UNIVERSITY OF GOTHENBURG Department of Earth Sciences Geovetarcentrum/Earth Science Centre

Choice of transportation

mode for non-work

related trips in relation

to public transport supply in

rural Kungsbacka municipality

Development of Public Transport Indicator

Natalia Kuska

ISSN 1400-3821 B799 Master of Science (120 credits) thesis Göteborg 2014

Mailing address Address Telephone Telefax Geovetarcentrum Geovetarcentrum Geovetarcentrum 031-786 19 56 031-786 19 86 Göteborg University S 405 30 Göteborg Guldhedsgatan 5A S-405 30 Göteborg SWEDEN Acknowledgements

First and foremost, I would like to sincerely thank my supervisors, Dr Anders Larsson, senior lecturer at department of Human Geography and Dr Fredrik Lindberg, researcher at department of Earth Science at University of Gothenburg and Mr Staffan Sandberg, traffic and urban planner in Ramböll, for their guidance and interesting ideas.

I would like to express my gratitude to Mr Per Kristersson, senior planner in Göteborg Region, for fruitful discussions during the thesis writing.

I own a deep of gratitude to Mr Anders Sjöholm, traffic planner in Ramböll, for inestimable assistance with Sampers model and to Johan Svensson, urban and traffic planner in Ramböll, for valuable hints and data sharing.

Moreover, I would like to thank Kungsbacka municipality for providing me with full access to travel survey for Kungsbacka municipality.

Finally I am grateful to Alexander Hellervik and Lars I Johansson from Trafikverket for sharing data and their knowledge.

Natalia Kuska Göteborg, 07.02.2014

Abstract

Due to the need of climate change mitigation switching transport from car oriented to more sustainable transport modes, such as public transport (PT), is of particular importance. In order to achieve this goal development of PT systems must be accompanied with sustainable spatial planning and change of attitude. Understanding of travel demand is a key issue for making decision regarding sustainable transportation and spatial planning. This thesis attempts to contribute to this effort by examining the capability of PT system and travel demand. The aim of this thesis is to find out how strong is the correlation between PT supply and choice of transport mode for other than work, business and school related trips (non-work trips). The analysis is performed for Kungsbacka municipality on “Sams area” level. Public transportation supply is reflected by Public Transport Indicator (PTI) developed for the need of this thesis. PTI aims to show the differences between public transport supplying from passengers´ point of view. PTI is built based on four service measures: service coverage, travel time difference by car and PT, frequency and hours of service. The data about modal split of non-work trips was obtained by analyzing Kungsbacka travel survey and by use of Sampers-Swedish National Transportation Model.

Table of Contents

1. INTRODUCTION, AIM AND RESEARCH QUESTIONS ...... 1 2. STUDY AREA...... 3 3. LITERATURE REVIEW ...... 5 3.1. FACTORS INFLUENCING TRANSPORT MODE CHOICE ...... 5 3.2. PUBLIC TRANSPORT PERFORMANCE MEASURES ...... 7 3.2.1. Differences between various performance measures ...... 8 3.2.2. Description of service measures ...... 9 3.2.3. Review of available methods ...... 12 3.2.4. Summary of available methods of transit performance measures ...... 20 3.3. PUBLIC TRANSPORT PERFORMANCE MEASURES IN RURAL AREAS ...... 21 4. METHODOLOGY AND DATA ...... 23 4.1. LEVEL OF MEASUREMENT-ZONES ...... 23 4.2. PERCENTAGE OF NON-WORK TRIPS BY PT-KUNGSBACKA TRAVEL SURVEY ...... 25 4.3. PERCENTAGE OF NON-WORK TRIPS BY PT-SAMPERS MODEL ...... 27 4.4. PUBLIC TRANSPORT INDICATOR ...... 29 4.4.1. Service coverage ...... 30 4.4.2. Travel time difference by car and public transport ...... 31 4.4.3. Frequency ...... 32 4.4.4. Hours of service ...... 33 4.4.5. Public Transport Indicator´s calculation ...... 33 5. BUILDING PUBLIC TRANSPORT INDICATOR ...... 35 6. THE CONTEXT OF THE CASE STUDY AREA ...... 41 6.1. REGIONAL TRANSPORTATION PLANS ...... 41 6.2. TRAVEL BEHAVIOR IN THE STUDY AREA ...... 41 7. ANALYSIS AND DISCUSSION ...... 44 7.1. PUBLIC TRANSPORT DEMAND FOR NON-WORK TRIPS IN KUNGSBACKA MUNICIPALITY ...... 44 7.1.1. Distribution of different transport modes for different non-work purposes ...... 47 7.1.2. Comparison of modal split of non-work trips to work trips ...... 47

7.2. SAMPERS VS. TRAVEL SURVEY ...... 48 7.3. PUBLIC TRANSPORT SUPPLY-PTI ...... 49 7.4. RELATIONSHIP BETWEEN PUBLIC TRANSPORT SUPPLY AND CHOICE OF TRANSPORT MODE ... 52 8. CONCLUSIONS ...... 54 REFERENCES ...... 56 APPENDIX 1 – Travel time to Kungsbacka city center and Göteborg city center by car and PT-maps ...... 62 APPENDIX 2 – Results of service mesures and PTI ...... 66 APPENDIX 3 – Sampers model ...... 69 APPENDIX 4 – Travel survey diary ...... 77

List of figures

Figure 2.1: Location of study area-Kungsbacka municipality ...... 4 Figure 3.1: Tranit performance measures categories and examples (Kittelson, 2003 b) ...... 9 Figure 3.2: Transit trip decision making process, transport availability ...... 16 Figure 4.1: General flow chart of thesis methodology ...... 23 Figure 4.2: Population in Kungsbacka municipality and arrangement of zones ...... 24 Figure 5.1: Service coverage-400m. Number of residential buildings within 400 m from stations. Weekdays condition ...... 36 Figure 5.2: Difference in travel time by car and public transport to Kungsbacka city ...... 38 Figure 5.3: Difference in travel time by car and public transport to Göteborg city ...... 39 Figure 6.1: Comparison of PT trips percentage for work and non-work purposes ...... 43 Figure 6.2: Comparison of private car trips percentage for work and non-work trips ...... 43 Figure 7.1: Percentage of non-work trips by public transport in Kungsbacka municipality based on Sampers data ...... 45 Figure 7.2: Percentage of non-work trips by public transport in Kungsbacka municipality based on data from travel survey ...... 46 Figure 7.3: Modal split for non-work trips, Kungsbacka travel survey ...... 47 Figure 7.4: Modal split for work trips, Kungsbacka travel survey ...... 47 Figure 7.5: Public Transport Indicator - weekdays ...... 50 Figure 7.6: Public Transport Indicator - weekends ...... 51 Figure 7.7: Relationship between PTI and percentage of PT trips (Sampers) ...... 53 Figure 7.8: Relationship between PTI and percentage of PT trips (travel survey) ...... 53 Figure appendix 1.1: Travel time to Kungsbacka city center by car ...... 62 Figure appendix 1.2: Travel time to Kungsbacka city center by public transport ...... 63 Figure appendix 1.3: Travel time to Göteborg city center by car ...... 64 Figure appendix 1.4: Travel time to Göteborg city center by public transport ...... 65

