How Do Train-Cyclists Navigate ?

How do train -cyclists ? navigate Exploring bike-train route choice behavior in the Amsterdam Metropolitan Area

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Author: Pedro Nieves Student number: 11659912 Email: [email protected] Submission Date: 11 June 2018 Supervisor: Dr. Marco te Brömmelstroet Second reader: Dr. Pieter Tordoir Master’s program: Urban & Regional Planning

How do train-cyclists navigate? Pedro Nieves

University of Amsterdam Faculty of Social and Behavioral Sciences Graduate School of Social Sciences

How do train-cyclists navigate? Exploring bike-train route choices in the Amsterdam Metropolitan Area

Supervised Research Project Written by Pedro Nieves

In partial fulfillment of the graduation requirements for the Master of Science in Urban and Regional Planning

June 2018

How do train-cyclists navigate? Pedro Nieves

Acknowledgments

First and foremost, I would like to thank my supervisor, Marco te Brömmelstroet (a.k.a. the fietsprofessor), for his guidance, inspiration, and constant feedback throughout my research. I am extremely grateful for his availability, attentiveness, and for his constructive criticism which challenged me to think outside of the box, encouraging me to write a better thesis within the timeframe.

I would also like to thank my colleagues from the University of Amsterdam with which I had the chance to discuss my study. I really appreciated their personal support and remarks throughout these past six months. Their friendship, cheerfulness, and dedication have inspired me and made this an unforgettable experience.

The past months living in Amsterdam have been very challenging yet rewarding – I came out of this program as a different person, feeling inspired and ready to contribute towards a better future in my home(is)land, Puerto Rico.

Finally, I want to express my deepest gratitude to my friends and family back home for their unconditional love, support, and comfort during my studies. Special thanks to my sister, Alexandra, and to my parents who have always been there for me throughout my academic career. I share this achievement with you. Gracias por tanto.

Sincerely,

______Pedro Nieves

How do train-cyclists navigate? Pedro Nieves

Abstract

Route choice studies have given attention to the factors that influence cyclists and transit users’ route decision making while disregarding the actual process of selecting a particular route from a different set of routes. This thesis has further explored this process by understanding the bicycle-train system as a distinct mode of transportation because it left open the question of how ‘train-cyclists’ make route choices, particularly from the complexity of train stations’ overlapping catchment areas. The study area which was chosen to delve into this aspect was the Amsterdam Metropolitan Area in the Netherlands because of its highly developed and integrated bicycle and railway infrastructure. Results indicate that bike-train route options increase exponentially when traveling to or from overlapping areas. All-in-all, the main finding suggest that train-cyclists are willing to have longer cycling journeys in order to shorten their train journeys.

Keywords: mobility, integrated transport, bike-train route choice, urban cycling, rail transport

Resumen

Los estudios de elección de ruta han prestado atención a los factores que influyen la selección ruta tanto de ciclistas y como de los usuarios de tránsito sin profundizar el proceso que involucra seleccionar una ruta particular sobre un conjunto de diferentes rutas. Esta tesis ha explorado este proceso conceptualizando el sistema bici-tren como un modo distinto de transporte debido a que dejó abierta la pregunta de cómo los/as ‘ciclistas-de-tren’ toman sus decisiones de ruta, particularmente desde la complejidad de las áreas de cobertura superpuestas de las estaciones de tren. El área de estudio que se eligió para explorar este aspecto fue el Área Metropolitana de Ámsterdam en los Países Bajos debido a que su infraestructura de ciclismo y ferroviaria está altamente desarrollada e integrada. Los resultados indican que las opciones de ruta aumentan exponencialmente cuando se viaja hacia o desde áreas superpuestas. En general, el principal hallazgo sugiere que los/as ciclistas-de-tren están dispuestos a tener viajes más largos en bicicleta para acortar el tiempo de viaje en tren.

Palabras clave: movilidad, transporte integrado, selección de ruta, ciclismo urbano, transporte ferroviario

How do train-cyclists navigate? Pedro Nieves

Table of Contents

Acknowledgments ...... 3 Abstract/Resumen ...... 4

1. Introduction ...... 7 2. Theoretical Framework ...... 9 2.1 Bike-train system ...... 9 2.2 Route choice behavior ...... 11 2.2.1 Bicycle route choice ...... 13 2.2.2 Transit route choice ...... 14 2.3 Conceptual framework (preliminary)...... 15 3. Methodology ...... 16 3.1 Problem Statement ...... 16 3.2 Research Question(s) ...... 17 3.3 Study Area ...... 17 3.4 Research Design ...... 19 3.4.1 How does the current bike-train system perform? ...... 19 3.4.2 What are the (in)direct attributes which influence potential bike-train route choices? ...... 20 3.4.3 To what extent are train-cyclists willing to trade-off travel time with other bike-train route choice attributes? ...... 22 3.4.4 How are bike-train route-related attributes playing a role in train-cyclists’ route choice behavior? ...... 23 4. Bicycle-train system performance ...... 25 4.1 Preparing the data ...... 25 4.2 Mapping the bike-train system ...... 25 4.3 Hypothetical travel situations ...... 28 4.4 Concluding remarks ...... 38 5. Bike-train route choice attributes ...... 40 5.1 Socio-demographic attributes ...... 40 5.2 Bicycle route choice attributes ...... 41 5.3 Transit route choice attributes ...... 44

How do train-cyclists navigate? Pedro Nieves

5.4 Concluding remarks ...... 48 6. Trade-offs between travel time and bike-train route choice attributes...... 50 6.1 Socio-demographic results ...... 50 6.2 Train-cyclists trade-offs ...... 51 6.3 Concluding remarks ...... 55 7. Train-cyclists route choice behavior ...... 57 7.1 Introducing the train-cyclists ...... 57 7.2 Navigating in overlapping catchment areas at the AMA ...... 59 7.3 The role of bike-train route-related attributes ...... 61 7.4 Concluding remarks ...... 64 8. Conclusions ...... 65 9. Discussion ...... 67 9.1 Limitations and (self)reflection ...... 67 9.2 Action points for transport planning practice ...... 68 9.3 Recommendations for further research ...... 69

Bibliography ...... 70 Appendices ...... 74 Appendix A: Bicycle route choice studies ...... 74 Appendix B: Transit route choice studies ...... 76 Appendix C: Web Expert Survey ...... 79 Appendix D: Trade-off Survey ...... 87 Appendix E: Interview Structure ...... 93 Appendix F: Informed Consent Letter ...... 96 Appendix G: Total bike-train route choices for the 3rd travel scenario ...... 98

How do train-cyclists navigate? Pedro Nieves

1. Introduction

Route choices are an outcome of our everyday mobility needs. Either going to work or meeting with a friend, every decision we make to engage in society entails a route choice. In most industrialized countries, people are becoming more and more dependent on mobility in order to participate in social and economic life (Bertolini, 2012). Unfortunately, the current mobility system is dominated by motorized means of transport which contributes to a large amount of CO₂ emissions (Faiz et al. 1996) and thus compromises the public health of future generations. Cities have therefore begun to take measures for the sake of transition towards a more sustainable urban mobility system.

The integration of the bicycle with transit systems is considered as one of the utmost tactics for competing with unsustainable motorized modes of transport (Rietveld, 2000; Brons et al.,2009). Luckily, there is an increasing international trend for developing public transportation in conjunction with the bicycle (Pucher & Buehler, 2012). Bike-and-ride1 trips provide several social and environmental benefits such as reducing air and noise pollution as well as congestion-mitigation benefits for communities (Martens, 2004; Krizek & Stonebraker, 2010). Various scholars have focused on the added value that the bike provides as a ‘feeder’ mode to public transport (e.g. Martens, 2007; Pucher & Buehler, 2009; Cervero et al., 2013; Halldórsdóttir et al., 2017), however, none of these studies accounted for the distinct characteristics that arise by the incorporation of the bicycle with transit systems.

On the contrary, Kager et al. (2016) addressed this matter by proposing the combined use of cycling and transit from a holistic approach stating that both systems ought to be considered as a particular mode of transport instead of two independent transport modes. This mode was conceptualized as the ‘bicycle- train system’ with regards to the underlying, distinct mechanism that it provides. Consequently, in order to upscale the usage of this system, research has given attention to modeling the route choices for both cyclists and transit users. Nonetheless, there is a limited amount of studies in which route choices are modeled for bike-train journeys (e.g. Fiorenzo-Catalano et al., 2004; La Paix & Geurs, 2015).

More significantly, there is also a lack of knowledge of how and why do bike-train users2 make route choices in accordance with the synergies caused by the bicycle-train combination. In a context with a highly developed bike-train infrastructure, the speed and spatial reach of the train combined with the flexibility and high accessibility of the bicycle has direct implications on train stations, not only extending their coverage (Advani & Tiwari, 2006) but also creating overlaps of the catchment areas (Kager & Harms, 2017) in which users can have more than one station to select from (Kager et al., 2016). Understanding

1 According to Martens (2007), bike-and-ride is a form of mobility which alludes to the joint use of the bicycle with public transportation (e.g. trains and buses). 2 For the purposes of this thesis, bike-train users or bike-and-ride commuters (i.e. people that make a trip using the bicycle and the train in combination) will also be regarded as train-cyclists.

How do train-cyclists navigate? Pedro Nieves this from the perspective of route choice is crucial because it can potentially reveal unknown patterns of travel behavior which can inform the transport planning practice. From a policy perspective, this research could contribute to the fields of integrated and sustainable transportation by encouraging an integration of urban planning and operation of both the bicycle and the train system with the development of effective bike-train policy initiatives. Lastly, the research findings could potentially encourage the idea of creating a route choice model of the bike-train system which is currently nonexistent.

The ultimate goal of this study is to inform the big theoretical debate on how do people make route choices by zooming in into the bike-train system. Such a system can help to amplify the different conceptions related to how people move within cities and around city-regions. In addition, the main objective is to enhance the understanding of factors that influence train-cyclists route choice behavior in order to better cater for their (travel) needs. Finally, it is also important to highlight that route choice has not been studied in a context where many available options are as the bike-train mode allows. Thus, the following thesis will further explore this by taking the Amsterdam Metropolitan Area in The Netherlands (NL) as a study area because of its relative maturity in bicycle-train integration. This will be done by applying a mixed methods research design addressing the following research question: How do train- cyclists make route choices while using the bike-train system?

How do train-cyclists navigate? Pedro Nieves

2. Theoretical Framework

This chapter outlines the theories and concepts which underlie the main research question of the thesis. Section 2.1 provides an overview of the bike-train system and its distinct characteristics. Section 2.2 discusses route choice behavior theory followed by an extensive literature review that was conducted in order to understand the state of knowledge concerning both cycling and transit route choice studies. Lastly, section 2.3 exhibits a preliminary conceptual scheme which was created based on the findings of the route choice studies.

2.1 Bike-train system Transportation is a system of utter importance for improving society’s welfare and wellbeing (Bertolini, 2012). As Meyer and Miller (2013:2) note, a system can be defined as “a group of interdependent and interrelated components that form a complex and unified whole intended to serve some purpose through the performance of its interacting parts.” This overarching definition was applied to transport systems in general in order to address their different performance characteristics based on travel speed and level of accessibility. The concept of speed indicates the amount of space that can be traveled in a particular amount of time whereas accessibility refers to the capacity an individual has to reach different locations and accomplish a social and/or economic activity (Rodrigue et al., 2006; Ferreira et al., 2017). When it comes to transport planning, both concepts are considered to be the most significant performance measures in determining the competence of a system for providing opportunities for mobility (Bertolini & le Clercq, 2003; Meyer & Miller, 2013).

From this system perspective, Kager et al. (2016) coined the term ‘bicycle-train system’ to distinguish a particular mode of transport whom performance integrates both the flexibility and high accessibility of the bicycle with the high speed and spatial reach of the transit system (Figure 1). ‘Train’ in this concept refers too high capacity transit services as defined by Meyer and Miller (2013:10) which includes rapid transit services that carry a high volume of passengers and whom networks connect central cities with regional centers. Thus, the concept excludes ‘feeder’ transit services such as trams and metro which generally have a lower speed. Therefore the differentiation between train versus ‘feeder’ services is based on whether a particular transit system is capable to amplify the traits of the bicycle at the level of a trip chain (Kager et al., 2016:210).

Furthermore, a journey made by the bicycle-train mode has three trip segments: (1) the home-end trip (or access travel) between the origin and the access station, (2) the train trip (or ‘main travel’) between the access and egress station, and (3) the activity-end trip (or egress travel) between the egress station and the destination (Ibid., p. 210). According to this, a bike-train trip was conditioned to whether or not it includes a combination of one or more trips made by train as part of the main travel and to whether or

How do train-cyclists navigate? Pedro Nieves not the home-end and/or activity-end trip is comprised of a cycling journey (Ibid., p. 210). Overall, this modal integration creates a powerful symbiotic relationship in which the bicycle and the train benefit from each other’s supply components.

Figure 1: The bicycle-train mode based on travel speed and level of accessibility as defined by Kager et al. (2016) and adapted from Meyer & Miller (2013).

Perhaps the greatest potential that the bicycle-train has is related to the synergetic effects that it provides to its users, a phenomenon that has been disregarded in the literature. Although Pucher and Buehler (2009:101) and Krizek & Stonebraker (2010:166) acknowledged that the integration of cycling with public transport creates synergies, they did not further elaborate on how they manifest in reality. On the contrary, Kager et al. (2016:212) outlined the following modal performance dimensions as potential bike- train synergies3:

(1) Speed and spatial reach: The high speed and spatial reach of the train compensate for the low speed and reach of the bicycle as people tend to cycle less as travel distances increase (Broach et al., 2012; Ton et al. 2017). (2) Adaptability: Cycling is a highly individualized means of mobility which offers door-to-door accessibility and more flexibility in departure time, speed, and route choice whereas railway transport is spatially confined to train cars. (3) Activity chain: Traveling by train provides the opportunity to fulfill other activities (e.g. reading, working) while cycling is confined to act of mobility. On the contrary, cycling itself is an activity that offers physical and mental health benefits (Garrard et al. 2012).

3 These three (3) synergies are an adaptation of the five (5) modal performance dimensions outlined by Kager et al. (2016:212).

How do train-cyclists navigate? Pedro Nieves

All these characteristics are precisely what makes the bike-train a distinct mode of transportation. In accordance with the first synergy, the speed and spatial reach of the bicycle has direct implications on train stations by extending their coverage area4 (Kager & Harms, 2017). This in turn creates overlaps between the stations’ catchment area in which train-cyclists may choose between more than one station according to a variety of reasons which can be related to either the services and characteristics that a station offers (La Paix & Geurs, 2015), to individual preferences (Meyer & Miller, 2013; Kager et al., 2016) and/or as a response to unexpected mishaps (Halldórsdóttir et al., 2017) such as weather or disruptions in the system5. By implication, the overlap of stations leads to overlapping routes which increases the variety of options to pick from when traveling to or from a station (Hoogendoorn-Lanser & Bovy, 2007). Bike-train synergies can thus help to understand train-cyclists’ route choice behavior by looking deeper into the complexity of catchment areas due to the overlaps of train stations.

2.2 Route choice behavior Understanding route choice behavior is pivotal to disclose travelers’ preferences in order to improve the predictive power of traffic forecasting models (Hood et al., 2011; Anderson et al., 2017). The process of choosing a specific route for a journey from a diverse set of routes is commonly regarded by scholars as a ‘black box’ (Figure 2), that is, as a complex travel phenomenon whose underlying mechanisms are not readily understood (Bovy & Stern, 1990; Chang & Chen, 1995). In order to address this, Bovy and Stern (1990:30) assert that individual route choice is a rational behavior mainly related to the following statements:

• the traveler, with his/her subjective needs, experiences, preferences, perceptions, etc.

• the physical environment, with its objective Figure 2: A general scheme of route selection as proposed by Bovy and Stern (1990:24). opportunities and their characteristics

In the case of the former, a subjective environment is highlighted as a domain which has an influence over how people navigate. This is partly determined by travelers’ socio-demographic backgrounds such as their sex, age, and employment status (Antipova et al., 2011). Nevertheless, route selection is also understood

4 The catchment area of train stations is generally defined based on a walking distance of approximately 0.5 to 1 km (Daniels & Mulley, 2013). On the contrary, cycling can cover up to three times this distance (Kager & Harms, 2017) which by implication increases stations’ catchment area. 5 The study of how disruptions in the system influence bike-train route choice is beyond the scope of this paper.

How do train-cyclists navigate? Pedro Nieves as a personal matter in which individual variations in terms of travel preferences6 will befall and thus cannot be strictly reduced to observable personal characteristics (Bovy & Stern, 1990). On the other hand, travelers’ prior experiences also play an important role when choosing a particular (set of) route(s). The feedback gained from the usage of previously selected routes between origins to destinations informs a traveler about the different alternatives that he/she has at his/her disposition. In turn, the traveler gains a certain route knowledge by learning about the routes’ “relevant attributes that influence [his/her] trade- off and choice” (Ibid. p. 32).

However, the traveler in question will not necessarily know the total amount of available route options he/she has whilst the ones that are known will not always be considered as genuine alternatives (Xu et al., 2011). Here is where the physical environment comes into to the picture which includes the built-up environs, the transport network infrastructure, and contextual occurrences (Bovy & Stern, 1990). After the traveler learns the different set of routes that he/she is able to choose, a ‘factor-importance- hierarchy’7 is made based on their various network characteristics in order to filter8 them and therefore decide which are more suitable and convenient for his/her journey (Ibid.). In this sense, travelers’ route choice behavior with regards to their subjective needs, experiences, and preferences is derived from the physical environment and its objective opportunities.

Furthermore, route choice models9 have been created in order to predict the flows on specific links of a transport network by identifying the attributes that influence observed behavior and therefore determine the relationship between route choice behavior and explanatory attributes (Ibid.). Among these models, logit and probit approaches10 are perhaps the most commonly used to predict the conditional probability of selecting a route from an identified choice set of origin-destination routes. The underlying logic of most available route choice models has its grounds on random utility theory11 which is based on the assumption that every person is a “rational decision-maker, maximizing utility relative to his or her choices”12 (Cascetta, 2009).

6 Travelers’ preferences are to some degree contingent upon their purpose for doing the journey (e.g. going to work or for leisure). 7 According to Bovy and Stern (1990), this term refers to a route choice process in which travelers rank their route alternatives taking into consideration their network characteristics in order to determine which route(s) he/she will select. Likewise, this hierarchy is considered to be the end state of travel decisions which comes after travelers’ destination choice and mode choice. 8 This filtering procedure has two dimensions: first, there is a perception filter in which the traveler has some degree of awareness of the existing route alternatives and their characteristics; and second, there is an evaluation filter in which a trade-off is made based on these perceptions which are then converted into a desirability scale (Bovy & Stern, 1990:33). 9 Some route choice models are also labeled as ‘discrete-choice models’ (Bovy & Stern, 1990). 10 Logit and probit models are both statistical models which account for behavioral variations such as route choice and mode choice. 11 This theory goes by the hand with the popular logic between transport economists in which travel time is regularly understood as wasted time based on financial terms. 12The conception that travelers generally tend to behave rationally has been confronted by a handful of transport scholars whom implicitly argue that irrationality is also constitutive of travel behavior (e.g. Bonsall & Cho, 2000; Avineri & Prashker, 2006; Xu et al., 2011).

