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

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.

@en

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

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.

@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

Governments worldwide are aiming to increase sustainable mode use to increase sustainability, livability, and accessibility. Integration of bicycle and transit can increase catchment areas of transit compared with walking and thus provide better competition to non-sustainable modes. To achieve this, effective measures have to be designed that require a better understanding of the factors influencing access mode and station choice. At the national/regional level this has been thoroughly studied, but there is a knowledge gap at the urban level. This study aims to investigate which factors influence the joint decision for tram access mode and tram station choice. The joint investigation can identify trade-offs between the access and transit journeys. Furthermore, the effect of each factor on the bicycle catchment area is investigated. Using data from tram travelers in The Hague, Netherlands, a joint simultaneous discrete choice model is estimated. Generally, walking is preferred to cycling. The findings of this study suggest that access distance is one of the main factors for explaining the choice, where walking distance is weighted 2.1 times cycling distance. Frequent cyclists are more likely also to cycle to the tram station, whereas frequent tram users are less inclined to cycle. Bicycle parking facilities increase the cycling catchment area by 234 m. The transit journey time has the largest impact on the catchment area of cyclists. Improvements to the system, such as fewer stops, higher frequency (like light rail transit), or both, therefore would result in a much longer accepted cycling distance.@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

Tijdens de ‘intelligente lockdown’ zag NS het aantal treinreizigers kelderen met maar liefst 93 procent. Dat is een ramp op zich. Maar de ov-organisatie vraagt zich ook bezorgd af wat al die klanten in de toekomst gaan doen. Om dat scherp te krijgen, benaderden NS en TU Delft het NS-panel met een serie vragen. Bijna 46.000 panelleden deden mee aan het onderzoek.@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 travel behaviour. Understanding the relationship between the attitude towards modes and the daily mobility pattern, can support policies that aim at increasing the active mode share. This paper investigates the daily mobility patterns of individuals using a latent class cluster analysis. The relationship between these classes and attitudes towards modes is investigated. Data of the Netherlands Mobility Panel (MPN) of the year 2016 is used, in combination with a companion survey focussing on active 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. Eight factors of attitudes towards modes 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 exhibits a multimodal daily mobility pattern. Generally, individuals are more positive toward used modes, compared to unused modes. Furthermore, a high level of travel mode consonance is found. When this is not the case (dissonance), often active modes or sustainable modes are preferred. Consequently, when the goal is achieving a higher active mode share, some individuals need to be targeted to change their mobility portfolio (exclusive car users and car and bicycle users), whereas others should be encouraged to increase the use of active modes at the cost of car use (public transport + users and car, walk, and bicycle users).@en

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.

@en

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.

@en

Due to increasing urbanisation rates worldwide combined with growing transportation demand, liveability of the urban environment is under pressure (UN, 2018). In response, many governments worldwide have set goals for increasing the share of trips made using sustainable modes of transport, such as walking and cycling. The use of active modes (i.e. walking and cycling) provides health benefits for individuals due to increased activity levels, and on a network level these modes (standalone or in combination with public transport) can potentially reduce traffic jams and the associated externalities (including air and noise pollution) when substituting the car. To achieve the desired increase in active mode shares, targeted policies need to be implemented. This requires a better understanding of who currently uses these modes, who could be persuaded to switch to active modes, and which determinants are driving active mode choice. This intended change towards active modes requires an adequate representation of walking and cycling in the transportation planning models in order to assess the effect of active mode policies on modal shares and distribution over the network. However, this is often not the case. Moreover, integration of active modes in these models occurs very slowly. Walking and cycling are often missing in transportation planning models, treated as a ‘rest’ category, or combined into slow/active modes, all of which result in incorrect estimates of the active mode shares, making it impossible to correctly identify the impact of potential policy measures on active mode shares. Examples of these policy measures are introduction of new infrastructure or changes to existing infrastructure, which impact route choice and distribution over the network, and reimbursement of using the bicycle to go to work, which impacts the mode choice of individuals. Investigating mode and route choice of active mode users increases the knowledge on active mode choice behaviour. By bridging this gap, the transportation planning models can potentially be improved. The objective of this thesis is ‘to understand and model mode and route choice behaviour of active mode users’. We identify six topics that are imperative to travel choices. First, we investigate the daily mobility patterns of individuals in relation to attitudes towards modes, because attitudes are considered to influence travel behaviour (Chapter 2). Afterwards, we zoom in on individual trips. We aim to understand which determinants drive the choice to walk or cycle (Chapter 3). In this topic we define the mode choice set as all feasible modes per individual and trip. However, not all feasible modes are used by individuals. Therefore, the third topic focuses on modes used over a long period of time, which we coin the experienced choice set. We investigate which determinants are relevant for including or excluding modes in this choice set (Chapter 4). Regarding cyclists’ route choice, we investigate the determinants influencing this choice (Chapter 5). This research is based on the experienced choice set. Accordingly, we compare this method to frequently used choice set generation methods to identify the added value of the experienced choice set (Chapter 6). Finally, we perform a literature review on how mode and route choice can be modelled simultaneously (Chapter 7). @en

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.

@en

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.

@en

In this research, we study trip chains from a new perspective. We analyze differences in trip chain complexity regarding the number of trips and the number of transport modes between five latent mobility pattern classes. The first results suggest that differences between the classes exist for both complexity indicators. However, the identified differences are challenging the commonly assumed role of mode choice on trip chain complexity.@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

The specification of the choice set for travel behaviour analysis is a non-trivial task, as 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 severe errors to the specified choice set: false negative (not generating observed routes) and false positive (including irrelevant alternatives) errors. Due to increased availability of revealed preference data, like GPS, it is possible to identify the choice set in different way: data-driven. The data-driven path identification approach (DDPI), introduced in this paper, combines all unique routes that are observed for one origin-destination pair into the choice set. This paper evaluates this DDPI approach, by comparing it to two choice set generation methods (breadth-first search on link elimination and labelling). The evaluation is based on three main purposes of choice sets: analysis of alternatives, model estimation and prediction. The conclusion is that the DDPI approach is a useful alternative for choice set identification. The findings indicate that in analysing alternatives, the DDPI approach is most suitable, as it is equal to the observed behaviour. For model estimation the DDPI approach provides a useful alternative to choice set generation methods, as it provides insights into the preferences of individuals. In terms of prediction, the DDPI approach is suitable on a network level, but not on the individual level. The average performance over all alternatives is similar for all choice sets, but on individual level the DDPI method does not predict well. @en

In de grootstedelijke gebieden van Nederland gaat het hard met de fiets. Het aandeel van deze slow mode is er inmiddels dusdanig hoog, dat de fietser een eigen verkeersmanagementaanpak verdient. Maar uitdaging één is dan: op welke data baseer je je? Is sturing mogelijk op basis van floating bike data? De auteurs nemen de mogelijkheden door en blikken vooruit naar de niet-eens-zo-verre toekomst van multimodaal netwerkmanagement.@en