List of tables

Table 2.1: Socio-economic characteristics of Kungsbacka municipality...... 3 Table 3.1: Availability and quality: service and performance measures ...... 17 Table 3.2:Service coverage LOS ...... 18 Table 3.3: Service frequency LOS: Urban scheduled Transit Service ...... 19 Table 3.4: Hours of service LOS ...... 19 Table 3.5: Transit/Auto Travel Time LOS...... 20 Table 3.6: Summary of methods of measuring public transport performance ...... 21 Table 4.1: Number of non-work trips by transport mode for each zone in Kungsbacka municipalty. Source: Kungsbacka travel survey ...... 26 Table 4.2: Service coverage-grades ...... 30 Table 4.3: Travel time difference by car and PT to Kungsbacka and Göteborg city-grades ...... 31 Table 4.4: Frequency-grades ...... 32 Table 4.5: Hours of service-grades ...... 33 Table 6.1: Modal split for GR´s municipalities with more than 70 000 travelers for weekdays in 2005 (Resvanor i Göteborgs-regionen, 2007) ...... 42 Table 6.2: Travel habits of students and employed in Göteborg and ten other GRs municipality in 2012 (Så reste Göteborgarna våren, 2012) ...... 42 Table 7.1: Modal splits for non-work trips by different purposes ...... 47 Table 7.2: Modal split for work and non-work trips, Sampers ...... 48 Table 7.3: Modal split for work and non-work trips, travel survey ...... 48 Table 7.4: Similarities and differences between modal split in Kungsbacka municipality according two source of data: Sampers and travel survey ...... 49 Table appendix 1.1: Results of Public Transport Indicator: for weekdays-PTI weekdays, for weekends-PTI weekends and averaged-PTI ...... 66 Table appendix 1.2: Results of all service measures for each zone: service coverage, difference in travel time, frequency, hours of service. For weekdays and weekends ...... 67 Table appendix 1.3: Results of all service measures after grading for each zone: service coverage, difference in travel time, frequency and hours of service. For weekdays and weekends ...... 68

Glossary

Headway – the time interval between two vehicles traveling in the same direction, over the same route. Level of Service – “six designed ranges of values for a particular service measure, graded from “A” (best) to “F” (worst) based on a transit passenger´s perception of a particular aspect of transit service” (Kittelson, 2003 b) Modal split –“the varying proportions of different transport modes which may be used at any one time”. Non-work trips – all trips except work, business and school related. For simplification hereafter these trips are called non-work. SAMS areas - division of Sweden into statistical areas. It includes around 9200 areas for whole Sweden. In this thesis analyses was performed on SAMS area level. Hereafter SAMS areas are called SAMS zones or simply zones Service Measure – “a quantitative performance measure that best describes a particular aspect of transit service and represents the passenger´s point of view” (Kittelson, 2003 b) Transit accessibility – “ability of travelers to reach transit facilities” (Murray, 2001) Transit connectivity – “a system´s provision of services between origins and destinations” (Mamun et al. 2013) Transit Performance Measure – “quantitative or qualitative factor used to evaluate a particular aspect of transit service” (Kittelson, 2003 b) Quality of Service – “the overall measured or perceived performance of transit service from the passenger’s point of view” (Kittelson, 2003 b) Västtrafik - the agency responsible for public transport services in the county of Västra Götaland, Sweden (Västtrafik).

List of abbreviations

GHG – Greenhouse Gas GR – Göteborg Region LITA – Local Index of Transit Availability (Rood, 1998) LOS – Level of Service O-D – Origin - destination PT – public transport PTI – Public Transport Indicator PTWAI – Public Transport and Walking Access Index (Mavoa et al. 2012) TAI – Transit Accessibility Index (Bhat et al., 2006) TCQSM – The Transit Capacity and Quality of Service Manual (Kittelson, 2003 b) TDI – Transit Dependence Index (Bhat et al., 2006) TOI – Transit Opportunity Index (Mamun et al. 2013) TSI – Transit Service Indicator (Fu and Xin, 2007)

1. Introduction, aim and research question

The population of Västra Götaland is expected to grow from 1.59 million in 2011 to 1.73 million by the year of 2025 (Befolkningsprognos Västra Götaland, 2012). Increased transportation demand appears as an inevitable consequence of population growth and an issue to be solved by regional authorities and planners (Road Transport Forecasts 2013). The large number of benefits resulting from developing transportation infrastructure is undeniable, such as reduced congestion and travel time (Litman, 2010). However, parallel with increasing travel demand GHG emission is growing also. It brings up a crucial issue of our time, how to reduce emission without negative effect on regional development (Klimat, transporter och regioner, 2007).

The transportation sector has a great potential to contribute to the effort of climate change mitigation. In Sweden transportation related GHG emission accounts for 33 % of total. Between 1990 and 2011 it showed a growing trend of 4 % (Transportsektorns utsläpp, 2013). The factors which contributed to the growth were population increase, economic growth and also urban sprawl, which is a particularly challenging issue for Västra Götaland´s effective transportation planning. More than 50 % of Västra Gotaland´s population lives in Gothenburg Region (GR), (Transportsektorns utsläpp, 2013). Moreover the research predicts further growth in GR, small growth in Sjuhärad and unchanged population in Fyrbodal and Skaraborg. The general pattern shows the population density decreases with distance from Gothenburg. The suburban areas tend to be relatively sparsely populated which promotes car oriented way of travelling and makes it difficult to ensure efficient public transportation with high and equal frequency for the whole area. The scattered way of development causes unequal accessibility to people or workplaces. By analyzing travel time to different destinations “Accessibility Tool” clearly points that from higher dense populated places such as Göteborg, Båras or Udevalla it´s possible to reach the largest number of jobs or people (Tillganglieghetsatlas over Västra Götaland, 2011). This brings up the question how to increase accessibility without substantial mobility growth.

The issue of low density development is addressed in GR´s plans. Project called “Hur 2050” intends to encourage sustainable urban design including more dense and diverse building structure and regional development around existing transportation nodes or local squares (Göteborg Region). Currently the spatial planners prioritize shortening travel distances. They focus on development on a local scale and using more sustainable transportation modes over increasing road capacity and road speed (Finnveden and Åkerman, 2011).

The impact of climate change forces transportation planners to include the climatic factors in decision-making process. Switching transport mode from car oriented to more sustainable transport modes is of particular importance. The emphasis is focused on increasing public transport trips. In order to achieve this goal development of public transport systems must be accompanied with sustainable spatial planning and change of attitude (Miola and Pridmore, 2011). This attitude could be expressed by the following sentence: “A developed country is not a place where the poor have cars. It is where the rich use public transportation.” (Gustavo Petro).

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In Göteborg Region project called K2020 was implemented in 2008. It aims to achieve 40 % of the trips by public transport by 2025 (K2020, 2008). The strategy acknowledges importance of developing buildings and workplaces in effort of increasing public transport demand.