How do train-cyclists navigate? Pedro Nieves

The following subsections will discuss the outcomes of several bicycle and transit route choice studies in order to identify the route-related attributes which were used for predicting route choice behavior. Appendix A and B exhibits an overview of all these studies.

2.2.1 Bicycle route choice There is a great diversity of factors that are commonly used for modeling bicycle route choice which can be grouped into socio-demographic and network attributes. In the case of the latter, cyclists’ sensitivity towards motorized traffic volume and steep hills (slope) has been found which indicates that they will tend to favor routes that have a flat topography and with a small amount of motorized vehicles passing by (e.g. Sener et al., 2009; Menghini et al., 2010; Hood et al., 2011; Beheshtitabar et al., 2014). Additionally, the findings presented by Broach et al. (2012) suggests that routes with fewer stop signs and traffic lights were preferred by cyclists, that they were also sensitive to travel distance, and that bike boulevards have a high value to cyclists during their journey. Furthermore, other studies have found how roadways’ speed limit and the absence or presence of a bike lane are likely to have a negative impact in bicycle route decision making (Hunt & Alberta, 2001; Casello & Usyukov, 2014).

On the contrary, in a recent study conducted by Ton et al. (2017), cyclists were found to be insensitive to separated bicycle paths and to motorized traffic yet readily affected towards contextual attributes such as rain and sunset/sunrise times. In addition, their results indicate that cyclists tend to minimize travel distance and the number of intersections per kilometer. On the other hand, cyclists’ characteristics such as age, gender/sex13, and cycling experience were used in some of these studies although their results suggest that these attributes have a lower impact upon bicycle route choice behavior as opposed to network attributes. Lastly, most of these studies strongly suggested either explicitly or implicitly that travel time is the most important attribute having an influence upon bicycle route choice behavior. Table 1 exhibits an overview of all the attributes that were identified as the most influential and the ones which were also repeated in the studies. 14

Table 1: Potential determinants of cyclists’ route choice behavior, based on findings of the above-cited studies.

13 Some studies did not differentiate between these two concepts, however, there is a distinction. When speaking of gender, it refers to the socially-constructed subjectivities used to express ones’ identity whereas sex expresses the physiological and biological differences (internal and external) that structure human corporeality (see Butler, 1990; Wittig, 1993; Preciado, 2002).

How do train-cyclists navigate? Pedro Nieves

2.2.2 Transit route choice Previous research has identified a fair amount of attributes that may be influencing transit route choice behavior which can also be grouped into socio-demographic, network, and contextual attributes. Gentile et al. (2005) used line waiting times and general headway distribution as route choice determinants for transit users. Their findings indicate that the availability of online information determines the probability of boarding a particular train at a station. Hoogendoorn-Lanser and Bovy (2007) estimated multimodal route choices considering a wide range of attributes for the three trip segments of train travel. Their main results suggest that overlaps in train journeys (the ‘main travel’) are valued positively which thus indicates that overlapping train routes are considered to be more attractive.

In another study conducted by Eluru et al. (2012), network attributes such as the number of transfers and waiting times as well as socio-demographic attributes were used to model transit route choices. The findings suggest that train users tend to favor fewer transfers which also have lower waiting time. However, as age increases, there appears to be less sensitivity towards waiting times. Moreover, Brands et al. (2014) study focused on network attributes taking into consideration the type of station, number of stops, and vehicle’s departure time. The general results insinuate that travel time is the principal factor influencing transit route choice. On the other hand, La Paix and Geurs (2015) study offer some interesting insights with regards to station choice. Their findings inform that the provision of unguarded bicycle parking facilities in stations may encourage travelers to reach a specific transit station and that car availability negatively influences a bike-train combination.

Other network attributes such as in-vehicle travel time and transfer times have also been found to be important factors determining transit route choice (e.g. Brahmaiah et al., 2017; Anderson et al., 2017) Lastly, Xu et al. (2018) focused on network attributes including in-vehicle crowding as a contextual attribute. Their findings suggest that crowding is also another important factor influencing route selection because it causes train delays and thus increases either in-vehicle travel time or transfer times. All-in-all, the majority of these transit route choice studies suggest that train commuters are inclined to select route alternatives which minimize their travel time. Table 2 shows an overview of the most influencing transit route choice attributes which were considered in these studies

Table 2: Potential determinants of transit users’ route choice behavior, based on findings of the above-cited studies.

How do train-cyclists navigate? Pedro Nieves

2.3 Conceptual framework (preliminary) The scheme below (Figure 3) was created to represent the preliminary conceptual framework of this thesis. The main objective of this scheme is to provide an overview of latent route choice attributes from the perspective of the bike-train system which altogether may be influencing train-cyclists route choice behavior. Further on, all bicycle and transit attributes consider in this scheme will be filtered in order to obtain a final conceptual scheme of bike-train route choice.

Route choice scholarship suggests that diverging factors are known to be influencing bicycle and transit route choices. In case of the former, network and contextual attributes appeared to be having a greater impact upon cyclists such as traffic volume, presence or absence of a bike lane, slope, and weather conditions. On the other hand, transit users’ route choices seem to be influenced mainly by network attributes such as the number of transfers and the type of station. For the socio-demographic attributes, they were the least ones suggesting a significant influence according to the overall results. More significantly, the findings from nearly all the route choice studies that were covered indicated that travel time is the utmost factor which plays a role in route decision making.

Figure 3: Preliminary conceptual scheme for this thesis. Source: Author’s illustration.

How do train-cyclists navigate? Pedro Nieves

3. Methodology

This chapter aims at outlining the systematic phases undertaken to conduct this research. Section 3.1 starts off by presenting the problem guiding the main objective of the thesis. Section 3.2 shows the main research question followed by four sub-questions whereas section 3.3 briefly discusses the study area selected for this study. Finally, section 3.4 exhibits the chosen research design and explains the methods chosen to assess each of the research sub-questions.

3.1 Problem Statement Several studies have tried to explain route choices by focusing on socio-demographic, network, and contextual characteristics of hypothetical or observed routes while disregarding the actual process of selecting a particular route. This ‘black box’ requires more theoretical development relating to its inner performance (Bovy & Stern, 1990) which becomes even more complex when there are overlaps between train stations’ catchment area. The phenomenon of overlapping stations has far-reaching implications for train-cyclists’ travel behavior because it increases the availability of routes to choose from (Kager & Harms, 2017) and, by implication, increases the capacity to personalize the whole journey based on various route-related attributes. Nevertheless, none of the studies mentioned above referred to the overlap of stations’ catchment area as an indicator that has an impact on either bicycle or transit route choice. There is therefore a lack of knowledge regarding how these overlaps are actually having an influence on route choice behavior.

By the time of writing, research on bike-transit route choice was found to be very limited. The literature clearly evidenced that bicycle and transit route choices are often modeled separately. In the same manner, multi-modal route choices have also been modeled but not exclusively for the bicycle-train system which by definition considers a journey made by bike and train as a single mode of transport. Even though Brands et al. (2014) examined multiple routing considering the bicycle as a feeder to transit systems, their route choice model in the end disregarded the bicycle because they focused only on interregional journeys. On the other hand, La Paix and Geurs (2015) model acknowledge bike-train share but only to estimate mode choice rather than route choice15.

Furthermore, in current route choice scholarship, travel time is widely considered as one, if not the most, significant factor influencing route choice behavior (Sun & Zu, 2012), especially during peak hours (Raveau et al., 2014; Ton et al., 2017). The theoretical insights suggested by the findings of the above-outlined route choice studies highlighted that it is usually a matter of the fastest route. However, when considering the complexity of catchment areas due to station overlaps, travel time may not be the greatest determinant for train-cyclists because of the variety of bike-train routes and the diversity of their

15 Although their study focused on mode choice, the route-related attributes that were used and their derived results exhibit crucial information that could inform potential bike-train route choices.

How do train-cyclists navigate? Pedro Nieves attributes. Therefore, understanding route choices within the complexity of the bike-train system seems to be both an interesting and urgent next line of research.

3.2 Research Question(s) In order to address the above problem statement, it is necessary to explore travelers’ process of making bike-train route choices with regards to the immense diversity of route-related attributes. Thus, the research question of this thesis is:

How do train-cyclists make route choices while using the bike-train system?

To answer this main question, the following sub-questions were drafted:

(1) How does the current bike-train system perform? (2) What are the (in)direct attributes which influence potential bike-train route choices? (3) To what extent are train-cyclists willing to trade-off travel time with other bike-train route choice attributes? (4) How are bike-train route-related attributes playing a role in train-cyclists’ route choice behavior?

These research questions are outlined in such a way to render a fruitful understanding of the behavioral complexity concerning train-cyclists’ route choices based on the theoretical underpinnings of the bike- train system. Likewise, having four sub-questions was chosen in order to explore the full range of the main research question considering how there is a lack of knowledge concerning bike-train route choices which are made in overlapping catchment areas.

3.3 Study Area The Amsterdam Metropolitan Region (AMA; Figure 4) is a city-region16 surrounding the city of Amsterdam, the capital of the NL. It is comprised of 33 municipalities and extends over two Dutch provinces composed of North Holland and Flevoland. Approximately 2.2 million people reside within this area which accounts for over 11 percent of the total Dutch population. The AMA is also the NL’s most robust economic region playing a central role in the international market. Furthermore, there are a total of 52 Nederlandse Spoorwegensystem (NS) railway stations within the AMA’s administrative boundaries of which inhabitants can use to easily move between municipalities. This amount accounts for over 13 percent of the total amount of train stations in the NL. Additionally, only 20 municipalities from the AMA currently have stations within their territories.

16 This concept is a political and analytical transition towards a new regionalism, coupled to encourage a re-escalation of State intervention. A city-region is generally defined based on adjacent built-up areas with high shares of economic and traffic flows (see Davoudi, 2009).

How do train-cyclists navigate? Pedro Nieves

Figure 4: Study area of the thesis. Source: Author’s illustration.

Since the beginning of the 1990s, the Dutch national government has dedicated plenty of its investments towards the integration of the bicycle with the NS railway system (Martens, 2007). Of all the 17 million inhabitants of the NL, it is estimated that around 1.2 million people are daily train commuters of which 47 percent use the bicycle to access stations and around 12 percent cycle from the egress stations (Kager et al., 2016:208). By the time of writing, there are a total of 398 railway stations17 in the NL and it is estimated that 69 percent of the Dutch population resides within a 5 km radius of the stations in cycling distance. Also within this reach, there are usually two or more stations of which to choose from which are interconnected with the bicycle network (Ibid., p. 213-214). Overall, this data strongly suggests why the Dutch have a high bike-train ridership. Given these points, the AMA was selected as a study area because of its relative maturity in bicycle-train integration.

17 This amount excludes both metro and tram stations.

How do train-cyclists navigate? Pedro Nieves

3.4 Research Design The empirical strategy chosen for this thesis was a mixed methods research approach. As Bryman (2012:628) argues, this research strategy is useful because it yields the combination of both quantitative and qualitative methods in such a way that “would seem to allow the various strengths to be capitalized upon and the weaknesses offset somewhat.” The strategy was selected because it allowed triangulating the data by, on the one hand, acquiring quantitative data by conducting web-based surveys but also, on the other hand, by having qualitative insights from interviews as a data source. The following sub-sections were divided per research sub-question explaining their methods of both data collection and analysis. Table 3 shows the summary of the research design selected for this study.

Table 3: Research design for this thesis.

3.4.1 How does the current bike-train system perform? In order to have a sound knowledge of the bike-train mode, it is necessary to illustrate its current system performance and the route opportunities it offers to its users. Therefore, the units of analysis for this research question were the NS railway stations and potential bike-train route choices.

To answer this sub-question, two phases were undergone. Firstly, Esri’s cartographic software known as Geographic Information Systems (GIS) was used as a research tool18. According to Lejano (2008), some of the traits of using GIS are its potential to uncover problems that arise in the planning profession and its capacity to visualize reality by mapping a large number of datasets. This software was thus chosen because it enabled to easily illustrate the system performance of the bike-train mode within the AMA. The data of the NS railway system required to answer this question was collected from a GIS open-data platform of a Dutch organization called Imergis19 which provides a wide variety of data sets of the Netherlands. The data was then analyzed by conducting a density, an overlay, and a proximity analysis using various

18 https://www.esri.com/en-us/what-is-gis/overview 19 http://www.imergis.nl/asp/47.asp

How do train-cyclists navigate? Pedro Nieves geoprocessing tools20 which results where then cartographically represented. The resulting maps were then used for the empirical strategy of the last research sub-question.

Thereafter, during the second phase of this question, three hypothetical travel situations were determined for train-cyclists that live in different cities of the AMA. This was done by estimating how much travel time did it take train-cyclists to get from their origins to their destinations. Data of both home- end and activity-end trips (cycling routes) was appraised using Google Maps™’ route planning service21 as a tool and its embedded algorithm which represents routes graphically and provides an estimation of the travel time per routes available that each traveler has at his or her disposal. On the other hand, for the main travel (train routes), the NS’s journey planner service22 was used to determine the fastest train trips. These two tools were chosen because they provided real-time travel data associated with rail tracks, roads, and traffic restrictions, therefore reassuring the validity of the travel scenarios (Santos et al., 2011; van Veenendaal, 1993). Afterward, both ArcGIS for Desktop and ArcGIS Online were used to map all the routes that each train-cyclist had available per trip segment.

The travel times for each trip segment were expressed in minutes and were then aggregated to display the totality of bike-train route options. In sum, the general aim of this question was to set the scene for the bike-train mode by exhibiting its system performance and by showing the number of similar route choices a train-cyclist has in terms of the travel time that he/she incurs when traveling from point A to point B.

3.4.2 What are the (in)direct attributes which influence potential bike-train route choices? Travelers route decision making is contingent upon a diverse set of route-related attributes which are (in)directly considered based on their preferences and needs (Bovy & Stern, 1990). Consequently, the units of analysis for this sub-question were the bicycle and transit route choice attributes outlined in the preliminary conceptual scheme (Figure 3). The main objective was to filter these attributes by conducting a web-based expert survey (see Appendix C) as a secondary data gathering method using the free online platform called Google Forms23. This method was chosen because it proved to be a fast and convenient strategy to acquire data from experts in a short amount of time considering their tight schedules. Furthermore, the experts invited to participate were those well-acquainted with bike-train research and developments in the NL. By using a Likert scale24 (Table 4), these experts were asked to rank each of Table 4: Likert scale used in the expert survey.

20 http://desktop.arcgis.com/en/arcmap/10.3/main/analyze/geoprocessing-tools.htm 21 https://www.google.com/maps 22 https://www.ns.nl/en/journeyplanner 23 https://www.google.com/forms/about/ 24 The Likert scale used in this study had seven values based on levels of importance as suggested by Vagias (2006). A scale of 7 was selected because it allows for better data distribution in the case of small samples (Finstad, 2010).

How do train-cyclists navigate? Pedro Nieves the route choice attributes in terms of importance based on their influence upon train-cyclists’ route choice behavior. A Likert scale was chosen because it is useful for revealing different degrees of opinion (Joshi et al., 2015) with regard to a specific topic, in this case, for route choice. Table 5 shows the list of the experts and their respective email addresses.

Luca Bertolini is a professor of urban and regional planning and director of the Centre for Urban Studies at the University of Amsterdam. Both his research and teaching are mainly focused on the integration of transport and land-use planning and on different ways for improving the interaction between theory and practice. Wouter de Koning, current head of Mobility Services for the NS, is responsible for the operation and development for all the NS stations in the NL. He oversees different tasks such as the management of bike parking at stations as well as the management of the ‘OV-fiets’25. Roland Kager is a data analyst of mobility, land-use, cycling, and transit at a Dutch company called Studio Bereikbaar26. His work mainly focuses on tracking and monitoring travel behavior from the combined use of bike and train. Likewise, he and Luca Bertolini are co-authors of a paper27 in which they coined the concept of ‘bicycle-train system’.

Lucas Harms is a senior researcher at the Kennisinstituut voor Mobiliteitsbeleid (KiM; Netherlands Institute for Transport Policy Analysis) working in the field of transport and mobility. He is also co-writer of a paper28 with Roland Kager in which they provided an overview of the synergies that arise from the integration of cycling with transit. Niels van Oort works as an assistant professor at Delft University of Technology and he is also a public transport consultant at a Dutch consultancy company called Goudappel Coffeng. His main area of expertise revolves around public transport planning. Finally, Olaf Jonkeren is also a researcher at the KiM working with urban transportation in fields related to freight, maritime, and inland waterway transport. He was also the leader of a project that studied bike-train users in the NL.

Table 5: List of experts with their respective emails.

After collecting all the data from the web survey, it was then analyzed using descriptive statistics with the help of Microsoft Excel in order to filter the route choice attributes based on their rankings. This method of analysis was selected because it allowed determining which attributes were considered most important according to their high percentage of responses and to their average values. Afterward, Tableau Software

25 The OV-fiets is a Dutch bicycle rental program available in most of the NS train stations. 26 https://www.studiobereikbaar.nl/ 27 Kager et al. (2016), Characterisation of and reflections on the synergy of bicycles and public transport, Transportation Research Part A, Volume 85, pp. 208-219. 28 Kager, R. and Harms, L. (2017) Synergies from improved bicycle-transit integration: Towards an integrated urban mobility system. International Transport Forum. pp. 1-27.

How do train-cyclists navigate? Pedro Nieves was used to create graphs representing the results of the analyses. Lastly, at the end of the survey, experts were asked to mention the existence of other underlying attributes that were not included in the preliminary conceptual scheme. This new set of bike-train route choice attributes was added to the list. As a result, a final conceptual scheme was created based on all these attributes which were then used in the empirical strategy of the remaining sub-questions.

3.4.3 To what extent are train-cyclists willing to trade-off travel time with other bike-train route choice attributes? Route choice scholarship suggests that travel time has the utmost influence on travelers as opposed to other route-related attributes. Therefore, in order to understand how these other bike-train route attributes may be exchanged with travel time, the units of analyses chosen to assess this sub-question were train-cyclists. These users were those who live, work, and/or study in the AMA which travel using the bicycle to reach an access station and/or to travel from an egress station, and whom main travel is composed of a train journey using the NS railway system.

To collect the quantitative data required to answer this question, Google Forms was also used to create another web-based survey (see Appendix D). This method of data collection was selected because one of the advantages of web surveys are their ability to filter questions in order to better target respondents (Bryman, 2012). In turn, this proved to be useful by assuring that respondents complied with the above- stated conditions and therefore excluding those who did not bear a likeness to the type of traveler using the bike-train mode. Likewise, respondents were recruited using a virtual snowball sampling technique (Baltar & Brunet, 2012) by posting the link of the survey on Facebook and Twitter and by distributing small papers with the link of the survey to cyclists who accessed or egressed from Amsterdam Lelylaan station and Amsterdam Centraal station29 . The survey was open for responses for a period of one month, from the 11th April 2018 until the 11th May 2018. To incentivize responses, participants were offered the opportunity to win one of three €10 e-gift cards for any purchase on Amazon.com.

