested that it has the potential to significantly improve equity outcomes with limited cost burdens to transit providers. Future research should focus on methods for identifying low-income users, best practices for implementing new payment structures such as fare capping, and different ways of using fares to increase transit accessibility.

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.

Railway systems are increasingly vulnerable to unexpected external events, such as extreme weather caused by climate change, cyber-attacks targeting critical infrastructure. These disruptions threaten the continuity of railway operations and underscore the necessity for railway organizations to enhance their overall resilience. Various studies have looked into the assessing the infrastructure resilience, however, having a resilient infrastructure is insufficient. The back-end decision making or organizational resilient is also essential and it should also be considered. This paper presents a semi-qualitative assessment tool that integrates both technical and organizational attributes identified against the established 4R Resilience Framework: Robustness, Redundancy, Resourcefulness, and Rapidity. The tool employs a structured five-stage cycle, combining expert voting, Rank Ordered Centroid-Technique for Order of Preference by Similarity to Ideal Solution (ROC-TOPSIS) methods to evaluate and determine the resilience level of each resilient attribute. Through surveys, the tool was validated for its relevance and practicality: over 60% of respondents affirmed the applicability of its resilience attributes and evaluation criteria, while 97% considered the tool at least moderately useful. The assessment process identifies low-performing attributes, which serve as guide for resilience-building initiatives and strategic planning. This work contributes an actionable approach to resilience assessment and planning that is adaptable for both national and international railway systems/organizations facing increasingly complex operational risks, and perhaps a universal assessment guideline.

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.

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.

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.

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.