Nevertheless, understanding travel demand seems to be a key issue for making decisions regarding cost-effective and clean transportation. Many studies done so far addressed the issue by analyzing different factors influencing travel behavior. The majority of research considers urban form, land use and socio-economic factors as the most influential drivers of travel patterns (Curtis and Perkins, 2006; Naess, 2012; Hong et al., 2012).

Plenty of research so far measured performance of public transport systems. The studies of public transport field tend to focus on work trips. However, number of non-work trips continuously increases (Steed and Bhat, 2000; Santos et al., 2011). In addition the non-work trips are assumed to be more influenced by lifestyle and built environment than work trips (Boarnet and Sarmiento, 1998; Meurs and Haaijer, 2001; Scheiner, 2010a). Furthermore, very little research of public transport was performed in rural areas (Majumdar and Lents, 2012).

The thesis fills the knowledge gap by studying choice of transport mode for non-work trips and exploring a supply level of public transport system in rural area.

Thesis aim The aim of this thesis is to find out how strong is the correlation between public transport supply and choice of transport mode for non-work trips in rural Kungsbacka municipality. Public transport supply is measured by use of Public Transport Indicator (PTI) developed for the need of this thesis. The role of PTI is to show the differences in level of public transport supply from passengers’ point of view. Choice of transport mode is expressed as percentage of non- work trips by public transport.

Research questions  Public transport demand for non-work trips in Kungsbacka municipality. . Distribution of different transport modes for different purposes: shopping, visiting friends etc. . Comparison of modal split for work and non-work trips.  Comparison of modal split results from two data sources: travel survey and Sampers model.  Public transport supply for non-work trips in Kungsbacka municipality: . Level of PT supply expressed by PTI. . Differences in PT supply during weekdays and weekends.

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2. Study area

In this chapter the location of study area is presented on the map. Short characteristic of the region is provided.

The study area is limited to Kungsbacka municipality, one of the 13 municipalities in Göteborg Region. The Göteborg Region Association of Local Authorities (GR) is the organization containing 13 municipalities in western Sweden. The main aim of the organization is to encourage cooperation between the municipalities, share experience and introduce a comprehensive planning aimed to address common issues of the whole region. The scope of organization´s activities is quite broad and among regional planning, environment and traffic includes issues such as: job market, welfare and social service or education (Göteborg Region).

Kungsbacka was chosen as a study area due to following reasons. Among other 13 municipalities of GR, Kungsbacka´s transportation is characterized by big amount of private car trips (see chapter 6). Moreover Kungsbacka travel survey was performed by Ramböll company what has facilitated full access to the survey. At last there was a need for limitation of the study area. Figure 2.1 presents a location of study area.

Kungsbacka municipality is located along the west coast of Sweden. It is just a one municipality from Göteborg Region which belongs to Halland County. The total Kungsbacka area is equal to 900 square kilometers, where land constitutes for 611 square kilometers (Kungsbacka Kommun, Geografi).

Total population of the municipality is equal to 77 275. The region is unequally and sparsely populated. Residential areas are concentrated more along the coast line with the highest population number in the cities: Kungsbacka, Vallda, Onsala, Åsa, Frilesås or Kullavik and Särö, which are considered as popular residential places (Kungsbacka Kommun, Befolkning).

Population 77 275 Age <20 28 % 20-65 55 % >65 17 % Higher education women 45 % men 38 % Unemployment 16-64 4.8 % 18-24 13 % Table 2.1: Socio-economic characteristics of Kungsbacka municipality. Data source: Age and Education- Kungsbacka kommun, Befolkning (2011), Employment-Kungsbacka kommun, Arbetsmarknad (2012).

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Figure 2.1: Location of study area-Kungsbacka municipality

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3. Literature review

Literature review is related to the most important issues of this thesis: choice of transport mode and public transport performance measures. Chapter 3.1 describes factors influencing transport mode choice with emphasis on non-work trips. Chapter 3.2 concentrates on public transport performance measures. Much attention is laid on chapter 3.2 in order to provide necessary background for developing PTI. The first part aims to introduce the concept. Then short characteristic of variety of transit performance measures is introduced. The commonly used service measures are discussed separately with focus on methodology of measurements. Finally, the number of studies is reviewed. Transit Capacity and Quality of Service Manual (TCQSM) is described more in depth than other methods since the similar concept was used for developing Public Transport Indicator. In final part, all described methods are summarized.

3.1. Factors influencing transport mode choice

The relationship between the build environment and daily travel behavior has been investigated precisely in many papers. The majority of research investigates influence of urban form or socio – economic and demographic factors on travel behavior (Naess, 2012; Hong et al., 2012).

There exists couple of ways to classify the factors. Commonly researchers divide factors for hard and soft. Hard factors are easier to measure. The other example is division for external and internal factors. Probably the most common way to classify variables is for objective and subjective (Lindström, 2003). Objectives factors include the one easier to measure or quantify such as travel time, land use and socio economic variables. The subjective variables are much more complex to quantify since they consider individuals´ perception, attitude and lifestyle.

Number of papers attempted to investigate the correlation between residential self-selection and the frequency and modal choice (Curtis and Perkins, 2006). Research by Cao et al (2008) suggests that possibly people living in the newly developed areas are more willing to take public transport or walk and cycle trip due to their own attitude towards sustainable transportation and environment. Meaning that, it is not only built environment influencing their decision about transport mode, but also their personal attitude. Due to this attitude the resident may make conscious decision to live in the area densely developed with easier access to public transport (Cao et al, 2008).

Urban form The choice of transportation mode is highly dependent on density and mixture of land use. More compact and dense urban form promotes use of public transportation or even encourages walking or cycling due to shorter travel distances (Cervero, 2002). On the contrary, remote and low-density areas favor the use of private cars. They tend to be difficult to provide with effective public transportation. Limited access to shopping or work centers further increases the problem (Soltani and Primerano, 2005).

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The number of studies so far confirmed this relationship. As for example study done by Cervero (2002) analyses separately influence of three dimensions of build environment: density, diversity and design. The study shows that these factors especially affect the decision to choose public transport (Cervero, 2002). Another study by Goudie (2002) investigates if location influence distance travelled and consequently amount of fuel used. The paper indicates that people living in suburban areas use about three times more fuel than those living in more central part of the city.

Socio – economic and demographic variables Another study for Southern California indicates relationship between land use and travel behavior as to be statistically insignificant. Instead it highlights socio-economic variables as more statistically relevant (Boarnet and Sarmiento, 1998). It is important to mention with relation to the thesis, that this study was performed for non – work trips. The most frequently examined factors include age, gender, household composition and income.

When considering gender, number of studies recognized the greater ability of women to adapt to more sustainable way of travelling (Curtis and Perkins, 2006). Study made in Sweden by Polk (2003) found that the reason why women are more willing to reduce car use is that they show more positive attitude towards environmental issues (Polk, 2003). Furthermore, considering gender and travel behavior, women are more likely to make trips in the maintenance purposes such as shopping (Best and Lanzendorf, 2005).

Reley´s research, focusing on household composition indicates that families with children use predominantly car as a main mode of transport. Households with students, unemployed or families without children are more dependent on public transport (Ryley, 2005).