Furthermore, the web survey was structured in two sections. The first part had different questions which were drafted according to the socio-demographic route-related attributes whereas the second part had 14 statements with a ‘fill-in the blanks’ format in which respondents had to choose how much travel time between 0 to 30 minutes were they willing to cycle in order to compensate other bike-train route choice attributes. Additionally, the survey filtered responses according to the type of bicycle30 that participants frequently used. This was done as a strategy to differentiate the trade-offs between bike-train users.

29 These stations were chosen because of their distinct characteristics. Amsterdam Lelylaan is a local station which only offers sprinter services whereas Amsterdam Centraal is an intercity station which also has different facilities. Also, these stations were selected because they were the ones that I frequently visited while conducting this study. 30 The following types of bicycle were considered: ordinary bike, e-bike, OV bike (OV-fiets), folding bike (vouwfiets), cargo bike (bakfiets), or a swap bike (swapfiets).

How do train-cyclists navigate? Pedro Nieves

After collecting all the secondary data, it was analyzed using descriptive statistics by showing the percentage of responses for each of the socio-demographic questions and by exhibiting the average values of the travel time that train-cyclists were willing to trade-off when making their route choices, whilst also showing the percentage of responses per each attribute. A trade-off analysis was chosen because it allowed to recognize which attributes were most valued by respondents by using a discrete choice approach (McCullough, 1998). In sum, the main purpose of this sub-question was to show the hierarchy between the attributes, that is, to see how train-cyclists rank different bike-train route attributes based on their willingness to cycle an extra amount of time. The assumptions derived from these results were triangulated with the insights provided from interviews of the following research sub- question.

3.4.4 How are bike-train route-related attributes playing a role in train-cyclists’ route choice behavior? The units of analysis chosen to assess this question were also bike-train users. In order to gather the qualitative data required to generate accounts of route choice, a total of five semi-structure key-informant interviews (see Appendix E) were conducted for people that lived, worked, and/or studied at the AMA. Participants were recruited using a snowball sampling technique by contacting respondents from the trade-off survey who provided their email addresses because of their interest to participate in a follow- up interview. The selection of particular interviewees was based on maximizing the diversity of train- cyclists’ socio-demographic backgrounds as a means to understand different route decision scenarios (Bryman, 2012). They were also selected because all of them frequently traveled to or from overlapping catchment areas. Likewise, pseudonyms31 were adopted to preserve the anonymity of all participants and to follow the line of reasoning used to explain their travel experiences.

Furthermore, these in-depth interviews covered a variety of open-ended questions which were structured on the basis of the findings derived from the previous research sub-questions to tighten their link. During the first phase, participants were asked about the answers they provided for the questions related to their socio-demographic attributes. For the second phase, participants were shown the different maps created to illustrate the bike-train’s system performance and had to reflect upon them based on their previous travel experiences. The third and final phase contained several questions in which interviewees had to reminisce their route choices according to each bike-train route-related attribute. In general, the decision to conduct semi-structured interviews was done because of their capacity to unravel insights with regards to how people perceive the object of study (Bryman, 2012), in this case, to how train-cyclists perceive route decision making according to bike-train route-related attributes. These interviews offered a compelling potential for being attuned to the uniqueness of different route scenarios.

31 This idea was inspired from reading a recent book written by David Bissell (2018), a thorough qualitative study which explores people’s everyday transit commuting experiences. Likewise, this book in general gave me the ideas and inspiration to draft the findings for this research sub-question.

How do train-cyclists navigate? Pedro Nieves

Each interview lasted approximately one hour. Once they were all carried out, a verbatim transcription32 was done for the five of them by capturing the exact wording and reactions of participants including for example laughter, pauses, and moments of silence. Thereafter, a line-by-line analysis of the interview transcripts was conducted based on the codes outlined in the operational scheme (see Table 6). Different quotes were gathered from the interviews which were then assembled according to these codes. As Charmaz (2006:50) argues, early coding of interview data helps to “gain a closer look at what participants say and, likely, struggle with.” In turn, this method was chosen because it allowed to refocus the interviews as they were being conducted. In the end, interpretations and conclusions were drawn on the basis of all coded data.

Table 6: The operational scheme for this study.

32 All participants granted their permission to record the interviews, transcribe them, and to use quotes from their narratives by signing an informed consent letter (see Appendix F).

How do train-cyclists navigate? Pedro Nieves

4. Bicycle-train system performance

This chapter will portray the system performance of the bike-train mode by discussing the following research sub-question: How does the current bike-train system perform? The succeeding sections represent the different stages that were undergone to answer this question. Section 4.1 briefly explains the data processing that was realized to empirically assess the question. Section 4.2 exhibit the GIS analyses that were carried out for the NS railway stations located within the AMA. Section 4.3 displays three hypothetical travel situations of train-cyclists, supplemented by route maps per each trip segment. Lastly, Section 4.4 discusses the general findings.

4.1 Preparing the data All the data was collected in a shapefile format (.shp) which is mainly used for ArcGIS desktop applications. It was then exported to a geodatabase33 as feature classes34 in order to start processing the information. First, the data of all Dutch municipalities was filtered out to obtain only the municipalities that form part of the Amsterdam Metropolitan Area (AMA). This was done to have a polygon feature of the AMA as the study area which was used for the analyses conducted. Afterwards, the data with all the NS railway stations was also filtered because it included both metro and train stations as well as tram stops. A query expression was done using the ‘Select by attributes’ tool to identify only the NS railway stations. Consequently, using the geoprocessing tool called ‘Clip’, the AMA was used as a base to snip only the stations within its confines.

4.2 Mapping the bike-train system

Density analysis

The AMA has 52 NS train stations which are relatively dispersed. In order to illustrate how some of them are clustered across space, the content management system known as ArcGIS Online was used to perform a density analysis. This type of analysis takes a certain amount of point or line features and spreads their density in a given area.

Firstly, the data of the NS train stations had to be uploaded from ArcMap to ArcGIS Online. Subsequently, their density was calculated using the so-called ‘Calculate density’35 tool which, by default, calculated in square kilometers an appropriate search distance for all stations and classified the output data with 10 classes. The density was symbolized based on different shades of purple in which the darker areas represent the places where most railway stations are agglomerated (See Figure 5). The densest area was

33 http://desktop.arcgis.com/en/arcmap/10.3/manage-data/geodatabases/what-is-a-geodatabase.htm 34 http://desktop.arcgis.com/en/arcmap/10.3/manage-data/geodatabases/feature-class-basics.htm 35 http://doc.arcgis.com/en/arcgis-online/analyze/calculate-density.htm

How do train-cyclists navigate? Pedro Nieves found in the center of the AMA between the municipal borders of Amsterdam, Diemen, and Ouder- Amstel. The second densest area was located at the west of the AMA between the municipal borders of Haarlem, Velsen, and Bloemendaal.

Figure 5: Density of NS railway stations within the AMA. Source: Author’s illustration.

Overlay analysis

Train stations’ catchment area are contingent upon the mode of transport that travelers use to access them. For an average pedestrian, walking distances are generally understood to be around 0.5 through 1 km. On the other hand, cycling distances are considered to be around 5 to 7 km which by implication increases the spatial reach of stations. In order to compare both catchment areas, their level of increase was verified in terms of impact areas that the stations had in the AMA which depended on whether a person walks or cycles to/from a train station. Consequently, a buffering method was conducted targeting the 52 NS stations (See Figure 6). This was done using the geoprocessing tool called ‘Buffer’ which created two polygon features: (1) a walking catchment area of 1 km and (2) a cycling catchment area of 5 km for all the stations. Afterward, these catchment areas were overlaid on top of the whole AMA region to calculate how much area did each of them covered. When the cycling distance was estimated,

How do train-cyclists navigate? Pedro Nieves approximately 70.1% of the whole area of the AMA (1793 km²) was identified as the bike-train catchment area (See Table 7). This was around 8 times bigger than the walking-based catchment area.

Access distances Walking (1 km) Cycling (5 km) Station impact area km² 154 (8.5%) 1258 (70.1%) (% out of the whole AMA area) Table 7: Comparison of station impact area between walking and cycling distance in the AMA.

Figure 6: NS stations impact area by access distances in the AMA. Source: Author’s illustration.

Proximity analysis

The findings provided by the previous analyses already suggests how the AMA offers a great amount of NS stations of which train-cyclists can choose from when making their route choices. To illustrate how stations’ cycling-based catchment area are overlapping with each other, a buffering method was also conducted by determining a 5 km distance for all NS stations within the AMA. This was done using ArcGIS Online instead of ArcGIS for Desktop because the former provides an output feature that exhibits overlapping areas in different shades of a color. The map below (Figure 7) exhibit results which are closely related to the above density map. However, in this map it can be seen how there are two areas that have

How do train-cyclists navigate? Pedro Nieves a greater amount of train stations in terms of proximity, one at the west with 8 stations and the other one at the center of the AMA with 12 stations. These overlapping catchment areas are what characterize the system performance of the bike-train mode in terms of its competence for providing opportunities for mobility.

Figure 7: Overlap of NS railway stations’ cycling-based catchment area. Source: Author’s illustration.

4.3 Hypothetical travel situations Bike-train route choices will vary depending on train-cyclists’ living location and on their travel purpose. In order to understand how they navigate in accordance to the system performance of the bike-train system, three hypothetical travel situations were predetermined which illustrate the different route options that are available when getting from A to B. Only cycling routes equal or less than 5km were mapped whereas for the train routes only the ones with the shortest travel time were considered. The home-end and activity-end route maps below exhibit different routes as indicated by Google Maps’ app, illustrating in color red the fastest routes. Furthermore, each travel scenario has two tables, the first one showing the route options available per trip segment and the second one showing the total combination of bike-train route choices. These scenarios were differentiated using a simple/complex relation based on the amount of overlapping railway stations that each train-cyclist had in close proximity.

How do train-cyclists navigate? Pedro Nieves

1st scenario: simple-simple

Karl, a man who lives in Boswijk36, needs to make a bike-train trip to the city of Weesp in order to work. From his home he only has the option to reach the intercity station called Lelystad Centrum and can pick between two cycling routes that take him to the station. These two available bike routes overlap in some segments which can be seen in Figure 8. Consequently, for the train trip, Karl has only one train station to egress from in proximity to his workplace which is Weesp, the only local railway station at the municipality (Figure 9). The fastest way from Lelystad Centrum to Weesp takes him 37 minutes via an intercity train which then has one transfer to a sprinter train at Almere Centrum. Once in Weesp, he is also able to choose between two different bike routes (Figure 10). In total, Karl has four bike-train routes to choose from which have almost the same travel times (Table 9).

Trip side Routes Travel time (minutes) Route #1 5 Home-end trip Route #2 5 Lelystad Centrum – Weesp #1 Train trip 37 (1 transfer) Route #1 6 Activity-end trip Route #2 7 Table 8: Bike-train route choices per trip segment for traveler #1.

Bike-train routes choices Total travel time 1 Home-end route #1 + Lelystad Centrum-Weesp + Activity-end route #1 48 2 Home-end route #1 + Lelystad Centrum-Weesp + Activity-end route #2 49 3 Home-end route #2 + Lelystad Centrum-Weesp + Activity-end route #1 48 4 Home-end route #2 + Lelystad Centrum-Weesp + Activity-end route #2 49 Table 9: Total combination of bike-train route choices for traveler #1.

36 A residential district of the city of Lelystad in the NL.

How do train-cyclists navigate? Pedro Nieves

Figure 8: Home-end routes for traveler #1. Figure 10: Activity-end routes for traveler #1. Source: Author’s illustration. Source: Author’s illustration.

Figure 9: Train route for traveler #1. Source: Author’s illustration.

How do train-cyclists navigate? Pedro Nieves

2nd scenario: simple-complex

Aqua, a woman who lives in Toolenburg, Hoofddorp37 will make a bike-train journey to Amsterdam in order to meet with a friend and go to the zoo called Natura Artis Magistra (ARTIS). From her home she only has in close proximity the local railway station called Hoofddorp. Aqua can pick between three bicycle routes in direction towards this station which are not too different in travel time and are also overlapping in some segments (Figure 11). From Hoofddorp station she can choose to egress in four train stations that are near to the zoo (Figure 12). These train routes vary in travel time and some of them have transfers while only one has a direct connection. Likewise, from all the egress stations she can pick between 11 activity-end routes which have different travel times. Some of these routes are overlapping in some portions as it can be seen in Figure 13. In total, Sarah is able to choose between 33 different bike-train routes from her home to ARTIS (Table 11).

Travel time Trip side Routes (minutes) Route #1 11 Home-end trip Route #2 12 Route #3 14 Hoofddorp - Amsterdam Central Station 23 (direct) Hoofddorp - Amsterdam Science Park 36 (1 transfer) Train trip Hoofddorp - Amsterdam Amstel 37 (2 transfers) Hoofddorp - Amsterdam Muiderpoort 33 (1 transfer) Route #1 8 Amsterdam Central Station - ARTIS Route #2 11 Route #3 11 Route #1 16 Science Park - ARTIS Route #2 16 Activity-end trip Route #1 12

Route #2 12 Amsterdam Amstel - ARTIS Route #3 12 Route #1 8 Muiderpoort - ARTIS Route #2 8 Route #3 11 Table 10: Bike-train route choices per trip segment for traveler #2.

37 The main town of Haarlemmermeer municipality in the Netherlands.

How do train-cyclists navigate? Pedro Nieves

Total travel Bike-train routes choices time 1 Home-end route #1 + Hoofddorp-Central Station + Activity-end route #1 42 2 Home-end route #1 + Hoofddorp-Central Station + Activity-end route #2 45 3 Home-end route #1 + Hoofddorp-Central Station + Activity-end route #3 45 4 Home-end route #2 + Hoofddorp-Central Station + Activity-end route #1 43 5 Home-end route #2 + Hoofddorp-Central Station + Activity-end route #2 46 6 Home-end route #2 + Hoofddorp-Central Station + Activity-end route #3 46 7 Home-end route #3 + Hoofddorp-Central Station + Activity-end route #1 45 8 Home-end route #3 + Hoofddorp-Central Station + Activity-end route #2 48 9 Home-end route #3 + Hoofddorp-Central Station + Activity-end route #3 48 10 Home-end route #1 + Hoofddorp-Science Park + Activity-end route #1 63 11 Home-end route #1 + Hoofddorp-Science Park + Activity-end route #2 63 12 Home-end route #2 + Hoofddorp-Science Park + Activity-end route #1 64 13 Home-end route #2 + Hoofddorp-Science Park + Activity-end route #2 64 14 Home-end route #3 + Hoofddorp-Science Park + Activity-end route #1 66 15 Home-end route #3 + Hoofddorp-Science Park + Activity-end route #2 66 16 Home-end route #1 + Hoofddorp-Amstel + Activity-end route #1 60 17 Home-end route #1 + Hoofddorp-Amstel + Activity-end route #2 60 18 Home-end route #1 + Hoofddorp-Amstel + Activity-end route #3 60 19 Home-end route #2 + Hoofddorp-Amstel + Activity-end route #1 61 20 Home-end route #2 + Hoofddorp-Amstel + Activity-end route #2 61 21 Home-end route #2 + Hoofddorp-Amstel + Activity-end route #3 61 22 Home-end route #3 + Hoofddorp-Amstel + Activity-end route #1 63 23 Home-end route #3 + Hoofddorp-Amstel + Activity-end route #2 63 24 Home-end route #3 + Hoofddorp-Amstel + Activity-end route #3 63 25 Home-end route #1 + Hoofddorp-Muiderpoort + Activity-end route #1 52 26 Home-end route #1 + Hoofddorp-Muiderpoort + Activity-end route #2 52 27 Home-end route #1 + Hoofddorp-Muiderpoort + Activity-end route #3 55 28 Home-end route #2 + Hoofddorp-Muiderpoort + Activity-end route #1 53 29 Home-end route #2 + Hoofddorp-Muiderpoort + Activity-end route #2 53 30 Home-end route #2 + Hoofddorp-Muiderpoort + Activity-end route #3 56 31 Home-end route #3 + Hoofddorp-Muiderpoort + Activity-end route #1 55 32 Home-end route #3 + Hoofddorp-Muiderpoort + Activity-end route #2 55 33 Home-end route #3 + Hoofddorp-Muiderpoort + Activity-end route #3 58 Table 11: Total combination of bike-train route choices for traveler #2.

How do train-cyclists navigate? Pedro Nieves

Figure 11: Home-end route for traveler #2. Figure 13: Activity-end routes for traveler #2. Source: Author’s illustration. Source: Author’s illustration.

Figure 12: Train routes for traveler #2. Source: Author’s illustration.

How do train-cyclists navigate? Pedro Nieves

3rd scenario: complex-complex

Alexa, a student who lives in Planetenwijk, Haarlem, needs to make a bike-train trip to Amsterdam because she has a lecture in Roeterseiland Campus at the University of Amsterdam (UvA). Since her home is located in an overlapping catchment area, she has the option to choose between six stations which are in close proximity. To reach these stations, there are a total of 17 home-end routes which vary in travel time and most of them are overlapping with each other (Figure 14). From all the access stations, Alexa can also pick between six egress stations that are near to the UvA (Figure 15). These train routes have different travel times, some have transfers while others are direct connections. Likewise, once Alexa arrives to any of the destination stations, she has a total of 18 activity-end routes to choose from which are also overlapping in some segments (Figure 16). In total, she is able to make 304 bike-train route combinations from her home in Haarlem to the UvA. Table 13 shows only the fastest routes; Appendix G exhibits the total amount of bike-train route choices for this scenario.

Travel time Trip side Routes (minutes) Route #1 7 Haarlem Route #2 10 Route #1 6

Route #2 7 Bloemendaal Route #3 8 Route #1 15

Route #2 15 Overveen Route #3 19 Home-end trip Route #1 14

Route #2 15 Santpoort-Noord Route #3 20 Route #1 10

Route #2 11 Santpoort-Zuid Route #3 12 Route #1 15

Route #2 16 Haarlem Spaarnwoude Route #3 16 Amsterdam Zuid (2 transfers) 42 Amsterdam Muiderpoort 29 (1 transfer) RAI 48 (2 transfers) Haarlem Amsterdam Science Park 32 (1 transfer) Amsterdam Central Station 15 (direct) Amsterdam Amstel 27 (1 transfer) Amsterdam Zuid 60 (2 transfers)

How do train-cyclists navigate? Pedro Nieves

Amsterdam Muiderpoort 49 (1 transfer) RAI 70 (2 transfers) Amsterdam Science Park 53 Bloemendaal (1 transfer) Amsterdam Central Station 25 (direct) Amsterdam Amstel 38 (1 transfer) Amsterdam Zuid 50 (2 transfers) Amsterdam Muiderpoort 43 (1 transfer) RAI 54 (2 transfers) Overveen Amsterdam Science Park 50 (1 transfer)

Amsterdam Central Station 25

(direct) Train trip Amsterdam Amstel 41 (1 transfer) Amsterdam Zuid 43 (2 transfers) Amsterdam Muiderpoort 54 (1 transfer) RAI 75 (2 transfers) Santpoort-Noord Amsterdam Science Park 58 (1 transfer) Amsterdam Central Station 30 (direct) Amsterdam Amstel 43 (1 transfer) Amsterdam Zuid 62 (2 transfers) Amsterdam Muiderpoort (1 transfer) 51 RAI 72 (2 transfers) Santpoort-Zuid Amsterdam Science Park (1 transfer) 55 Amsterdam Central Station 27 (direct) Amsterdam Amstel 40 (1 transfer) Amsterdam Zuid 41 (2 transfers) Amsterdam Muiderpoort 34 (1 transfer) RAI 45 Haarlem Spaarnwoude (2 transfers)

How do train-cyclists navigate? Pedro Nieves

Amsterdam Science Park 41 (1 transfer) Amsterdam Central Station 16 (direct) Amsterdam Amstel 29 (1 transfer) Route #1 20

Route #2 20 Amsterdam Zuid - UvA Route #3 21 Route #1 8

Route #2 9 Amsterdam Muiderpoort - UvA Route #3 9 Route #1 17

Route #2 17 RAI - UvA Activity-end trip Route #3 18 Route #1 15

Route #2 16 Amsterdam Science Park - UvA Route #3 17 Route #1 12

Route #2 13 Amsterdam Central Station - UvA Route #3 14 Route #1 9

Route #2 10 Amsterdam Amstel - UvA Route #3 11 Table 12: Bike-train route choices per trip segment for traveler #3.

