West, East Or South, Which Railwaystation in Hudiksvall Is

West, East or South, which Railway Station in Hudiksvall is Preferable? A Predictive Study of Future Climate Scenarios from an Accessibility Perspective

Sofia Moberg

Master Thesis Spatial planning and development, Umeå university Spring 2021

Author: Sofia Moberg Contact: [email protected] Master thesis (30 ects) Program: Master in Spatial Planning and Development Department of Geography Umeå University Supervisor: Magnus Strömgren Abstract in English An expansion of the railway, East Coast Line is essential in order to ensure transportation of passenger and goods back and forth to Northern Sweden. The preliminary studies of the planned expansion to a double track have identified vulnerabilities linked to how our climate changes. Because of these risks and vulnerabilities, the railway station in Hudiksvall needs to be relocated or the Current station needs to be adapted to potential future climate scenarios. Furthermore, social sustainability and the aspect of accessibility is also a vital perspective to consider during the development of railway infrastructure. This study compares the three different station locations from an accessibility perspective and from different climate scenarios through Network Analyst in ArcGIS Pro. To visualize future climate scenarios, two RCP-scenarios (Representative Concentration Pathways) are considered, which is RCP 4.5 and RCP8.5. Additionally, the GTFS specification in ArcGIS Pro is used to model public transit to these railway stations in an accessibility perspective. Because one strategy when developing the East Coast Line is to increase the active transportation in comparison to car transportation.

Results from this study indicates that the Current station, which is located in a coastal area will be worst affected of potential future climate scenarios from an accessibility perspective. Other findings are that vulnerable groups in the society, such as low-income earners and elderly will be most affected, if the railway station remains in the current location. The results from the performed Service area analysis and Location-allocation analysis advocates the Eastern station as a location for the new railway station.

Keywords: accessibility, active transportation, RCP-scenarios, public transit, GTFS.

Abstract in Swedish – Abstrakt på svenska En expansion av Ostkustbanan är viktigt för att kunna säkerställa transport av passagerare och gods till och från norra Sverige. Förstudierna av den planerade expansionen till ett dubbelspår har identifierat sårbarheter kopplat till hur vårt klimat förändras. Som en följd av dessa risker och sårbarheter behöver järnvägsstationen i Hudiksvall flyttas. Alternativt behöver den nuvarande klimatanpassas. Detta ställer krav på att ta hänsyn till den sociala hållbarheten och tillgängligheten för befolkningen i Hudiksvall. Denna studie ämnar att jämför de tre olika stationslägena ur ett tillgänglighetsperspektiv samt utifrån olika klimatscenarier i ArcGIS Pro. För att visualisera framtida klimatscenarier beaktas två RCP-scenarier, det vill säga representativa koncentrationsvägar, vilket är RCP4.5 och RCP8.5. Dessutom används GTFS- specifikationen i ArcGIS Pro för att modellera kollektivtrafik till dessa järnvägsstationer ur ett tillgänglighetsperspektiv, då ett mål med utvecklingen av Ostkustbanan är att utöka andelen av personer som väljer aktiv transport i jämförelse med biltransporter.

Resultatet av denna studie visar att den nuvarande stationen, som är placerad i närheten av Hudiksvalls kust, kommer att vara hårdast drabbad av potentiella framtida klimatscenarier ur ett tillgänglighetsperspektiv. Vidare visar studien på att låginkomsttagare och äldre kommer att drabbas hårdast om järnvägsstationen ligger kvar på den nuvarande platsen. Resultatet från de utförda Service area analyserna och Location-allocation analyserna, visar att det östra alternativet är det alternativ som kommer att vara minst påverkad av ett framtida klimat från ett tillgänglighetsperspektiv.

Nyckelord: tillgänglighet, aktiv transport, RCP-scenarier, kollektivtrafik, GTFS.

Acknowledgements First of all, I would like to thank my supervisor at Umeå University, Magnus Strömgren, for helping me to develop my research and introducing me to the GTFS-specification in ArcGIS. Most of all, thanks for your patience when mine was non-existing. I would like to express my gratitude to my supervisor at the Swedish Transport Administration, (STA), Katarina Lind, for enabling me to do this thesis in collaboration with the STA. Thank you for emphasizing the importance of my work and your faith in me when I need it the most. Also, to Isabelle Jonsson, thank you for the time you have spent introducing me to SCALGO Live and helping me to understand the different conditions of the RCP- scenarios. I would also like to take this opportunity to thank everyone in the Planning- department at STA for your warm welcome of me during these special times. A very big thank you to Hans Gyllow at Hudiksvall’s municipality and to Agneta Davidsson, project leader at STA, for your time going through the different conditions of the East Coast Line project. To my father-in-law, Roger Söderberg, M.A. in English linguistic, thank you for your time and effort you have spent on this thesis with your carefully reading of my texts. Additionally, I would like to thank my family and friends who have supported me through this journey. A personal thanks to my father, Hans Moberg, who has stopped asking finally what I am going to be when I grow up. At last, I would like to express my gratitude to my fiancé Pontus Söderberg, who always supports and pushes me to challenge myself. Without you I would probably not even have started my master studies. You are my greatest source of inspiration.

Tabel of Content 1. Introduction ...... 10 1.1. Problem Formulation ...... 11 1.1.2. Aim ...... 13 2. Theoretical framework & Literature review ...... 15 2.1. Theoretical Framework ...... 15 2.1.1. How can Accessibility & Active transportation be Achieved? ...... 15 2.1.2. Concepts of Planning for Climate Changes ...... 16 2.1.3. Representative Concentration Pathways (RCP) ...... 16 2.2. Literature review ...... 18 2.2.1. Accessibility of Public Transit Systems ...... 18 2.2.2. Active Transportation & Accessibility ...... 20 2.2.3. Challenges with Implementation of Climate Adaptation ...... 21 2.2.4. Expected Events in a Future Climate ...... 23 3. Method ...... 25 3.1. GIS as a Quantitative Method ...... 25 3.1.3. Why Network Analyst? ...... 27 3.2. Data sample & Methodological settings ...... 28 3.2.1. Methodological Choices for the Network Analysis ...... 28 3.2.2. Methodological Choices for the Climate Layers ...... 30 3.2.3. Restrictions & Barriers in the Network ...... 30 3.2.4. Methodological Choices for the Service Area Analysis ...... 31 3.2.5. Methodological Choices for Location-allocation Analysis ...... 32 3.3. Limitations...... 33 4. East Coast Line & Travel Patterns ...... 34 4.1. Travel Patterns of Today ...... 34 4.2. Routes of the East Coast Line ...... 37 5. Result ...... 38 5.1. How will the Accessibility be Affected Regarding Different Locations? ...... 38 5.1.1. Catchment Area of Various Groups ...... 43 5.2. How will the Concerned Locations be Affected Regarding RCP-scenario 4.5 & 8.5? ...... 47 5.2.1. Expected Outcomes of Accessibility for Pedestrians ...... 51 5.2.2. Expected Outcomes of Accessibility for Bicyclists ...... 53 5.2.3. Expected Outcomes of Accessibility for E-bicyclists ...... 55 5.2.4. Expected Outcomes for Various Groups ...... 56 5.3. What Location is Preferable Regarding both the RCP-scenarios and Accessibility?...... 62 5.3.1. Preferable Location with RCP4.5 ...... 65 5.3.2. Preferable Location with RCP8.5 ...... 67 6. Discussion ...... 71 6.1. Preferable Location in a Socio-accessible Perspective ...... 71 6.2. Preferable Location in a Socio-environmental Perspective ...... 73 6.3. Socio-environmental Preferable Location ...... 75 7. Conclusion ...... 77 8. References ...... 79

List of figures. Figure 1. Overview of the East Coast Line and the study area...... 12 Figure 2. Developments curves for emission of greenhouse gases with different RCPs ...... 17 Figure 3. Global mean sea level rise depending on RCP- scenarios ...... 24 Figure 4. Road network of Hudiksvall’s municipality...... 25 Figure 5. Road network around the potential railway stations...... 26 Figure 6. Work commuters from Hudiksvall ...... 34 Figure 7. Work commuters to Hudiksvall...... 35 Figure 8. High School commuters from Hudiksvall ...... 36 Figure 9. Overview of alternative route of the railway depending in chosen location...... 37 Figure 10. Overview of the catchment areas with different travel modes ...... 39 Figure 11. Number & share of population (%) within each of the cutoff times with walking as travel mode...... 40 Figure 12. Number & share of population (%) within each of the cutoff times with cycle as travel mode...... 41 Figure 13. Number & share of population (%) within each of the cutoff times with e-bike as travel mode...... 42 Figure 14. Number & share of the total female population (%) within each of the cutoff times with pedestrian as travel mode ...... 46 Figure 15. Share of the elderly population (%) within each of the cutoff times, with pedestrian as travel mode ...... 47 Figure 16. Overview of flooded areas with RCP4.5 & RCP8.5 ...... 48 Figure 17. Comparison between the usual catchment area for pedestrians and the catchment area of pedestrians with climate barriers RCP4.5 & RCP8.5...... 49 Figure 18. Comparison between the usual catchment area for bicyclists and the catchment area of bicyclists with climate barriers RCP4.5 & RCP8.5...... 50 Figure 19. Comparison between the usual catchment area for e-bicyclist and the catchment area of e- bicyclists with climate barriers RCP4.5 & RCP8.5...... 50 Figure 20. Number & share of population (%) within each of the cutoff times with pedestrian as travel mode & RCP4.5 ...... 51 Figure 21. Number & share of the population (%) within each of the cutoff times with pedestrian as travel mode & RCP8.5...... 52 Figure 22. Number & share of population (%) within each of the cutoff times with bicycle as travel mode & RCP4.5...... 54 Figure 23. Number & share of population (%) within each of the cutoff times with bicycle as travel mode & RCP8.5...... 54 Figure 24. Number & share of population (%) within each of the cutoff times with e-bike as travel mode & RCP4.5...... 55 Figure 25. Number & share of population (%) within each of the cutoff times with e-bike as travel mode & RCP8.5...... 56 Figure 26. Number & share of the total female population (%) within each of the cutoff times, with pedestrian as travel mode and RCP4.5...... 59 Figure 27. Number & share of the total female population (%) within each of the cutoff times, with pedestrian as travel mode and RCP8.5...... 60 Figure 28. Number & share of the elderly population (%) within each of the cutoff times, with pedestrian as travel mode and RCP4.5...... 61 Figure 29. Number & share of the elderly population (%) within each of the cutoff times, with pedestrian as travel mode and RCP8.5...... 61 Figure 30. Chosen facility based on location-allocation analysis with pedestrian as travel mode...... 62 Figure 31. Chosen facility based on location-allocation analysis with bicycle as travel mode...... 62 Figure 32. Chosen facility based on location-allocation analysis with e-bike as travel mode...... 64 Figure 33. Chosen facility based on location-allocation analysis with RCP4.5 and pedestrian as travel mode...... 65 Figure 34. Chosen facility based on location-allocation analysis with RCP4.5 and bicycle as travel mode...... 66 Figure 35. Chosen facility based on location-allocation analysis with RCP4.5 and e-bike as travel mode...... 67 Figure 36. Chosen facility based on location-allocation analysis with RCP8.5 and pedestrian as travel mode...... 68 Figure 37. Chosen facility based on location-allocation analysis with RCP8.5 and bicycle as travel mode...... 69 Figure 38. Chosen facility based on location-allocation analysis with RCP8.5 and e-bike as travel mode...... 70

List of tables Table 1. Data gathering and source...... 28 Table 2. Average speed per travel mode. Source: Bedogni et al., (2016)...... 29 Table 3. Settings in SCALGO Live ...... 30 Table 4. Total amount of the population that each of the station captures 2017 & 2019 ...... 43 Table 5. Number of Low-income earners in each cutoff time per station (Pedestrian) ...... 44 Table 6. Number of Low-income earners in each cutoff time per station (Bicycle) ...... 44 Table 7. Number of Middle-high-income earners in each cutoff time per station (Pedestrian)...... 44 Table 8. Number of Middle-high income earners in each cutoff time per station (Bicycle) ...... 45 Table 9. Share of Low-income earners in each cutoff time & station for different RCP-scenario & the differential from today (Pedestrian) ...... 57 Table 10. Share of Low-income earners in each cutoff time and station with RCP4.5 & RCP8.5 (Bicycle)...... 57 Table 11. Share of Middle-high income earners in each cutoff time and station with RCP4.5 & RCP8.5 (Pedestrian)...... 58 Table 12. Share of Middle-high income earners in each cutoff time and station with RCP4.5 & RCP8.5 (Bicycle) ...... 58

1. Introduction Events of extreme weather all around the world has caused damage on the infrastructure and affect the population. This has shed light on the importance to considering future climate scenarios in spatial planning (Monterio & Ferreira, 2020). But for decades, the significant impact of the population everyday life as a direct effect of environmental changes have been less prioritized in spatial planning (Nyström & Tonell, 2012). Instead, focus has relied on economic growth and urban sprawl was a planning phenomenon that after the Second World War was a way of developing it (Arnstberg, 2005).

