With the arrival of COVID-19 in the Netherlands in Spring 2020 and the start of the “intelligent lockdown”, daily life changed drastically. The working population was urged to telework as much as possible. However, not everyone had a suitable job for teleworking or liked teleworking. From a mobility perspective, teleworking was considered a suitable means to alleviate travel. Even after the pandemic it can (continue to) reduce pressure on the mobility system during peak hours, thereby improving efficiency and level of service of transport services. Additionally, this could reduce transport externalities, such as emissions and unsafety. The structural impact from teleworking offers opportunities, but also challenges for the planning and operations of public transport. The aim of this study is to better understand teleworking during and after COVID-19 among train travellers, to support operators and authorities in their policy making and design. We study the telework behaviour, attitude towards teleworking, and future intentions through a longitudinal data collection. By applying a latent class cluster analysis, we identified six types of teleworkers, varying in their frequency of teleworking, attitude towards teleworking, intentions to the future, socio-demographics and employer policy. In terms of willingness-to-telework in the future, we distinguish three groups: the high willingness-to-telework group (71%), the low willingness-to-telework group (16%), and the least-impacted self-employed (12%). Those with high willingness are expected to have lasting changes in their travel patterns, where especially public transport is impacted. For this group, policy is required to ensure when (which days) and where (geographical) telework takes place, such that public transport operators can better plan and operate their services. For those with low willingness, it is essential that the government provides tools to companies (especially in education and vital sector) such that they can be better prepared for teleworking (mostly during but also after the pandemic). Employers on the other hand need to better support their employees, such that they stay in contact with colleagues and their concentration and productivity can increase.@en

This study investigates whether the decline in public transit ridership is a temporary phenomenon or indicative of a structural shift in travel patterns and attitudes. We estimate a latent class trajectory model using data from a comprehensive and large-scale survey administered by the Dutch national train operator conducted at eight different points in time after the onset of the pandemic. Six latent trajectories in train use and stated future intentions to use the train are revealed, showing different ‘recovery’ pathways. Whereas low-educated frequent commuters travel almost as much as before, highly educated frequent commuters and mixed-purpose travellers still travel much less, even in the last wave when all restrictions are lifted. The results indicate that travellers belonging to these classes have structurally changed their behaviour. The shift to working from home is more pronounced than the shift to private car use.@en

Many studies about conducting activities while traveling start from the hypothesis that conducting onboard activities reduces the value of time (VoT). However, surprisingly limited empirical evidence is provided for this hypothesis. The few studies that aim at providing this evidence face methodological problems in the sense that effects attributed to conducting onboard activities are confounded with differences between groups. This paper further develops and applies a solution for this problem proposed by Wardman and Lyons (2016). In essence, this method includes constructing a within-person choice experiment, which involves that the same respondents make choices in a context that enables conducting activities, as well as in a context that does not enable conducting activities. This method is applied in a study that collected data from 820 train travelers in the Netherlands. The results show that as expected, the VoT in the activity context is significantly lower than the VoT in the non-activity context, which thus supports the hypothesis. Reduction in VoT due to conducting onboard activities is around 30% for commuters, while leisure travelers who prefer to read lose almost half their VoT value. In addition, this paper discusses how the estimated VoT reduction values can be interpreted as the Value of Activity (VoA), which can be used for appraising investments aimed at reducing the disutility of travel other than by means of reducing travel time, such as improving Internet connections.@en

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.@en

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

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

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

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.@en

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.@en

Studies all over the world have been performed during Covid-19 to investigate the characteristics of anxious public transport travellers and the reduction in public transport usage. This indicates that reduction in public transport usage related to anxiety from Covid-19 is a problem everywhere and important to investigate this group further. For this project, longitudinal surveys are used to gain insights into the groups of anxious and non-anxious train travellers in the Netherlands. This project is part of a larger project, which focuses on the impacts Covid-19 has on train travelling behaviour, by NS and TU Delft (Van Hagen et al. (2021). Covid-19 and train travel behavior. Paper presented at the European Transport Conference). This subproject focuses on the effects of anxiety on train travelling behaviour during and after Covid-19. The data from the surveys are used to divide the participants into groups based on their anxiety levels: anxious, neutral, and non-anxious. The anxious group consists of people that do not feel free to travel by train during Covid-19 and the non-anxious group does feel free to travel by train during Covid-19. To analyse the characteristics and travel behaviour, the data from the survey of April 2021 are used, and statistical tests such as chi-square test and classification tree analysis are used to analyse the differences between the groups. The main purpose of this project is to investigate the group of anxious train travellers during Covid-19 to gain more insights into their characteristics, attitude, and behaviour. This study finds that the main factors that influence anxiety levels are age, gender, and vaccination status. Our research shows that females and older people are more likely to be anxious. As a result, a typical profile of an anxious person is a female, older than 25 years old and not vaccinated. Furthermore, a non-anxious person is likely to be male, 25 years old or younger, and fully vaccinated. Since attitude has a strong relationship with (travel) behaviour, the anxious group is compared to the non-anxious group to investigate the effects of anxiety on attitude and travel behaviour. The results show that anxiety has a negative effect on attitude which leads to less train usage, both current and expected usage in the future. Anxious people generally tend to have a negative attitude towards the train, while non-anxious people usually have a positive attitude towards the train. In current train travelling behaviour, anxiety has the effect of people travelling less, and are more likely to not travel at all. For future expected travel, anxious people are more likely to plan to travel less than non-anxious people. The number of anxious people fluctuates over time and seems to be related to the number of cases or hospitalizations. The size of the anxious group is higher when there are peaks in number of cases and hospitalizations, and lower when things are calmer. Additionally, vaccinations seem to influence the number of anxious people as well, where the size of the anxious group reduces when a lot of people in the Netherlands are fully vaccinated. During the first year (April 2020 to April 2021), the anxious group has been between 40 and 70% of train travellers. It can be assumed that there will still be a group of people that are anxious after Covid-19, because in September 2021, when cases had been low for some time, 20% of train travellers were still anxious and a small group of 6% was still feeling very anxious. The results of this paper help to identify the anxious group and establish the effect of anxiety on attitude and behaviour, which helps with designing future timetables and planning rolling stock purchases. For future research, it is recommended to look further into the relationship between the number of anxious people and the number of cases or hospitalizations as that relationship can help predict train usage in the future. Furthermore, it is recommended to investigate why people are still feeling anxious even after a time of low number of cases and no restrictions. That information can help with reducing the size of the anxious group and increase train usage.@en

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