Total Bike-train route choices travel time 1 Home-Harlem route #1 + Haarlem-Amsterdam Central Station + Central Station-UvA route #1 34 2 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #1 43 3 Home-Overveen route #1 + Overveen-Amsterdam Central Station + Central Station-UvA route #1 52 4 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Central Station + Central Station-UvA route #1 56 5 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #1 49 6 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #1 43 Table 13: Fastest bike-train route combinations for traveler #3.

How do train-cyclists navigate? Pedro Nieves

Figure 14: Home-end route for traveler #3. Source: Figure 15: Activity-end route for traveler #3. Source: Author’s illustration. Author’s illustration.

Figure 16: Train routes for traveler #3. Source: Author’s illustration.

How do train-cyclists navigate? Pedro Nieves

4.4 Concluding remarks The system performance of the current bike-train mode appears to be relatively high in some areas of the AMA. The level of accessibility of the bicycle combined with the high speed and spatial reach provided by NS trains nearly covers the whole extent of the AMA. This mainly has to do with the fair amount of railway stations that are dispersed within the AMA’s administrative boundaries. Additionally, there appear to be two areas that exhibited a greater density which in a like manner coincided with the stations’ overlapping catchment areas. The hypothetical travel situations illustrated how in these areas the system performance of the bike-train mode was much higher because they resulted to be the most propitious context for train- cyclists in terms of the availability of route choices per trip segment.

Furthermore, the travel scenarios showed how the total amount of bike-train route choices increase exponentially based on train-cyclists’ purpose when traveling from A to B (see Table Table 14: Bike-train route choices for each travel scenario. 14). In the simple-simple scen ario, the home and work location of the train-cyclist gives him no other option but to choose the nearest station and the travel time that he can trade-off between his bike-train route options is only one minute. Therefore, the system performance of the bike-train mode in these locations appeared to be relatively low. On the contrary, for the simple-complex scenario, although this train-cyclists is only able to reach one station from her home, the location of her destination gives her the option to choose between four egress stations which by implication increases her route choices. Also, she has the opportunity to trade-off 24 minutes of her travel time between the 33 bike-train route options and 21 minutes between the four fastest routes per each train station that she can egress. Lastly, in the case of the complex-complex scenario, traveling between the two overlapping catchment areas of the AMA significantly increased the bike-train route choices that the train-cyclist is able to make. Of all the 304 route options that this traveler has, she is able to trade-off 79 minutes of travel time whereas for the fastest bike-train combinations she can trade-off 22 minutes.

All-in-all, since the route choice tables for the simple-complex and complex-complex scenarios show that travel times for most options are quite different, this indicates that travelers have the possibility to trade- off these minutes with other route-related attributes in order to satisfy their mobility needs. Additionally, considering that most home-end and activity-end routes as well as the train routes were overlapping, this suggests the potential that travelers have for personalizing their journeys (Kager & Harms, 2017). It is important to note that the exponential growth of route choices would be doubled because some travelers also cycle back to the stations and may even decide to do a trip chain while navigating (Kager et al., 2016).

How do train-cyclists navigate? Pedro Nieves

In sum, it appears that living or traveling to/from overlapping areas offers a portfolio of diverse route options in which train-cyclists can make all kinds of combinations based on different bike-train route choice attributes.

How do train-cyclists navigate? Pedro Nieves

5. Bike-train route choice attributes

This chapter outlines the various bike and transit route choice attributes discussed in the theoretical framework and translates them into bike-train route choice attributes by discussing the following research sub-question: What are the (in)direct attributes which influence potential bike-train route choices? Of all the six experts targeted to assess this question, only five of them completed the online survey. The following sections are divided per each group of attributes showing their descriptive outcomes. The last section outlines the extra set of attributes suggested by the experts whilst discussing the overall results.

5.1 Socio-demographic attributes A total of six socio-demographic attributes were identified from the literature which could be having an influence over train-cyclists route choice behavior. In the case of ‘Age’, it was mostly ranked as neutral whereas ‘Gender’ was considered to be the less influencing attribute with 40 percent of responses ranking it as ‘not at all important’. The ‘Employment status’ was also predominantly ranked as neutral while ‘Parental status’ was considered to be of both low importance and moderately important. The two attributes which were considered to have more influence upon train-cyclists route choices were ‘Cycling experience’ and ‘Car availability’. From a scale of 5 through 7, both attributes received a total of 60 percent of responses indicating a higher degree of importance. In sum, Table 15 shows the percentage of responses for all socio-demographic attributes.

Table 15: Percentage of responses with regard to the socio-demographic attributes.

The average values per each attribute can be seen in Figure 17 represented by a gray-color circle. This bar graph visually summarizes the results for this group of attributes. Each attribute has a bar which is segmented by the percentage of their level of importance. The reason why some attributes do not have a bar for each level of importance is because they did not receive any ranking for that particular value. Moreover, it can be clearly seen how ‘Car availability’ and ‘Cycling experience’ were the two most influencing attributes. The former had an average value of 5 while the later had 5.4 being the highest average value and thus the most important socio-demographic attribute influencing bike-train route

How do train-cyclists navigate? Pedro Nieves choices.

Figure 17: Ranking of socio-demographic attributes based on their level of importance according to bike-train experts.

5.2 Bicycle route choice attributes

Network attributes

A total of 16 bicycle network attributes were identified from the route choice studies. ‘On-street parking’ was the network attribute mostly ranked from a scale of 1 through 3 and can thus be considered as the least influencing one. In the case of ‘Number of traffic lights’, ‘Slope’, and ‘Number of intersections’, these attributes were all ranked the same with a 60 percent of responses considering them as moderately important. Likewise, ‘Number of lanes’, ‘Number of bridges’, and ‘Bike boulevards’ were also ranked the same but with a 60 percent of responses valuating them as neutral. ‘Number of stop signs’ and ‘Percentage of bike lane’ were equally ranked receiving 40 percent of responses for both neutral and moderately important. The attributes that obtained the most diverging rankings were ‘Presence/absence of bike lane’ with 40 percent of responses for both neutral and very important, ‘Roadway speed limit’ which received 40 percent of responses considering it as very important, and ‘Unguarded bike parking’ with 40 percent of responses indicating it as slightly important. From a scale of 5 through 7, ‘Traffic volume’, ‘Travel time’, and ‘Guarded bike parking’ received a total of 80 percent of responses which suggests their high degree of importance. Lastly, ‘Travel distance’ was the network attribute which obtained the highest ranking with 80 percent of responses valuating it as very important. Table 16 shows the percentage of responses for all bicycle network attributes.

How do train-cyclists navigate? Pedro Nieves

Table 16: Percentage of responses with regards to the bicycle network attributes.

Figure 18 is a visual summary of the results from experts’ ranking which also illustrates the average values for all bicycle network attributes. Overall, it can be seen how most of these attributes received an average value equal to or greater than 4 (neutral). In accordance to these results, it appears that ‘Traffic volume’ (5.0), ‘Travel distance’ (5.8), ‘Travel time’ (5.6), and ‘Guarded bike parking’ (5.2) are the most important bicycle network attributes influencing potential bike-train route choices.

How do train-cyclists navigate? Pedro Nieves

Figure 18: Ranking of bicycle network attributes based on their level of importance according to bike-train experts.

Contextual attributes

A total of four bicycle contextual attributes were singled out from the route choice studies. According to the overall results, the level of importance for these attributes appeared to diverge based on experts’ rankings. In the case of ‘Crime’, it was mostly valuated as slightly important whereas ‘Rush hours’ was mainly considered to be of low importance. ‘Sunset/sunrise’ was ranked differently by all the experts, therefore its level of importance cannot be generalized. Lastly, ‘Weather’ was ranked as the most important attribute having a total of 80 percent of responses from a scale of 5 through 7. Table 17 shows the percentage of responses for all bicycle contextual attributes.

Table 17: Percentage of responses with regards to the bicycle contextual attributes.

How do train-cyclists navigate? Pedro Nieves

Figure 19 illustrates the level of importance and the average values for the four bicycle contextual attributes. ‘Rush hours’ and ‘Sunset/sunrise” both had an average value of 3.8 and can thus be considered as the less influencing factors. On the other hand, it can be clearly seen how ‘Weather’ received the highest ranking with an average value of 5.2 which makes it the most important bicycle contextual attribute influencing potential bike-train route choices.

Figure 19: Ranking of bicycle contextual attributes based on their level of importance according to bike-train experts.

5.3 Transit route choice attributes

Network attributes

A total of 16 transit network attributes were identified from the literature. Experts’ rankings suggest that ‘Number of sprinter trains’ was the attribute which received the lowest ranking having a total of 60 percent of responses from a scale of 1 through 3. ‘General headway distribution’ was the attribute mostly ranked as neutral with a 60 percent of responses. From a scale of 5 through 7, various attributes received a total of 80 percent of responses; these were: ‘Type of station’, ‘Number of transfers’, ‘Guarded bike parking’, ‘Frequency of trains’, and ‘Travel time (for entire trip)” which indicates that they were all considered to have a higher degree of importance. Also between a scale of 5 through 7, a fair amount of attributes had a total of 60 percent of responses which were ‘’Number of stops’, ‘Vehicle’s departure time’, ‘In-vehicle travel time’, ‘Travel cost’, and ‘Unguarded bike parking’. This also indicates their relatively high level of importance. On the other hand, the rankings for ‘Shelter on platform’ and ‘Connection to other PT systems’ were quite varied although both of them were mostly considered as neutral with a 40 percent of responses. Lastly, ‘Transfer time’ received 100 percent of responses from a

How do train-cyclists navigate? Pedro Nieves scale of 5 through 7 which makes it the most important transit network attribute in terms of the percentage of responses. Table 17 displays these percentages per each level of importance for all the attributes.

Table 18: Percentage of responses with regards to the transit networks attributes.

As it can be observed in Figure 18, a great amount of these attributes were ranked greater than 4 (neutral). Three attributes had an average value lower than 4 which makes them the less important ones; these were: ‘Connection to other PT systems’ (3.4), ‘General headway distribution’ (3.2), and ‘Number of sprinter trains’ (3.0). On the other hand, a total of five attributes received an average value greater than 5 (moderately important) which makes them the most important. These attributes were ‘Type of station’ (5.8), ‘Travel time (for the entire trip)’ (5.6), ‘Transfer time’, Number of transfer’ (5.8), and ‘Frequency of trains’ (5.2). Nonetheless, since both ‘Type of station’ and ‘Number of sprinter trains’ received the highest average values, they can be considered as the attributes with the highest importance influencing potential bike-train route choices.

How do train-cyclists navigate? Pedro Nieves

Figure 18: Ranking of transit network attributes based on their level of importance according to bike-train experts.

Contextual attributes

According to the transit route choice studies mentioned above, a total of 4 contextual attributes were identified. All the attributes appear to diverge in their rankings based on experts’ opinion. In the case of ‘Weather’, it was ranked differently by all respondents which means that its level of importance cannot be generalized. ‘Social safety’ obtained 40 percent of responses ranking it with low importance whereas ‘Rush hours’ was mostly considered as neutral with also 40 percent of responses. Lastly, ‘In-vehicle crowding’ was the attribute mostly ranked as neutral with 60 percent of responses. Table 19 provides an overview of all the attributes with their respective percentage of responses per each level of importance.

Table 19: Percentage of responses with regards to the transit contextual attributes.

How do train-cyclists navigate? Pedro Nieves

As Figure 19 illustrates, none of these attributes received an average value between 5 to 7 which indicates their neutral or lower degree of importance. However, considering that ‘In-vehicle crowding’ obtained the highest average value (4.6), it can be considered as the contextual attribute with the highest level of importance influencing potential bike-train route choices.

Figure 19: Ranking of transit contextual attributes based on their level of importance according to bike-train experts.

How do train-cyclists navigate? Pedro Nieves

5.4 Concluding remarks At the end of the online survey, experts were asked if they considered the existence of other attributes that were not outlined in the preliminary conceptual framework which could also be influencing train- cyclists route choice behavior. The following attributes were identified from their responses:

1) Attractiveness of bike route 3) Travel motive/trip purpose 2) Bike route crowding 4) Bike parking (cost and capacity) 3) Seat availability 6) Directness

Taking these six attributes into consideration and according to the findings for all the group of attributes, a final conceptual scheme for bike-train route choice was created which can be seen in Figure 20. The attributes from the preliminary conceptual scheme were filtered out based on the rankings provided by the five bike-train experts who completed the web survey. This was done by selecting all the bicycle and transit route choice attributes that received the highest percentage of responses from a Likert scale of 5 through 7 which also obtained an average value equal or greater than 5. Likewise, from each group of attributes, at least one attribute was selected which had the highest average value. This was the case for transit contextual attributes whose mostly ranked attribute was ‘in-vehicle crowding’.

These bike-train route-related attributes are the ones that may be indicating an (in)direct influence upon potential bike-train route choices according to experts’ opinion. A direct influence alludes to those network and/or contextual attributes that are explicitly considered such as travel time and weather conditions whereas an indirect influence refers to socio-demographic attributes which are constitutive of train-cyclists’ subjective experiences and preferences (Bovy & Stern, 1990). In sum, this new set of bike- train attributes would be the ones which train-cyclists can trade-off between each other in order to make their route decision when there is a great amount of bike-train route options as it was illustrated in the travel scenarios of the previous chapter. In other words, each traveler is expected to (in)directly rank their route alternatives based on their various characteristics.

How do train-cyclists navigate? Pedro Nieves

Figure 20: Conceptual scheme of bike-train route choice.

How do train-cyclists navigate? Pedro Nieves

6. Trade-offs between travel time and bike-train route choice attributes

This chapter contains the results from the trade-off analyses undergone in order to answer the following research sub-question: To what extent are train-cyclists willing to trade-off travel time with other bike- train route choice attributes? A total of 41 responses (N = 41) was gathered from participants during the timeframe in which the online survey was open for responses. The following sections outline the findings: section 6.1 reports the socio-demographic results, section 6.2 exhibits the overall findings from the trade- offs between travel time and bike-train route choice attributes whilst reporting the results according to socio-demographic variables. Lastly, section 6.3 concludes and illustrates the total rankings of the attributes based on train-cyclists willingness to incur an extra amount of travel time.

6.1 Socio-demographic results A bit more than half of the participants were men with 25 responses (61%) whereas 16 (39%) were from women38. Likewise, 49% of responses were in an age range between 20 and 29 years old, 27% were people between 30 and 39 years, 14% between 50 and 59 years, and 5% for both age ranges between 40-49 and 60 years or older. In terms of the employment status of respondents, 46% were employed, 35% were students, 7% were self-employed, 5% were employed students, and 3% (1 respondent) was unemployed and another respondent (3%) was retired.

Participants also had to mention their level of cycling experience from a scale of 1 through 7. More than half of them considered themselves as advanced cyclists with a total of 63% of responses indicating their experience from a scale of 6 to 7. The rest of participants (37%) indicated an intermediate cycling experience from a scale of 3 to 5 whereas none of them considered themselves as beginner cyclists39 from a scale of 1 to 2. On the other hand, most respondents (78%) were not car owners while only 22% did own a car. Participants also had to indicate their main travel purpose when performing a bike-train combination. 49% answered that they do it when traveling to work, 29% mentioned that it was for leisure, 20% indicated that it was for educational purposes, while 2% (1 respondent) added that he/she does it for everything.

Additionally, respondents had to indicate at what side of the journey did they frequently use the bicycle when traveling to/from train stations. 39% mentioned that they tend to cycle for the home-end side (access travel) whereas 12% answered that they cycle for the activity-end side (egress travel). However, the majority of participants (49%) indicated that they tend to cycle at both sides of the journey. Lastly, in

38 It should be clear that these results are only considering the biological sex of participants and therefore do not refer to the gender identity of them. 39 These different categories of bicyclist (beginners, intermediate, and advanced) were chosen in order to differentiate between the levels of cycling experience. The next chapter with the results from the interviews will delve into this aspect.

How do train-cyclists navigate? Pedro Nieves terms of the type of bicycles regularly used by respondents, nearly all of them used an ordinary bicycle (81%), four of them indicated to use an OV-fiets (10%), three used a folding bicycle (7%), and only one of them used an electric bicycle (2%).

6.2 Train-cyclists trade-offs

Overall results (N = 41)

Respondents were generally adamant when they were asked their willingness to cycle an extra amount of time to comply with attributes related to bike parking. The majority of participants were not willing to trade-off any minutes to reach a train station with guarded (51%) and unguarded bike parking (73%). Same goes for the price of bike parking at stations which most respondents (63%) indicated their unwillingness to trade-off any of their cycling travel time to reach a station with lower fares. Furthermore, for the type of station40, most participants (48%) were indifferent to this network attribute which was expressed in their indisposition to cycle more minutes to access a station which offers different facilities. In terms of in-vehicle travel time, this was the only network attribute whom statement referred to a trade-off between train travel time instead of cycling travel time. Again, a preponderance of responses (48%) suggested an unwillingness to spend any amount of minutes inside a train carriage in order to have time to carry out another activity while traveling.

On the other hand, the survey respondents were amenable to trade-off the remaining network and contextual attributes. For instance, during the journey to/from a train station, the majority of participants (44%) indicated that they are willing to cycle five more minutes by choosing another route in order to avoid a bike route that is crowded. In the case of the time incurred when transferring to other platforms, most respondents (44%) were disposed to avoid this by cycling an extra five more minutes towards another station. This links to two other network attributes which are train transfers and directness. For the former, most participants (32%) indicated their willingness to cycle an additional five minutes to reach a station where they can board a train with fewer transfers. For the latter, more than half of respondents (41%) were willing to cycle 10 more minutes to a station where they can catch a train with a direct connection.

In terms of traffic volume, the majority of participants (44%) seemed to be sensitive to this network attribute which was expressed by their willingness to cycle five more minutes to/from a station via another route with less traffic. Likewise, 41% of responses were inclined to cycle an extra five minutes to/from a station in order to make a detour and choose a bike route that is more attractive. In the case of in-vehicle crowding, most participants (29%) were willing to cycle 15 more minutes to reach a station and

40 The type of a train station can also be defined based on whether it is a rural or urban station and/or a local or intercity station. However, in this study, stations were defined based on the facilities that they offer considering that some stations within the AMA have shops and restaurants which may be influencing station choice and, by implication, route choice.