Increased urban sprawl and decentralization expanded the car dependence (Banister, 2008). A wealthier Western World contributes to that more people own a car, which increases the usage and is one reason of elevated rate of greenhouse gas emissions (Norström & Losciale, 1995). In order to measure how the climate changes due to greenhouse gas emissions, different Representative Concentration Pathways (RCP)1 has been developed (Van Vuuren et al., 2011). These climate scenarios are common in climate adaptive planning (SMHI, 2020).

The national strategy of climate adaptations that the Swedish government has established, as a direct effect of the Paris Agreement, puts work of climate and vulnerability analysis to a central part of the planning procedure (Prop. 2017/18:163). To consider risks for landslides, erosion, cloudburst, droughts, and sea level rise when planning for new development creates the condition to plan for a robust and sustainable transportation system (Nyström & Tonell, 2012), with uncertainty considered (Coaffee & Lee, 2016). Also, this ensures that we can live with developments impacts of nature (Harvey, 2003). An ecological sustainable transport system is the system that ensures good water, ground and air quality and take the biodiversity in account for both humans and nature (Nyström & Tonell, 2012).

However, decentralization and increased usage of car has not only elevated the carbon dioxide emissions (Norström & Losciale, 1995), it has also affected the mobility. Contributing to less attractiveness of active transportation, such as walking, and cycling (Banister, 2008). Another used terminology of active transportation is non-motorized transport (Sallis, Frank, Saelens & Kraft, 2004) or slow modes travel (Elldér, Larsson, Solá & Vilhelmson, 2018). However,

1 Over the years different climate scenarios has been adapted to describes a possible development of the climate based on assumptions of the human activities and how much energy that is retained in the atmosphere. RCP- scenarios one way of describing how the climate changes based on emission of greenhouse gases, air pollution and land-use. Today, four RCP-scenarios has been estimated, which is 2.6, 4.5, 6 and 8,5. The higher RCP, the more emission of greenhouse gases. The adapted RCP-scenarios and its meaning will be explained further in the theoretical part. 10

Banister, (2008) has also seen in his study that local public transport has also been less attractive because of urban sprawl. Additionally, less active transportation can e.g., lead to disparities in health (Gray et al., 2011). The same study has also stated that a wider sub- regional pattern of housing, economic development, land use and transportation are factors of social exclusion (ibid.). To prevent this, efforts in the planning process are required to achieve a transportation system that is social sustainable. Implying that the system should create opportunities for positive experiences. A good life of culture, public service, good health and include everyone (Nyström & Tonell, 2012).

But planning for a robust transportation network and social sustainability is challenging and complex. Planning for a robust transportation network because future climate scenarios are predictions. Even if we today know much about the climate change, much is yet to be discovered (Lennartsson & Simonsson, 2007). To fulfill social sustainability goal is difficult because they are soft values that are challenging to measure. The challenge with the discourse of social sustainability is that it has become a collective concept across the whole population. Leading to difficulties to identify which measures are aimed for which individuals (Nyström & Tonell, 2012).

Questions that need to be asked are whose right’s and whose city? Lefebvre’s theory of the right to the city refers to urban environment and the context to everyday life and social relations (Lefebvre, 1996; Shields, 2011). While Marcuse (2012, p. 34) defines the right to the city as individual justice for all. The understanding of specific life situations needs to be considered to be able to create a good environment in spatial planning. Life situations is to be understood as the environmental, economic, and social aspects and the differences in transportation patterns that come with it (Nyström & Tonell, 2012).

1.1. Problem Formulation Passenger transportation on the East Coast Line is extensive due to the relationship with metropolitan areas, such as Stockholm and Gothenburg. Capacity utilization is already high and with increased population growth in the cities around the East Coast Line, there is a possibility that travelling times will increase. Because of this it is of high priority to with take measures in order to ensure a robust and sustainable transport system for passenger and goods to and from Northern Sweden. The plan is to expand the railway line to a double track and increase the travels with train in comparison to car travels. This would have positive effects on both the environment and social sustainability (Åström, 2021).

When planning for large-scale infrastructure, it is vital to consider climate change and adaption that may be bedded in a future climate in consideration, as that ensures that we can live with the developments (Harvey, 2003). While it is equally important to consider social sustainability and equity (Garcia, Macário, Menezes & Lourerio, 2018; Åström, 2021). In that way, climate change and social sustainability is interlinked. Because if we manage to fulfill the 13th Sustainable Development Goal, to fight climate change. We are on a good way to achieve social sustainability (AtKisson, 2017, 17 May).

Considering climate change as a part of the National Climate Adaption Strategy (Prop. 2017/18:163), the planned expansion of the East Coast Line has identified vulnerabilities of the existing transportation system (Åström, 2021). But the scope of this study is not to investigate the whole route Gävle-Västeraspby, because it would have been too extensive in relation to the time. The aim is rather to investigate the adaptations that is needed and what social impacts it may have on one municipality along the East Coast Line, namely Hudiksvall. See figure 1. By studying the development of the East Coast Line and the relocation of the railway station in Hudiksvall, this study Figure 1. Overview of the East Coast Line and the study area. contributes to accessibility perspectives in transportation planning by analyzing how the accessibility for various groups in society can be affected when performing large redevelopments of infrastructure. Also, this study contributes to how

12 accessibility for various groups in society can be affected by climate change. Additionally, a part of this research is to analyze active travel modes, such as, pedestrians, with the possibility to use public transit, and bicyclist’s accessibility to different alternatives of a potential new railway station. As this study aims to apply the GTFS-specification, which makes it possible to apply public transit data to Network analyst in ArcGIS Pro. Therefore, car transportation is excluded in this study.

Identified vulnerabilities around the traffic lane in Hudiksvall are landslides and erosion. Plus, flooding, sea level rise interlinked to different RCP-scenarios. This has led the Swedish Transport administration, (STA) to consider other options for the location of the railway station (Åström, 2021). This study aims to compare the three alternative locations from an accessibility perspective and how accessibility can be affected based on different climate scenarios.

If measures are not taken, these identified risks and vulnerabilities will cause affects from a social sustainability perspective as well (Åström, 2021). After all, the importance of a robust and accessible transportation system is essential to prevent social exclusion (Gray et al., 2011). Because an accessible transportation system makes it possible for marginal groups in the society, without the possibility to own a car, to access employments or other daily activities (Garcia et al., 2018). Therefore, accessibility to public transport creates the conditions for successful integration of vulnerable groups in society (Gray et al., 2011; Nyström & Tonell. 2012).

1.1.2. Aim As a consequence of the identified risks and vulnerabilities of the existing system along the East Coast Line, the location of the railway station in Hudiksvall will need to be reviewed. The STA has developed proposals for three possible locations for a new railway station, which one is the existing one. Depending on where this new station will be located, different conditions are created for access to the transport system from an accessibility and equality perspective. At the same time, different climate scenarios can cause disturbances and affect the accessibility. This study aims to contribute to accessibility planning from a climate perspective, by analyzing various groups of the society’s accessibility towards the possible locations from a climate scenario of today and in the future. Hence, the aim of this study is to conduct a predictive study of the possible locations of the railway station and answer following questions:

1. How will the accessibility be affected regarding the different locations? 2. How will the concerned locations be affected regarding RCP-scenario 4.5 and 8.5? 3. What location is preferable regarding both the RCP-scenarios and accessibility?

2. Theoretical framework & Literature review This episode consists of two parts. The first one is theoretical framework, which deals with the theoretical definitions of accessibility, adaptive planning, and definitions of the RCP- scenarios. The second part is the literature review episode, which is there to guide the decisions that were made when setting important parameters in for my Network analysis in ArcGIS.

2.1. Theoretical Framework The first part of this episode will define definitions of accessibility and how sustainable mobility can be achieved with active transportation in transportation planning. The second part of this episode consists of theories of planning for climate change and the signification of RCP-scenarios.

2.1.1. How can Accessibility & Active transportation be Achieved? A sustainable mobility paradigm involves overcoming the car dependency and make room for local public transit and active transportation. However, in order to overcome the car dependency, the public transit system, together with the active transportation, must precede car traffic and be prioritized in planning. But the problem with the sustainable mobility paradigm is that people overall tend to measure their travels in time. At the same time that travels are not something that people wish to undertake for their own sake. Therefore, to live up to the sustainable mobility paradigm, proximity to different facilities, services and housing is needed (Banister, 2008). This is where accessibility comes in. Accessibility has long been associated with speed (Alfonzo, 2005). But in a sustainable mobility paradigm (Bansiter, 2012) it is rather enabled with proximity (Alfonzo, 2005).

Halden’s (2012) theory about accessibility lands in a new accessibility paradigm. Accessibility is an overall terminology used to describe proximity to access different services and is defined different for different individuals. The theory strives to achieve accessibility for various groups of people. In order to live up to the accessibility paradigm a more user centric approach needs to be adapted in accessibility planning (ibid.). This is in line with Lefebvre’s theory of the right to the city, referring to different needs for different groups of people. The meaning is that planning needs to consider different life situations due to that it requires different measurements (Lefebvre, 1996; Shield, 2011). But in order to know which measurements are required for different individuals: Who the new station is planned for needs to be considered. Is it the people in charge or people in society? This is something that Harvey’s (2003) theory about the right to the city highlights and builds on. Different

15 perspectives of various groups of people needs to take more space in pre-studies in order to build inclusive societies (Harvey, 2003; Nyström & Tonell, 2012).

2.1.2. Concepts of Planning for Climate Changes Coaffee & Lee’s (2016) theory about adaptive planning, refers to involving climate change and uncertainty when planning for development. How resilient a system is, depends on adaption and adaptability. Adaptation and adaptability of a system is linked to policymaking by urban governance. In order to classify planning as adaptive planning, (to make sure that the building environment is resilient against perturbation), it has to consider risks and vulnerabilities for shock events and future climate scenarios (Coaffee & Lee, 2016).

The opposite to adaptive planning is maladaptive planning. The theory about maladaptive planning refers to planning that is no longer fit for purpose or increases vulnerability. Rather than reducing risk and vulnerability to the impacts of climate change. Examples of maladaptive planning is how the leader of governmental sectors can fail to build resilience and undermine other objectives and increase vulnerability. Building resilience on just one level, e.g., local level, can risk undermining it at others, such as regional and national levels, affecting the local level (Coaffee & Lee, 2016).

2.1.3. Representative Concentration Pathways (RCP) One climate scenario describes a possible development of the climate based on assumptions of the human activities and how much energy that is retained in the atmosphere (van Vuuren et al., 2011). IPCC2 has developed four global climate scenarios connected to the average temperature (SGU, 2020) called RCP-scenarios. Those are future climate estimations based on exiting literature and components of radioactive forcing as input for climate modeling. These components are emission from greenhouse gases, air pollution and land use and aims to give an indicator of how the climate changes bases on emissions (van Vuuren et al., 2011). The RCP-scenarios can be reached with a combination of economical, technological, demographic, and political developments (IPCC, 2014). These scenarios extend over a period to year 2100 (van Vuuren et al., 2011). Mapping and studying these different RCP-scenarios, it is called to add a climate factor3. This means that for instance the rain intensity increases, as

2 Intergovernmental Panel on Climate Change is UN: s climate panels who have developed an evaluation report (SGU, 2020). 3 Applicate a climate factor is to increase the rain intensity of different rain-events. E.g., 100-year rainfall. Adopting a climate factor means that the rain intensity corresponds to a future RCP-scenario (MSB, 2017). See page 21 & 29 for further explanation and adapted climate factor. 16 an effect of how the climate is changing (MSB, 2017). Figure 2 illustrates the emission of

CO2 and the different scenarios.

2.1.3.1. RCP4.5 In order to live up to RCP4.5, stricter climate policies need to be adapted. We need to have low energy intensity and adapt more plans for afforestation to capture emissions of CO2. The needed area for agriculture production is lower as we must make room for larger harvests due to our consumption. The carbon dioxide emissions will increase and stagnate year 2040 (SMHI, 2020).

2.1.3.2. RCP8.5 RCP-scenario 8.5 is the scenario that is predicted if the carbon dioxide emissions increase to three times more Figure 2. Developments curves for emission of greenhouse than today. The population of the Earth will gases with different RCPs. The green line represents RCP2.6, the red RCP4.5, the black increase to 12 billion by the year of 2100, RCP 6, and the blue RCP8.5. Source: van Vuuren et al., contributing to larger areas of pastureland is (2011). needed. The population are dependent on fossil fuels. The energy intensity is high and there is a no additional climate policy (SMHI, 2020).

2.2. Literature review This study focuses on active transportation, also called slow modes travel to access the public transit system. Therefore, this episode consists of three parts; previous studies of transportation planning and accessibility of public transit system, definitions of active transportation and measures taken to increase the rate of active transportation. The last part consists of previous research about future climate scenarios and expected events caused by these scenarios and its effects on the infrastructure and planning.

