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

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