How do train-cyclists navigate? Pedro Nieves board a train that is less crowded. This is related to the answers provided for seat availability in which 34% of respondents were disposed to cycle 10 more minutes during their access travel to catch a train where they can have a guaranteed seat. Finally, more than half of participants (54%) indicated their willingness to cycle 10 extra minutes to reach a station that offers a higher frequency of trains. In sum, Table 20 illustrates all the results per each of the trade-off statements where the bolded values represent the higher percentage of responses.

Table 20: Percentage of responses for the trade-off between travel time and other bike-train route-related attribute.

How do train-cyclists navigate? Pedro Nieves

Results according to bike-train trip purpose

Overall, participants who indicated to frequently travel for leisure when doing a bike-train combination were disposed to cycle more minutes on average in contrast to others. For example, most of them were willing to cycle an extra 9 minutes towards a station by choosing a bike route for its attractiveness as well as cycling 8 more minutes by picking another route which is less crowded. This suggests that these travelers are flexible to incur in additional travel time considering their freedom from the demands of work and/or duties. On the contrary, in the case of those whose trip is mainly for work purposes, they were willing to spend an additional 7 minutes on average during their main travel.

This denotes that these respondents are probably using that Table 21: Travel time trade-offs time in order to accomplish another activity throughout their according to travelers’ trip purpose. train journey, possibly work-related. Lastly, all participants indicated a willingness to cycle more minutes on average to board a station where they can board a direct train towards their destination. This hints how train-cyclists, regardless of their trip purpose, may be inclined to incur in longer cycling journeys to have a shorter train travel.

Results according to cycling experience

The trade-offs according to the different levels of cycling were slightly dissimilar. On average, most intermediate cyclists were more willing to cycle additional minutes in contrast to advanced cyclists which resonates with Ton et al. (2017) findings with regard to how cycling experience influences route choice. For example, in the case of bike routes that seem to be crowded, intermediate bicyclists prefer to cycle an extra 7 minutes by selecting another route to or from a station which seems more comfortable and less crowded. The same goes for traffic volume where these participants were inclined to cycle 6 more minutes by choosing another bike route which is less congested by motorized vehicles. On the other hand, advanced cyclists indicated their willingness to trade-off more Table 22: Travel time trade-offs according travel minutes to reach a station and board a train with less to travelers’ cycling experience.

How do train-cyclists navigate? Pedro Nieves transfers or preferably with a direct connection. This also suggests that advanced cyclists may be more disposed to have a lengthy cycling travel time in order to have a shorter train journey.

Results according to the trip segment

When comparing the results per bike-train trip segment, slight differences were found between respondents. For instance, as opposed to others, travelers who frequently use the bicycle during the activity-end journey were willing to cycle 7 more minutes on average to reach a station which has guarded bike parking. This may indicate that those participants are likely to leave their bicycles at stations in order to use them for their egress travel which becomes clearer when considering their willingness to cycle 6 more minutes on average to a station that offers low-priced parking. Likewise, those who regularly use the bicycle on both sides of Table 23: Travel time trade-offs according to trip the journey were disposed to cycle 9 extra minutes segment. towards a station where they can board a train that is less crowded. This echoes Xu et al. (2018) findings, suggesting that some of these travelers are possibly taking their bicycles on board and therefore prefer the comfort of an uncrowded train carriage.

Results according to the type of bicycle used

Although the majority of respondents indicated to regularly use an ordinary bicycle when traveling to/from stations, minor differences were found between those you use another type of bicycle. In the case of the four travelers who usually travel using an OV-bike (OV-fiet), most of them were willing to spend more minutes during their train trips as opposed to those who use a folding bicycle. Likewise, folding bikers were generally reluctant to trade-off their cycling travel time in order to satisfy any of the bike parking attributes. Lastly, the only respondent who frequently used an electric bicycle was willing to trade- off 15 minutes on average for most bike-train route-related attributes.

How do train-cyclists navigate? Pedro Nieves

6.3 Concluding remarks According to the data results from the online survey, train-cyclists indicated a willingness to trade-off some minutes of their whole travel time. Figure 21 exhibits the overall results by illustrating in horizontal bars a distribution of the percentage of responses as well as the average values (in minutes) that travelers were willing to trade-off for each attribute. These average values are suggesting a ‘factor-importance- hierarchy’ (Bovy & Stern, 1990) between the route-related attributes that are (in)directly taken into consideration during bike-train route decision making. The main finding of this chapter goes by the hand of the three most ranked attributes which were frequency of trains, train transfers, and directness. This indicates that train-cyclists are generally willing to spend longer times cycling during their access travel in order to reduce the travel time of their train journey.

Figure 21: Desirability scale according to train-cyclists’ willingness to incur in an extra amount of travel time (in minutes). 41

Since train-cyclists were willing to cycle between 2 to 13 extra minutes on average, this suggests that bike- train route choices for the simple-complex and complex-complex travel scenarios are not entirely about travel time and therefore other attributes may also be compensated. For instance, because of all the route options in the simple-complex scenario where the traveler has the possibility to trade-off 21 minutes between the four fastest bike-train route combinations, the decision to choose one of them could be

41 The averages per each of the attributes were rounded to obtain their nearest absolute values in minutes. It should also be emphasized that ‘in-vehicle travel time’ was the only network attribute which indicated a trade-off between the time spent traveling during the train journey in order to perform another activity.

How do train-cyclists navigate? Pedro Nieves contingent upon the attractiveness of the bike route, the traffic volume, or the availability of a seat inside a train carriage. Same goes for the complex-complex scenario where the traveler could trade-off 22 minutes between the six quickest bike-train routes by ranking their attributes based on her subjective preferences and needs, and according to her previous travel experience (Bovy & Stern, 1990). However, it still remains unclear how exactly are these attributes playing a role in train-cyclists process of route decision making. The next chapter will further explore this aspect with the insights provided from a series of interviews.

How do train-cyclists navigate? Pedro Nieves

7. Train-cyclists route choice behavior

This chapter aims to unravel train-cyclists’ route choice behavioral complexity by delving into their travel experiences addressing the following research sub-question: How are bike-train route-related attributes playing a role in train-cyclists’ route choice behavior? The following sections outline the findings according to the structure of the interviews. Section 7.1 briefly explains the socio-demographic background of each interviewee while section 7.2 covers their input according to the above-cited maps of the bike-train’s system performance. Section 7.3 goes more in-depth about the role of different bike-train network and contextual attributes. Lastly, section 7.4 concludes by linking the empirical findings with the theory.

7.1 Introducing the train-cyclists The majority of interviewees were students between their twenties who are also employed and regularly use an ordinary bicycle to travel to or from overlapping catchment areas in the AMA. Their recurrent bike- train journeys were done for different purposes. For example, Sora is a train-cyclist who lives in Utrecht and frequently travels to Amsterdam during the week to study. From his home he usually walks towards Utrecht Centraal station and boards a direct train towards Amsterdam Amstel station where he picks up his bicycle which is stored in a guarded bike parking and rides it towards the UvA. Likewise, Namine lives in Amsterdam Nieuw-West and regularly cycles to Amsterdam Lelylaan station, leaves her bike in an unguarded parking and embarks a train to Amsterdam Centraal station where she then takes a tram to go to work. Kairi, who lives in Amsterdam-Oost, usually makes a bike-train combination during the weekends. She first rides her bike towards Amsterdam Amstel station, locks it in an unguarded parking and then catches a direct train towards Den Helder to visit her mother.

On the contrary, Axel is a train-cyclist who most of the times rides a folding bicycle. Flexibility is the main reason why he chooses this type of bike, generally because he can reroute his cycling journeys to/from stations in case of any mishaps while still taking the bike with him in the train without worrying where to park it. Axel’s most frequent bike-train route starts from his home in Utrecht where he cycles to Utrecht Centraal station, boards the train with his bike and travels towards Amstel station and rides again his bike to the UvA where he works. Lastly, Riku who lives in Amsterdam-Noord is a recurring user and great fan of the OV-fiets system. From his home he cycles to Amsterdam Centraal station, parks his bike for free in any unguarded parking and boards a train with a direct connection towards Haarlem station. Once there he picks up an OV-bike and uses it in order to visit his parents and/or to meet with friends. His main motives for using these bicycles are also flexibility and because he considers that it is faster to travel by bike than by public transport.

Most of the interviewees considered to have a high level of cycling experience but on different grounds. Axel and Namine both referred to Amsterdam’s traffic and hectic cycling culture as main reasons for why living or working in this city requires quite the competence to travel by bike. Namine, however, who

How do train-cyclists navigate? Pedro Nieves indicated to have an intermediate experience felted distressed stating that in Amsterdam “everybody does their own thing, makes up their own rules. So for me that’s ok, but sometimes I really get annoyed by how it goes” (personal communication, 25 April 2018). On the other hand, Sora mentioned how being exposed to cycling since he was in elementary school is what gave him an above average experience at handling the bike. Riku made a similar expression but also pointed out that the main sport he practices is cycling and therefore is what makes him a competent bicyclist. Interestingly, on a different note, Kairi associated her high cycling experience with the sense of freedom when she was recalling to “feel in control because I don’t depend on everyone else” (personal communication, 30 April 2018).

Furthermore, nearly all train-cyclists were not car owners, and some were even reluctant to it. For instance, when asked if they would combine the car when making their route choices, Sora and Riku refused by referring to the additional expenses which are incurred. “I can hardly imagine taking the car to go to Haarlem because first of all you have to pay parking. Second, it takes gasoline… so I think the [travel] costs would be a lot cheaper by cycling and taking the train” (Riku, personal communication, 4 May 2018). Axel, although he owns a car, expressed similarly stating that he would not travel with it to Amsterdam due the limited parking at the inner city and because it would take him much longer as opposed to a bike- train journey (personal communication, 2 May 2018). Given these points, it becomes clear how the bike- train’s system performance is relatively high and prevails over the car in cities that are located within overlapping areas at the AMA such as Amsterdam and Haarlem.

Table 24: A summary of interviewees’ socio-demographic background.

How do train-cyclists navigate? Pedro Nieves

7.2 Navigating in overlapping catchment areas at the AMA During the second phase of the interviews, train-cyclists had to reflect upon their route choices based on two of the maps which were created to illustrate the system performance of the bike-train mode. The first one was the density map (Figure 22) which showed the total amount of NS stations, their spatial distribution, and their density across the AMA. Both Namine and Riku were shocked when they first saw the map since they were not aware of all the existing stations and on how some of them are in proximity to each other. “I have the feeling that many of them are close together and rarely being used”, said Riku (personal communication, 4 May 2018). Axel, on the other hand, was surprised about the density around Haarlem whereas for Amsterdam he was aware of it because it is the area where he works and regularly Figure 22: Density of NS railway stations within the AMA. travels. Likewise, Sora recalled how growing up in Hilversum where he frequently traveled back and forward to Amsterdam made him conscious about the different stations that are available at the center and southeast side of the AMA. He adds, however, that “the fact that I’m aware of that doesn’t mean I use them all” (personal communication, 24 April 2018). On the same line, Kairi was aware of these ‘hubs’ as she calls them because “it’s something you experience, you can see the rail connections and [the train] always mentions the biggest stations, like Utrecht and Leiden” (personal communication, 30 April 2018).

Afterward, interviewees were shown the second map (Figure 23) which illustrated the overlaps of train station’s catchment area. When asked to reflect upon it, most of them recalled their previous travel experiences when selecting which stations to access and/or egress in overlapping areas while (in)directly referring to different bike-train route- related attributes. For example, when traveling a long distance, Sora prefers to board an intercity train with a direct connection which is what he does when traveling to/from either Utrecht or Hilversum. He was also quite Figure 23: Overlap of NS railway stations’ cycling-based catchment area familiarized with the railway network when he outlined that “between Amsterdam Central station and Amstel there’s only one railway track in

How do train-cyclists navigate? Pedro Nieves each direction. So if something were to happen, it’s immediately noticed that no trains would run. Whereas from Bijlmer Arena [station] to Utrecht there are more tracks, so it rarely happens that there are no train services at all” (personal communication, 24 April 2018).

Likewise, Axel was also well-acquainted with the bike-train journeys he often embarks on from Amsterdam, like for example when he visits his parents in Rotterdam. While making this travel, he prefers to cycle towards Amsterdam Zuid station, board a train to Schiphol Airport station and then transfer to an intercity train directly towards Rotterdam. Even though that from Amstel station he could also catch a direct train, he avoids it because–as he recounts–“it’s a commuter train that stops at every station and it’s inconvenient because it’s the only direct one which doesn’t have any relaxed chairs” (personal communication, 2 May 2018). Moreover, when reflecting upon the map, Kairi expressed that albeit she lives right next to Amsterdam Science Park station, she almost never visits it because “it is not well connected, it has only one direction, and usually in the weekend when I travel it is very crowded” (personal communication, 30 April 2018). Therefore, she prefers to cycle towards Amstel station because from there she can take an intercity train directly towards Den Helder.

On the other hand, Namine expressed that when making a bike-train journey in Amsterdam, she prefers to go by habit and always travel towards Lelylaan station because–as she says–“it’s just what I’m used to and since it’s so close to me then I stick with that choice. Also I know there would be a spot [to park] there so” (personal communication, 25 April 2018). Therefore, although she sometimes cycles towards Amstel station, she tends to avoid it because of the long distance from her home. Thus the close proximity to Lelylaan station and a guaranteed bike parking is what wins her choice over Amstel station. In a like manner, Riku also prefers to travel to the nearest station which is Amsterdam Centraal station and because from there he can catch a direct train towards Haarlem. However, when he travels to Utrecht to visit friends, he makes a longer cycling journey to Amstel station because it his ‘cheapest option’ considering that the train from Centraal is also stopping at Amstel (personal communication, 4 May 2018).

All-in-all, the insights provided on the basis of these maps resonates with Body and Stern (1990) arguments regarding route-learning process. It appears that these train-cyclists become versed from their previous travel experiences and therefore have some degree of awareness of existing bike-train route alternatives and their respective attributes. The following section will delve more on how these attributes are actually playing a role in train cyclists’ route decision making.

How do train-cyclists navigate? Pedro Nieves

7.3 The role of bike-train route-related attributes The third and final phase of the interview asked train-cyclists specifically about the different bike-train route-related attributes. While they were recounting their route choices, all of them indicated that on some occasions they use different smartphone apps to navigate. For instance, Sora, Axel, and Riku tend to check Google Maps before cycling to unfamiliar places in Amsterdam just to have an idea of the route, however, they never use the app while on the move. In contrast, Namine mentioned that she uses Google Maps almost every time before and during her cycling journeys. Additionally, sometimes she checks if trains are crowded and verifies their departure times while using the NS journey planner app. In the case of Kairi, she checks an app called 929242 for her train trips and tends to use Google Maps when traveling by bike because of the app’s GPS service.

Nonetheless, interviewees bike-train routes were not entirely chosen on the basis of these apps. Some seemed to be ad hoc decided, mostly according to contextual attributes. For example, different weather conditions were highlighted as important factors influencing route choice. Both Axel and Sora mentioned that when cycling towards the UvA during winter, if it starts to snow they would reroute their journey to a metro station and pay the extra travel fee in order to avoid slippery roads. Additionally, rain was also regarded as a determinant factor. “If it rains quite heavily then I would already grab a train because I would at least have to cycle for 25 minutes [to work] so then I would definitely reconsider a bike-train trip” said Namine (personal communication, 25 April 2018). For Kairi it is the combination of wind and rain or if it is cold when she chooses a shorter cycling route to the nearest station. Riku, in contrast, was insensitive to the weather. “For rain and snow it’s not really affecting me, I still go bike. So a lot of times I end up wet” replying with peals of laughter (personal communication, 4 May 2018).

Weather conditions also appeared to be related with how train-cyclists choose between bike routes based on their attractiveness. “When it’s really sunny and nice I tend to cycle alongside the Amstel river”, said Axel when recounting his trip towards the UvA (personal communication, 2 May 2018). Likewise, when heading back to Utrecht, Sora prefers to take a detour along the Amstel river if it is sunny and because he knows that he can catch a train every ten minutes from Amstel station. “That has really improved the comfort of traveling because it used to be very crowded but now it’s ok”, he adds (personal communication, 24 April 2018). Thus, for Sora, the frequency of trains as a network attribute seems to have an interplay with the weather and, by implication, with the attractiveness of a bike route.

On a similar line, Namine expressed that when she does cycles towards Amstel station as opposed to other stations it is also mainly because of the high amount of trains departing and arriving from there. For Axel this is also an important factor–“I would be less happy when the train connection would be half an hour only, it’s just more inconvenient. But now the trains are every ten minutes, before they were every

42 https://9292.nl/en

How do train-cyclists navigate? Pedro Nieves fifteen minutes. So in the worst case you have to wait ten minutes” (personal communication, 2 May 2018).

In the case of Kairi, when cycling towards Amstel station she sometimes prefers to choose a route that goes through Frankendael park because–as she notes– “it’s more greener and especially now in the spring I like it better” (personal communication, 30 April 2018). However, when cycling from Amstel to her home, she recalled having three route alternatives. There is the first one which crosses through the park, a second one which has a supermarket in the way, and a third one that has a segment which is hilly. But albeit the last one is her fastest option, she sometimes chooses the second one when she returns from Den Helder in order to do groceries before arriving to her house. However, when asked about how she chooses between these three bike routes on a regular basis, she replied while laughing “it really just depends if I’m on the mood to cycle the hill!” (personal communication, 30 April 2018).

Moreover, some train-cyclists indicated an interplay between in-vehicle travel time, travel distance, and seat availability. Sora, for example, prefers to avoid crowded trains to be able to sit inside the train when traveling towards Hilversum because of the long train travel. Kairi also agrees and mostly because when she travels to Den Helder– “I prefer to sit because I want to read or do something on my laptop. And I just don’t like to be standing for an hour or so” (personal communication, 30 April 2018). When Riku and Namine travel for long distances they as well like to have a guaranteed seat in order to work on their laptops. As Namine mentioned, “When I have to do something I would prefer to stay as long as possible in one train, usually to work on my laptop” (personal communication, 25 April 2018).

When train-cyclists were asked about traffic volume, Sora was the only one that referred to motorized vehicles. During his frequent journeys from Amstel station to the UvA, he chooses to cycle through Wibaustraat, a street which is regularly busy with car drivers. Even though that street is his fastest route to get to the UvA, he wishes that “there would be another route that could be really convenient for me without any cars” (personal communication, 24 April 2018). On the other hand, when cycling to Amstel station, Riku said that he tends to avoid traffic lights at crossroads by maneuvering his way through them, pinpointing his high cycling experience. Likewise, Axel prefers to evade traffic lights by “picking a route because of a certain crossing where the traffic light is slower or faster.” He also adds that “For some it’s even possible to skip the red light and just go” (Axel, personal communication, 2 May 2018).

The type of stations in terms of the facilitates that they offer was only considered by Namine and Kairi. Both of them mentioned that they would cycle to Amsterdam Centraal station or make a train stop there because on some occasions they go shopping to the stores. However, in terms of the bike parking available at station, Riku for example would prefer to visit a guarded one with high capacity but only when he is using his racing bicycle. Likewise, when traveling to the UvA, Sora mentioned that another reason why he takes a train to Amstel is mainly because he has the chance to leave his bike there at a guarded parking while paying a small fee.