2.2.1. Accessibility of Public Transit Systems Accessibility is a complex part of transport planning because the terminology is defined different for different individuals (Halden, 2012). But lately, different goals have been adapted with the concept of accessibility of the transport system (Bertolini, le Clercq & Kapoen, 2005). For instance, accessibility has long been associated with travel speed to facilities and services (Alfonzo, 2005), and research has focused on accessibility of the transportation system to economic clusters (Grengs, 2015). Therefore, an economic and social goal of accessibility has been to develop the ability for workers and customers to access the system for daily use and access employments (Bertolini et al., 2005). However, as a growing interest of active transportation to access public transit system the meaning of accessibility has shift (Chan & Farber, 2020).

Still, conflicts can be seen in the concept of accessibility, because accessibility is defined different among various groups (Halden, 2012). For instance, some have argued that a transportation system is social sustainable and accessible if people in an urban area can walk, bike, or go by public transport to work (Nyström & Tonell, 2012). This conclusion certainly supports Elldér et al., (2018) study that accessibility to the public transit system is essential. Since the main part of the Swedish population does not live close enough to the workplace to get there by slow modes of travel (ibid.). Therefore, in order to decrease the car dependency, which would have good impacts on public health and the environment, planning for accessibility of the public transit system with active transportation is vital (Chan & Farber, 2020). However, this requires a reliability of the transportation network and reasonable travel time for people to choose public transit before car transportation (Noland & Polak, 2002).

Unfortunately, lack of strategic thinking about accessibility and mobility contributes to inefficiency in the transportation system and makes accessibility a multifaceted concept (Garcia et al., 2018). A reason for that is that studies of social patterns and accessibility of various groups of people is have not been studied in an extent that is needed (Nyström &

Tonell, 2012; Garcia et al., 2018). Garcia et al., (2018) suggests that accessibility should be seen as a tool to improve the social equity. With that meaning that every person in a society would have access to a transportation system, independently of their needs (ibid.).

However, how different groups of people use the public transportation system is something that Trivector, (2018a) has studied. They saw a correlation between usage of public transit system and income, education, and gender. Females tend to choose public transportation in a larger extent compared to males, due to environmental benefits (Trivector, 2018a). Meanwhile other studies have shown that the financial resources and the possibility to own a car is a factor whether to travel by public transit or not. This is in line with Sayfoor, (2015) study that high-income takers prefer the car as a transportation means in front of public transit and that more men are high-income earners (ibid.). This is something that Trivector, (2018c) also has concluded, that more males own a car compared to females (ibid.).

However, even if the concept of accessibility among different groups to a large extent is missing, another study by Trivector, (2018b) has identified females and elderly as an exposed group of the public transportation system. Because of the feeling of precariousness, they tend to limit the usage of the transportation system to specific times of the day. This relates to Garcia et al., (2018) suggestion that more studies of social patterns connected to accessibility like Trivector’s study needs to be performed in order to achieve equity in the transportation system. Harvey’s, (2003) theory of the right to the city and the questions of who we plan for also shed lights on equity in planning. Referring to that accessibility problems in spatial environments, individual mobility and travel patterns must take a bigger place in the planning procedure of mobility systems (Shield, 2011; Nyström & Tonell, 2012; Garcia et al., 2018). Urban planners need to change the mindset and put people in the center of the planning procedure (Harvey, 2003). This is in line with Lubitow, Rainer and Bassett, (2017) study that in order to build an equal transit system (Garcia et al., 2018) the transit dependent people need to be identified. With that meaning, where the people without private transportation, elderly, youths, and people below a median income level life (Lubitow et al., 2017). Yet, even if the transit dependent population is identified, this is not an easy task and is something that usually is failing during developments of public transit systems (Toms & Song, 2016). Planners strive to develop an equal transportation system for low-income inhabitants and elderly so that they can access employments and daily activities but usually ends up planning for the majority of the population (ibid.).

2.2.2. Active Transportation & Accessibility Another aspect that makes it vital to increase the active transportation is that physical inactivity in the Western World is high (Bourne et al., 2018). At the same time, increasing the active transportation towards public transit systems has founded to be a good alternative to decreasing the car dependency and achieve more balance of sustainable transportation (Chan & Farber, 2020). Therefore, to achieve a sustainable mobility paradigm, planning for accessibility that is enabled with proximity is essential, in order to with take measurements for people to choose active transportation (Banister, 2008). But that accessibility for various groups of people has been downplayed in accessibility research can also be seen when looking at a factor such as proximity (Halden, 2012; Garcia et al., 2018), which is stated to be a fundamental factor for people to choose active transportation (Bansiter, 2008: Elldér et al., 2018). Improving the accessibility towards facilities and services also improves the social equity for groups like youths, elderly and low-income workers, which has less opportunities for car transportation (Elldér et al., 2018).

Proximity can be defined with terms of physical distance but also travel time. Previous studies use distance and travel time in combination to define proximity, because of that the physical landscape is a vital factor (Elldér et al., 2018). That aspect is also something that Alfonzo, (2005) concludes, that the time it takes to travel is a crucial factor in the decision to walk and bike in contrast to using the car (Alfonzo, 2005). Willingness to walk varies among different people and the purpose of the trip. There is small part of the population that is willing to walk more than one kilometer for everyday errands (Elldér et al., 2018). Relating to that the accessibility is defined different for different individuals (Halden, 2012).

Simultaneously, physical infrastructure and the landscape in combination with transportation planning can be an obstacle for people’s accessibility and mobility regarding active transportation. To have accessibility, an overall land use planning that allows a mixture of facilities and services is needed (Bertolini et al., 2005). Therefore, planners have focused on the built environment. Planning for safe active transportation, with shorter distance are factors which contributes to environmentally friendly and slow modes of travel (Carlson et al., 2014; Elldér et al., 2018). This proves Nyström & Tonell, (2012) statement that a mixture of facilities is essential in order for people to choose active transportation. At the same time Banister, (2008) states that the lengths of trips need to be kept below the threshold for walking and biking modes to attract people to choose active transportation (ibid.). A reason to why this

20 is challenging is that the threshold differs from individual to individual (Halden, 2012; Garcia et al., 2018).

A general rule for trips lengths, based on European studies, is to have one-way journeys ≤5 km for bicycle, or ≤2.5 for walking (Rabl & de Nazelle, 2012). However, other studies of Carlson et al., (2014) saw a trend among young people that the environmental benefits and sustainability is a contributing driver of active transportation, instead of keeping the travel distance under the accepted threshold for travel distance (ibid.). This is in line with Chan & Farber, (2020) conclusion that younger population and households with low access to cars tend to combine active transportation with public transit. Although, this conclusion is not surprising since these groups often has less resources, which forces them to combine these two travel modes (ibid.). Meanwhile, other previous studies have come up with the conclusion that habit is the only factor of adult’s choices between active transportation and car transportation, regardless of the distance (de Bruijn, Kremers, Singh, van den Putte & van Mechelen, 2009). Yet the most positive correlation of active transportation in terms of walking and bicycling is the correlation of high-income and education, meaning that high- educated people with higher income tend to choose active transportation in a larger degree than low educated, low-income earners (ibid.).

2.2.2.1. Travel Speed of Active Transportation The average speed for the usual biking and walking is studied in previous research by Bedogni, Felice & Bononi (2016). The study consists of 5,400 sampled GPS-coordinates from smart phone devices of eight different participants. The result ended with an average speed for pedestrians with 5 km/h and 20 km/h for bicyclists (ibid.).

E-bikes has become a major part of active transportation. The technological progress has increased the number of bicycles worldwide and the motor of the bike helps to come up to a speed of 25km/h. This progress has made travel times shorter than with usual bikes and has made it possible to travel longer distances. However, the distances have not yet increased, but the number of trips with a bicycle has (Stenner et al., 2020).

2.2.3. Challenges with Implementation of Climate Adaptation Planning for new developments involves different priorities, such as economic development, political will, and protection of nature. In the role of a planner, it is not always clear what to prioritize as these three priorities usually end up with three conflicts. Economic growth usually ends in a conflict of fairly growth in society and not degrading the ecosystem in the process (Campbell, 1996). Additionally, previous studies have shown that population growth

21 leads to pressure on the environment, resulting in loss of biodiversity, pollution, and natural resource consumption (Monterio & Ferreria, 2020).

Road networks are important part of infrastructure as it physically interconnects people with the world. Previous events of extreme weather around the world shows how much damage it can cause to the infrastructure and the accessibility for the population (Toma-Danila, 2018; Li & Kaewunruen, 2018). Events related to climate change are the most common cause of disruption of the railway systems (Lindgren, Jonsson & Carlsson-Kanyama, 2009). Expected events such as flooding, sea level rise, erosion and storms will most likely increase in frequency and hit hard on the already vulnerable high-populated low-elevation coastal areas (Monterio & Ferreira, 2020). Both hot and cold climates can affect railway networks, which may lead to deterioration and rail buckling (Li & Kaewunruen, 2018). The ability of a network to function and adapt to new and sudden events is an important aspect that need to be achieved in order to prevent social and economic losses (Toma-Danila, 2018). Therefore, by identifying risks and vulnerabilities connected to a changing climate, is a way of planning for developments that is resilient and adaptive. If the risks are considered (Coaffee & Lee, 2016). Although, this is challenging since long term planning that is associated with infrastructure makes is difficult to plan for climate changes that possibly can occur in the future (Lindgren, Jonsson & Carlsson-Kanyama, 2009). But already today increased frequency of flooding damaging the railway network can be identified in Sweden and other parts of Europe (ibid.).

This proves that the uncertainty about climate change and future climate scenarios will make the resilience process a continuous journey. Especially with identifying the problems and planning for new infrastructure solutions and developments, that either mitigates the already existing risks and vulnerabilities. Alternatively, planning new developments that are adapted to a future climate to get an adaptive planning procedure. Learning from past of extreme events and do risk assessments is also vital in order to prevent the planning for being maladaptive (Coaffe & Lee, 2016). For that reason, research has started to focus on simulating and estimating disasters and events connected to climate change using GIS (Toma-Danila, 2018).

It is vital to implement climate policy documents and measures for adaptation. Yet, it will be challenging for decision-makers at every level. These policy documents require navigation of information and facts generated at different scales into options of adaptation. These options should also be socially and politically acceptable, due to significant degrees of uncertainty (McEvoy, 2013). Referring to Campbell, (1996) conclusion that planning often ends up in

22 conflicts of prioritized areas. This in turn is in line with Bertolini et al., (2005) conclusion that the policy documents should be acceptable on other levels than just the environmental level because of degrees of uncertainty. Because of this governments often want to include other goals in the environmental goal, either economic or social, to access space in plan-making (ibid.). This is in line with Marcuse’s (1976) statement that the role of a planner often comes down to narrow interest of the political will from governance and authorities. A matter of fact is that it has been difficult to adopt adaptation and adaptability in policymaking because it is uncertain and costly. Thus, it is one reason why it has been less prioritized to have adaptive planning (Coaffee & Lee, 2016). All the different aspects need to be weighted and considered (Bertolini et al., 2005) and “the right plan” is the plan that does least harm in a public interest (Campbell, 1996).

2.2.4. Expected Events in a Future Climate Dahlström (2010) has developed a formula to estimate rainfall intensities with duration from 5 minutes to 24 hours. This formula is commonly used by planners in Sweden to handle rain intensities in urban structures and is applicable if local precipitation statistics are limited or missing in a specific area (SMHI, 2015c). This formula is used to estimate the 100-years rainfall adopted for the climate barriers in the Network analysis (Dahlström, 2010; MSB, 2017). Depending on different RCP-scenarios, the annual precipitation estimates to increase. Namely 10–30 % for RCP4.5 and 15–40% for RCP8.5 in the whole of Sweden (SMHI, 2015a). Cloudbursts are estimated to increase both in intensity and in frequency in the whole of Sweden due to a warmer climate (SMHI, 2017). The definition of a cloudbursts is that it could rain 50 mm within an hour (SMHI, 2015c). While a 100-years rain events is precipitation of 44 mm rain with a duration of 30 minutes (Dahlström, 2010; Svenskt vatten, 2016; MSB, 2017). To know how much the rain intensity is increased for events like a 100- year rain and for specific RCP-scenarios, a climate factor could be adapted. Using a climate factor means that the rain volume increases with 20–50 percent based on today’s knowledge (MSB, 2017).

Depending on different RCP-scenarios the global sea level will rise with different magnitude. The sea level is estimated to rise with one meter by the year of 2100 with RCP-scenario 8.5. The worst affected areas would be the coastal regions (SMHI, 2015b). Se figure 3.

Figure 3. Global mean sea level rise depending on RCP- scenarios. Source: IPCC, (2014).

