In recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to many, and it is considered a good alternative for certain car trips by policy-makers and planners. A major limitation of many studies that investigate such substitution effects of the e-bike, is their reliance on cross-sectional data which do not allow an assessment of within-person travel mode changes. As a consequence, there is currently no consensus about the e-bike’s potential to replace car trips. Furthermore, there has been little research focusing on heterogeneity among e-bike users. In this respect, it is likely that different groups exist that use the e-bike for different reasons (e.g. leisure vs commute travel), something which will also influence possible substitution patterns. This paper contributes to the literature in two ways: (1) it presents a statistical analysis to assess the extent to which e-bike trips are substituting trips by other travel modes based on longitudinal data; (2) it reveals different user groups among the e-bike population. A Random Intercept Cross-Lagged Panel Model is estimated using five waves of data from the Netherlands Mobility Panel. Furthermore, a Latent Class Analysis is performed using data from the Dutch national travel survey. Results show that, when using longitudinal data, the substitution effects between e-bike and the competing travel modes of car and public transport are not as significant as reported in earlier research. In general, e-bike trips only significantly reduce conventional bicycle trips in the Netherlands, which can be regarded an unwanted effect from a policy-viewpoint. For commuting, the e-bike also substitutes car trips. Furthermore, results show that there are five different user groups with their own distinct behaviour patterns and socio-demographic characteristics. They also show that groups that use the e-bike primarily for commuting or education are growing at a much higher rate than groups that mainly use the e-bike for leisure and shopping purposes.@en

Physical distancing has been an important asset in limiting the SARS-CoV-2 virus spread during the COVID-19 pandemic. This study aims to assess compliance with physical distancing and to evaluate the combination of observed and self-reported data used. This research shows that it is difficult to operationalize new rules, that context affects compliance, that there needs to be a need for compliance, and that rules require upkeep. From a methodological point of view, this study found that the combined methods provide a comprehensive picture of compliance behaviour, that it is challenging but essential to mitigate response fatigue in long-term monitoring studies, and that it would be interesting in future research to learn how actual behaviour is influenced by personal narratives.@en

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.@en

This paper studies the relationship between trip chain complexity and daily travel behaviour of travellers. While trip chain complexity is conventionally investigated between travel modes, our scope is the more aggregated level of a person’s activity-travel pattern. Using data from the Netherlands Mobility Panel, a latent class cluster analysis was performed to group people with similar mode choice behaviour in distinct mobility pattern classes. All trip chains were assigned to both a travel mode and the mobility pattern class of the traveller. Subsequently, differences in trip chain complexity distributions were analysed between travel modes and between mobility pattern classes. Results indicate considerable differences between travel modes, particularly between multimodal and unimodal trip chains, but also between the unimodal travel modes car, bicycle, walking and public transport trip chains. No substantial differences in trip chain complexity were found between mobility pattern classes. Independently of the included travel modes, the distributions of trip chain complexity degrees were similar across mobility pattern classes. This means that personal circumstances such as the number of working hours or household members are not systematically translated into specific mobility patterns.@en

As the introduction of automated vehicles (AVs) into road traffic accelerates, establishing user acceptance is increasingly crucial. User comfort, largely influenced by the AVs' driving styles, is one of the essential factors influencing acceptance. This video submission provides a methodological overview of a qualitative interview study, which used a Wizard-of-Oz method to investigate participants' comfort levels during automated driving on real roads. By understanding the specific comfort experiences of both older and younger users, we can inform the design process for AVs, thereby enhancing user experience and facilitating broader acceptance of technology across a more diverse and inclusive demographic spectrum.@en

Introduction: It has been estimated that physical inactivity accounts for roughly 10% of premature mortality globally in any given year. Active travel (walking and cycling) has been promoted as an effective means to stimulate physical activity. However, many of the available studies on the relation between active travel and health are based on cross-sectional data and are therefore unable to determine the direction of causation. This study aims to unravel the bidirectional relationships between active travel measured by the active modes bicycle, e-bike and walking, on the one hand, and two health outcomes, namely body-mass index (BMI) and self-rated health (SRH), on the other. Methods: To provide an initial assessment of the relationship between active travel and the two health outcomes, multivariate regression models are estimated. To study the direction of causation, Random-Intercept Cross-Lagged Panel Models (RI-CLPM) are estimated using three waves of the Netherlands Mobility Panel (MPN). Active travel is measured as travelled distances and trips with the bicycle, e-bike and walking. BMI is calculated based on weight and height, SRH is measured with a single question. Results: The regression models show that a higher BMI and lower SRH are associated with less walking and cycling, while being obese is associated with more e-bike use. The results of the RI-CLPM indicate that cycling distance has a positive effect on SRH. Furthermore, walking distance has a negative effect on BMI and BMI has a negative effect on bicycle use among people without obesity. No relationships between BMI and active travel are found for people with obesity. Conclusion: The results highlight the importance of longitudinal analyses when estimating the relationship between active travel and health. In addition, the results suggest that, relatively speaking, the increasing overweight and obesity rates may result in a decrease of bicycle use.@en