How do train-cyclists navigate? Pedro Nieves

Another recurring factor that interviewees mentioned without being asked about it was the connection of other modes of public transport that are available at some train stations. For example, in case of adverse weather conditions, most of the interviewees chose to visit a train station that has connections to the metro. “When I arrived at Amstel and I look out and I realize ‘ok, it’s really bad weather, I don’t want to cycle like this’ then I jump to the metro” said Axel when traveling towards the UvA (personal communication, 2 May 2018). On the other hand, Kairi also prefers to cycle towards Amstel station instead of Science Park station because the latter is not interconnected with the metro. Also, as she adds, “in case I miss the train at Amstel I can always take the metro to Centraal and get the same train from there to Den Helder” (personal communication, 30 April 2018).

An additional reason why Kairi disregards Science Park station is because of train transfers. “I think that’s the main reason I choose to go to Amstel station because there’s a direct connection” she adds (personal communication, 30 April 2018). Riku also tries to avoid transfers because– “if one of the trains is delayed then you will miss it and I have too many experiences with this” –which is why he is willing to cycle more time to reach a station and board a train with less stops and even sometimes he decides to cycle directly to Haarlem (personal communication, 4 May 2018). Likewise, Sora and Axel were both contented by the fact that they do not have to change trains because their travel from Utrecht is via an intercity train directly towards Amsterdam. For Axel, the transfers would be one of the chief reasons for him to travel by car.

Lastly, at the end of the interviews, train-cyclists were asked if they actually take the above-outline bike- train attributes into account when choosing their routes. Axel replied that he does consider some of them but that the selection of his bike-train routes is mainly contingent upon the activities that he is able to chain altogether throughout the day which is generally possible because of the flexibility provided by his folding bike. Kairi paused for some seconds to think about the question and expressed that “I think the more often you travel, the more you get to know your route preferences. Because if I would travel once, I wouldn’t really have something to reflect on” (personal communication, 30 April 2018). On the other hand, Sora stated that route choice is “a very fluid process, it’s more of a happening” (personal communication, 24 April 2018). For him, route decision making seems to be something which is not entirely predefined. Moreover, Namine was fascinated for thinking more in-depth about her route choices and was eager to make use of other stations near her which she did not know about. Finally, Riku replied that he tends to mix between his cycling route alternatives without thinking of their attributes. “I really don’t know how I do this, I just cycle in many different ways all the time. What I do know is that I don’t mind cycling longer distances for having a shorter travel by train” he confidently assured (personal communication, 4 May 2018).

How do train-cyclists navigate? Pedro Nieves

7.4 Concluding remarks Train-cyclists’ subjective needs, preferences, and experiences were clearly outlined throughout their narratives. Their individual route choices were (in)directly derived from the physical environment according to network and contextual attributes which to some extent reverberates with Bovy and Stern’s (1990) route choice behavior theory. Likewise, the fact that they indicating to sometimes navigate using Google Maps and the NS app gives leverage to the empirical strategy chosen in chapter 4 for determining potential bike-train route choices at the AMA.

Most bike-train route-related attributes seem to interact with each other when train-cyclists are processing their route alternatives. These interactions seem to be occurring as a consequence of the synergies that arise from the bike-train mode. For instance, there was an interplay between how a bike route to or from a station is perceived as attractive on the basis of weather conditions or on how the need to have a guaranteed seat inside a train wagon is contingent upon the travel distance. In case of the latter, what appears to be happening here is a trade-off between the time spent traveling by train in order to carry out another activity which echoes one of the modal performance dimensions of the bike-train mode, that is, the ‘activity chain’ (Kager et al. 2016). Additionally, Axel route experiences highlighted another modal performance dimension which is ‘adaptability’ (Ibid.) and was mainly due to his folding bicycle. Not having to worry where to park his bike gives him more flexibility to choose his departure times and to mix between train stations.

Furthermore, train-cyclists also mentioned other network attributes which were filtered out during the expert survey. For example, the presence of traffic lights or a steep slope were considered as factors which deviate from the selection of a particular bike route. Same goes for public transport connections at train stations, such as the metro, which was found to have a relative influence upon the decision to access or egress a specific station. What these points suggest is that the bike-train attributes used in this study are not exclusively the only ones playing a role in train-cyclists route choice behavior.

Although the clock ticked loudly throughout train-cyclists’ reflections, travel time was not the utmost factor having an influence over their route choice behavior. The time spent while traveling with the bike- train system seems to be something which is actively produced through the various routes that are undertaken. How train-cyclists are experiencing time is much more variable in accordance with the diversity of bike-train route-related attributes. This gives them the chance to experiment different routes during their travel which in turn changes how they value travel time, thus generating other opportunities. Perhaps Riku is a suited example because his willingness to cycle more time as a way to reduce his train travel signals another interplay whilst shedding light to one of the possibilities that people can have when navigating using the bike-train mode in cities located at overlapping catchment areas such as Amsterdam.

How do train-cyclists navigate? Pedro Nieves

8. Conclusions

This thesis began by understanding the bicycle-train system as a distinct mode of transportation because it left open the question of how people who use the bicycle and train in combination make route choices, particularly from the complexity of overlapping train stations. The study area which was chosen to delve into this aspect was the Amsterdam Metropolitan Area (AMA) because of it's highly developed and integrated bicycle and train infrastructure.

First of all, the current system performance of the bike-train mode was mapped. Results suggest that when considering the spatial impact area of all the NS stations, almost three-quarters of the AMA appears to be covered by the bike-train’s catchment area. Likewise, the two densest areas which concentrated more NS stations were also the areas where there was a higher overlap between the stations’ catchment area. These overlapping areas resulted to be the ones in which the bike-train’s system performance was superior in terms of its competence for providing opportunities for mobility. The findings from the hypothetical travel scenarios evidenced this by exhibiting how bike-train route options increase exponentially when traveling to or from overlapping areas. For instance, in the simple-complex scenario, the traveler was able to choose between 33 bike-train routes whereas in the complex-complex scenario there were a total of 304 route alternatives, and most of these routes were overlapping at some segments. Thus, with so many similar routes to get from A to B, what this indicates is that travelers can choose one out of all these options based on different route attributes.

But what are these attributes which are influencing potential bike-train route choices? To answer this question, the variety of bicycle and transit route choice attributes which were outlined in the preliminary conceptual scheme were filtered into bike-train attributes according to experts’ ranking. As a result, a final conceptual scheme of bike-train route choice was proposed including the socio-demographic, network, and contextual attributes that were considered as the most influencing ones upon train-cyclists’ route choice behavior. These attributes were then used to further understand how train-cyclists rank them based on their willingness to cycle an extra amount of time as a means to offset them. A desirability scale was created which results indicated that when it comes to bike-train route choices other attributes seem to be also having an important role. This became clear considering that train-cyclists were willing to cycle between 2 to 13 minutes more on average to satisfy other bike-train route-related attributes. Overall, although scholars consider that cyclists tend to cycle less as travel distances increase, the main finding here suggests that train-cyclists are actually willing to have longer cycling journeys in order to shorten their train trips.

Given these points, how then are train-cyclists making route choices while using the bike-train system? The answer to this main research question was finally unraveled by the insights gained from interviewing train-cyclists because it allowed for a contextual understanding of bike-train route choices made when

How do train-cyclists navigate? Pedro Nieves traveling to or from overlapping catchment areas. The synergetic effects which emanate from the bike- train mode seem to have a direct implication on how bike-train route-related attributes are playing a role in route decision making. An interplay between most of these attributes was clearly evidenced by the interviewees which in turn underlies the modal performance dimensions of the bike-train system. The bicycle as a highly individualized means of mobility gave train-cyclists the possibility to adapt their route choices to or from stations in the case of unfavorable contextual occurrences. Likewise, when accessing or egressing stations, some train-cyclists indicated to reroute their cycling journeys in order to chain different activities. It therefore becomes clear that the high speed and spatial reach of the train complemented with the high accessibility of the bicycle gives train-cyclists the opportunity to personalize their whole journey based on various route-related attributes.

Overall, most of the findings seem to be consistent with the literature. For instance, train-cyclists’ route choice behavior according to their subjective needs, experiences, and preferences were generally derived from the physical environment and its objective opportunities, echoing Bovy and Stern’s (1990) route choice behavior theory. Nonetheless, by looking deeper into their experiences, it appears that route choice may not entirely be a rational and utility-optimizing behavior. As Kairi notes, “I don’t how or why, like unconsciously you make rational [route] decisions, but you are never really aware of it” (personal communication, 30 April 2018). Likewise, as Riku says, “I always change my cycling routes without reasons… I just like to get to know cities” (personal communication, 4 May 2018). Therefore, what these narratives seem to signal is that route choices are dynamic and even slightly irrational, making train- cyclists’ feel differently about their journeys. In this sense, train-cyclists’ route choice behavior may as well be about the lived experience of being on the move.

How do train-cyclists navigate? Pedro Nieves

9. Discussion

9.1 Limitations and (self)reflection

This study had several limitations which should be addressed and reflected upon. In general, having chosen to answer four research sub-questions within the given timeframe to conduct this research certainly limited the depth in which each of them was assessed.

To start off, one of the limitations is related to the method of data collection chosen for the first research sub-question. Using Google Maps and the NS app to collect and to estimate the travel time of potential bike-route choices for different hypothetical travel scenarios disregarded those routes that are actually chosen by people when traveling from A to B. Therefore, adopting these apps as a research tool assuming that it is how travelers often navigate undeniably neglected those who do not use them in order to make route choices.

Another important limitation is related to the sample size of the trade-off survey which was conducted for the third research sub-question. A total of forty-one responses is certainly not representative of the whole population and is therefore only accounting for a small portion of the train-cyclists that either lived, worked, and/or study in the AMA. As a consequence, the assumptions derived from the data results cannot be generalized for the entire population nor to other contexts, suggesting that further research is needed. Additionally, how the statements in the survey were written are to a certain extent biased by my own understanding of how travel trade-offs are made. They could have also been drafted differently in order to depict the various ways in which bike-train route-related attributes are traded-off with travel time. Furthermore, considering that a web survey was employed as a data gathering method, this inevitably excluded people that did not own a computer or any smart devices with internet connection. Also, it should be acknowledged that not being fluent in Dutch was a major limitation when recruiting users at the train stations that were visited.

Lastly, a brief reflection ought to be made regarding the interviews that were conducted. The idea that speaking with people provides a passageway into their subjective reality is polemic, mainly because of the context-dependent nature of the interview. To make a firm diagnosis of train-cyclists route choice behavior would overlook the nuances that surfaced throughout the interviews. Therefore, each interview was treated as an encounter in its own right, highlighting the multiple perceptions and diverse perspectives which are embedded in a given social reality. As these interviews were conducted, I became more attuned to how route choices are made throughout the study area coupled with my own traveling experiences as a user of the bike-train mode.

How do train-cyclists navigate? Pedro Nieves

9.2 Action points for transport planning practice

The overall findings outlined in this thesis offer some pointers which can potentially contribute to transport planning practice. The theoretical underpinnings of the bike-train system suggested here are intended to further encourage the growing international trend of integrating the bicycle with public transportation. However, transport practitioners must drift away from the idea that the bicycle is merely a feeder to other modes of transports and start framing it as an integrated part of the public transport system. Such an understanding can help to foster the creation of bike-train policies that, for instance, are targeted to improve the conditions of train stations for people that access or egress them by bicycle.

Another key contribution of this thesis is in line with the provided insights into the behavior of people who as a means of mobility use the bicycle and the train in combination, hence the train-cyclists. The subjective preferences and travel time trade-offs exhibited by these users can inform transport planners in terms of route planning, by taking into account the diversity of bike-train route choice attributes in order to develop or improve the interconnections between home/activity-end cycling routes and public transport hubs as well as the coupling between all trip segments. This would mean for example planning bike routes towards stations which are more attractive or equipping stations with stores and with cheap and safe bike parking. In turn, by giving priority to the bicycle when developing a station area, the combined use of bicycle and transit can be further enhanced.

A third and final contribution to transport planning goes by the hand of the study area chosen for this thesis, but more particularly for Amsterdam. By the time of writing, the Amsterdam Centraal station counts with over 185,000 daily passengers and is expected to increase to 275,000 by 2030, similarly to passengers arriving at other stations such as Zuid and Sloterdijk which are also expected to boom43. Considering that 47% of daily train users in the NL access stations by bike (Kager et al., 2017), it is likely that most of these passengers are train-cyclists. As a consequence, ProRail44 will invest two billion euros in the coming years to improve the railway system in the AMA in order to tackle this growth. However, given the fact that Amsterdam is located in an overlapping area where, for instance, the cycling catchment area of both Amstel and Centraal station are overlapping, ProRail does not necessarily have to cater for both stations. Because if indeed train-cyclists are willing to spend longer times cycling during their access travel in order to have a shorter train journey, they would be disposed to access any station in proximity where they can board a direct train. Therefore, the frequency of direct trains at these intercity stations could be decentralized by redistributing them to other local stations at Amsterdam and thus balancing the expected growth of passengers.

43 https://www.parool.nl/amsterdam/enorme-investering-in-spoor-om-amsterdam-bereikbaar-te-houden~a4599131/ 44 ProRail is a Dutch government entity in charge of maintaining and further extending the national railway infrastructure in the Netherlands.

How do train-cyclists navigate? Pedro Nieves

9.3 Recommendations for further research

By providing a conceptual scheme for understanding bike-train route choice, it is hoped to encourage scholars in further delving into this topic. The following themes have been proposed as potential research topics:

1) Bike-transit route choice in general: The analyses conducted in this study were limited to the conceptual understandings of the bike-train system. However, further illustration and exploration of route choices made by the combined use of the bicycle with other transit systems seem to be another promising research topic. Different synergetic effects between the metro, bus, or tram and their implications for route choice could be researched. For instance, bike-metro route choices may be studied in places where metro station’s catchment areas are overlapping with each other. This would offer a comparison between the insights of bike-train route choices outlined in this thesis.

2) Bike-train route choice contextual variations: Researchers can delve into context-specific explorations of bike-train route choices in different countries in order to test the validity of this concept under diverging conditions. The findings provided here can stimulate and facilitate comparative case studies between cities or city-regions that also have a relative maturity in bicycle-train integration.

3) Bicycle-train route choice model: In this thesis it was shown how route choice scholarship has generally focused on modeling routes separately for either the bicycle or for the transit system. By the time of writing, a route choice model combining both modes is still nonexistent; even popular journey planner apps such as Google Maps have yet deciphered how to model bike-train route choices. Therefore, the route choice attributes outlined in the conceptual scheme could potentially inform this idea, encouraging researchers to take on the challenge of creating a multinomial logit and/or probit model for the bike-train mode.

How do train-cyclists navigate? Pedro Nieves

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Appendices Appendix A: Bicycle route choice studies Articles Algorithm/Model/ Indicators/Criteria/ Study Area Method Parameters/Attributes • Facility characteristics Hunt and Alberta (2001) Influences on • Stated preference • Non-cycle traffic characteristics Edmonton, Canada bicycle use • Logit choice modelling • Individual and trip characteristics • Environment/situation characteristics • Cyclists’ characteristics o age, gender, employment characteristics, bicycling experience, reason of bicycling • On-street parking

o parking type, parking turnover rate,

length of parking area, and parking

occupancy rate

• Bicycle facility type and amenities Texas, USA o bicycle lane, wide-outside lane, and

facility continuity Sener et al. (2009) An analysis of bicycle • Stated preference elicitation approach • Roadway physical characteristics route choice preferences • Web survey o roadway grade, number of stop signs, in Texas, US red lights, and cross streets • Roadway functional characteristics o traffic volume and roadway speed limit • Roadway operational characteristics o Travel time • Route length (in meters) • Gradient • Bike path (percentage of marked bike paths along

the route

• Number of traffic lights • Mode detection algorithm of GPS-observed Menghini et al. (2010) Route choice of • Path size Zürich, Switzerland person days cyclists in Zurich • Speed o Stated preference (SP) surveys • Traffic volume • Type of intersection (traffic lights vs. roundabouts)

• Network attributes:

o Length (in miles)

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Hood et al. (2011) A GPS-based bicycle • Path Size Multinomial Logit model o Traffic volumes San Francisco, California, route choice model for San Francisco, o GPS data collection: CycleTracks app o Speed USA California o Number of lanes o Bike class (bike facility) o Up-slope o Turns • Crime • Network attributes: o Bridge with on-street bike lane o Bridge with separated bike facility o Up-slope

o Path size

o Turns

o Bike boulevard

o Off-street regional bike path

o Annual Average Daily Traffic (AADT) Portland, Oregon, USA

o Traffic signals

o Stop signs Broach et al. (2012) Where do cyclists ride? • Choice set generation algorithm o Unsignalized intersections A route choice model developed o Revealed preference GPS data with revealed preference GPS data • Socio-demographic: • Gender • Age • Parental status • Path parameters: o Path length • Multinomial Logit Model (MNL) o Auto speed • GPS Casello & Usyukov (2014) Modeling o Auto volumes • GIS Waterloo, Ontario, Canada Cyclists’ Route Choice Based on GPS Data o Elevation (gradient, slope) o Presence/absence of bike lane • Distance (travel time) Beheshtitabar et al. (2014) Route Choice Norrköping, Östergötland, • PostGIS • Slope Modelling for Bicycle Trips Sweden • Existence/absence of cycle facilities • Network attributes: o Distance o Ton et al. (2017) How do people cycle in % of cycle path o Number of intersections (per km) Amsterdam? Estimating cyclists’s route • MNL & Path Size Logit (PSL) model Amsterdam (inner city), the • Contextual attributes: choice determinants using GPS data from o GPS data Netherlands o Rain an urban area o Sunset/sunrise times o Trip purpose o Rush hours

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Appendix B: Transit route choice studies Articles Algorithm/Model/ Indicators/Criteria/ Study Area Method Parameters/Attributes Gentile et al. (2005) Route Choice on Transit • “A new stop model” • Line waiting times (independent variable) Networks with Online Information at Stops • General headway distributions - • Home-end & Activity-end o Mode indicators o Walk o Bike o Car o Bus o Tram o Metro • Station indicators o Intercity station o Express station o Local stations • Whole trip o Train indicators Hoogendoorn-Lanser, S. and Bovy, P. (2007) • MNL model ▪ Intercity train Dordrecht, Rotterdam, The Modeling Overlap in Multimodal Route Choice • PSL model ▪ Express train Hague, and Leiden; the by Including Trip Part–Specific Path Size ▪ Local train Netherlands Factors • In-vehicle times (min) o Walk o Bike o Car o Bus o Tram o Metro o Train • Number of legs o Low frequency o High frequency • Costs (euros) o Parking costs o UPT cists • Other (min) o Total UTP headway o Walk time to UPT stops • Path size o Basic o Home • Level of service attributes:

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o Travel time o Number of transfers o Walking time Eluru et al. (2012) Travel Mode Choice and • Mixed Multinomial logit model o Initial wait time Montreal, Quebec, Canada Transit Route Choice Behavior in Montreal • Socio-demographics in accordance to travel time: o Gender o Age o Employment status Brands et al. (2014) Modelling Public • Zenith Method • distance radius Transport Route Choice, with Multiple Access • type of station Amsterdam Metropolitan and Egress modes • minimal number of stops Area, the Netherlands • vehicles’ departure time • Socio-demographic variables o Gender o Age o Student’s occupation (based on student OV chip-card) o Car availability o Average income in thousands (per year) • Travel-related attributes o Discount La Paix, L. and Geurs, K. (2015) Modelling o Work purpose observed and unobserved factors in cycling to • Hybrid choice models for mode choice o Business purpose Randstad South Wing, the railway stations: application to transit- o Rush hour based on departure time Netherlands oriented-developments o In-vehicle travel time in the Netherlands • Land use variables o Length of access route by bicycle o Number of dwellings o Jobs per inhabitant o Population density o Ratio of job types o Traffic nuisance o Density of bicycle network • Station characteristics o Number of sprinter trains o Number of BTM lines o Station type o Easiness to find travel information o Lighting o Number of bike parking spaces at departure station • Psychometric indicators o Punctuality of trains

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o Connection train to train at departure station o Shelter on platform at departure station o Frequency of trains o Social safety during the day and night o Quality of guarded bicycle parking o Quality of unguarded bicycle o General judgement of the departure station o Connection of other public transport by train o Quality of roads to access the departure station • Network attributes Brahmaiah et al. (2017) A Performance • VISSIM (simulation modal) software o Route travel time Analysis Of Modelling Route Choice Behavior • Discrete choice (multinomial logit) o Travel cost Delhi, India On Urban Bus And Multi Mode Transit Route model o Transfer time o Waiting time o Line haul time o Quality of service o In-vehicle travel • Socio-demographic attributes o Age o Gender o Income • Doubly stochastic path generation • In-vehicle travel times Anderson et al. (2017) Multimodal route algorithm (for choice set generation) • Number of transfers choice models of public transport passengers • A mix path size correction logit (PSC logit • Transfer time Greater Copenhagen Area, in the Greater Copenhagen Area model) o Waiting time at transfers Denmark • Revealed Preference o Walking time at transfers • Number of transfers • Bayesian inference approach o Discrete logit model of route Xu et al. (2018) Learning the route choice choice • In-vehicle travel time behavior of subway passengers from AFC data o Data mining approach • Transfer time Beijing, China • Metropolis-Hasting sampling • In-vehicle crowding • Gray relation analysis (GRA) • Stated Preference (SP) Survey • Revealed Preference

How do train-cyclists navigate? Pedro Nieves

Appendix C: Web Expert Survey

Bike-train route choices The following web-based expert survey was created as part of the research design for my master's thesis. As experts, you will be asked to rank a number of attributes based on their level of importance concerning bike-train route choices. A bike-train trip is understood here as the use of a bicycle to reach an access station and/or to travel from an egress station and whom main travel is composed of a train journey.