2.2.4.2. Water Depth and Flooded Areas These future climate scenarios and the extreme weather will most likely cause floods in built areas, such as areas of housing, which will affect the physical infrastructure (DHI, 2019). Nevertheless, other factors, such as densification of properties in urban environments causes lack of drainage systems and easily creates floods during extreme weathers (Svenskt vatten, 2016). The consequences will of course depend on the water depth of where the flood occurs. By water depth, meaning the amount of water that is gathered at the specific location. Mapping consequences of extreme floods the guideline is to have a value of 10–30 cm of water depth. This guideline is based on that the accessibility is made difficult at those water depths. Water depths around 30–50 cm make the accessibility impossible. While water depth ≥50 cm causes risks of damages on housing and infrastructure (DHI, 2016).

3. Method This chapter consists of two major parts. The first part explains the Network Analyst and why this method is suitable for answer the research questions. The second part consists of information and clarification of the collected data, the attributes, settings, and parameters in the network.

3.1. GIS as a Quantitative Method To model transportation network, GIS is an appropriate tool. One among many platforms for road network models is ArcGIS, with the extension Network Analyst (Toma- Danila, 2018). This study is a quantitative study using ArcGIS Desktop and ArcGIS Pro to model the transport network in Hudiksvall. In order to see how the possible re-location of the railway station will affect the accessibility for the population in Hudiksvall and how potential future climate scenarios may affect the accessibility of the different station locations. The result is based on two analyses, Service area analysis and Location-allocation analysis. These analyses will be explained in the second part. 3.1.1. Network Analyst as a Quantitative Tool In the network model, data of links, nodes, edges, and junctions becomes a cohesive set of a network, which can represent car roads, cycle- and pedestrian roads and railways, just to name a few. All the data that characterizes the network, such as the attributes named above, are stored as attribute information in a vector database (Heywood, Cornelius & Carver, 2011). In this network the node can also represent bus stops and the potential railway stations.

Figure 4 illustrates the overall the network in Hudiksvall. While figure 5 illustrates a closer picture of the network around the railway Figure 4. Road network of Hudiksvall’s municipality. stations.

Figure 5. Road network around the potential railway stations. When building the network, impedance is a feature, representing the cost of traveling in network links and stops. Impedance values are important in determining routes finding, allocation and spatial interactions (Heywood et al., 2011). In this case the impedance is the time it takes to travel by bike or walk with an assumption of the average speed to the possible locations for the railway stations.

Supply and demand are other factors that stores in the vector database and are equally important as the impedance. Supply is the quantity of a resource available at a center that can satisfy demand associated with the links of a network (Heywood et al., 2011). Supply represents the amount of people within an area for each location of the railway. The network centers are the different locations for the railway station. However, other centers that is important to have in mind are center or stops that serves a large amount of people or people that are dependent on public transports. These locations can be hospitals and schools (Mitchell, 1999; Heywood et al., 2011). Therefore, it is important to have railway stations in proximity to these other centers. The demand is the utilization of a resource by an entity that is associated with a network link or node (Mitchell, 1999; Heywood et al., 2011), with other words it is the catchment area for each location of each railway station.

3.1.3. Why Network Analyst? A network is a system of interconnected linear features through which materials, goods and people are transported. In GIS, network models are abstract representations of the components and characteristics of their real-world counterparts (Heywood et al., 2011). Network analysis makes it possible analyze movements and model potential paths (ArcGIS Desktop, 2020). Therefore, Network Analyst is an appropriate tool as this study aims to answer how the accessibility may be affected regarding the different locations and how these locations will be affected in a future climate.

Another reason to why Network Analyst is beneficial in this study is that the tool is efficient during strategic decisions due to that it gives an understanding of current market dynamics and potential market dynamics. Network Analyst helps to solving problems like transportation costs and finding best stop, etc. (ArcGIS Desktop, 2020). In this case the focus lies on the accessibility for citizens to the potential railway stations and how the location can be affected by a future climate.

3.1.3.1. General Transit Feed Specifications (GTFS) With ArcGIS Pro and Network Analyst it is possible to model public transit, such as buses, trains and subways (ArcGIS Pro, 2021a). GTFS is a detached specification that supports the network and the performed analysis (ArcGIS Pro, 2021d). GTFS is data consisted of locations of transit lines and stops and actual time schedules. It makes it possible to answer questions like: how well the public transit system serves its riders? How easily can people access important destinations using public transit? (ArcGIS Pro, 2021a). This tool is essential for this study of the public transit system, in this case, the bus system is crucial for groups like elderly and socio-economic exposed groups in order for them to access other public transit systems.

With GTFS it is possible to model travel time that involves the time for waiting until the next scheduled transit trip. Plus, the travel time along the line segment from one end to the other, plus the time it takes to walk to the final destination. Before this application was available at ArcGIS Pro, analysis of public transit was made with assumptions about travel time in the network. This new adopted specification contributes to network analyzes that represents the reality. Also, this tool contributes to that pre-studies of relocations of facilities that is dependent on the accessibility of another transportation system, such as railway stations, has been developed and corresponds to the real-world (ArcGIS Pro, 2021d).

3.2. Data sample & Methodological settings Secondary data is data that has been collected previously in case of other studies. The advantage of secondary data is that it saves time. Also, the data is often of good quality. Disadvantages of secondary data are that other researchers are not familiar with the data and its structure (Bryman, 2008). In this study all collected data are secondary data. The data of the road network are gathered from The Swedish Transport Administration (STA) open database, Lastkajen. While climate related data and other variables are a collected from several other authorities, municipalities, and organizations open databases. See table 1.

Climate barriers, such as flooding is gathered from SCALGO Live which estimates flooded areas and precipitation. See table 1. SCALGO Live is a platform that has gathered data about the Earth’s surface through sensors and satellites. The platform provides a snapshot of flooded areas, with the assumption that the ground is saturated on water. With this platform it is possible to increase and decrease rain intensities to be able to identify areas with risks of flooding due to estimation of where water is gathered (https://scalgo.com).

Table 1. Data gathering and source. Source Data Geodata Extraction Tool (GET) Population 250m x 250m (Urban area) (2017, 2019) Geodata Extraction Tool (GET) Population 1000m x 1000m (Periphery area) (2017, 2019) Geodata Extraction Tool (GET) Population by income (2017) Hudiksvall’s municiapality Student commuters (high school) Lastkajen National road network, Sweden SCB.se Work commuters SMHI Future sea levels (average) SCALGO Live Precipitation Trafiklab GTFS Public Transit data: Xtrafik

3.2.1. Methodological Choices for the Network Analysis Each of the attributes of average speed for pedestrians and bicyclists are set according to the literature studies previously in the text (Bedogni et al., 2016; Stenner et al., 2020). These values are presented in table 2.

Because of the significant impact e-bikes have had on the active transportation (Stenner et al., 2020), the decision to include e-bikes as a separate attribute are essential. Therefore, a separated cost attribute e-bikes is added. Also, this makes it possible to compare the catchment areas of the two bicycle types as e-bikes has become very common.

Table 2. Average speed per travel mode. Source: Bedogni et al., (2016).

Traveling mode Average speed (km/h) E-bike 25km/h Bicycle 20 km/h Walk 5 km/h

In order to estimate driving time for walking and bicycling, each of the created attribute of the different travel modes is calculated for each road segment, according to this formula.

([Road length in meters]) ÷ ([Assumption of average speed in km/h] × 1000 ÷ 60)

Regarding the pedestrian travel mode, the different cutoffs times are set to 10, 20 and 30 minutes, considering the general rule to have one-way trip length ≤2,5 km for pedestrians (Rabl & de Nazelle, 2012) and with the assumption that we walk 5 km/h (Bedogni et al., 2016). This means that the break point of choosing active transportation before car transportation goes with 2,5 km or 30 minutes of walking. Therefore, the maximum cutoff time for pedestrians is set to 30 minutes.

The same assumption is used for the bicyclists, which is also based on previous literature from Rabl & de Nazelle (2012) that a one-way journey with bike should be ≤5 km and Bedogni et al. (2016) research that the average speed of a bicyclist is 20 km/h. The settings for the e-bicyclists are based on the same trip length, considering the studies from Stenner et al., (2020) that has shown that the trip length does not increase with e-bikes, but the number of trips do. Anyhow, this means that is takes 15 minutes for a bicyclist to travel 5 km if the average speed is 20 km/h. While it takes 12 minutes to travel 5 km for e-bicyclists if the average speed is 25km/h. Therefore, the cutoff times for the two bicyclist modes is set to 5, 10 and 15 minutes. However, it is essential to also analyze farther distances, which the distance was increased to 7 km for cyclists so therefore a cutoff time of 20 minutes is also added.

Once this has been applied, the GTFS sources are added in the network dataset. Public transit is added for the pedestrian travel mode. Public transit data are excluded with bicycle and e- bike due to that the city bus service in Hudiksvall does not allow bicycles on the busses (X- trafik, 2021). The time used for travel for all the performed analysis is set to Monday 8 a.m. This means that the person would start the journey in the network 8 a.m, with the assumption that the person will leave Hudiksvall by train for work or other activities.

3.2.2. Methodological Choices for the Climate Layers The Swedish Transport Administration has taken a government decision that estimates of climate change should be made for roads and rail networks where climate change can be affected during the facility’s technical lifespan. These analyses done by SMHI are estimated to year 2100, therefore the same time frame for climate analysis of STA’s facilities is adapted (STA, 2020).

STA has internal guidelines adopting climate factors when mapping rain events and floods. These internal guidelines have come up with a decision to have a climate factor at 1.3 simulating rain with a duration of ≤60 minutes. A climate factor 1.2 is adapted for rain with a duration ≥60 minutes. Implying that a 100-year rainfall with 44 mm with a duration of 60 minutes is added with a climate factor of 1.2 if it rains ≥60 minutes, representing RCP4.5. Estimating the same 100-year rainfall with a climate factor of 1.3 represent RCP8.5 (STA, 2020).

According to DHIs guidelines of water depth causing problems with accessibility, already at a 10 cm water depth the traffic could be redirected (DHI, 2016). Therefore 10 cm of water depth has been applied in SCALGO Live estimating 100-years rain events. See table 3 for settings in SCALGO Live.

Table 3. Settings in SCALGO Live

Layer Water depth (cm) Rain (mm) Climate RCP- Sea-level factor scenario 100-years rain 10 44 1.2 4.5 0.4 m event 100-years rain 10 44 1.34 8.5 1 m event

3.2.3. Restrictions & Barriers in the Network Restrictions are set in the Network analysis and can be used for certain travel modes. The alternatives of setting the restriction attributes of traversing streets are prohibited, avoided, or preferred (ArcGIS Desktop, 2019). In this case restrictions for pedestrian and bicycle modes are set to prohibited on highways, due to that vehicle with speed below 40 km/h are not allowed (Trafiksäkerhet, 2021).

Other restrictions can be to prefer certain roads. For instance, to access the closest way to the railway station by walk or bike, other ways than pedestrian- and bicycle roads can be faster. However, in the traffic safety aspect, car roads should be avoided as far as possible. However, it may be inevitable for pedestrians and bicyclist not to traversing car roads to access the

30 railway station. Therefore, restrictions for pedestrian and bicyclist are set to prefer pedestrian and bicycle road.

When water is gathered on the streets due to e.g., sea level rise and 100-year rainfall, barriers of water are added to the network analysis. These water barriers are polygons gathered from SCALGO and SMHI. Depending on the analyzed RCP-scenario, different water barriers are added.

3.2.4. Methodological Choices for the Service Area Analysis To answer the first and the second research question Service area analysis is performed with different settings regarding different travel modes. Service area analysis makes it possible to answer questions like: What areas are within 10 minutes from a railway station? (ArcGIS Pro, 2021c). This is an appropriate analysis to answer these research questions as the catchment area of the three different locations are essential in order to gain understanding of the population’s accessibility of the different locations.

Doing a service area analysis is creating a buffer around a point, with specifications about distance. Although, unlike an ordinary buffer, it represents the maximum distance that can be traveled along the road network. As seen earlier in the method part the cutoff times for pedestrians are set to 10-, 20- and 30 minutes. While for bicyclist it is set to 5-, 10-, 15- and 20 minutes cutoffs. This means that the maximum cutoff time, in this case are therefore 20- or 30 minutes and a buffer is created around the roads that can be reached within that time (ArGIS Pro, 2021c).

Adding the GTFS specification on the Service area analysis, creates the opportunity to combine e.g., the pedestrian travel mode with public transit and model the catchment area of the population. This is significant for this study because it makes it possible to see how well the public transportation system catches the different train stations (ArcGIS Pro, 2021d).

The boundary type for each of the analysis is set to overlap due to that this type creates individual polygons for each facility and they overlap each other (ArcGIS Pro, 2021c). This is a suitable setting because it makes it possible to visualize the catchment area for each alternative.

By setting the parameter “Exclude modes” in the network dataset it is possible to exclude certain public transit modes (ArcGIS Pro, 2021d). In this case public transit by train is excluded as buses are the only alternative for local public transport in Hudiksvall.

To answer the second research question the same settings in the Service area analysis is applied with an addition to the climate layers of sea level rise and 100-year rainfalls. To better illustrate that accessibility is deteriorating, the “Exclude lines” parameter is added. This makes is possible to exclude certain bus lines due to that the road e.g., is flooded (ArcGIS Pro, 2021d).

