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.

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.

In this study a unique bicycle sharing program (BSP) is studied: a BSP initiated by an urban transit provider (buses and trams). The idea is that the combined use of BSPs and buses and trams could increase the catchment area of urban transit alone, therefore offering a more competitive alternative for the car. However, in the scientific literature hardly any knowledge is available regarding to what extent, by whom and how this bicycle – urban transit combination is used. This study explores the so-called ‘HTM-fiets’ programme in The Hague, the Netherlands, operated by urban transit operator HTM. Within the case, data was collected through a survey among the users of this program. The results indicate that, in this case, only 9% of the respondents use HTM-fiets in combination with urban transit. Of bike users who use HTM-fiets as a stand-alone mobility option (i.e. without combining it with transit), 46% have used the HTM-bike as substitute for bus and tram. Our results imply that the transit provider of ‘HTM-fiets’ faces difficult policy choices. The large degree of substitution may negatively influence their business case. However, a large degree of substitution is at the same time not a problem per se for them, because this substitution may alleviate crowding problems in transit and ‘HTM-fiets’ can be seen as an extra service by them offered to people in the Hague to ensure better accessibility of the city. The main lesson would be to focus on an integrated design of BSP and public transport in case a complementary system is aimed for, since our case shows clearly that without an integrated design especially substitution will take place from urban transit to the bicycle.

Globally, the need for more sustainable modes of transport is rising. One of the main contenders of the car is the electrical bike (e-bike). To promote the use of e-bikes, pilots are being organised worldwide (e.g. in the USA, Norway, and the Netherlands). Studies have shown that providing a free e-bike to people for a limited period of time changes their mode choice behaviour during the pilot period. Only few studies have also investigated the long-term effects of these free e-bike trial periods, which show increase in e-bike use in general. However, these studies have failed to investigate why some participants of the trials change behaviour on the long-term, whereas others continued their former behaviour. This study aims to bridge this gap. A pilot with e-bikes was organised at Delft University of Technology, The Netherlands, with the goal of reducing car use for commuter trips towards the university. Data was collected at various moments during and after the trial period to evaluate the long-term changes in commuting behaviour and to identify potential reasons for these changes. A total of 82 participants are included in this study. Overall, car use for commuting decreased from 88% before the pilot to 63% three months after the pilot. E-bike use went up from 2% to 18% in the same time period. A binary logistic regression model shows that the most important variables to explain the decrease in car use are 1) purchase of an e-bike, 2) the participant's perception regarding e-bike safety, and 3) the aim of the participant to use the pilot to change their current behaviour. Besides that, the most important predictor of increase in e-bike use is the purchase of an e-bike. Furthermore, participants identify the investment costs of an e-bike as the strongest reason for not purchasing an e-bike and, thus, not changing their commuting behaviour. Future pilot programs could consider the potential of incrementally purchasing an e-bike over a longer period of time, instead of at once, to increase e-bike adoption rate.

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.

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.

Pollution and congestion are important issues in urban mobility. These can potentially be solved by multimodal transport, such as the bicycle-Transit combination, which benefits from the flexible aspect of the bicycle and the wider spatial range of public transport. In addition, the bicycle can increase the catchment areas of public transport stops. Most transit operators consider a fixed 400m buffer catchment area. Currently, not much is known about what influences the size of catchment areas, especially for the bicycle as a feeder mode. Bicycles allow for reaching a further stop in order to avoid a transfer, but it is not clear whether travelers actually do this. This paper aims to fill this knowledge gap by assessing which factors affect feeder distance and feeder mode choice. Data are collected by an on-board transit revealed preference survey among tram travelers in The Hague, The Netherlands. Both regression models and a qualitative analysis are performed to identify the factors that influence feeder distance and feeder mode choice. Results show that the median walking feeder distance is 380m, and the median cycling feeder distance is 1025m. The tram stop density and chosen feeder mode are most important in feeder distance. For feeder mode choice, the following factors are found to be influential: Tram stop density, availability of a bicycle, and frequency of cycling of the tram passenger. Furthermore, the motives of respondents for choosing a stop further away are mostly related to the quality of the transit service and comfort matters, of which avoiding a transfer is named most often. In contrast, the motives for cycling relate mostly to travel time reduction and the built environment. Three important barriers for the bicycle-Tram combination have been discovered: unavailability of a bicycle, insufficient and unsafe bicycle parking places. Infrequent users of the bicycle-Tram combination are more inclined to travel further to a stop that suits them better.