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.@en

Active modes take up an increasingly important place on the global policy-making agenda. In the Netherlands, a country that is well-known for its high shares of walking and cycling, the government aims at achieving a modal shift among 200,000 commuting car drivers towards using the bicycle. To this end, policy measures need to be introduced. When the aim is to achieve a modal switch over an enduring period of time, it is more relevant to know the likelihood of including or excluding a mode in the mode choice set, compared to choosing a mode for a single trip. Therefore, we investigate the formation of the experienced choice set (set of modes used over a long period of time), where the aim is to identify determinants that influence the inclusion or exclusion of a mode in this set. We estimate discrete choice models, based on survey data from the Netherlands Mobility Panel (MPN) and a complementary survey, where individuals were asked to report the frequency of using certain modes of transport for commuting trips over the course of half a year. This study shows that the experienced choice set for commuting is unimodal for the majority of the individuals, and remains constant over time for most individuals. Reimbursement by the employer for using a certain mode is the most important determinant influencing the experienced choice set, followed by ownership characteristics and urban density. We show that the mode choice set formation depends on more determinants than previously assumed.@en

Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.@en

The primary goal of this study is to quantify the expected effects of Mobility-as-a-Service on travel preferences and travel behaviour in the Netherlands. We do this by studying the expected changes in preferences and behaviour related to the following determinants of travel mode choice: travel time, travel costs, convenience and comfort, flexibility and freedom, willingness to share and certainty. We achieve this by collecting and analysing survey data, including a choice experiment. This allows to detect preferences and trade-offs that people would be willing to make for a system which still does not completely exist in reality. To make choice sets relevant for respondents (e.g. relating their choices to current travel behaviour), we select respondents from the Netherlands Mobility Panel (MPN), which is a household panel started in 2013 that maps the travel behaviour of a certain group of people and households over a period of consecutive years. Our research makes several contributions, including shedding light on the extent to which the promise of MaaS as a tool to assist in the shift towards more sustainable travel can be kept. @en

The goal of this study is to investigate the relationships between wayfinding styles and mobility patterns and navigational preferences. Urban wayfinding behavior is defined by the strategies that people use to decide how to move from one place to another within a city (Montello 1995). It relates to the preferences, selection and application of navigation strategies, the attitude towards travelling, and ability to reach the intended destination. The research question is to what extent do wayfinding styles differ for groups of travellers and their mobility patterns and navigation preferences? The hypotheses are that more active mobility patterns correlate to more wayfinding abilities, and that with more wayfinding abilities a stronger preference occurs for taking short cuts, while the preferences for time, distance and number of turns may depend on the travel mode and urban environment. First a theoretical framework has been developed for the identification of wayfinding styles based on literature and a factor analysis derived from 23 self-reported preferences towards wayfinding and navigation. Furthermore, the results illustrate fourteen variables (relating to socio-demographic, mobility patterns and navigational preferences) that exercise significant differences among the clusters of wayfinding styles, while the built and urban environment did not yield any significant differences. The contribution of relating wayfinding behavior to revealed mobility patterns and navigational preferences could provide new insights into the decision-making process of people while travelling and improve the content of travel information.@en

The goal of this study is to investigate the relationships between wayfinding styles and mobility patterns and navigational preferences. Urban wayfinding behavior is defined by the strategies that people use to decide how to move from one place to another within a city (Montello 1995). It relates to the preferences, selection and application of navigation strategies, the attitude towards travelling, and ability to reach the intended destination. The research question is to what extent do wayfinding styles differ for groups of travellers and their mobility patterns and navigation preferences? The hypotheses are that more active mobility patterns correlate to more wayfinding abilities, and that with more wayfinding abilities a stronger preference occurs for taking short cuts, while the preferences for time, distance and number of turns may depend on the travel mode and urban environment. First a theoretical framework has been developed for the identification of wayfinding styles based on literature and a factor analysis derived from 23 self-reported preferences towards wayfinding and navigation. Furthermore, the results illustrate fourteen variables (relating to socio-demographic, mobility patterns and navigational preferences) that exercise significant differences among the clusters of wayfinding styles, while the built and urban environment did not yield any significant differences. The contribution of relating wayfinding behavior to revealed mobility patterns and navigational preferences could provide new insights into the decision-making process of people while travelling and improve the content of travel information.@en