3.2.5. Methodological Choices for Location-allocation Analysis To answer the third research question a Location-allocation analysis is performed. Location- allocation analysis statistically points out a good location based on the planned expansion and the surroundings (ArcGIS Pro, 2021b). Location-allocation analysis makes it possible to define a set of service facilities within a limited area (Kotavaara, Pohjosenperä & Rusanen, 2018). The goal with location-allocation is to locate the facilities in a way that supplies the demands points most efficiently (ArcGIS Pro, 2021b). Additionally, applying public transit data for pedestrians will result in a time-based Location-allocation to define optimal catchment areas (Kotavaara et al., 2018). This is an appropriate analysis for the third research question because it can statistically point out a location based on the population. However, a Location-allocation analysis can give multiple solutions based on how many facilities that will be developed, therefore this analysis is a good complement with Service area analysis.

The considered facilities in the Location-allocation analysis are the same locations that STA has suggested, and the chosen facility is set to one. This means that only one of the three alternatives can be chosen. Location-allocation analysis has seven different problem types that answers different kinds of questions. The used problem type for these analyses is Maximize Coverage. This problem type is used so that so many demand points (the population) as possible are allocated to solution facilities within the impedance cutoff. This problem type is useful trying to locate stores or other society services so it can catch most of the people within a certain drive time (ArcGIS Pro, 2021b). After all, an essential part of locating the railway station is how many of the population that can reach it within a certain travel time. This problem type is a good alternative to answer the third research question. The cutoff time is set to 30 minutes for pedestrians and 20 minutes for bicyclist, due to that these are the maximum cutoff times used in the Service area analysis.

3.3. Limitations This study is done within a collaboration with the Swedish Transport Administration, which for instance has given me access to the platform SCALGO Live. During this process I have participated in discussions with both the STA and Hudiksvall’s municipality. To be able to get an in-depth understanding about the different alternatives. With this, it is important to determine that this study is done for scientific purposes, objectively and independently of this collaboration.

This study focuses on active transportation from a perspective of a sustainable mobility paradigm (Bansiter, 2008). At the same time as the strategy from the STA is to increase the share of people choosing active transportation because of the environmental and health related factors that active transportation has on the population. Therefore, the alternative of using car is excluded in the different analysis. In further research it would have been interesting to analyze the accessibility with car transportation to the three alternatives, but for now the focus of this study is active transportation.

Due to lack of available data, I have decided to exclude the RCP-scenario 2.6 and 6. Data for extreme weather events such as 100-year rain and a 100-year flow connected to these scenarios are missing. These models could have been performed and calculated but is time consuming. Models for sea level rise and precipitation for RCP2.6 exists, but other data is missing a decision was made to exclude these scenarios completely. Because otherwise the material could simply have been misleading.

Other aspects as higher average temperature are also other factors for RCP-scenarios. That variable is not considered in the result because they are not specific points that are measured along or within the railway or railway stations. Still, what needs to be considered in future climate scenarios is that a higher average temperature can cause solar curves and sparks on the railway which affects the average speed. Aspects of erosion and landslide are also aspects of future climate scenarios but are difficult to connect to different RCPs and therefore these aspects are also excluded in the analysis.

In the fourth chapter, the current commuting patterns are presented. It would obviously been interesting to analyze the university student commuting pattern, as well as student commuters to Hudiksvall, however due to lack of available data these travel patterns has not been analyzed.

4. East Coast Line & Travel Patterns This chapter aim to give a background of the travel patterns of today along the East Coast Line and to and from the municipalities in proximity. This in order to give an understanding of commuting pattern and important groups to consider in transport planning. The second part of this chapter will give an overview of the different routes that is considered for new development.

4.1. Travel Patterns of Today As mentioned earlier, a robust transportation system creates enlargements of labor market regions. Already today the work commuting along the East Coast Line is an important traffic lane in that regard. Figure 6 illustrates the number of commuters to each of the municipalities in Västernorrland’s and Gävleborg’s county, regardless of the transportation mode. The green arrows represent the lowest number of commuters, while the red arrows symbolize the higher number of commuters. As seen in figure 6, it is a slightly difference in commuting patterns between male and female. In total more male’s commutes compared than females, for instance, more females commute to one of the biggest labor market regions, Gävle (SCB, 2019).

Figure 6. Work commuters from Hudiksvall. Source: SCB, (2019).

Figure 7 illustrates the work commuting to Hudiksvall. As visualized, there are more commuters that commute to Hudiksvall rather than the opposite (SCB, 2019). Nordanstig’s municipality stands out in number of commuters in both directions. A reason for this can be that the municipality does not have an independent local labor market and the fact that Hudiksvall municipality is a dependent local labor market (SCB, 2009). The collected picture is that most of work commuting exist to/from the nearest municipalities and to the two bigger labor market regions, Gävle and Sundsvall from Hudiksvall (SCB, 2019). Due to that, it is of priority to have a robust public transportation system that is faster.

Figure 7. Work commuters to Hudiksvall. Source: SCB, (2019).

The public transportation system also involves other commuters, such as commuting for education. High school students and university students are not just a group in societies that are more dependent on public transportation systems. Students also tend to choose a public transportation before car transportations. Therefore, this is an important group to consider (Lubitow et al., 2017).

As seen in figure 8, there is a majority that commutes to Ljusdal’s municipality. As also illustrated is that the high school commuters from Hudiksvall commutes to one bigger labor market region, namely Gävle (Hudiksvall’s Figure 8. High School commuters from Hudiksvall. Source: Hudiksvall’s municipality, (2021). municipality, 2021).

4.2. Routes of the East Coast Line If there will be a new location for the railway station, the route of the railway needs to be replaced. Depending on the outcome and which location that will be chosen two alternatives of a route is optional. Figure 9 aims to give an overview of how these routes will be placed, depending on the station location and the buildability. As illustrated in the map, the first option is that the route will pass another center South of Hudiksvall, called Iggesund. While the other option is that the route will go more West than the center of Iggesund. A reason for the second alternative in the right map is that the railway is that case would follow the highway, E4.

Figure 9. Overview of alternative route of the railway depending in chosen location.

5. Result In this chapter, the result linked to each of the research questions will be presented. The chapters start with a comparison of the catchment area of the different travel modes. Then the result of how accessibility will be affected for varies groups based on the different station locations is presented. Secondly, these aspects will be compared with the two considered RCP-scenarios. The result chapter ends with a Location-allocation analysis where the aspects of accessibility for the total population together with the RCP-scenarios is analyzed.

5.1. How will the Accessibility be Affected Regarding Different Locations? As mobility increases, train traffic contributes to opportunities for labor market enlargements and a more environmentally friendly alternative of transportation compared to car traffic. However, due to an enlargement of the labor market regions in the Gävleborg’s county, where jobs are moved outside municipality and county boundaries, a robust and reliable train transport system is needed. In addition, climate change has made it more important to create opportunities for the population to choose an environmentally friendly transport option. Out of which the planning for public transit must be available (Gävleborg’s region, 2020). Therefore, the catchment area of each of the alternative railway stations are analyzed, together with the opportunity to use public transit for pedestrians.

Figure 10 illustrates an overall picture how the catchment areas distinguish between the different travel modes, walking, bicycling and e-bike towards all three alternatives. In map A, the catchment area of pedestrian’s is visualized. The larger layer behind is the catchment area of pedestrians with public transit. Meaning that the catchment area if a person walks to a bus stop and use the bus to reach the station within a given time. The larger red/brown areas that can be seen West in map A are bus stops that can be reached within 30 minutes from the Current and the Eastern station, with walking as travel mode.

Figure 10. Overview of the catchment areas with different travel modes. Figure: 10a: Catchment area for pedestrian. 10b: Catchment area for bicycle. 10c: Catchment area for E-bike. Even if the catchment areas on a broad spectrum is important, an even more important aspect is how many people that live at an accessible distance from the transport system. After all the accessibility is a vital part for the population to choose active transportation as a part of their everyday life. This is an essential aspect to increase the proportion of people who choose active transport (Gävleborg’s region, 2020). Figure 11 illustrates the catchment area for each of the train stations with walking as a travel mode with the possibility to use public transit. From now on, when pedestrians are considered in this study, it is pedestrians with access to the public transit system. Illustrated in the map is the cutoff times (10-, 20- and 30 minutes), called ToBreak in the legend, to the railway station, the total number of inhabitants and the share of the total population within those areas. The population data in the maps are from 2017.

Figure 11. Number & share of population (%) within each of the cutoff times with walking as travel mode. Figure 11a: West station. Figure 11b: Current station. Figure 11c: East station. Source: SCB (2017). As seen in map C the Eastern station captures the largest share of the population within the 30- and 20-minutes break points for walking as a travel mode. The Current station captures the largest share of people within 10 minutes, which is not surprising since the other facilities, housing and physical infrastructures are built around the existing railway station. The Western station captures the lowest share of the population within 10 minutes, which is not surprising since areas around that station are to a larger extent a shopping area. With walking as a travel mode, the East station captures the largest share of population in total.

As seen in figure 12 the catchment area for the various stations with bicycle as travel modes are greater, as is the catchment area of the proportion of inhabitants. The Current station captures 16% of the inhabitants within 5 minutes, contrasted with the Eastern station that captures 15% of the total population within 5 minutes. While the Western captures 6% of the total population within 5 minutes. Within 10 minutes, the Current station captures 20%, while the Eastern station has a catchment area of 22% of the inhabitants. Of which the Western station will bring in 25% of the population within 10 minutes.

Figure 12. Number & share of population (%) within each of the cutoff times with cycle as travel mode. Figure 12a: West station. Figure 12b: Current station. Figure 12c: East station. Source: SCB (2017).

The catchment areas of the different cutoff units become bigger with e-bike as travel mode, see figure 13. This means that the 5 minutes catchment area becomes slightly bigger. For the Western station, the share of total population is 12% within 5 minutes, while for the Current- and the Eastern station it has increased to 22% of the total population. Implying that 22% of the total population has 5 minutes access to one of these two alternatives with an e-bike.

As seen in the figures 12-13 is that the percentage of the population is lower within the 15- and 20 minutes cutoff times, compared to the catchment area of pedestrians. This because of that the catchment areas have increased. This also show tendencies of that there is more people living in the center rather than within 5–7 kilometers from the different alternatives.

Figure 13. Number & share of population (%) within each of the cutoff times with e-bike as travel mode. Figure 13a: West station. Figure 13b: Current station. Figure 13c: East station. Source: SCB (2017).

The aggregated picture based on the figures 11-13 is that all three of the stations capture almost equally amount of the population. There is a slight difference that the Eastern station captures 35% of the population with walking as travel mode, see table 4. Besides that, the Eastern station also has a relatively good catchment area, which does not distinguish so much from the Current station’s catchment area in North, South, East, and West. The Eastern- and the Current station are the two alternatives that captures the largest number of the population within 5 minutes. Nevertheless, each station in this manner can be seen as accessible as an accessible distance differs among various groups of people (Halden, 2012; Garcia et al., 2018). Still, a main factor is that the chosen station is accessible under the threshold of choosing active transportation (Rabl & de Nazelle, 2012; Bedogni et al., 2016). To be able to encourage people to travel by active transportation.

Table 4 summarizes the total share of the population that each of the stations capture. The year of 2017 Hudiksvall’s municipality had 37,401 inhabitants. That number had later increased to 37,607 inhabitants by the year of 2019. As the most recent income statistics is from 2017, the population data are also from 2017 to be able to compare them. But to give a

42 more accurate picture of the population, data from 2019 is also added in table 4, which is the most recent available population data.

Table 4. Total amount of the population that each of the station captures 2017 & 2019. Source: GET, (2017;2019); Hudiksvall’s municipality, (2021); SCB, (2021).

Population 2017 Station Pedestrian Population (%) Bicycle Population (%) E-bike Population (%) West 27 43 45 Current 32 43 45 East 35 44 44 Population 2019 Station Pedestrian Population (%) Bicycle Population (%) E-bike Population (%) West 27,9 44 45 Current 33 43 45 East 36 43 45

As table 4 illustrates, 2019 the Current station captures 33% of the total population with walking as travel modes, when the total population is summarized within all the cutoff times. The Current station also captures 43% respectively 45% of the population with bicycle or e- bike as travel mode. Comparably the East station has a bigger catchment area of the population that walk compared to the Western and the Current station, but the catchment area with bicycle or e-bike are similar between the three alternatives (GET, 2017; Hudiksvall’s municipality 2021; SCB, 2021).

5.1.1. Catchment Area of Various Groups Various groups of people have different needs of the public transport system. Income, gender, and education are factors that influence the travel mode. Income for instance influence the possibility to own a car. While educational level often goes hand in hand with income level (Trivector, 2018a). As it is important to consider all groups in the society when planning for a transportation system, one vital group is low-income earners. Because they tend to be in bigger need of an accessible public transportation system in proximity. Therefore, it is essential to consider where vulnerable groups in the society live to plan for equity in the transport system (Lubitow, 2017; Garcia et al., 2018).