All the attributes mentioned in the survey were collected from a variety of studies which used them in their route choice models in order to estimate bicycle and transit route choices. Throughout the sections, you have to rank socio-demographic, network, and contextual attributes which you considered to be relevant with regards to train-cyclists' route choices. The aim is to filter some of these attributes based on the results of the survey. In the end, you will be asked to mention other attributes that you consider to be missing which could also be having an influence on bike-train route choices.

Thank you for your willingness to participate!

Socio-demographic attributes How important are the following socio-demographic attributes for making bike-train route choices?

1 = not at all important, 2 = low importance, 3 = slightly important, 4 = neutral, 5 = moderately important, 6 = very important, 7 = extremely important

1) Age

2) Gender/Sex

3) Employment status

How do train-cyclists navigate? Pedro Nieves

4) Car availability

5) Cycling experience

6) Parental status

Transit network attributes How important are the following transit network attributes for making bike-train route choices?

1 = not at all important, 2 = low importance, 3 = slightly important, 4 = neutral, 5 = moderately important, 6 = very important, 7 = extremely important

1) Type of station

2) Number of stops

How do train-cyclists navigate? Pedro Nieves

3) Vehicle’s departure time

4) In-vehicle travel time

5) Number of transfers

6) Transfer time

7) Travel time (for the entire trip)

8) Number of sprinter trains

9) Unguarded bike parking (at station)

10) Frequency of trains

How do train-cyclists navigate? Pedro Nieves

11) Shelter on platform

12) Connection to other public transport systems

Transit contextual attributes How important are the following transit contextual attributes for making bike-train route choices?

1 = not at all important, 2 = low importance, 3 = slightly important, 4 = neutral, 5 = moderately important, 6 = very important, 7 = extremely important

1) In-vehicle crowding

2) Weather

3) Social safety

4) Rush hours

How do train-cyclists navigate? Pedro Nieves

Bicycle network attributes How important are the following bicycle network attributes for making bike-train route choices?

1 = not at all important, 2 = low importance, 3 = slightly important, 4 = neutral, 5 = moderately important, 6 = very important, 7 = extremely important

1) On-street parking

2) Number of stop signs

3) Number of traffic lights

4) Slope

5) Number of intersections

6) Traffic volume

How do train-cyclists navigate? Pedro Nieves

7) Roadway speed limit

8) Travel time

9) Percentage of bike lane

10) Unguarded bike parking

11) Guarded bike parking

12) Number of bridges

13) Bike boulevards

How do train-cyclists navigate? Pedro Nieves

14) Presence/absence of bike lane

15) Travel distance

Bicycle contextual attributes How important are the following bicycle contextual attributes for making bike-train route choices?

1 = not at all important, 2 = low importance, 3 = slightly important, 4 = neutral, 5 = moderately important, 6 = very important, 7 = extremely important

1) Crime

2) Weather

3) Sunset/sunrise

4) Rush hours

How do train-cyclists navigate? Pedro Nieves

Train-cyclists’ route choice "Train-cyclists" is another way to name bike-train users; it refers to those people that make a trip using the bicycle and the train in combination. The conceptual scheme below illustrates all the aforementioned attributes used for the modelling of bicycle and transit route choices which altogether may determine potential bike- train route choices.

What do you think of the following conceptual scheme? Do you consider other attributes which could also be influencing train-cyclists route choices? If so, please mention them and provide a brief explanation.

How do train-cyclists navigate? Pedro Nieves

Appendix D: Trade-off Survey

Exploring bike-train route choices The following survey was created by Pedro Nieves, MSc candidate of urban and regional planning at the University of Amsterdam. In the frame of my master's thesis, I'm researching the process of making route choices when people decide to travel using the bicycle and the train in combination. As 'train-cyclists', you will have to answer different questions related to your bike-train route choices. In the first section you will be asked various socio-demographic questions. For the second section you are going to indicate (1) trade-offs between travel time and other travel factors and (2) your level of agreement according to a few statements.

All the data obtain from this survey will be kept confidential and only used for research purposes. If you are interested in the results, or have any questions, concerns, and/or remarks, feel free to contact me at [email protected]

Thank you for your willingness to participate!

Socio-demographics Please answer all of the following questions. Bear in mind that this survey targets people that (i) currently live and/or work in the Amsterdam Metropolitan Area and (ii) use the bicycle to get to/from train stations. If you do not comply with these two criteria, you can disregard completing the survey.

1) What is your sex? o Man o Woman

2) What is your age? Answer: ______

3) What is your employment status? o Employed o Self-employed o Unemployed o Retired o A student o A homemaker o Other: ______

4) Do you currently live, work and/or study in the Amsterdam Metropolitan Area (AMA)? o Yes o No

How do train-cyclists navigate? Pedro Nieves

5) Do you use the bicycle to get to or from train stations? o Yes o No

6) Which of the following bicycles do you frequently use to get to or from a train station? o Ordinary bicycle o Electric bicycle (e-bike) o OV bicycle (OV-fiets) o Folding bicycle (vouwfiets) o Cargo bicycle (bakfiets) o Swap bicycle (swapfiets)

7) At what side of the trip to you tend to ride your bicycle? o From my home to the train station o From the train station to my destination o From both sides of the trip

8) What is your usual purpose when doing a bike-train trip? o Work o Education o Leisure o Other: ______

9) How would you consider your cycling experience?

10) Do you currently own a car? o Yes o No

How do train-cyclists navigate? Pedro Nieves

Train-cyclists For the following sentences, please choose or indicate the amount of travel time in MINUTES that you are willing to cycle. Consider this extra time as part of your total commuting time.

1) I am willing to cycle ____ minutes more if I can board a train with less transfers. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

2) I am willing to cycle ____ minutes more if I can board a train that has a direct connection to my origin/destination. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

3) During rush hours, I am willing to cycle ____ minutes more if I can board a train that is less crowded. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

4) I am willing to cycle____ minutes more in order to reach a train station that has guarded bike parking. o 0 o 5

How do train-cyclists navigate? Pedro Nieves

o 10 o 15 o 20 o 25 o 30 o Other: ______

5) I am willing to cycle ____ minutes more in order to reach a train station that has unguarded bike parking. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

6) I am willing to cycle ____ minutes more in order to reach a train station that has low-priced bike parking. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

7) I am willing to cycle ____ minutes more to reach a train station that a has a variety of facilities such as coffee places, snack bars and/or stores. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

How do train-cyclists navigate? Pedro Nieves

8) When taking the train, I am willing to travel ____ minutes more in order to have time to study and/or work. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

9) I am willing to cycle ____ minutes more to/from a train station in order to avoid streets with a lot of traffic. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

10) I am willing to cycle ____ minutes more to/from a train station if I can take a bike route that is more attractive. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

11) I am willing to cycle ____ minutes more to a train station if I know that I can have a guaranteed seat inside the train carriage. o 0 o 5 o 10 o 15 o 20 o 25

How do train-cyclists navigate? Pedro Nieves

o 30 o Other: ______

12) I am willing to cycle ____ minutes more to/from a train station if I can take a bike route that is less crowded. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

13) I am willing to cycle ____ minutes more to reach a station that has a higher frequency of trains. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

14) I am willing to cycle ____ minutes more to a train station if I can spend less time transferring to other platforms. o 0 o 5 o 10 o 15 o 20 o 25 o 30 o Other: ______

How do train-cyclists navigate? Pedro Nieves

Appendix E: Interview Structure

Comments

• Briefly introduce the interviewee.

Introduction • You indicated that you live, study, and/or work in the AMA. Which one of these is what brings you to Area? Where do you live, study, and/or work?

Questions Comments

Socio-demographic attributes: 1) Cycling experience: For your cycling experience, you indicated that from a scale from 1 through 7 you consider to have a ___. Can you explain why? 2) Trip purpose: You mentioned that your usual purpose when doing a bike-train trip is to/for ______. Is there a specific reason for this? 3) Car availability: You also stated that you don’t own a car. If you would, do you think that this would change your current bike-train route choices? Phase 1: You also stated that you own a car. On which occasions do you Socio-demographic tend to use it instead of making a bike-train trip? attributes 4) You mentioned that you frequently use an ______bicycle to get to or from train stations. Do you sometimes use another type of bicycle? Why? 5) Do you own a second bicycle? If so, how do you use this to your favor when travelling? 6) What are your most frequent bike-train routes? 7) Why do you choose this (or these) route(s) instead of other ones? Do you always stick with the same routes?

8) Can you explain your decision process when making this (or these) route(s)? a. What travel factors do you take into consideration?

How do train-cyclists navigate? Pedro Nieves

Comments

Map #1: NS Stations within the AMA

This is a map of all the NS train stations within the AMA. There are currently a total of 52 railway stations which are relatively dispersed…

Map #2: Density of NS Stations

Phase 2: However, as you can see in this density map, there are two areas where the Maps stations appear to be more conglomerated. These are between the municipal border lines of Amsterdam, Diemen, and Ouder-Amstel and between Haarlem, Velsen, and Bloemendaal. • What are your thoughts on this?

Map #3: Overlapping catchment areas

In this map you can see train stations’ catchment areas based on a 5km cycling distance. What this suggests is that bike-train users may have the possibility of choosing between different stations when traveling. • Where you also aware of this? If so, how do you use it to your favor when making your route choices? Comments Contextual attributes: 1) Weather

On what ways do weather conditions influence your bike-train route choices? 2) Bike-route crowding Do you prefer to cycle to or from a train station through a route that is less crowded? How would you consider a bike route that is crowded? 3) In-vehicle crowding Do you prefer to avoid trains that are more crowded? How do you Phase 3: know if they are? Bike-train route choice 4) Attractiveness of bike route attributes When you’re planning your bike-train route, do you tend to cycle to or from a train station taking a route that more attractive? Why?

Network attributes 1) Traffic volume

How do train-cyclists navigate? Pedro Nieves

How does the traffic volume in streets influencing your decision of cycling to or from a train station? 2) Travel time How does travel time play a role when making your bike-train route choices? Would you consider it as the most important factor influencing your travel choices? Why? 3) Travel distance Do you tend to consider the distances you travel when your making a bike-train trip? If so, why? 4) Frequency of trains Do you prefer to cycle towards a train station that has a higher frequency of trains? Why? 5) (Un)guarded bike parking: Does it matter to you if the train station you visit has guarded or unguarded bike parking? Why? Would you prefer to cycle towards a train station that has bike parking with high capacity? Why? Would you prefer to leave your bicycle at a station whose bike parking is less occupied? Why? If any, what would be the ideal characteristics that a bike parking at a train station should have? 6) Train transfers: How do you consider train transfers when planning your train trip? Do you tend to avoid them? 7) Type of station When cycling towards a station, do you take into account the facilities that the station offers? (grocery shops, coffee shops) 8) Seat availability Is having a guaranteed seat when boarding a train important for you? Why?

How do train-cyclists navigate? Pedro Nieves

Appendix F: Informed Consent Letter

Description

You have been invited to participate in a research about how people make bike-train route choices in the Amsterdam Metropolitan Area. This research is being carried out by Pedro Nieves, a graduate student of Urban & Regional Planning at the University of Amsterdam in the Netherlands. The main purpose of this research is to enhance the understanding of factors that influence bike-train users’ route choice behavior in order to better cater for their travel needs. You have been selected to take part in this research based on your willingness to participate in a follow-up interview as you stated in the web survey. The choices for selecting the interviewees were based on maximizing the diversity of people’s socio-demographic background. Moreover, it is expected that approximately 10 interviewees will participate in this study as volunteers. After agreeing to participate in the study, you will be asked (1) elaborate on some of the answers you provided for the survey, and (2) your opinion on a few maps. Participating in this interview will take approximately 45 minutes. Risks and benefits

The risks associated for this study are mainly based on the discomfort that participants may feel when providing locational and routing information. On the other hand, the expected benefits for taking part in this research are (1) the chance of winning one of three €10 Amazon gift cards for completing the web survey and (2) the results of this study when it is finished in case the participant is interested.

Confidentiality

All the information or data that can identify the participant will be handled confidentially. In order to preserve the anonymity, pseudonyms will be used for the names of all the participants. Additionally, in case the participant gives permission for recording the interview, a verbatim transcription will be done to transfer into text everything of the audio file which will then be used only for research purposes. My thesis supervisor, Marco te Brömmelstroet, will also have access to the raw data that can directly or indirectly identify a participant, including this consent letter. Participants’ rights

If you have read this document and have decided to participate, please understand that your participation is completely voluntary and that you have the right to refrain from participating or withdrawing from the study at any time, without any penalty. You also have the right not to answer any particular question. In addition, you have the right to receive a copy of this document. If you have any questions or want more information about this research, please contact:

How do train-cyclists navigate? Pedro Nieves

Researcher: Pedro Nieves Thesis supervisor: Marco te Brömmelstroet. Ph.D. [email protected] // +31 6 45 527870 [email protected]

Your signature on this document means that you have decided to participate after having read and discussed the information presented on this consent letter and that you have received a copy of this document.

______

Name of participant Signature Date

I have discussed the contents of this consent form with the above signer.

______

Name of researcher Signature Date

How do train-cyclists navigate? Pedro Nieves

Appendix G: Total bike-train route choices for the 3rd travel scenario

Total Bike-train route choices travel time 1 Home-Harlem route #1 + Haarlem-Amsterdam Zuid + Zuid-UvA Route #1 69 2 Home-Harlem route #1 + Haarlem-Amsterdam Zuid + Zuid-UvA Route #2 69 3 Home-Harlem route #1 + Haarlem-Amsterdam Zuid + Zuid-UvA Route #3 70 4 Home-Harlem route #2 + Haarlem-Amsterdam Zuid + Zuid-UvA Route #1 72 5 Home-Harlem route #2 + Haarlem-Amsterdam Zuid + Zuid-UvA Route #2 72 6 Home-Harlem route #2 + Haarlem-Amsterdam Zuid + Zuid-UvA Route #3 73 7 Home-Harlem route #1 + Haarlem-Amsterdam Muiderpoort + Muiderpoort-UvA Route #1 44 8 Home-Harlem route #1 + Haarlem-Amsterdam Muiderpoort + Muiderpoort-UvA Route #2 45 9 Home-Harlem route #1 + Haarlem-Amsterdam Muiderpoort + Muiderpoort-UvA Route #3 45 10 Home-Harlem route #2 + Haarlem-Amsterdam Muiderpoort + Muiderpoort-UvA Route #1 47 11 Home-Harlem route #2 + Haarlem-Amsterdam Muiderpoort + Muiderpoort-UvA Route #2 48 12 Home-Harlem route #2 + Haarlem-Amsterdam Muiderpoort + Muiderpoort-UvA Route #3 48 13 Home-Harlem route #1 + Haarlem-RAI + RAI-UvA route #1 72 14 Home-Harlem route #1 + Haarlem-RAI + RAI-UvA route #2 72 15 Home-Harlem route #1 + Haarlem-RAI + RAI-UvA route #3 73 16 Home-Harlem route #2 + Haarlem-RAI + RAI-UvA route #1 75 17 Home-Harlem route #2 + Haarlem-RAI + RAI-UvA route #2 75 18 Home-Harlem route #2 + Haarlem-RAI + RAI-UvA route #3 76 19 Home-Harlem route #1 + Haarlem-Amsterdam Science Park + Science Park-UvA route #1 54 20 Home-Harlem route #1 + Haarlem-Amsterdam Science Park + Science Park-UvA route #2 55 21 Home-Harlem route #1 + Haarlem-Amsterdam Science Park + Science Park-UvA route #3 56 22 Home-Harlem route #2 + Haarlem-Amsterdam Science Park + Science Park-UvA route #1 57 23 Home-Harlem route #2 + Haarlem-Amsterdam Science Park + Science Park-UvA route #2 58 24 Home-Harlem route #2 + Haarlem-Amsterdam Science Park + Science Park-UvA route #3 59 25 Home-Harlem route #1 + Haarlem-Amsterdam Central Station + Central Station-UvA route #1 34 26 Home-Harlem route #1 + Haarlem-Amsterdam Central Station + Central Station-UvA route #2 35 27 Home-Harlem route #1 + Haarlem-Amsterdam Central Station + Central Station-UvA route #3 36 28 Home-Harlem route #2 + Haarlem-Amsterdam Central Station + Central Station-UvA route #1 37 29 Home-Harlem route #2 + Haarlem-Amsterdam Central Station + Central Station-UvA route #2 38 30 Home-Harlem route #2 + Haarlem-Amsterdam Central Station + Central Station-UvA route #3 39 31 Home-Harlem route #1 + Haarlem-Amsterdam Amstel + Amstel-UvA route #1 43 32 Home-Harlem route #1 + Haarlem-Amsterdam Amstel + Amstel-UvA route #2 44 33 Home-Harlem route #1 + Haarlem-Amsterdam Amstel + Amstel-UvA route #3 45 34 Home-Harlem route #2 + Haarlem-Amsterdam Amstel + Amstel-UvA route #1 46 35 Home-Harlem route #2 + Haarlem-Amsterdam Amstel + Amstel-UvA route #2 47