Psycho – social variables Significantly lower number of papers considers influence of different psycho and social variables on choice of transport mode. Hiscock et al (2002) studied the possible psycho – social benefits of car use. The results show that car users feel more secure and gaining prestige from their car ownership. Moreover car was perceived as ensuring the protection from “undesirable” people, providing autonomy and higher access to different destinations than public transport. Additionally, car ownership was thought to increase socially desirable attributes such as masculinity (Hiscock et al, 2002).

Importance of subjective variables The majority of studies use rather measurable variables when determining drivers of travel behavior. However, not all people with high incomes or families with children’s will use car due to their own beliefs. Similarly not everyone living in the conditions considered as a promoting public transport will use it. Therefore recent research focuses on the need to include subjective variables such as lifestyle, individual´s preferences and attitude when studying the relation between built environment and modal choice. Other research states even that focusing just on correlation between built environment and transport mode might lead to wrong conclusions

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(Holz-Rau and Scheiner, 2010). Especially modal choice for leisure trips is affected by subjective variables (Van Acker et al, 2011). Important role in choice of transport mode is played also by locational self-selection. People which reside in a newly urbanized areas may be more willing to use public transportation due to their personal attitude, while decision about transport mode may be the consequence of aware choice of their location. So far not many research attempted to valuate importance of subjective variables on modal choice. Holz-Rau and Scheiner (2010) investigated the relationship between life situation, lifestyle, choice of residential location, urban form and travel mode by using structural equation modelling (Holz-Rau and Scheiner, 2010). Overall results showed that choice of transport mode is influenced more by life situation than by lifestyle. On the other hand the role of lifestyle is emphasized in decision about location, what in turn influence transport mode choice. Therefore, for people preferring outgoing lifestyle, easy access to public transport and services is more important and may be the reason why they choose to live in urbanized areas. By contrast, individuals´ with high social status or family oriented tend to use cars more and residential choice may be less important. This research doesn´t provide answer regarding share of subjective and objective variables on travel mode. Nevertheless it confirmed the importance of inclusion both types of variables.

3.2. Public transport performance measures

Introduction Developing of transport accessibility indicators was introduced in 1950 and continuously grows in significance (Schoon et al., 1999). Initially attention was emphasized on car transport due to following reasons. Firstly, providing of efficient public transport was not as important as ensuring effective transport by car at the time when car was considered as current and future dominant transport mode. Nutley considered for instance whether the rural transit problems may be solved by very high car ownership rate (Nutley, 1996). Secondly, the measure of transit performance was limited due to difficulties with collecting data mainly in terms of costs and time requirements (Bertini and El-Geneidy, 2003). Finally, modelling of transit trips is more complex compared to car (Martin et al., 2002).

In turn problems which must be faced by today´s transport such as increasing GHG emissions, traffic congestion and traffic accidents forces to perceive a car in different categories (Litman, 1999, 2003). Public transport is considered to help mitigate the negative effects of car use. Thus, the need for better understanding of transit performance appeared to have accelerated the number of research in this field. Besides data collection although still not satisfying, does not cause so much problems as before. Furthermore, there has always been and will likely be the population who don´t have an access to a car and are totally dependent on public transport, especially youth, elderly, marginalized (Martin et al., 2008).

Nowadays efficiently operating transit is considered as key component of sustainable transportation system and the most effective way to relieve car orientated transportation (Al Mamun and Lownes, 2011). Measuring the performance of transit is important in evaluating

7 potential areas of transit improvement. It also helps to allocate new investment or make decision on land development (Al Mamun and Lownes, 2011).

3.2.1. Differences between various performance measures

Transit performance measures differ among the available studies in many respects. Firstly transit performance may be measured from different points of view, such as operator´s or passenger´s (Al Mamun and Lownes, 2011).

Furthermore different transit components may be chosen to analyze, depending on purpose of study. For accessibility measures the following components are most commonly used (Al Mamun and Lownes, 2011):  Spatial coverage: “transit must be available within reasonable physical proximity to the origin and destination” (Al Mamun and Lownes, 2011).  Temporal coverage: “transit must be available in the time when travelers want to travel” (Al Mamun and Lownes, 2011).  Trip coverage: “transit must be available both in origin and destination of a trip” (Al Mamun and Lownes, 2011).  Comfort: “is sufficient space available on the public transit at the desired time?” (Kittelson, 2003 b). Majority of the studies use spatial coverage and temporal coverage components. Plenty of the studies introduce trip coverage as a third component, such as Transit Opportunity Index (TOI) (Mamun et al. 2013) or Transit Capacity and Quality of Service Manual (TCQSM) (Kittelson, 2003 b). In turn, Local Index of Transit Availability (LITA) use comfort among spatial and temporal coverage (Rood, 1998).

Moreover, different studies prioritized various service measures such as frequency and distance to transit facilities. One service measure may represent different components e.g. travel time may constitute for a measure of both accessibility and quality.

Another difference in the available research is the different types of measures used: individual measure, ratio, index, level of service (Bhat et al. 2006). An individual measure considers just one service measures, similarly the ratio. In order to combine different service measure index approach was introduced.

Finally, some of the studies among transit performance measure have other goals, for example assessing changes in accessibility after introducing new bus lines into the area (Calvin and Zandbergen, 2012).

It is important to point out differences among the studies in order to avoid misunderstanding of the methods. The terminology used in the literature may be confusing, thus some definitions are provided in glossary. Additionally figure 3.1 seeks to understand mentioned differences.

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Figure 3.1: Transit Performance Measure Categories and Examples (Kittelson, 2003 b).

3.2.2. Description of service measures

Access to transit facility Proximity to the transit stops is the most common measure of transit accessibility (Biba et al. 2010). It is confirmed that only in a presence of spatial accessibility the other factors such as costs, comfort, or security will by consider by user (Beimborn et al. 2003). The most commonly indicator reflects the ratio of population living within the transit stop service area.

The service area usually is calculated by using GIS tool to create buffers, usually around bus stations or transit network. The buffer method is quite simple but introduces some mistakes to

9 calculation. It assumes that all people living in the buffered areas have walking access to transit facilities. Furthermore, actual walking distances differ from the distances calculated using buffers, thus service area may be overestimated (Biba et al. 2010). Noteworthy is the difference between buffering around bus stations and transit lines. In the first case there is a risk of overlapping service areas when the stops are placed close to each other. In turn buffering around transit lines may cause overestimation of service areas along the line where stops are spaces farther from each other (Mamun et al. 2013). The other, more precise GIS method uses pedestrian network to calculate real walking distance to transit facilities in GIS network analyst tool. The measure of service area is more precise when it includes all possible obstacles on the way to transit stops such as: rivers, lakes, walls, huge buildings or busy roads. Since the network databases usually don´t contain such information it requires time to collect the data. Frequently the assumption that no significant barriers to pedestrian can be made along the walking paths is used (Zhao et. al. 2003).