In table 5 the catchment areas of low-income earners in percentage are presented with walking as travel mode to the different alternatives (GET, 2017). In this example, low-income earners are a household that has ≤167,400 SEK per year as disponible income (SCB, 2017). When looking at statistics of low-income takers, the pedestrian and the bicycle modes is the

43 two modes that is considered. These two travel modes are the only considered travel modes when comparing the catchment area of various groups, to easier be able to compare them. In table 6 the catchment area of low-income earners with bicycle as travel mode is presented, also in percentage.

Table 5. Number of Low-income earners in each cutoff time per station (Pedestrian) Source: SCB, 2017. Travel time West station Current station East station 10 min 40% 26% 34% 20 min 33% 29% 26% 30 min 26% 33% 30%

Table 6. Number of Low-income earners in each cutoff time per station (Bicycle) Source: SCB, 2017. Travel time West station Current station East station 5 min 30% 27% 27% 10 min 28% 30% 30% 15 min 27% 20% 17% 20 min 9% 12% 11%

As the result of the two tables shows, the Western station covers the biggest number of low- income earners concerning pedestrian as travel mode. The Western station also has the largest catchment area of low-income takers within 5 minutes from the station with a bicycle. But there is only a marginal difference between all three stations (SCB, 2017).

Due to that previous studies have shown that high-income earners prefer to use the car (Sayfoor, 2015), and that this study focuses on active transportation to access the railway station. High-income earners are excluded, and middle-high income earners is the studied group together with low-income earners. Table 7 shows how many in the population (in percentage) who is middle-high income takers within each of the cutoff times to the different stations, with pedestrian as travel mode. While table 8 shows the middle-high income earners, but with bicycle as a travel mode. Middle-high income earners is a household that have 241,456–333 192 as disposable income (SCB, 2017).

Table 7. Number of Middle-high income earners in each cutoff time per station (Pedestrian) Source: SCB, 2017. Travel time West station Current station East station

10 min 13% 26% 18% 20 min 22% 22% 24% 30 min 23% 20% 21%

Table 8. Number of Middle-high income earners in each cutoff time per station (Bicycle) Source: SCB, 2017. Travel time West station Current station East station 5 min 22% 24% 23% 10 min 23% 22% 22% 15 min 23% 24% 29% 20 min 32% 34% 31%

The result from table 7–8 shows that the Current station captures the largest share of middle- high income earners within 5 minutes with walking as travel mode. However, the Eastern station captures the largest share of the population within 20 minutes and the Western is the station that has the largest catchment area within 30 minutes. With bicycle as travel mode, it is marginal difference between the three alternatives.

To summarize table 5–8 two aspects is essential. The first aspect is that the Western station captures a more diverse group area in the municipality. As the station has a relatively good catchment of middle-high income earners, but mainly of socio-economic vulnerable groups. On the other hand, the Western station has the lowest number of inhabitants in the catchment area, which could explain the diversity (SCB, 2017; 2019). Considering the total catchment area of these considered groups, the Current- or the Eastern station are two appropriated alternatives.

Nevertheless, additional groups to consider in transport planning is the female’s and elderly, due to that research has shown that gender is a significant impact between the decision of active transportation. Where females tend to choose active transportation because of environmental benefits. But female’s and elderly often tend to limit their public transit travel due to lack of accessibility connected to safety aspects (Trivector, 2018b).

At the year of 2017, 18,637 women was living in Hudiksvall (Hudiksvall’s municipality, 2021). Figure 14 illustrates the total number of-, and share of the female population, within each of the cutoff times to the different railway stations, considering walking as travel mode. As shown in the map the Current station captures the largest share of the female population within 10 minutes. But the Eastern station captures 18% of the female population within 30 minutes. As also seen in the map is that only 258 women lives within 10 minutes from the Western station in comparison to that 1,438 women lives within 10 minutes from the Current station (SCB, 2017).

Figure 14. Number & share of the total female population (%) within each of the cutoff times with pedestrian as travel mode. Source: SCB (2017).

Concerning the elderly population (65 years or older), figure 15 illustrates the catchment area of each of the locations with the total number of elderly and the share of the elderly within each of the cutoff time, with pedestrian as travel mode. Within 10 minutes from the Western station, 66% of the population are 65 years or older. However, within 10 minutes from the Eastern station, 54% of the population are elderly. The is a slight difference of the proportion of elderly between the Western and the Eastern station, where the Western station has the highest share of elderly compared to the total population within 30 minutes from the station. The Current station captures the lowest share of elderly within all three cutoff times. Where the proportion of elderly is 27–29% of the total population within all cutoff times (SCB, 2017). See figure 15. Taking these groups of the population in consideration, related to a safety aspect and to be able to increase equity (Garcia et al., 2018; Trivector, 2018b), the Western- or the Eastern location is a good alternative of a new railway station, because each of these stations captures a large share of females and elderly within an accessible distance of walking.

Figure 15. Share of the elderly population (%) within each of the cutoff times, with pedestrian as travel mode. Source: SCB, (2017).

5.2. How will the Concerned Locations be Affected Regarding RCP-scenario 4.5 & 8.5? Two different outcome in form of flooding are what can be expected associated with the two different RCP-scenarios. Seen in figure 16 is a 100-year rain event together with sea level rise. These events do not necessarily have to occur at the same time but give a good picture of how extreme weather events can affect the infrastructure. However, in Hudiksvall, the two scenarios in form of flooded areas attend quite similar. The main difference is that the water level along the coast is significantly higher. The same is for the water level around Lillfjärden. Specifically, because that the water level is expected to rise from 0.4 meters with RCP4.5 to 1 meter with RCP8.5 (IPCC, 2014). Figure 16 visualizes the differences of flooding with the two RCP-scenarios, where the red areas are flooded areas.

Figure 16. Overview of flooded areas with RCP4.5 & RCP8.5. Source: SMHI, (2020); SCALGO, (2021). Adding RCP-scenario 4.5 to the Service area analysis all the catchment areas become smaller for all three of the station locations. Most affected is the Current stations catchment area. But what to consider is that the Current station is located in a more critical area of flooding. With RCP8.5 the catchment area becomes even smaller compared to RCP4.5 and a usual scenario of today. Figure 17 illustrates a comparison of how the different catchment areas distinguish for pedestrians without flooded areas in comparison with the catchment areas of RCP4.5 and RCP8.5. However, a note of caution is that the land rise it not considered in the performed analysis.

As seen in the maps, the RCP-scenario 4.5 cause a smaller catchment area to all the stations within 10 minutes. Also seen in the Northeast in map C is that the catchment for the Eastern station is also affected due to potential flooding. The Western station is the least affected station of flooding.

Figure 17. Comparison between the usual catchment area for pedestrians and the catchment area of pedestrians with climate barriers RCP4.5 & RCP8.5.

Figure 18 visualizes the differences in catchment for bicycle as travel mode while figure 19 illustrates the differences for e-bikes. As seen in the maps for figure 18 and 19 the catchment areas between the both RCP-scenarios are almost similar. Overall, the catchment area from a scenario of today compared to the two RCP-scenarios is very similar. The difference is that the RCP-scenario 8.5 has a marginal smaller catchment area along the coastline.

Figure 18. Comparison between the usual catchment area for bicyclists and the catchment area of bicyclists with climate barriers RCP4.5 & RCP8.5.

Figure 19. Comparison between the usual catchment area for e-bicyclist and the catchment area of e-bicyclists with climate barriers RCP4.5 & RCP8.5.

5.2.1. Expected Outcomes of Accessibility for Pedestrians As the catchment area becomes smaller, the accessibility for the population is affected. Leading to that the share of the population within each cutoff time for e.g., pedestrians has decreased. This is illustrated in figure 20. Worst affected by RCP4.5 within the shortest cutoff time for pedestrians will the Current station be, comparing to the two other alternatives. Concerning the RCP8.5 the Current station is then again, the station that will be worst affected of a potential future climate scenario, which is visualized in figure 21.

Figure 20. Number & share of population (%) within each of the cutoff times with pedestrian as travel mode & RCP4.5. Figure 20a: West station. Figure 20b: Current station. Figure 20c: East station. Source: SCB (2017).

Figure 21. Number & share of the population (%) within each of the cutoff times with pedestrian as travel mode & RCP8.5. Figure 21a: West station. Figure 21b: Current station. Figure 21c: East station. Source: SCB (2017). When comparing these two scenarios it is clear that the accessibility is affected in several aspects. One is that more people get a longer distance to the railway station in the future due to water barriers and flooding. Another is that the accessibility will be affected, both for access to using the public transit system to access the railway station, but also to reach the railway station with active transportation. The Eastern station is affected due to the fact it is also located nearby a lake, but not in the same extent as the Current. The Western station is the alternative that is almost not affected.

In terms of population, it is shown that the Eastern station has a catchment area of 31 people less with RCP8.5 compared to RCP4.5 within 10 minutes. However, the catchment area of number of inhabitants between a present-day scenario and RCP4.5 does not differ within 10 minutes. While the Western station captures the same amount of the population within 10 minutes today as well as for both the scenarios. Nevertheless, a note of caution, is that the Western station captures the lowest percentage of the population within 10 minutes.

5.2.2. Expected Outcomes of Accessibility for Bicyclists Figure 22 and 23 shows the catchment area for the population for each of the alternatives with bicycle as travel mode. Compared with RCP4.5 the Current station loses 1% of the total population within 5 minutes with RCP8.5. While comparing RCP4.5 with today it represents a loss of 1% of the population within 5 minutes. This means that the Current station loses 2% of the total population if RCP8.5 would occur.

The Western station also loses 1% of the population within 5 minutes with RCP4.5 compared with today, but then again that station captures the least of the population. Around the Western- and the Current station tendencies can be shown that more people get a longer travel time to these stations due to the flooding. For instance, with RCP4.5 the Western station captures 26% of the total population within 10 minutes, but with RCP8.5 that number has decreased to 25%, while the share of the population within 15 minutes has increased from 9% with RCP4.5 to 10% with RCP8.5. The same thing happens around the Current station. That station loses 1% of the total population within 5 minutes that is instead capture within 10 minutes for RCP8.5.

Although, the first impression is that the Eastern station captures an equal percentage of the population for both the scenarios. The fact is that the station loses 55 people within 5 minutes with RCP4.5 compared to today and other 31 with RCP8.5. This represents a loss of 86 people within 5 minutes with RCP8.5. However, the population within the other cutoff times are not affected when comparing RCP4.5 and RCP8.5. Why the Eastern station is not that affected compared to the other alternatives can by the fact that the bicycle roads are well developed around that area. Or that there are other alternative ways to the station, which make it around the flooding.

Figure 22. Number & share of population (%) within each of the cutoff times with bicycle as travel mode & RCP4.5. Figure 22a: West station. Figure 22b: Current station. Figure 22c: East station. Source: SCB (2017).

Figure 23. Number & share of population (%) within each of the cutoff times with bicycle as travel mode & RCP8.5. Figure 23a: West station. Figure 23b: Current station. Figure 23c: East station. Source: SCB (2017).

5.2.3. Expected Outcomes of Accessibility for E-bicyclists When comparing a scenario of today for e-bicyclist, the Eastern station loses 228 people within 5 minutes with RCP4.5 and other 31 people with RCP8.5. See figure 24-25. The Current station has a smaller catchment area by 3 percent of the total population within 5 minutes for RCP8.5 compared to RCP4.5. Taking a closer look at the actual numbers, the Current station loses 318 people with RCP4.5 within 5 minutes compared with today and another 948 people with RCP8.5. If RCP8.5 would occur, this means that the accessibility will be affected for 1,266 people within 5 minutes to the Current station.

The Western station captures almost equally share of the total population, but loss 31 people within 5 minutes and 1% of the population within 15 minutes with RCP8.5. Compared with a scenario of today the Western station loses 267 people with RCP4.5. Together this represents a loss of total 298 people with RCP8.5 compared with today.

Figure 24. Number & share of population (%) within each of the cutoff times with e-bike as travel mode & RCP4.5. Figure 24a: West station. Figure 24b: Current station. Figure 24c: East station. Source: SCB (2017).

Figure 25. Number & share of population (%) within each of the cutoff times with e-bike as travel mode & RCP8.5. Figure 25a: West station. Figure 25b: Current station. Figure 25c: East station. Source: SCB (2017). 5.2.4. Expected Outcomes for Various Groups The catchment area of the population is affected due to potential flooding. It is inevitable that it also affected various groups in the society. Table 9 shows the share of low-income earners for the two RCP-scenarios, for each of the railway stations with walking as travel mode. In the parentheses the differential number from a usual scenario of today is represented.

As the results of table 9 shows, accessibility will be worst affected for low-income residents if the Current station remains the built location, in both scenarios. But to keep in mind is that the Current station, along with the Eastern station, are the options that catch the highest proportion of the population. Thus, it suggests a greater diversity among the population.

