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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Het verduurzamen van de mobiliteitssector is een essentieel onderdeel van de transitie naar een klimaat- en CO2-neutrale samenleving. Voor verplaatsingen die te lang zijn voor actieve vervoerwijzen (lopen, fietsen, …), is het openbaar vervoer over het algemeen het meest duurzame alternatief voor reizigers. Met een hoge capaciteit en efficiënt gebruik van middelen is het ideaal voor het vervoeren van grote aantallen mensen over langere afstanden. Een zwakte echter bij het gebruik van het openbaar vervoer is het bekende first/last mile-probleem: voor- en natransport naar en van de halte of station. Gedeelde micromobiliteit kan hier een oplossing bieden.@en

In maart 2020, nadat de coronacrisis ook Nederland had bereikt, startten TU Delft en NS een groots longitudinaal onderzoek onder treinreizigers (Van Hagen et al. 2021). Het onderzoek loopt inmiddels al ruim tweeëneenhalf jaar, met zeven deelonderzoeken en een achtste op komst. Menno de Bruyn (NS) en Niels van Oort (TU Delft) beschrijven hun onderzoek.@en

This paper uses stated preference data collected in the city of Rotterdam and discrete choice modelling techniques to study the relationship between public transport and shared micromobility. It assumes a hypothetical condition of integrated systems and studies the relationships of complement and competition between these modes. The findings suggest that shared micromobility modes are viable alternatives as egress modes for metro trips. Shared micromobility can be seen as a complement to metro, yet shared e-mopeds proved to also be a viable option as individual modes for long-distance trips. Different characteristics proved to be important in choices in this context: frequency of public transport use, previous use of shared micromobility, and age. Considering the results obtained, collaboration between shared micromobility and transit operators might benefit them as well as travellers. Collaborations should be designed so that they help travellers to decrease total travel time, even if it implies longer egress legs. However, the costs of these shared modes should not be as high as to prevent travellers to use them as egress alternatives. Finally, young travellers and frequent transit users could be specifically targeted, as they showed to have a better perception of shared micromobility.

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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.@en

Validation of travel demand models, although recognised as important, is seldom undertaken. This study adds to the scarce literature in this field by undertaking an external validation of a multi-modal transit route choice model. The model was estimated using smart card data for the urban transit network of Amsterdam before the introduction of a new metro line and is used to predict changes in travel behaviour after the network change. To validate, the model was checked for changes in estimated parameters between the two time periods, and predictive ability was evaluated at different aggregation levels. Although most model parameters were found to be unstable between the two contexts, the predictive performance at all levels was similar to the locally estimated model. Moreover, individual choices and transit mode-share predictions were found to be close to the observed ones. The errors were relatively larger for the link and route-level predictions, some of which could be attributed to the assumptions made regarding consideration choice set given as input to the model. On comparing alternative model specifications, using generic instead of mode-specific travel attributes lead to a strong degradation in predictive performance. Conversely, a model incorporating overlap between routes, with a better model fit in the base period, did not offer a clear improvement in prediction performance. The study highlights the need to validate transit route choice models before using them for deriving policy recommendations, especially in this data-rich age in which it can often be undertaken at a relatively low additional cost.

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Nowadays, urban areas are exposed to various challenges such as climate change, social inequalities, and congestion. Shared mobility hubs present the opportunity to reshape our cities and mitigate the previously mentioned challenges by contributing to a more sustainable transport system. These are places where shared cars, mopeds, and e-bikes are offered to improve connectivity in urban areas. In this paper, we investigate the impact of efficiently allocating multimodal shared mobility hubs on modal split, service level, and environmental factors while assuring economic feasibility. Given a limited budget, cities would like to optimize the hubs’ locations to maximize the population's benefits. For that purpose, we introduce a multi-stage design algorithm model that distributes the hubs and allocates fleets of shared cars, mopeds, and e-bikes to maximize travel utility for all the population traveling using traditional and/or shared modes while accounting for multimodal trips. The model is divided into several modules: computational modules that calculate the demand for the hubs; an optimization module to optimize the hubs’ capacities, availability, and relocation of shared vehicles; and finally, a genetic algorithm to find the optimal hub distribution. Our proposed model is one of the first that optimizes the location and capacity of multimodal hubs by considering multimodal trips in a large network. Additionally, it allows to assess mobility, spatial, and environmental impact of shared modes. The model is applied to the case of Amsterdam, the capital of The Netherlands, with around 800,000 inhabitants. After running several scenarios with different budgets allocated to build the hubs, results show that having more hubs with a lower number of shared vehicles is more beneficial than having fewer hubs with higher capacity. That is because the travel time savings increase considerably when investments lead to complete coverage of the area by the hubs network. A modal split of 5% for the shared modes is expected when Amsterdam is covered by 288 hubs. From an environmental point of view, only 32% of the shared trips replace trips previously made by ICE and electric cars, leading to a limited CO2 emissions reduction of 1.27%. Hence, introducing shared modes and mobility hubs without push measures for the use of private cars appears to offer limited benefits to decrease the negative impacts of private car usage.