The crucial issue in service area calculation is to define the distance people are willing to walk to transit stop. Commonly used by planners´ and researchers distance is equal to 400 m to transit stop and 800 m to railway station (El-Geneidy et al. 2009). However, it is quite a big simplification since the willingness to walk differs with age, attitudes or quality of walking paths (Biba et al. 2010). Moreover, the short distance not always ensure the possibility of walking, which may be difficult due to lack or bad maintained sidewalks, lack of street lights or different types of barriers, such as a plot. Frequently, the “land environment” don´t encourage us to walk.

Research shows that the distance passengers are willing to walk is usually greater than 400 m (Burke and Brown 2007a, 2007b). The other research indicates that for each additionally 500 m from the station the probability of taking the walk to station decrease by 50 % (Loutzenheiser, 1997).

Access to destinations The proximity to transit stop is considered to be the most common measure of public transport availability. However, just a short physical access to transit stop doesn´t guarantee that desirable destination can be reached from the particular station. Thus it´s significant to know places which can be reached by public transport (Lei and Church, 2010). For that purpose the accessibility models are used. Initially the accessibility was restricted to generating maps of areas accessible by public transport (O´Sullivan et al., 2000). Current approach aims to examine the access to different types of destinations. It is important to identify the particular destinations, services or activities which are not reachable by transit.

The example of accessibility tools is GIS based Land Use and Public Transport Accessibility Index (LUPTAI). This index measures access to common land use destinations and gives outputs for each grid cell (Yigitcanlar et al. 2008).

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Travel time Access to different destinations must be considered together with travel time needed to reach the particular destination. Current accessibility models usually use travel time as a measure of accessibility.

Travel time undoubtedly influences decisions about transport mode. It seems to be more meaningful to determine travel time difference by different modes than just travel time to destination. Due to variety in individuals travel attitude the value of travel time may be however considered in two different perspectives: one see travel by bus as additional time to read, relax or even work. For another the time for unnecessary long trips is lost. Nevertheless, the majority of passengers perceive car trip as more valuable than this by public transport, from the value of time perspective (Russell, 2012).

The example of accessibility tool using travel time as a main measure was developed at University of Gothenburg (Ellder et al., 2012). It is significant tool used in this thesis and is described in methods chapter.

Frequency Plenty of studies emphasize the importance of frequency among other accessibility service measures such as measure of PTWAI and frequency (Mavoa et al. 2012). Some of the papers use frequency measure as an independent measure, other as a supplement to transit access measures (Mavoa et al. 2012). Frequency may be also quality measure. Frequent schedules don´t require planning the trips in advance and in case of missed bus waiting time is not long what increase the perceived quality level.

Service frequency usually differs significantly between peak and peak off travel time and between weekend and weekday. This diversity of transit schedules impedes introducing one common measure. Hence different approaches exist to measuring this transit service. The one of the measure excludes all trips below the established minimum service frequency, as for example 30 min during weekday in – peak (Curtis and Scheurer, 2010). The other approach takes into account all trips but measures the service at two different ways. The first one, measures the number of trips per week for each bus station (Currie, 2010). The second, uses headways in transit scheduling to categorize transit service frequency, such as: each 15 min, 30 min (Yigitcanlar et al. 2008).

Hours of service Hours of service is a measure of number of hours during the day when transit service is provided. Hours of service play crucial role in taking decision about transit use. Even when transit is provided within reasonable distance and the frequency is satisfying it loses significance when the desired travel time is not covered by transit (Kittelson, 2003 b).

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Importance/Weighting Each of the service measures presented above plays important role in transit performance determination. It may be misleading to consider which one is the most significant since it depends on the purpose of a study.

Nevertheless, in order to get the most valuable measure of accessibility all service measures should be combined since all measures are dependent on each other. For example if transit service is not provided at the time of a day when passenger needs to take a trip, it does not matter where or how often service is provided the rest of the day (Kittelson, 2003 b). Similarly one may say that proximity to the transit service facility plays crucial role, but if there is no transit provided to the particular destination the trip is impossible. This may lead to conclusion that all service measures play equally important role, at least in accessibility measure.

3.2.3. Review of available methods

Public Transport and Walking Access (PTWAI) and frequency (Mavoa et al. 2012) Understanding of public transport accessibility is the first step to change the transport dominated by cars to public transport. The paper particularly highlights the importance of frequency among other accessibility service measures. Service frequency is treated as a separate measure. Combined public transit and walking accessibility index (PTWAI) and frequency measure, are considered separately. The accessibility is treated as a measure of access to every day destinations, assuming that people are restricted to public transport and walking modes of travel. The service measures involves: time needed to access transit stop, waiting times, duration of a trip and destinations available at the end of the trip. Analysis is performed at the land parcel level what is seen as an improvement in comparison to other research performed on administrative areas level.

As the measure of accessibility, travel time is used as more important than cost or distance when deciding about transport mode (Frank et al., 2008).

For accessibility calculations the multimodal network was used which combines transit and walking modes. Calculation was performed from each destination to land parcels. The destinations data includes 17 different types of destinations, grouped into 5 categories: education, financial, health, shopping and social/recreational. For each destination there were service areas calculated for five groups of travel times: 0-10, 10-20, 20-40, 40-60, >60 min. The service area for each travel time group is a combination of 50 m Euclidean buffer around the road centerlines and a 50 m buffer around only these transit stops which are accessible from the destination within the travel time group. All land parcels within different service areas were scored from 0 to 4 according to different travel times. As a result to each land parcel there are 17 accessibility scores assigned. The final PTWAI index was calculated for each land parcel as a sum of all averaged accessibility scores of each of 5 categories.

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The frequency was calculated as an average number of trips per hour for each bus stop with distinction of commuting and non-commuting hours.

Especially worthy of attention in respect to this thesis topic is the importance of transit frequency measure in accessibility analysis which is confirmed by results of the study. The final results show that 94.4 % of the regions populations can reach the destinations within 40 min using walking and public transport but only 26.5 % of the population has access to at least two trips per hour. Moreover only 5 % of population lives in areas supported with transit frequency of more than five departures per hour.

Transit Opportunity Index (TOI) (Mamun et al. 2013) The Transit Opportunity Index (TOI) acknowledges the importance of ease of reaching the destination in transit performance measure. Simply it measures “the ability of a service to provide the origin with access to destinations” (Mamun et al. 2013). The index combines two main measures: accessibility and connectivity where the first concerns access to transit system and the second attributes of the system between origin and destination. The index aims to integrate spatial and temporal coverage with O-D trips coverage (travel time, topological network connection) (Mamun et al. 2013). The calculation of the TOI is made as follows:  Transit accessibility:  Spatial coverage: ratio of the area covered by 400 m buffer around transit line to the total area.  Per capita service frequency: daily available seats per capita from an origin to a destination.  Connectivity parameter: binary connectivity parameter measures access to destinations from the origin. It takes value 1 when transit line directly connects origin and destination and value 0 when direct connection doesn´t exist. The trip distance does not influence the connectivity parameter.  Travel time-based logistic functions: a logistic decay function based on travel time estimation is introduced in order to address connectivity decreases with trip distance. It applies that the level of connectivity decreases gradually up to the certain travel time, more rapidly in the middle and gradually again in the final range of travel time. Travel time is calculated as a function of access time, wait time, in-vehicle time and egress time.  Transit opportunity index: the connectivity parameter is multiplied with decay factor and accessibility factor. The procedure is done for each transit line for each O-D pair. Results are summed and divided by the sum across all O-D pairs.