Table 9. Share of Low-income earners in each cutoff time & station for different RCP- scenario & the differential from today (Pedestrian). Source: SCB, (2017).

Travel time West station Current station East station RCP 10 min 40% (0%) 26% (0%) 34% (0%) 4.5 20 min 25% (-8%) 24% (-5%) 21% (-5%) 4.5 30 min 26% (0%) 28% ( -5%) 29% ( -1%) 4.5

10 min 40% (0%) 23% (-3%) 33% (-1%) 8.5 20 min 25% (-8%) 22% (-7%) 21% (-5%) 8.5 30 min 25% (-1%) 27% (-6%) 29% (-1%) 8.5

Table 10 shows the share of the low-income earners with bicycle as travel mode, while it compares the catchment area of the two RCP-scenarios. The Eastern station captures almost equally share of low-income earners with both the RCP-scenarios, except for the 5 minutes cutoff time, where it loses 1% of the low-income earners with RCP8.5. This is in line with previous result that the catchment area for the population of the Eastern station was least affected by the different scenarios, in relation to how many people the Eastern station serves. Meanwhile the Current station has a decreased catchment area of 30% of the low-income population within 20 minutes. Considering the low-income takers, the Eastern station is the alternative that is the least affected.

Table 10. Share of Low-income earners in each cutoff time and station with RCP4.5 & RCP8.5 (Bicycle). Source: SCB, (2017).

Travel time West station Current station East station RCP 5 min 26% (-4%) 24% (-3%) 25% (-2%) 4.5 10 min 27% (-1%) 30% (0%) 29% (-1%) 4.5 15 min 26% (-1%) 20% (0%) 17% (0%) 4.5 20 min 7% (- 2%) 1% (- 11%) 11% (0%) 4.5

5 min 25% (-5%) 23% (-4%) 24% (-3%) 8.5 10 min 26% (-2%) 29% (-1%) 29% (-1%) 8.5 15 min 29% (+2%) 29% (+9%) 17% (0%) 8.5 20 min 7% (+2%) 1% (-30%) 11% (0%) 8.5

Taking a closer look at the middle-high income earners the Eastern station is least affected in comparison to the two other station locations with walking as travel mode. The accessibility for the middle-high income earners to the Eastern station is almost not affected by RCP4.5. The Current station loses total 5% of the middle-high income takers with RCP4.5 and 10%

57 with RCP8.5. While the Western station loses 8% of the middle-high income earners with RCP4.5 and 9% with RCP8.5.

Table 11. Share of Middle-high income earners in each cutoff time and station with RCP4.5 & RCP8.5 (Pedestrian). Source: SCB (2017).

Travel time West station Current station East station RCP 10 min 12% (-1%) 25% (-1%) 18% (0%) 4.5 20 min 16% (-6%) 18% (-4%) 24% (0%) 4.5 30 min 22% ( -1%) 20% (0%) 22% ( -1%) 4.5

10 min 12% (-1%) 22% (-4%) 17% (-1%) 8.5 20 min 16% (-6%) 16% (-6%) 20% (-4%) 8.5 30 min 21% (-2%) 21% (+1%) 20% (-3%) 8.5

For middle-high income earners with bicycle as travel mode, table 12 shows that the accessibility to the Current station is almost not affected considering both scenarios. The accessibility for the middle-high income earners is most affected to the Western station within 5 minutes, even if there is a marginal difference. A reason for this is that the Western station is located in a further distance from the center and the main part of the population. Another aspect could be that there are not so many alternative ways to take to the Western station if the road network would be flooded in connection to that station. If this station should be the chosen location of the new railway station it puts effort to build a more functional road network around that station, so that there are more potential ways to the station if other roads would be flooded.

Table 12. Share of Middle-high income earners in each cutoff time and station with RCP4.5 & RCP8.5 (Bicycle). Source: SCB, (2017).

Travel time West station Current station East station RCP 5 min 20% (-2%) 24% (0%) 24% (-1%) 4.5 10 min 23% (0%) 22% (0%) 22% (0%) 4.5 15 min 23% (0%) 24% (0%) 29% (0%) 4.5 20 min 31% ( -1%) 33% ( -1%) 32% ( -1%) 4.5

5 min 20% (-2%) 24% (0%) 24% (-1%) 8.5 10 min 23% (0%) 22% (0%) 22% (0%) 8.5 15 min 24% (-1%) 24% (0%) 29% (0%) 8.5 20 min 34% (+2%) 33% (-1%) 32% (-1%) 8.5

In table 10–12 it is shown that the Western station captures more of the population for certain scenarios. This can be explained by the fact that some of the population gets a farther distance to travel to the station or that it captures people from another station due to potential flooding.

This indicates that distance for instance to the Eastern station is to long due to flooded ways, so the population is instead captured by the Western or the Current alternative.

In addition, given how the female population will be affected by potential future climate scenarios. Is the assessment that the Eastern station is the location that captures a relatively large share of the female population for both the potential scenarios.

The Western station captures 709 females less with RCP4.5 compared to a scenario of today and loses another 129 females with RCP8.5. Implying that the Western station loses a total of 838 females, if RCP8.5 will occur. While the Eastern station loses 539 females with RCP4.5 and other 154 females with RCP8.5, representing a total of 693 females. The Current station loses 602 females with RCP4.5 and another 247 female with RCP8.5, which is 849 females compared from today. This is a higher number of females compared to the Western station, but in total the Current station captures a larger share of the female population. See figures 26–27.

Figure 26. Number & share of the total female population (%) within each of the cutoff times, with pedestrian as travel mode and RCP4.5. Source: SCB, (2017).

Figure 27. Number & share of the total female population (%) within each of the cutoff times, with pedestrian as travel mode and RCP8.5. Source: SCB, (2017).

The accessibility for the elderly population of the two RCP-scenarios is illustrated in figure 27–28. The accessibility is not that affected for the elderly to the Western station. However, the high share of the elderly population within the 10 minutes cutoff time to the Western station can be explained by the fact that there are a relatively low share of the total population living in that area. For instance, 54% of the population within 10 minutes from the Eastern station are 65 years and older, which represents a larger number of inhabitants.

Shown in the maps is that the accessibility for elderly population around the Eastern station will not be significantly affected by the two RCP-scenarios. Not as much as for the Current station. The Eastern station also captures the largest amount of elderly.

Figure 28. Number & share of the elderly population (%) within each of the cutoff times, with pedestrian as travel mode and RCP4.5. Source: SCB, (2017).

Figure 29. Number & share of the elderly population (%) within each of the cutoff times, with pedestrian as travel mode and RCP8.5. Source: SCB, (2017).

5.3. What Location is Preferable Regarding both the RCP-scenarios and Accessibility? The third research question aim to answer which location is preferable considering the three different travel modes. To answer this question, a location- allocation analysis is performed for the three different travel modes and then adding the polygon barriers representing the two RCP-scenarios.

Figure 30 illustrates the chosen location for the new railway station by pedestrians with accessibility to the public transit system. The result is based on the demand points, representing the red circles in the map. In this case the demand point are population Figure 30. Chosen facility based on location-allocation analysis with pedestrian data from 2017. The calculation as travel mode. that is made have come up with the solution that the East station is a suitable location for a railway station. The lines in the map represent minutes that it takes to travel to the chosen facility. The yellow lines determine that it takes 24.07–29.70 minutes to travel from that demand point to the Eastern station. As the cutoff time are set to 30 minutes. There are few red lines around the Eastern station that points to a demand point of 0–169 people. Implying that 0–169 people would have 1.11–6.85 minutes’ walk to the Eastern station.

The with bicycle as travel mode, the Eastern station also becomes the chosen alternative, according to the calculation from the location-allocation analysis, see figure 31. The principle is the same as for the pedestrian travel mode, thus is not the public transit system accessible for bicyclist and the cutoff time is set to 20 minutes, which is the same for the service area analysis with bicycle as travel mode.

Figure 31. Chosen facility based on location-allocation analysis with bicycle as travel mode.

With bicycle as travel mode, it is shown that people from almost every demand point category can reach the Eastern station within almost 8 minutes. Also, seen Southwest in the map is that the largest demand point category (680–849 people) can reach the station within almost 8 minutes, 8.16 minutes to be precise. A total number of 4,868 inhabitants can reach the Eastern station within 5 minutes.

However, with e-bike as travel mode and with a cutoff time of 20 minutes the Western station is the chosen alternative for the new railway station, see figure 32. This can be explained by the fact that with e-bike as travel mode. It is possible for the population in the inlands (more West in the map) to reach the Western station within 20 minutes. But also, the travel mode of e-bicyclists makes it possible to reach a big spread of the population geographically, from the West to the East. 3,211 people has approximately 5 minutes to the Western station with e-bike as travel mode.

Figure 32. Chosen facility based on location-allocation analysis with e-bike as travel mode. However, what needs to be taken in consideration, if suggesting this location based on this analysis. It is how many people in Hudiksvall owns a e-bike? Also, that cycling is a limited means of transport to certain time of the year due to potential snow in this part of Sweden.

5.3.1. Preferable Location with RCP4.5 Adding the RCP4.5 scenario to the location allocation analysis with pedestrian as travel mode, the Eastern station will be the chosen station. Despite that is loses six demand points, due to flooding, see figure 33. The lost demand points represent a number of 3–152 people, that in this case gets a disturbed accessibility to the railway station. The travel time for the other demand points does not appear to be significantly affected by RCP4.5. With RCP4.5, 767 people have under 10 minutes’ walk, in combination with public transportation to the Eastern station, which does not distinguish from a scenario of today if the Eastern station would be the chosen station. Figure 33. Chosen facility based on location-allocation analysis with RCP4.5 and pedestrian as travel mode. With bicycle as travel mode the Eastern alternative also continues to the be chosen station location when considering RCP4.5, as seen in figure 34. Interesting with the travel mode of the bicyclist is that the accessibility for the demand points is not affected at all. It captures an equally amount of the population as with a scenario of today. However, this is in line with previous result, that the bicyclist’s accessibility will be little affected around the Eastern station.

Figure 34. Chosen facility based on location-allocation analysis with RCP4.5 and bicycle as travel mode. Figure 35 shows that the Eastern station is the chosen location with RCP4.5 for e-bicyclists. It is also contrasted with that the Western station was the chosen one with a scenario of today. A result of that is that the station loses eight demand points that was assigned to the West station, containing a total number of 86 inhabitants within 15–19 minutes. Instead, the Eastern alternative captures six other demand points in the East, representing 88 inhabitants in total. A result of that the chosen location has shifted, the nearest travel time has decreased with approximately one minute for the population that is nearest. For the population that is captured within the chosen cutoff time (20 minutes) the travel time has decreased with 30 seconds.

3,211 people have 5 minutes to the Western station with e-bike as travel mode, comparably with 6,722 people that has 5 minutes to the Eastern station with e-bike as travel mode.

Figure 35. Chosen facility based on location-allocation analysis with RCP4.5 and e-bike as travel mode.

5.3.2. Preferable Location with RCP8.5 With RCP8.5 and walking as travel mode, the Eastern station loses six demand points, which is the same demand points as with RCP4.5. But the chosen station location is still the Eastern station, see figure 36. The number of people that have under 10 minutes to the Eastern station are the same as of a climate scenario of today.

For bicyclists and RCP8.5, the Eastern station is the chosen one. However, compared to a scenario of today and RCP4.5, RCP8.5 loses one demand point, containing 14 people within 16-20 minutes, due to potential flooding, see figure 37. But the accessibility for very closest demand points of population with bicycle as travel mode are not affected by RCP8.5.

Figure 36. Chosen facility based on location-allocation analysis with RCP8.5 and pedestrian as travel mode.

Figure 37. Chosen facility based on location-allocation analysis with RCP8.5 and bicycle as travel mode.

Figure 38 illustrates the chosen station location with e-bike as travel mode concerning RCP8.5. Compared to RCP4.5 the accessibility for e-bicyclist is not affected if the station would be placed at the Eastern alternative. The result of this analysis is in line with previous result from the service area analysis, that the accessibility for the e-bicyclist was not that interfered with RCP8.5 compared to RCP4.5.

Figure 38. Chosen facility based on location-allocation analysis with RCP8.5 and e-bike as travel mode.

6. Discussion In this part each of the research questions will be discussed based on the presented result in the previous chapter with connection to the previous literature review. In the next chapter, the conclusion will be presented, together with limitations of this study and potential future studies.

6.1. Preferable Location in a Socio-accessible Perspective To summarize the first part of the result. With the GTFS-specification it is possible to visualize the catchment area for pedestrians as well as the share of the total population to each of the alternative railway stations with public transit (see Figure 10).

By the assumption that work commuting distances are too large in order to get there by active transportation (Elldér et al., 2018), it is vital that people in a large extent can access the public transit system by walking or bicycling (Chan & Farber, 2019). Therefore, has this study has focused on the population in Hudiksvall accessibility towards the potential railway stations with active transportation. In that perspective the Current or the Eastern station is the preferable alternatives of a new station location (see Figure 11–13). Due to the fact that these locations capture a large share of the population under the threshold of active transportation (Bansiter, 2008). However, this requires that the transportation system is reliable (Noland & Polak, 2002).