How do train-cyclists navigate? Pedro Nieves

36 Home-Harlem route #2 + Haarlem-Amsterdam Amstel + Amstel-UvA route #3 48 37 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Zuid + Zuid-UvA route #1 86 38 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Zuid + Zuid-UvA route #2 86 39 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Zuid + Zuid-UvA route #3 87 40 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Zuid + Zuid-UvA route #1 87 41 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Zuid + Zuid-UvA route #2 87 42 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Zuid + Zuid-UvA route #3 88 43 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Zuid + Zuid-UvA route #1 88 44 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Zuid + Zuid-UvA route #2 88 45 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Zuid + Zuid-UvA route #3 89 46 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 63 47 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 64 48 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 64 49 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 64 50 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 65 51 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 65 52 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 65 53 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 66 54 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 66 55 Home-Bloemendaal route #1 + Bloemendaal-RAI + RAI-UvA route #1 93 56 Home-Bloemendaal route #1 + Bloemendaal-RAI + RAI-UvA route #2 93 57 Home-Bloemendaal route #1 + Bloemendaal-RAI + RAI-UvA route #3 94 58 Home-Bloemendaal route #2 + Bloemendaal-RAI + RAI-UvA route #1 94 59 Home-Bloemendaal route #2 + Bloemendaal-RAI + RAI-UvA route #2 94 60 Home-Bloemendaal route #2 + Bloemendaal-RAI + RAI-UvA route #3 95 61 Home-Bloemendaal route #3 + Bloemendaal-RAI + RAI-UvA route #1 95 62 Home-Bloemendaal route #3 + Bloemendaal-RAI + RAI-UvA route #2 95 63 Home-Bloemendaal route #3 + Bloemendaal-RAI + RAI-UvA route #3 96 64 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Science Park – Science Park-UvA route #1 74 65 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Science Park – Science Park-UvA route #2 75 66 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Science Park – Science Park-UvA route #3 76 68 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Science Park – Science Park-UvA route #1 75 69 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Science Park – Science Park-UvA route #2 76 70 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Science Park – Science Park-UvA route #3 77 71 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Science Park – Science Park-UvA route #1 76 72 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Science Park – Science Park-UvA route #2 77 73 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Science Park – Science Park-UvA route #3 78 74 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #1 43 75 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #2 44 76 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #3 45

How do train-cyclists navigate? Pedro Nieves

77 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #1 44 78 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #2 45 79 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #3 46 80 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #1 45 81 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #2 46 82 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Central Station + Central Station-UvA route #3 47 83 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Amstel + Amstel-UvA route #1 53 84 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Amstel + Amstel-UvA route #2 54 85 Home-Bloemendaal route #1 + Bloemendaal-Amsterdam Amstel + Amstel-UvA route #3 55 86 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Amstel + Amstel-UvA route #1 54 87 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Amstel + Amstel-UvA route #2 55 88 Home-Bloemendaal route #2 + Bloemendaal-Amsterdam Amstel + Amstel-UvA route #3 56 89 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Amstel + Amstel-UvA route #1 55 90 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Amstel + Amstel-UvA route #2 56 91 Home-Bloemendaal route #3 + Bloemendaal-Amsterdam Amstel + Amstel-UvA route #3 57 91 Home-Overveen route #1 + Overveen-Amsterdam Zuid + Zuid-UvA route #1 85 92 Home-Overveen route #1 + Overveen-Amsterdam Zuid + Zuid-UvA route #2 85 93 Home-Overveen route #1 + Overveen-Amsterdam Zuid + Zuid-UvA route #3 86 94 Home-Overveen route #2 + Overveen-Amsterdam Zuid + Zuid-UvA route #1 85 95 Home-Overveen route #2 + Overveen-Amsterdam Zuid + Zuid-UvA route #2 85 96 Home-Overveen route #2 + Overveen-Amsterdam Zuid + Zuid-UvA route #3 86 97 Home-Overveen route #3 + Overveen-Amsterdam Zuid + Zuid-UvA route #1 89 98 Home-Overveen route #3 + Overveen-Amsterdam Zuid + Zuid-UvA route #2 89 99 Home-Overveen route #3 + Overveen-Amsterdam Zuid + Zuid-UvA route #3 90 100 Home-Overveen route #1 + Overveen-Amsterdam Muiderpoort + Muiderpoort route #1 66 101 Home-Overveen route #1 + Overveen-Amsterdam Muiderpoort + Muiderpoort route #2 67 102 Home-Overveen route #1 + Overveen-Amsterdam Muiderpoort + Muiderpoort route #3 67 102 Home-Overveen route #2 + Overveen-Amsterdam Muiderpoort + Muiderpoort route #1 66 103 Home-Overveen route #2 + Overveen-Amsterdam Muiderpoort + Muiderpoort route #2 67 104 Home-Overveen route #2 + Overveen-Amsterdam Muiderpoort + Muiderpoort route #3 67 105 Home-Overveen route #3 + Overveen-Amsterdam Muiderpoort + Muiderpoort route #1 70 106 Home-Overveen route #3 + Overveen-Amsterdam Muiderpoort + Muiderpoort route #2 71 107 Home-Overveen route #3 + Overveen-Amsterdam Muiderpoort + Muiderpoort route #3 71 108 Home-Overveen route #1 + Overveen-RAI + RAI-UvA route #1 86 109 Home-Overveen route #1 + Overveen-RAI + RAI-UvA route #2 86 110 Home-Overveen route #1 + Overveen-RAI + RAI-UvA route #3 87 111 Home-Overveen route #2 + Overveen-RAI + RAI-UvA route #1 86 112 Home-Overveen route #2 + Overveen-RAI + RAI-UvA route #2 86 113 Home-Overveen route #2 + Overveen-RAI + RAI-UvA route #3 87 114 Home-Overveen route #3 + Overveen-RAI + RAI-UvA route #1 90

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How do train-cyclists navigate? Pedro Nieves

115 Home-Overveen route #3 + Overveen-RAI + RAI-UvA route #2 90 116 Home-Overveen route #3 + Overveen-RAI + RAI-UvA route #3 91 117 Home-Overveen route #1 + Overveen-Amsterdam Science Park + Science Park-UvA route #1 80 118 Home-Overveen route #1 + Overveen-Amsterdam Science Park + Science Park-UvA route #2 81 119 Home-Overveen route #1 + Overveen-Amsterdam Science Park + Science Park-UvA route #3 82 120 Home-Overveen route #2 + Overveen-Amsterdam Science Park + Science Park-UvA route #1 80 121 Home-Overveen route #2 + Overveen-Amsterdam Science Park + Science Park-UvA route #2 81 122 Home-Overveen route #2 + Overveen-Amsterdam Science Park + Science Park-UvA route #3 82 123 Home-Overveen route #3 + Overveen-Amsterdam Science Park + Science Park-UvA route #1 84 124 Home-Overveen route #3 + Overveen-Amsterdam Science Park + Science Park-UvA route #2 85 125 Home-Overveen route #3 + Overveen-Amsterdam Science Park + Science Park-UvA route #3 86 126 Home-Overveen route #1 + Overveen-Amsterdam Central Station + Science Park-UvA route #1 52 127 Home-Overveen route #1 + Overveen-Amsterdam Central Station + Central Station-UvA route #2 53 128 Home-Overveen route #1 + Overveen-Amsterdam Central Station + Central Station-UvA route #3 54 129 Home-Overveen route #2 + Overveen-Amsterdam Central Station + Central Station-UvA route #1 52 130 Home-Overveen route #2 + Overveen-Amsterdam Central Station + Central Station-UvA route #2 53 131 Home-Overveen route #2 + Overveen-Amsterdam Central Station + Central Station-UvA route #3 54 132 Home-Overveen route #3 + Overveen-Amsterdam Central Station + Central Station-UvA route #1 56 133 Home-Overveen route #3 + Overveen-Amsterdam Central Station + Central Station-UvA route #2 57 134 Home-Overveen route #3 + Overveen-Amsterdam Central Station + Central Station-UvA route #3 58 135 Home-Overveen route #1 + Overveen-Amsterdam Amstel + Amstel-UvA route #1 65 136 Home-Overveen route #1 + Overveen-Amsterdam Amstel + Amstel-UvA route #2 66 137 Home-Overveen route #1 + Overveen-Amsterdam Amstel + Amstel-UvA route #3 67 138 Home-Overveen route #2 + Overveen-Amsterdam Amstel + Amstel-UvA route #1 65 139 Home-Overveen route #2 + Overveen-Amsterdam Amstel + Amstel-UvA route #2 66 140 Home-Overveen route #2 + Overveen-Amsterdam Amstel + Amstel-UvA route #3 67 141 Home-Overveen route #3 + Overveen-Amsterdam Amstel + Amstel-UvA route #1 69 142 Home-Overveen route #3 + Overveen-Amsterdam Amstel + Amstel-UvA route #2 70 143 Home-Overveen route #3 + Overveen-Amsterdam Amstel + Amstel-UvA route #3 71 144 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Zuid + Zuid-UvA route #1 77 145 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Zuid + Zuid-UvA route #2 77 146 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Zuid + Zuid-UvA route #3 78 147 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Zuid + Zuid-UvA route #1 78 148 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Zuid + Zuid-UvA route #2 78 149 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Zuid + Zuid-UvA route #3 79 150 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Zuid + Zuid-UvA route #1 84 151 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Zuid + Zuid-UvA route #2 84 152 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Zuid + Zuid-UvA route #3 85 153 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 76 154 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 77

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How do train-cyclists navigate? Pedro Nieves

155 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 77 156 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 77 157 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 78 158 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 78 159 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 82 160 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 83 161 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 83 162 Home-SantpoortNoord route #1 + SantpoortNoord-RAI + RAI-UvA route #1 106 163 Home-SantpoortNoord route #1 + SantpoortNoord-RAI + RAI-UvA route #2 106 164 Home-SantpoortNoord route #1 + SantpoortNoord-RAI + RAI-UvA route #3 107 165 Home-SantpoortNoord route #2 + SantpoortNoord-RAI + RAI-UvA route #1 107 166 Home-SantpoortNoord route #2 + SantpoortNoord-RAI + RAI-UvA route #2 107 167 Home-SantpoortNoord route #2 + SantpoortNoord-RAI + RAI-UvA route #3 108 168 Home-SantpoortNoord route #3 + SantpoortNoord-RAI + RAI-UvA route #1 112 169 Home-SantpoortNoord route #3 + SantpoortNoord-RAI + RAI-UvA route #2 112 170 Home-SantpoortNoord route #3 + SantpoortNoord-RAI + RAI-UvA route #3 113 171 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Science Park + Science Park-UvA route #1 87 172 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Science Park + Science Park-UvA route #2 88 173 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Science Park + Science Park-UvA route #3 89 174 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Science Park + Science Park-UvA route #1 88 175 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Science Park + Science Park-UvA route #2 89 176 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Science Park + Science Park-UvA route #3 90 177 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Science Park + Science Park-UvA route #1 93 178 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Science Park + Science Park-UvA route #2 94 179 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Science Park + Science Park-UvA route #3 95 180 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Central Station + Central Station-UvA route #1 56 181 Home-SantpoortNoord route #1 + SantpoortNoord- Amsterdam Central Station + Central Station -UvA route #2 57 182 Home-SantpoortNoord route #1 + SantpoortNoord- Amsterdam Central Station + Central Station -UvA route #3 58 183 Home-SantpoortNoord route #2 + SantpoortNoord- Amsterdam Central Station + Central Station -UvA route #1 57 184 Home-SantpoortNoord route #2 + SantpoortNoord- Amsterdam Central Station + Central Station -UvA route #2 58 185 Home-SantpoortNoord route #2 + SantpoortNoord- Amsterdam Central Station + Central Station -UvA route #3 59 186 Home-SantpoortNoord route #3 + SantpoortNoord- Amsterdam Central Station + Central Station -UvA route #1 62 187 Home-SantpoortNoord route #3 + SantpoortNoord- Amsterdam Central Station + Central Station -UvA route #2 63 188 Home-SantpoortNoord route #3 + SantpoortNoord- Amsterdam Central Station + Central Station -UvA route #3 64 189 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Amstel + Amstel-UvA route #1 66 190 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Amstel + Amstel-UvA route #2 67 191 Home-SantpoortNoord route #1 + SantpoortNoord-Amsterdam Amstel + Amstel-UvA route #3 68 192 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Amstel + Amstel-UvA route #1 67 193 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Amstel + Amstel-UvA route #2 68 194 Home-SantpoortNoord route #2 + SantpoortNoord-Amsterdam Amstel + Amstel-UvA route #3 69

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How do train-cyclists navigate? Pedro Nieves

195 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Amstel + Amstel-UvA route #1 72 196 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Amstel + Amstel-UvA route #2 73 197 Home-SantpoortNoord route #3 + SantpoortNoord-Amsterdam Amstel + Amstel-UvA route #3 74 198 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Zuid + Zuid-UvA route #1 92 199 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Zuid + Zuid-UvA route #2 92 200 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Zuid + Zuid-UvA route #3 93 201 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Zuid + Zuid-UvA route #1 93 202 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Zuid + Zuid-UvA route #2 93 203 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Zuid + Zuid-UvA route #3 94 204 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Zuid + Zuid-UvA route #1 94 205 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Zuid + Zuid-UvA route #2 94 206 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Zuid + Zuid-UvA route #3 95 207 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 69 208 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 70 209 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 70 210 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 70 211 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 71 212 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 71 213 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 71 214 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 72 215 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 72 216 Home-SantpoortZuid route #1 + SantpoortZuid-RAI + RAI-UvA route #1 99 217 Home-SantpoortZuid route #1 + SantpoortZuid-RAI + RAI-UvA route #2 99 218 Home-SantpoortZuid route #1 + SantpoortZuid-RAI + RAI-UvA route #3 100 219 Home-SantpoortZuid route #2 + SantpoortZuid-RAI + RAI-UvA route #1 100 220 Home-SantpoortZuid route #2 + SantpoortZuid-RAI + RAI-UvA route #2 100 221 Home-SantpoortZuid route #2 + SantpoortZuid-RAI + RAI-UvA route #3 101 222 Home-SantpoortZuid route #3 + SantpoortZuid-RAI + RAI-UvA route #1 101 223 Home-SantpoortZuid route #3 + SantpoortZuid-RAI + RAI-UvA route #2 102 224 Home-SantpoortZuid route #3 + SantpoortZuid-RAI + RAI-UvA route #3 102 225 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Science Park + Science Park-UvA route #1 80 226 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Science Park + Science Park-UvA route #2 81 227 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Science Park + Science Park-UvA route #3 82 228 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Science Park + Science Park-UvA route #1 81 229 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Science Park + Science Park-UvA route #2 82 230 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Science Park + Science Park-UvA route #3 83 231 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Science Park + Science Park-UvA route #1 82 232 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Science Park + Science Park-UvA route #2 82 233 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Science Park + Science Park-UvA route #3 84 234 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #1 49

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How do train-cyclists navigate? Pedro Nieves

235 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #2 50 236 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #3 51 237 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #1 50 238 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #2 51 239 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #3 52 240 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #1 51 241 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #2 52 242 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Central Station + Central Station-UvA route #3 53 243 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Amstel + Amstel-UvA route #1 59 244 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Amstel + Amstel-UvA route #2 60 245 Home-SantpoortZuid route #1 + SantpoortZuid-Amsterdam Amstel + Amstel-UvA route #3 61 246 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Amstel + Amstel-UvA route #1 60 246 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Amstel + Amstel-UvA route #2 61 247 Home-SantpoortZuid route #2 + SantpoortZuid-Amsterdam Amstel + Amstel-UvA route #3 62 248 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Amstel + Amstel-UvA route #1 61 249 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Amstel + Amstel-UvA route #2 62 250 Home-SantpoortZuid route #3 + SantpoortZuid-Amsterdam Amstel + Amstel-UvA route #3 63 251 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Zuid + Zuid-UvA route #1 76 252 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Zuid + Zuid-UvA route #2 76 253 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Zuid + Zuid-UvA route #3 77 254 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Zuid + Zuid-UvA route #1 77 255 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Zuid + Zuid-UvA route #2 77 256 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Zuid + Zuid-UvA route #3 78 257 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Zuid + Zuid-UvA route #1 77 258 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Zuid + Zuid-UvA route #2 77 259 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Zuid + Zuid-UvA route #3 78 260 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 57 261 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 58 262 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 58 263 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 58 264 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 59 265 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 59 266 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Muiderpoort + Muiderpoort-UvA route #1 58 267 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Muiderpoort + Muiderpoort-UvA route #2 59 268 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Muiderpoort + Muiderpoort-UvA route #3 59 269 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-RAI + RAI-UvA route #1 77 270 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-RAI + RAI-UvA route #2 77 271 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-RAI + RAI-UvA route #3 78 272 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-RAI + RAI-UvA route #1 78 273 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-RAI + RAI-UvA route #2 79

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How do train-cyclists navigate? Pedro Nieves

274 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-RAI + RAI-UvA route #3 79 275 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-RAI + RAI-UvA route #1 78 276 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-RAI + RAI-UvA route #2 79 277 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-RAI + RAI-UvA route #3 79 278 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Science Park + Science Park-UvA route #1 71 279 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Science Park + Science Park-UvA route #2 72 280 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Science Park + Science Park-UvA route #3 73 281 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Science Park + Science Park-UvA route #1 72 282 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Science Park + Science Park-UvA route #2 73 283 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Science Park + Science Park-UvA route #3 74 284 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Science Park + Science Park-UvA route #1 72 285 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Science Park + Science Park-UvA route #2 73 286 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Science Park + Science Park-UvA route #3 74 287 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #1 43 288 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #2 44 289 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #3 45 290 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #1 44 291 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #2 45 292 Home-Haarlem Spaarnwoude route #2 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #3 46 293 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #1 44 294 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #2 45 295 Home-Haarlem Spaarnwoude route #3 + Haarlem Spaarnwoude-Amsterdam Central Station + Central Station-UvA route #3 46 296 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Amstel + Amstel-UvA route #1 53 297 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Amstel + Amstel-UvA route #2 54 298 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Amstel + Amstel-UvA route #3 55 299 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Amstel + Amstel-UvA route #1 54 300 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Amstel + Amstel-UvA route #2 55 301 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Amstel + Amstel-UvA route #3 56 302 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Amstel + Amstel-UvA route #1 54 303 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Amstel + Amstel-UvA route #2 55 304 Home-Haarlem Spaarnwoude route #1 + Haarlem Spaarnwoude-Amsterdam Amstel + Amstel-UvA route #3 56

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How do train-cyclists navigate? Pedro Nieves

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