TOI is a comprehensive measure which highlights importance of trips coverage in transit performance measure. The big advantage of this method is possibility to calculate index between an O-D pair as well as from or to an origin or destination. The index however is not totally applicable for multi-modal systems since maximum one transfer is considered between O-D pair not directly connected.

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Transit Service Indicator (TSI) (Fu and Xin, 2007) Transit Service Indicator (TSI) is developed to measure the quality of transit service including diverse performance measures such as service frequency, hours of service, route coverage or travel time which is crucial component of the index.

The index considers quality of service from two perspectives: single trip maker and multiply trip makers´. The assumption is made that individual most likely perceive difference between total travel time by transit and car as a measure of public transport quality. Thus TSI for a particular trip and particular individual is measured simply as a ratio of the weighted door-to-door travel time by car to the weighted door-to-door travel time by transit (Fu and Xin, 2007).

In order to reflect perspective of multiply trip makers´ not only travel time but spatial and temporal components are included. The procedure is performed for several areas:  TSI of a travel corridor. The view of all trip makers´ is expressed in a corridor linking two activity areas: where all trips start and where all trips end. Base on known activity pattern of an area the random trips are generated. Transit index is calculated as an average of TSI for each generated trip.  TSI of an activity area. It is calculated as quality of transit service from the activity area to all desired destination.  TSI of a service area. It is calculated as quality of transit service representing total demand from all origins to all destinations in the given period of time.

Travel time component as a ground to build the index has been given extra attention in the study. However, due to complexity of travel time study several assumptions were undertaken. The following assumptions are commonly used in this type of studies as for the example in TOI (Mamun et al. 2013).  Both passengers of public transport and cars are expected to choose “the best” path – shortest, fastest.  An average passengers waiting time at transit stop is equal to half of the service headway.  Access and egress times for car passengers are neglected.

An essential part of the index development is assigning weighting factors to each of the travel time components: walk time, wait time and in-vehicle time. Available studies differ in passengers´ attitude towards importance of time components. They may be perceived differently with respect to location or trip purposes, thus should be established in accordance to local conditions (Pratt, 2000). On the other hand some studies showed that out-of-vehicle time is at least twice as important as in-vehicle time (Schultz 1991).

The index integrates well time travel components into one comprehensive measure together with special and temporal coverage components. However, the quality of service is reflected

14 only by travel time components while other measures such as comfort or travel cost are not considered. The index is quite difficult to calculate.

High resolution spatio-temporal modelling of public transit accessibility (Calvin and Zandbergen, 2012) The model uses travel time as a main service measure as in TSI but as a measure of accessibility not quality. The main idea behind the model is to combine transit network and walking network into a high resolution spatio-temporal GIS-based multimodal model which allows calculating all travel time components base on common network: walking, waiting, in-vehicle time. The model may be used as transit performance measure or demand measures but the main aim of this model development was to assess changes in accessibility level after addition two express bus lines.

The model acknowledges the importance of integrating temporal components into accessibility measure. Referring to multimodal network it´s commonly practiced by linking transit schedules or other service characteristics as attributes of the network. A comprehensively built network gives a great potential for assessing travel time components with respect to service attributes.

Particular attention is laid on transportation equity issue which aims to provide the same access to social and economic opportunities by providing equitable access to all destinations (Sanchez et al., 2003). Regarding to public transport generally the service must be provided in the way that allows equitable access to all destinations. Among many different ways of considering transit equality concepts the most commonly one focus on equity of distribution emphasizing the importance of “need”, meaning that transit service must reaching those in need (Deka, 2004).

The model was built as a GIS based walking and transit network. Walking network however did not contain pedestrian paths but was constructed from roads which were assumed to be walkable. In turn, transit network consists of bus lines with temporal attributes assigned: bus schedules with distinction on morning peak afternoon peak and off-peak bus travel time. That level of detail allows calculating all travel time components: walking time to bus station, waiting time, in-vehicle travel time and all possible transfers.

Since one of the main model focuses was the social justice issue, the results of model were compared with socio-economic factors on “census block group level”. In order to do that the travel time results for each residential were disaggregated to block level. To each building point the weighting was assigned aiming to include number of residents living in. The socio economic variables were chosen in the way that the index reflects the level of social need for transit: income, car ownership, age (more than 65 and less than 19).

The authors point out that social sustainability issues, precisely social equity, lose importance currently when the magnitude of attention is directed towards environmental sustainability (Martens, 2006).

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The Transit Capacity and Quality of Service Manual (TCQSM) (Kittelson, 2003 b) TCQSM measures both availability of service and its comfort. The availability factors constitute a first step aiming to assess if transit may be an option. If transit stops are available within walking distance, destination is reachable by transit, the information is available and the service is possible at desirable time, the trip by transit is possible. Moving to second step which measure the comfort and convenience of service makes sense only when the first step is fulfilled. Figure 3.2 illustrates the first step of decision-making process.

Figure 3.2: Transit Trip Decision-Making Process: Transit Availability (Kittelson, 2003 b)

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The Manual for each performance measure: availability and comfort, assigned different service measures depending on transit system elements as presented in table 3.1. To each category more than one service measure may be assigned. One service measure may reflects also both availability and quality.

Service and performance measures Category Transit Stop Route Segment System FREQUENCY SERVICE COVERAGE HOURS OF SERVICE Availability accessibility % person-minutes accessibility passenger loads served RELIABILITY TRANSIT/AUTO PASSENGER LOADS travel speed TRAVEL TIME Quality amenities transit/auto travel travel time reliability time safety Table 3.1: Availability and quality: service and performance measures

The service measures highlighted in blue were chosen for developing Public Transport Indicator. For that reason they are described here more in details.

Service coverage Service coverage is a measure of area within walking distance from public transport service. Commonly used by planners´ and researchers distance is equal to 400 m to transit stop and 800 m to railway station (El-Geneidy et al. 2009).

TCQSMs approach is that service coverage area does not provide a complete picture of transit availability by itself but when combined with frequency and hours of service helps to identify the availability of transit service from different areas. The measure of service coverage area in TCQSM is considered as not sufficient when it does not take into account population density and employment density. Simply unpopulated areas, without number of work places does not required to be covered by public transit. In order to assess the transit performance of the areas in need of public transport, TCQSM uses the concept of a transit-supportive area which provides minimum population and job densities that must support hourly transit service (Kittelson, 2003 b). Thus the minimum household density in order to support area with at least hourly transit service is 11 units per net hectare. In turn area with density of 10 jobs per gross hectare produces the same amount of transit trips as an area with density of 11 units per net hectare (Kittelson, 2003 b).

The manual presents GIS based method of service coverage area evaluation as follows:  Creating buffers within 400 m from bus stations and 800 m from railway stations.  Calculation the household and job densities  Identify the transit-supportive areas - the areas which meet the requirements to provide at least hourly service transit presented above.  Identify how big part of transit-supportive area is covered by transit.