Since a strategy developed by STA and Gävleborg’s region is to increase the rate of active transportation and public transportation (Gävelborg’s region, 2020; Åström, 2021), this study is built on the assumption that the population in Hudiksvall want to travel to the station by walking or bicycling. This is essential in other perspectives, such as that rate of physical inactivity is high and affected people’s quality of the everyday life (Gray et al., 2011; Bourne et al., 2018). In that perspective, the Western station is less preferable, since it captures the smallest part of the population with active transportation (see Figure 11–139. This means that more people will have a longer distance to the railway station, and probably go there by car. Because previous studies have concluded that there is a small part of the population that is willing to walk more than one kilometer per day for everyday errands (Elldér et al., 2018).

But the challenging aspect when planning for social sustainability and equity of the transportation system is that accessibility is defined differently for different individuals (Halden, 2012). However, by keeping distances under a general threshold of active transportation (Banister, 2008; Rabl & Nazelle, 2012), while analyzing the catchment area of

71 the different station locations, can shed some lights on which alternative that is preferable with those aspects. In that sense the Current- or the Eastern station are the alternatives that can be seen as accessible, because these two locations are placed in a functional center, with the possibility to reach different services.

However, analyzing various groups accessibility to the railway stations, in order to plan for a transportation system that is equal (Garcia et al., 2017), there is marginally difference between all three different stations considering low-income earners, where the Western station captures the highest share (see Table 5–6). With a note of caution that the alterative captures the lowest share of the total population. If the only considered aspect was to place the station in an area of where people that are transit dependent live, according to Lubitow et al., (2017), to achieve social equity of an urban transportation system (Garcia et al., 2018). The Western- or the Eastern location are the two preferable alternatives (see Table 5–6). However, other aspects to consider is the previous studies by Trivector, (2018a; 2018b) that has seen patterns that females, together with elderly tend to limit their travels to specific times of the day, due to safety aspects. Whereas they can be seen as a vulnerable group of the public transit system. Considering this group of the society as a group that uses the public transit system to a larger extent compared to male’s (ibid.) is therefore a crucial aspect in determining the new station location. Where the Eastern station captures the largest share of the female population (see Figure 14). But the Current station captures the highest number of elderlies in total (see Figure 15). It can also be seen that there are a relatively good diversity of groups living around the Current station. However, the Western station captures the highest share of elderly, but a relatively low share of the female population (see Table 5–8, Figure 14–15). In total number of elderly inhabitants there is a marginal difference between all three alternatives, where the generational changes within the next upcoming years will have a more values considering location of this aspect. Most important is to consider these groups in the society so that the location is favorable for the transit dependent population (Toms & Song, 2016). Therefore, by considering the low-income population, with less access to car transportation, together with elderly and females that uses public transit in a larger extent, the Eastern station is the preferable location, because that station has a good catchment area of both females and elderly. At the same time that the station location captures a relatively good share of low-income earners (see Table 4–8, Figure 11–15).

But the lack of observed social patterns of the transportation system (Garcia et al., 2017), makes it challenging to advocate a location based on these groups in the society. Even though

72 it might be necessary since previous research has stated that measurements to include marginal groups in the society usual fails (Toms & Song, 2016). This is something that Nyström & Tonell, (2012) also has identified, that the discourse of social sustainability is challenging because it is difficult to identify which measurements that are aim for which individuals. Additionally, results of the willingness of choosing active transportation in combination with public transit among the population in Hudiksvall is missing in this study. More in-depth analysis of mobility patterns need to be done in order to determine a good station location that is favorable for marginal groups in the society or people that previous studies have proved to use the system. For instance, previous research concludes that habits are the only factor in the decision of active transportation or not. Where the high-income earners are the devoted group of active transportation (de Bruijn et al., 2009). Unfortunately, high-income earners are a devoted group of active transportation but not public transit. This makes is more essential to plan for accessibility for the public transit system together with active transportation. Since this might have good effects on high-income earners habits of using the public transit system as well. In that concern the Current station is the preferable alternative (see Table 7–8). Considering that the station has a good catchment area of both low-income earners and middle-high income earners. Also, because other studies, such as Carlson et al., (2014) has shown that the environmental benefits are the contributing factor, which requires proximity to different services and facilities (Alfonzo, 2015). Therefore, it is preferable to place the station in a location with a mixture facilities and services, which is supported by Nyström & Tonell, (2012) theory about mobility systems in urban environments.

6.2. Preferable Location in a Socio-environmental Perspective Planning for infrastructure developments is associated with long-term planning. Therefore, considering potential extreme weather events during relocations of e.g., a railway station is essential, since events in the past has caused disruption of railway systems (Lindgren, Jonsson & Carlsson-Kanyama, 2009; Monterio & Ferreria, 2020). These events have affected the accessibility for the population worldwide (Toma-Danila, 2018) and Hudiksvall is no exception. Analyzing these three potential locations of a new railway station by adding a climate factor, highlights how the accessibility for the population might be affected, today and in the future. Flooding and sea level rise will most likely affect the accessibility to the transportation system for the population in Hudiksvall, where the Current station is worst affected. Since the station is located near the coast (Figure 16 & Figure 24–25). This kind of

73 locations is also the places that will be most affected according to Monteiro & Ferreira, (2020). On top of that, the area around the Current station is expected to have a growth in population (Åström, 2021), which will lead to more pressure on the environment around these areas (Monterio & Ferreria, 2020). Therefore, implementing climate adaptive planning (Coaffee & Lee, 2016) makes sure that the public transit system in Hudiksvall is robust in the future and that the population can rely on the system.

Based in the performed analysis of this study, planning for a developed around the Current station would be seen as maladaptive planning and ignoring a potential future climate (Coaffee & Lee, 2016). That form of maladaptive planning of infrastructure can lead to consequences of population loses due to increased accidents, as well as disruption of the system (Lindgren, Jonsson & Carlsson-Kanyama, 2009; Toma-Danila, 2018). In that regard the Western station is more appropriate alternative as that station’s catchment area is not significantly affected of potential flooding and sea level rise (Figure 16 & Figure 20–25). But then, other perspectives such as proximity measured in travel time are a factor to consider (Alfonzo, 2015). The Western station is located in a place that is farther from the center with the lowest catchment area of the population (see Table 4, Figure 20–25). This aspect is vital as studies of Elldér et al., (2018) claims that few people are willing to walk more than one kilometer for everyday errands. From the perspectives of this study, which is focusing on active transportation in combination with public transit, the Western station is not an appropriate location and would probably increase the car dependency.

Additionally, comparing the number of various groups of the population with the different RCP-scenarios, shed lights on which group in Hudiksvall that will be most affected by a future climate scenario. Based on the results of the performed analysis presented in table 9-12 the accessibility for population around the Current station will be worst affected by a future climate. Planning for long-term perspectives that consider affects caused by extreme events is essential for a robust and reliable transportation system (Lindgren, Jonsson & Carlsson- Kayama, 2009; Coaffee & Lee, 2016). This conclusion points towards that the Current location are a location related to risks and vulnerabilities with much potential of flooding. That most likely would impair accessibility for the population that is dependent on the public transit system, such as low-income earners (Li & Kaewuren, 2018; Lubitow et al., 2017). Comparing the accessibility of the different travel modes of active transportation with different groups of the society, low-income earners are least affected around the Eastern station and worst affected around the Current station (see Table 9–10). Considering the

74 potential effects of various groups in the society that has a variety of needs of the public transport system refers to who we plan the new railway station for (Harvey, 2003). Only considering the climate aspect and buildability the Western station is the safest location as that location has a very low risk of flooding. While on other aspects, such as social sustainability and accessibility, the Current station is preferable. Because it is located in a functional city center and creates the opportunity for connectivity to economic clusters (Grengs, 2015). The Gäveborg’s region, regional development strategy, (2020) claims will be more common in the future. In that case, the Current or the Eastern alternatives would be preferable considering Bertolini et al., (2005) study about accessibility goals. If the plan is to achieve economic and social goals with the accessibility to the railway station. As these locations make it possible for commuters from Hudiksvall to access the system for daily use and for employments, with active transportation. Also, as Elldér et al., (2018) states that the distance to work is too large for the main part of the Swedish population for it to change to active transportation. Whereas it is even more essential that the railway station is accessible in order for the population to choose active transportation to railway station independent of future climate scenarios.

6.3. Socio-environmental Preferable Location Planning infrastructure developments is challenging for both governmental urban planning and local urban planning, because there is a lot of uncertainty connected to a climate change (Coaffee & Lee, 2016). Although the risk and climate analyses carried out for this study show a weakness in the current infrastructure system, it is still uncertain how the effects of it will affect the developments and the population. The uncertainty about future climate instead requires developments to meet other requirements, such as social or economic criteria. This is something that has also been stressed in previous studies by Bertolini et al., (2005) and McEvoy, (2013). The social and economic requirements are something that the Current station can fulfill based on the performed Service area analysis (see Figure 11–13).

However, the performed GIS-analysis of this study has shown that the Current station is a risk-full location due to potential flooding caused by future climate scenarios. If it is still determined that the Current station would be the best alternative, due to significant degrees of uncertainty about the future climate and the potential of accessibility for various groups. The process of having adaptive planning (Coaffee & Lee, 2016) will probably be a continuous job in order to mitigate and adapt the public transit system. Another aspect, if the station remains in the Current location, while STA make adaptations for other parts of the East Coast Line,

75 due to potential future climate scenarios, there is of high risk that the location will undermine STA: s resilience process (Coaffee & Lee, 2016). While the station in Hudiksvall remains unadopted. This would probably cause disturbance and increases the travel time.

Nevertheless, as there is a marginal difference of how the Eastern and the Western station is affected by the studied RCP-scenarios, an accessibility perspective for who the location is planned (Harvey, 2003) for should be decisive. As this is something that studies by Toms & Song, (2016) has concluded to be failing during previous developments of public transit systems. Large redevelopments like this have much to gain from that conclusion, which means that measures should be aimed for the transit dependent population. Whereas the Eastern alternative serves the total population best, proved by the performed Location- allocation analysis (see figure 30–38). But has also a good catchment area of various groups in Hudiksvall (see Table 5–12, Figure 14–15, 26–29). However, because of economic aspects of building the station is not always consistent with social and ecological goals, as previous studies show (Bertolini et al., 2005). It is important to distinguish between the role of urban planning and the state interest, as is also pointed out by Marcuse, (1976). However, the Location-allocation analysis together with the Service area analysis, show a clear result, that from an accessibility and environmental perspective, the Eastern station is preferable. The Eastern alternative is a good location considering a future climate scenario of today and in the future from an accessibility perspective. This refers to Campbell’s, (1996) conclusion about planning, that the right plan is the plan that does the least harm in a public interest.

7. Conclusion This study aimed at analyzing the three different alternative locations of a new railway station in Hudiksvall, due to identified risks and vulnerabilities of the existing system. Based on the performed analysis it has been identified that the Current station will probably be the station location that is worst affected from an accessibility perspective, caused by the both RCP- scenarios. Other findings of this study indicate that the both the Western- and the Eastern station is not significantly affected by none of the two RCP-scenarios, in an accessibility perspective of active transportation. However, the Western station captures a relatively low share of the population in Hudiksvall. As the result based on the analyses carried out in this study advocate the Eastern station as the location for the new railway station from an active transportation perspective. This because that station is accessible for the population today and will not be significantly affected by potential future climate scenarios.

Despite this, determining a new location for railway station is complex and multifaceted. Because each of the alternatives brings both positive and negative aspects. For instance, aspects of technical solutions that are beyond the scope of this study and might be decisive in determining the location. As well as economical aspects that is not considered in this study and therefore, there is no result indicating on which option is the most technically or economically justifiable solution. Other aspects not considered is the travel pattern towards Hudiksvall and how the accessibility will be occurred dependent of the chosen location. Therefore, before determining a station location further studies of travel patterns to Hudiksvall, such as work commuter’s accessibility would be beneficial on a broader perspective.

This study has analyzed the accessibility of different travel modes of active transportation to the different alternatives. However, even if previous studies are dealing with people’s mobility and that socio-economical exposed groups are a transit dependent people. It would have been interesting in future research do a more in-depth analysis of the social pattern in Hudiksvall, which most certain would have made the decision from a social sustainability and accessibility perspective easier. This is vital aspects to consider when planning for new infrastructure developments, because who developments are planned for would have more space in those decisions. For future research, it would have been interesting to study the whole route of the East Coast Line throughout Hudiksvall municipality from a social and ecological sustainability perspective by applying more aspects of climate and vulnerability analyses such as erosion and landslides. These aspects are also essential in the decision to

77 place new infrastructure developments. After all, such efforts are meant to last over generations, meaning that this goes hand in hand with the social aspects and who we plan for.

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