Everyday people find their way towards work, supermarkets, or unfamiliar places are explored for a social visit. Understanding how differences in urban wayfinding behaviour relate to daily travel patterns is important to describe route choice behaviour, identify potential navigation problems, design more legible cities, and provide comprehensible travel information. Therefore, the goal of this study is to jointly investigate the differences between urban wayfinding styles and the relations with socio-demographic, motility, urban environment, navigational preferences, and daily travel behaviour. The findings of this study are based on a sample of the Dutch population of 1101 respondents. All respondents completed a three-day travel diary as part of the Mobility Panel Netherlands (MPN), and an additional cross-sectional survey designed to capture perceptions, attitudes, and wayfinding for active modes (PAW-AM). A Factor Analysis is conducted to identify urban wayfinding styles based on a Dutch version of the self-report questionnaire of environmental spatial skills originally developed in Santa Barbara (SBSOD). Generalized Linear Models (GLMs) are used to estimate to what extent various determinants affect two hypothesized urban wayfinding styles, in this study coined as Orientation Ability (OA) and Knowledge Gathering & Processing Ability (KA). The main findings of the study are an associated effect of gender and age on both urban wayfinding styles, while the navigational preference to follow the bearing line and average daily distance travelled by car have disassociated effects. The remaining determinants are only significant in either OA or KA, providing evidence that mainly different processes describe each wayfinding style.

Interest into active modes (i.e. walking and cycling) has increased significantly over the past decades, with governments worldwide ultimately aiming for a modal shift towards active modes. To devise policies that promote this goal, understanding the determinants that influence the choice for an active mode is essential. The Netherlands is country with a large and demographically diverse active mode user population, mature and complete active mode infrastructure, and safe environment. Mode choice research from the Netherlands enables a comparison on relevant determinants with countries that have a low active mode share. Furthermore, it can provide quantitative input for policies aiming at an active mode shift. This paper estimates a mode choice model focusing on active modes, while including a more comprehensive set of modes (i.e. walking, cycling, public transport and car). Based on data from the Netherlands Mobility Panel (MPN) in combination with an additional survey focused on active modes (coined PAW-AM), this study estimates which determinants influence mode choice. The determinants can be categorized as individual characteristics, household characteristics, season and weather characteristics, trip characteristics, built environment, and work conditions. The results show that all categories of determinants influence both walking and cycling. However, the choice for cycling or walking is affected by different determinants and to a different extent. In addition, no active mode nest was found in the model estimation. Cycling and walking should thus be regarded as two distinguished alternatives. Furthermore, the results show that active mode use is most sensitive to changes in the trip characteristics and the built environment.

Specifying the choice set for travel behaviour analysis is a non-trivial task. Its size and composition are known to influence the results of model estimation and prediction. Most studies specify the choice set using choice set generation algorithms. These methods can introduce two types of errors to the specified choice set: false negative (not generating observed routes) and false positive (including irrelevant routes). Due to increased availability of revealed preference data, like GPS, it is now possible to identify the choice set using a data-driven approach. The data-driven path identification approach (DDPI) combines all unique routes that are observed for one origin-destination pair into a choice set. This paper evaluates this DDPI approach by comparing it to two commonly used choice set generation methods (breadth-first search on link elimination and labelling). The evaluation considers the three main purposes of choice sets: analysis of alternatives in the choice set, model estimation and prediction. The conclusion is that the DDPI approach is a useful addition to the current choice set identification methods. The findings indicate that in analysing alternatives in the choice set, the DDPI approach is most suitable, as it reflects the observed behaviour. For model estimation the DDPI approach provides a useful addition to the current choice set generation methods, as it provides insights into the preferences of individuals without requiring network-data for additional information or generating routes. In terms of prediction, the DDPI approach is not suitable, as it is not able to perform well with out-of-sample data.