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 Calculate the level of service (LOS) – percentage of transit-supportive area covered by transit (see table 3.2).

LOS Percentage of Transit- Supportive Area Covered (TCQSM) A 90.0 – 100.0 B 80.0 – 89.9 C 70.0 – 79.9 D 60.0 – 69.9 E 50.0 – 59.9 F < 50.0 Table 3.2: Service coverage LOS

Frequency The Manual presents the LOS measure both as headway and the number of vehicles per hour. As in the case of service coverage, frequency only when combined with other service measures provides the information about the availability of service. Frequency may be also view as a transit service quality measure while determining convenience of service and the waiting time for passenger.

The Manual points that several transit lines may support one bus stop but not all of them may lead to the same destination. Thus, determination of service frequency LOS considers destination from a given transit stop. Some of the stations are supported by schedules characterized by equal headways especially the one supplied with one line. This is often not a case when several lines serve one stop and quite often buses are cumulated in a certain time. The passenger´s perception is considerably different in both of these cases. Thus the manual assumes that if the buses on separate lines that serve the same destination arrive at a stop within 3 minutes difference are taken as a one departure.

According to passenger´s perception toward transit service frequency in urban areas the manual presents the thresholds for each level of service in table 3.3. Thus for example 10 minutes is a barrier below which passenger´s do not check a schedule. The lower, once per hour, threshold corresponds to the minimum frequency when determining hours of service LOS.

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LOS Headway Veh/h Comments (min) A <10 >6 Passengers don’t need schedules B 10-14 5-6 Frequent service, passengers consult schedules C 15-20 3-4 Maximum desirable time to wait if bus/train missed D 21-30 2 Service unattractive to choice riders E 31-60 1 Service available during hour F >60 <1 Service unattractive to all riders Table 3.3 Service frequency LOS: Urban Scheduled Transit Service

Hours of service Hours of service is a measure of number of hours during the day when transit service is provided. The Manual calculates the hours of service at the minimum level of frequency equal to one hour, corresponding to LOS E in a frequency table. In the same way as for frequency the hours measured may differ for weekdays and weekends.

The Manual provides calculation of hours of service considering different possible scenarios; e.g. limited daytime service, transit available only for peak hours or only in the evenings. The procedure looks as follows: hours of service is calculated for each period of time when transit operates at the frequency at least one hour; time difference between first and last departure time is determined and one hour is added; the hours are sum for each period in the day what gives daily hours of service measure.

The Manual presents the following LOS for hours of service coverage in table 3.4:

LOS Hours per day Comments A 19-24 Night or owl service provided B 17-18 Late evening service provided C 14-16 Early evening service provided D 12-13 Daytime service provided E 4-11 Peak hour service/limited midday service F 0-3 Very limited or no service Table 3.4: Hours of service LOS

Travel time The Manual calculates total travel time as a time needed for door to door trip. For transit trips this calculation seems to be far more complex than for car trips since it includes: walking time to transit stop, waiting time, in vehicle time, eventual time needed for transfers and finally again walking time form final station to destination.

The manual uses difference in travel time by car and PT as a travel time measure (Table 3.5).

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LOS Travel time Comments difference (min) A <=0 Faster by transit than by car B 1-15 About as fast by transit as by automobile C 16-30 Tolerable for choice riders D 31-45 Round-trip at least an hour longer by transit E 46-60 Tedious for all riders; may be best possible in small cities F >60 Unacceptable to most riders Table 3.5: Transit/Auto Travel Time LOS

3.2.4. Summary of available methods of transit performance measures

Table 3.6 summarizes all the methods of PT performance measures, described above. Additionally three other methods, frequently mentioned in literature, are described: Time-of- Day tool (Polzin et al. 2002), LITA (Rood, 1998) and TAI & TDI (Bhat et al., 2006). The summary aims to highlight differences, similarities and characteristics of each method. Thus, the most of the methods uses three main components: spatial, temporal and trip coverage. Depending on the purpose of the study different transit measures are determined: availability, accessibility or comfort. Different service measures are used to estimate particular transit measure: travel time, frequency. Some methods prioritized individual service measure. Furthermore the methods differ in level of complexity, methodology or perspective of measuring: passenger or agency (see also chapter 3.2.1.).

This summary provides a necessary background to build PTI suitable for the needs of the thesis. Presentation in form of the table facilitates interpretation and PTI building. Based on the literature review and summary, the PTI was built as follow. It considers spatial, temporal and trip coverage. Four service measures are used: service coverage, frequency, hours of service and travel time. All service measures are equally important. PTI considers passenger´s point of view.

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Components Transit Important Measure name Spatial Temporal Trip component service Characteristic Coverage Coverage Coverage measured measure TCQSM Availability yes yes yes LOS concept (Kittelson,2003) and quality Comprehensive GIS PTWAI and analyses; frequency On land parcel level; yes yes yes Accessibility Frequency (Mavoa et al. Accessibility and 2012) frequency treated separately. Focus on ease of reaching the destination; TOI Accessibility Spatial Importance of trip (Mamun et al. yes yes yes and coverage, coverage: incorporating 2013) connectivity frequency O-D trip coverage; Calculated for transit lines. Travel time accounts for effect of both supply and TSI Weighted yes yes yes Quality demand; (Fu et al. 2005) travel time Complex calculations; Quite many assumptions High resolution spatio-temporal GIS based comprehensive walking and transit model yes yes yes Accessibility Travel time network; (Calvin, P.T., Importance of transit Zandbergen socio-economic equity P.A., 2012) Time-of-Day Demand side is reflected tool by travel demand time-of- yes yes no Accessibility Travel time (Polzin et al. day distribution on an 2002) hourly basis Distinctive from LITA Comfort and passengers´ point of view yes yes no Accessibility (Rood, 1998) convenience due to inclusion of comfort Customer-orientated TAI & TDI methodology. Measures level of transit supply and (Bhat et al. yes yes no Accessibility level of need for transit 2006) considering socio- economic variables. Table 3.6: Summary of methods of measuring public transport performance

3.3. Public transport performance measures in rural areas

Supplying the rural areas with transit is difficult due to low population density and long distances between the transit stops. This generates high costs of transport and in the same time brings relatively low profits (Brown, 2008). There exists relatively low amount of studies

21 concerning transit in rural areas. Particularly missing is the knowledge about passengers’ perception and expectation towards public transport service in rural areas.

Individuals´ expectation towards public transport in rural areas The number of papers attempt to know the individuals expectations towards urban transit quality in order to meet their needs as it is believed the key to increase public transport usage is to design it in a way that is desired by passengers (Beirao and Cabral, 2007). While the studies concerning urban areas concentrate on the need to improve the available transit service the studies of rural areas attempt to answer whether there would be an interest of use the transit if it would be better developed (Majumdar and Lents, 2012).