The goal of this study is to investigate the relationships between wayfinding styles and mobility patterns and navigational preferences. Urban wayfinding behavior is defined by the strategies that people use to decide how to move from one place to another within a city (Montello 1995). It relates to the preferences, selection and application of navigation strategies, the attitude towards travelling, and ability to reach the intended destination. The research question is to what extent do wayfinding styles differ for groups of travellers and their mobility patterns and navigation preferences? The hypotheses are that more active mobility patterns correlate to more wayfinding abilities, and that with more wayfinding abilities a stronger preference occurs for taking short cuts, while the preferences for time, distance and number of turns may depend on the travel mode and urban environment. First a theoretical framework has been developed for the identification of wayfinding styles based on literature and a factor analysis derived from 23 self-reported preferences towards wayfinding and navigation. Furthermore, the results illustrate fourteen variables (relating to socio-demographic, mobility patterns and navigational preferences) that exercise significant differences among the clusters of wayfinding styles, while the built and urban environment did not yield any significant differences. The contribution of relating wayfinding behavior to revealed mobility patterns and navigational preferences could provide new insights into the decision-making process of people while travelling and improve the content of travel information.@en

Active modes (i.e. walking and cycling) have received significant attention by governments worldwide, due to the benefits related to the use of these modes. Consequently, governments are aiming for a modal shift from motorised to active modes. Attitudes are generally considered to play an important role in determining mode choice and travel behaviour. Therefore, many studies have investigated the relationship between attitudes and behaviour. Understanding the relationship between the attitude towards modes and the daily mobility pattern, can help in providing input for efficient and effective policies that aim at increasing the active mode share. This research investigates patterns in the daily mobility patterns of individuals in the Netherlands and tests the relationship between these patterns and attitudes towards modes. This study identifies five classes of mobility patterns: 1) car and bicycle users, 2) exclusive car users, 3) car, walk, and bicycle users, 4) public transport users, and 5) exclusive bicycle users. Regarding attitudes towards modes, eight factors are identified: five mode related attitudes, two public transport related attitudes, and one related to the prestige of using modes. The results show that the majority of the users is multimodal in their daily mobility pattern. Furthermore, individuals are more positive towards modes that are used in the daily mobility pattern, compared to unused modes. The exclusive car users are most negative to the unused modes. Consequently, when the goal is achieving a higher active mode share, it might be more fruitful to target the multimodal classes and/or classes that contain active mode use, compared to the habitual car users. @en

Active modes (i.e. walking and cycling) have received significant attention by governments worldwide, due to the benefits related to the use of these modes. Consequently, governments are aiming for a modal shift from motorised to active modes. Attitudes are generally considered to play an important role in determining mode choice and travel behaviour. Therefore, many studies have investigated the relationship between attitudes and behaviour. Understanding the relationship between the attitude towards modes and the daily mobility pattern, can help in providing input for efficient and effective policies that aim at increasing the active mode share. This research investigates patterns in the daily mobility patterns of individuals in the Netherlands and tests the relationship between these patterns and attitudes towards modes. This study identifies five classes of mobility patterns: 1) car and bicycle users, 2) exclusive car users, 3) car, walk, and bicycle users, 4) public transport users, and 5) exclusive bicycle users. Regarding attitudes towards modes, eight factors are identified: five mode related attitudes, two public transport related attitudes, and one related to the prestige of using modes. The results show that the majority of the users is multimodal in their daily mobility pattern. Furthermore, individuals are more positive towards modes that are used in the daily mobility pattern, compared to unused modes. The exclusive car users are most negative to the unused modes. Consequently, when the goal is achieving a higher active mode share, it might be more fruitful to target the multimodal classes and/or classes that contain active mode use, compared to the habitual car users. @en

Simulation studies suggest that pooled on-demand services (also referred to as Demand Responsive Transport, ridesharing, shared ride-hailing or shared ridesourcing services) have the potential to bring large benefits to urban areas while inducing limited time losses for their users. However, in reality, the large majority of users request individual rides (and not pooled rides) in existing on-demand services, leading to increases in motorised vehicle miles travelled. In this study, we investigate to what extent fare discounts, additional travel time, and the (un)willingness to share the ride with (different numbers of) other passengers play a role in the decision of individuals to share rides. To this end, we design a stated preference study targeting Dutch urban individuals. In our research, we (1) disentangle the sharing aspect from related time–cost trade-offs (e.g. detours), (2) investigate preference heterogeneity regarding the studied attributes and identify distinct market segments, and (3) simulate scenarios to understand the impact of the obtained parameters in the breakdown between individual and pooled services. We find that less than one third of respondents have strong preferences against sharing their rides. Also, we find that different market segments vary not only in their values of the willingness to share, but also in how they perceive this willingness to share (per-ride or proportional to the in-vehicle time). Further, the scenario analysis demonstrates that the share of individuals who are willing to share rides depends primarily on the time–cost trade-offs, rather than on the disutility stemming from pooling rides per se.@en