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Digitalisation in public transport has become pervasive over the past decade, especially in urban areas. While it benefits many, it also leaves some behind. Previous research shows that older adults, people with a lower education level, people with impairments and people with a migration background are more likely to be negatively impacted by digitalisation in transport services. In order to uncover mechanisms behind digital inequality in public transport, we interviewed 39 people belonging to these groups. They experience difficulties due to low digital skills, not using digital technologies on-the-go, not possessing the right devices and due to a complex design of technologies, among others. Many participants reap some benefits of digitalisation though. In fact, individuals can experience benefits on one aspect and difficulties on another. Nevertheless, experiencing difficulties with digitalisation does not necessarily equal to exclusion from public transport thanks to coping strategies like support from one's social network. Still, many coping strategies come with pitfalls such as hidden work and costs. Digital technologies facilitate a self-service approach that paradoxically makes some people more dependent on others. This study can support practitioners and researchers in developing a better understanding of the (sometimes insidious) consequences of technological innovations on individuals.

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As digitalisation is making its way into public transport (PT) services, policy approaches to ensure that such services remain inclusive are at best fragmented, at worst inexistant. This study pieces together existing initiatives and lessons learnt in the transport sector itself, and takes inspiration from other fields with a more mature understanding of digitalisation. We interviewed twenty-two experts working either in the PT sector or in other sectors such as healthcare and public administration to present an overview of possible measures to foster inclusion in PT in the digital era. We used both triangulation and a two-step respondent validation process to improve results’ trustworthiness. We conclude that there is no one-size-fits-all, but a series of complementary strategies to address digital inequality. A focus on an inclusive design from the start, courses, showing the added value of digital tools, specialist products and non-digital alternatives are building blocks to foster a more inclusive PT system in the era of digitalisation. The role of the public transport staff ought not to be underestimated in digital transformations. Importantly, securing the issue of unequal access to public transport due to digitalisation at a decision-making level is essential. Nevertheless, there is only so much that the transport sector can do. Tackling more systemic issues that often underlie digital barriers like poverty and low literacy is crucially relevant. While the present study was conducted in the Netherlands, the presented measures can be applied in other countries by stakeholders working on inclusive digital transformations in (public) transport services.

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Hydrogen fuel cell multiple unit vehicles are acquiring a central role in the transition process towards carbon neutral trains operation in non-electrified regional railway networks. In addition to their primary role as a transport mean, these vehicles offer significant potential for applications in innovative concepts such as smart grids. Compared to the pure electric propulsion systems, fuel cell technology allows for cogeneration processes by recovering generated heat in addition to the provision of the electrical power. This paper presents the analysis of fuel cell hybrid-electric multiple unit vehicle employed in regional railway transport during regular service, and in vehicle-to-grid application during the off-service hours, where it provides the electrical and thermal energy for stationary consumers in terminal stations. The system dynamics are modelled using a backward-looking quasi-static simulation approach, with implemented real-time optimization-based control strategy for managing the power flows between different components. In a case study of selected vehicle and railway services in the Netherlands, the fuel cell system showed average hydrogen consumption of 0.4 kg/km, with the overall electrical efficiency of 38.89%. In vehicle-to-grid scenario, the system satisfied complete stationary power demand, and provided about 327 kWh of thermal energy during two-hours operation, reaching overall cogeneration efficiency of 66.81%.@en

Hydrogen fuel cell multiple unit vehicles are acquiring a central role in the transition process towards carbon neutral trains operation in non-electrified regional railway networks. In addition to their primary role as a transport mean, these vehicles offer significant potential for applications in innovative concepts such as smart grids. Compared to the pure electric propulsion systems, fuel cell technology allows for cogeneration processes by recovering generated heat in addition to the provision of the electrical power. This paper presents the analysis of fuel cell hybrid-electric multiple unit vehicle employed in regional railway transport during regular service, and in vehicle-to-grid application during the off-service hours, providing the electrical and thermal energy for stationary consumers in terminal stations. The system dynamics are modelled using a backward-looking quasi-static simulation approach, with implemented real-time optimization-based control strategy for managing the power flows between different components. In a case study of selected vehicle and railway services in the Netherlands, the fuel cell system showed average hydrogen consumption of 0.4 kg/km, with the overall electrical efficiency of 38.89%. In vehicle-to-grid scenario, the system satisfied complete stationary power demand, and provided about 327 kWh of thermal energy during 2-h operation, reaching the overall cogeneration efficiency of 66.81%.

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On-demand mobility services (FLEX) are often proposed as a solution for the first/last mile problem. We study the potential of using FLEX to improve train station access by means of a three-step sequential stated preference survey. We compare FLEX with the bicycle, car and public transport for accessing two alternative train stations. We estimate a joint access mode and train station choice model. Estimating a latent class choice model with different nesting structures, we uncover four distinct segments in the population. Two segments (∼50%) with a lower Willingness-to-Pay seem to be more likely to take-up FLEX. Ex-urban car drivers seem to be the most likely segment to adopt FLEX, showing great, since members of this segment are currently frequent users of the private car. Our case study also shows that while FLEX competes primarily with public transport when accessing local stations, it competes primarily with car for reaching distant stations.

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The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on- board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on- the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.

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