Digitalisation in transport services offers many benefits for travellers. However, not everyone is willing or able to follow the new, more or less formal requirements digitalisation has brought along. Existing reviews on the intersection between Information and Communication Technologies (ICTs) and mobility cover a range of vantage points, but the perspective of how various levels of engagement with digital technologies affect access and navigation of transport services has not been addressed yet. In communication science, studying disparities in terms of ICT appropriation and their consequences is known as digital inequality research. This review paper aims at shedding light on what digital inequality in the context of transport services consists of and what its consequences are. To do so, we define and use a conceptual framework for the analysis of digital inequality in transport services. The review of the twenty-five papers, as selected in our systematic literature search, shows that there is a burgeoning interest in this topic. Vulnerability to digitalisation in transport services exists along dimensions of age, income, education, ethnicity, gender and geographical region. We find that motivations and material access get more attention than digital skills and effective usage. Nevertheless, literature acknowledges that having material access to technology does not mean that people benefit from what technology has to offer. Furthermore, the characteristics of ICTs impact one’s possibilities to access digital technologies, such as how user-friendly a technology is. Data-driven and algorithm-based decision-making present a particularly pernicious form of digital exclusion from transport services. As digital technologies are progressively becoming indispensable to navigate the world of transport services, low levels of digital engagement may create a new layer of transport disadvantage, possibly on top of existing ones. Although digitalisation can be part of the solution to transport disadvantage, it can also be part of the problem. With network effects at play, what might start as a relative disadvantage may turn into an absolute disadvantage. Given the nascent state of research on digital inequality in transport services, much remains to be understood. Suggested research avenues include mechanisms of digital exclusion from transport services, the contribution of digital inequality to transport disadvantage, and importantly, solutions to mitigate its impacts.@en

On-demand mobility services are promising to revolutionise urban travel, but preliminary studies are showing they may actually increase total vehicle miles travelled, worsening road congestion in cities. In this study, we assess the demand for on-demand mobility services in urban areas, using a stated preference survey, to understand the potential impact of introducing on-demand services on the current modal split. The survey was carried out in the Netherlands and offered respondents a choice between bike, car, public transport and on-demand services. 1,063 valid responses are analysed with a multinomial logit and a latent class choice model. By means of the latter, we uncover four distinctive groups of travellers based on the observed choice behaviour. The majority of the sample, the Sharing-ready cyclists (55%), are avid cyclists and do not see on-demand mobility as an alternative for making urban trips. Two classes, Tech-ready individuals (27%) and Flex-ready individuals (9%) would potentially use on-demand services: the former is fairly time-sensitive and would thus use on-demand service if they were sufficiently fast. The latter is highly cost-sensitive, and would therefore use the service primarily if it is cheap. The fourth class, Flex-sceptic individuals (9%) shows very limited potential for using on-demand services.@en

The concept of Mobility-as-a-Service (MaaS) is rapidly gaining momentum. Parties involved are eager to learn more about its potential uptake, effects on travel behaviour, and users. We focus on the latter, as we attempt to reveal the profile of groups within the Dutch population that have a relatively high likelihood of adopting MaaS in the near future, apart from the actual supply side. MaaS is a transport concept integrating existing and new mobility services on a digital platform, providing customised door-to-door transportation options. Based on common denominators of MaaS as found in the literature, we have established five indicators to identify early adopters: innovativeness, being tech-savvy, needing travel information, having a multimodal mindset, and wanting freedom of choice. These five indicators are the building blocks of our Latent Demand for MaaS Index (LDMI), and were constructed using 26 statements and questions from a special survey conducted in 2018 among participants of the Netherlands Mobility Panel (MPN). The features derived from the MPN serve as independent variables in a regression analysis of the indicators used to ascertain the profile of early adopters. The results of our model indicate that early adopters are likely to be highly mobile, have a high socio-economic status, high levels of education and high personal incomes. Young people are more eager to adopt MaaS than older adults. Early adopters are healthy, active and frequent users of trains and planes. The characteristics of MaaS's early adopters overlap in numerous ways with those of innovative mobility services users and with the general characteristics of early adopters as found in innovation studies.@en