The most common problem with investigating this issue is low number of responds, such as in a case of research by Majumdar and Lentz. They attempted to answer whether or not commuters would be willing to use public transit if it would be provided and which factors influence their decision (Majumdar and Lents, 2012). Passengers asked about willingness to transfer from car to PT answered as follows: 24 % said yes, 21 % may be interested if they knew the progress of the project, 21 % were willing to try and 34 % said no (Majumdar and Lents, 2012). The results show also that those who drive alone by private cars are less willing to switch into public transport than those who share the ride in car or van pooling what was explained by authors as a strong attachment to convenience, privacy and comfort and perception of losses this attributes (Majumdar and Lents, 2012). Number of variables was listed as influencing decision about choice of transit trip such as: travel cost savings, frequency of service, time savings, accessibility to jobs, a variety of payments types and the opportunity to do other things while traveling (Majumdar and Lents, 2012). The number of responds however was too low to be able to draw valuable conclusion about individuals’ attitude towards public transport and factors influencing their decision. Therefore the hypothesis presented before in other studies is brought: “individuals’ attitude may not differ much for rural and urban areas, however it needs to be further investigated” (Majumdar and Lents, 2012).

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4. Methodology and data

In the first part of this chapter methodology of determining percentage of non-work trips by PT is presented, separately for Sampers model and Kungsbacka travel survey in sections 4.2 and 4.3 respectively. In section 4.4 methodology of building PTI is provided. Firstly the methods of determining service measures are presented: service coverage, difference in travel time by car and PT, frequency and hours of service. Finally the method of calculating PTI is described.

The methodology used in this thesis includes the following important steps:  Determination of modal split for non-work trips in Kungsbacka municipality. Two types of data were used: Travel survey made for Kungsbacka municipality and Swedish Transportation Demand Model called Sampers.  Development of Public Transport Indicator (PTI). The indicator takes into account: - physical distance to the nearest bus station, - differences in travel time by car and public transport, - frequency of bus departures and hours of service operating.

Figure 4.1 shows model which aims to explain research question solving.

Research question

RELATIONSHIP

SUPPLY DEMAND

PTI Percentage of PT trips

GIS analyses, Sampers model,

Västtrafik data travel survey

Figure 4.1: General flow chart of thesis methodology

4.1. Level of measurement-zones

Initially data on percentage of non-work trips by PT was intended to be derived from Kungsbacka travel survey only. However after disaggregation to the “Sams areas” level number of responds was too low for some areas. For that reason data from Sampers model was incorporated.

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Since data are available from different sources, there was a need to introduce common level for measurement. Sampers gives results on “traffic areas” level, which are almost the same as “Sams areas”. In order to introduce the same unit for analysis all data are presented on “Sams areas” level. For that reason PTI was calculated also in the same level. Hereafter for simplifications “Sams areas” will be called zones.

In further part of the thesis, the results are discussed using the number of each zone, thus figure 4.2 illustrates arrangement of each zone with zones numbers. The map shows also number of population in each zone as it is important when interpreting the results. Two zones: number 12 and 52, were excluded from further analysis. Due to very low number of population living in these zones Sampers modeled unrealistic number of trips there.

Figure 4.2: Population in Kungsbacka municipality and arrangement of zones.

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4.2. Percentage of non-work trips by PT-Kungsbacka travel survey

The travel survey was conducted during October 2012. The main purpose of the survey was to identify travel pattern in Kungsbacka for better sustainable travel planning. The other goal was to know if, or how introduced congestion tax in Göteborg affects travel pattern over the time. The letters were addressed to 6400 Kungsbackas inhabitants of age between 15 and 84. The questionnaires contain two parts. The first one includes questions about age, gender or education and questions about access to different modes of travel. In second part people were asked to describe all “movements” made in a day they have received the letter. Full travel survey is shown in appendix 4. One movement means a move from one place to another for specific purpose, for example: first movement form home to kinder garden; second from kinder garden to work, the third from work to shopping center and the fourth from shopping center to home.

The trips are divided for following purposes: 1 – Work/school 2 – Business 3 – Shopping groceries 4 – Other shopping 5 – Leisure time activities 6 – Visiting relatives and friends 7 – Service 8 – Drive/pick up children 9 – Drive/pick up others 10 – Home 11 – Other than work, business and not mentioned above

Methods The travel survey results were available as a final report but also as an excel database containing all trips made by respondents during a day. Thus there was possible to extract from database only non-work trips. By non-work trips the following purposes are meant: regular food shopping, other shopping, free time trips, visiting friends and relatives, services, drive and pick up children, drive and pick up others. All non-work trips have been categorized in respect to the trip purposes and trip origination. The classification of the trips base on trip origination was performed in order to use only trip originating from home as they are directly related to the public transport performance at the place. However, due to low number of responds the trips originating from other than home place were included also.

All non-work trips were aggregated to zones level. On this level data were not statistically valuable, thus some of the zones were excluded from further analysis, namely each zone with responds number equal or lower than 30.

The table 4.1 shows number of non-work trips for each zone. The zones marked with blue are not included when analyzing the relationship between PTI and trips number.

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Zone C Cpass PT B W SUM C Cpass PT B W Number Percentage 2 122 12 12 11 26 183 67 7 7 6 14 3 101 12 9 9 10 141 72 9 6 6 7 4 69 11 5 7 6 98 70 11 5 7 6 5 19 3 1 0 2 25 76 12 4 0 8 6 57 4 1 3 1 66 86 6 2 5 2 7 22 0 0 0 0 22 100 0 0 0 0 8 21 2 3 0 3 29 72 7 10 0 10 9 40 7 1 3 1 52 77 13 2 6 2 10 8 0 0 0 1 9 89 0 0 0 11 11 37 10 4 1 2 54 69 19 7 2 4 12 0 0 0 0 0 0 0 0 0 0 0 13 76 12 5 4 2 99 77 12 5 4 2 14 61 16 9 1 4 91 67 18 10 1 4 15 25 2 0 0 1 28 89 7 0 0 4 16 52 6 1 0 2 61 85 10 2 0 3 17 12 2 1 0 0 15 80 13 7 0 0 18 24 2 0 0 0 26 92 8 0 0 0 19 46 12 3 0 0 61 75 20 5 0 0 20 3 0 0 0 0 3 100 0 0 0 0 21 19 4 2 0 0 25 76 16 8 0 0 22 13 3 3 0 3 22 59 14 14 0 14 23 92 6 9 1 11 119 77 5 8 1 9 24 10 0 0 0 0 10 100 0 0 0 0 25 62 8 1 3 5 79 78 10 1 4 6 26 27 5 5 2 7 46 59 11 11 4 15 27 13 0 0 0 0 13 100 0 0 0 0 28 23 1 3 1 2 30 77 3 10 3 7 29 3 0 0 1 4 8 38 0 0 13 50 30 71 9 5 1 11 97 73 9 5 1 11 31 37 9 2 1 3 52 71 17 4 2 6 32 25 0 0 0 1 26 96 0 0 0 4 33 27 4 3 0 1 35 77 11 9 0 3 34 40 5 3 0 3 51 78 10 6 0 6 35 6 0 0 2 0 8 75 0 0 25 0 36 23 3 1 0 0 27 85 11 4 0 0 37 46 11 2 5 5 69 67 16 3 7 7 38 45 11 2 2 4 64 70 17 3 3 6 39 27 4 6 0 0 37 73 11 16 0 0 40 11 2 1 1 0 15 73 13 7 7 0 41 41 8 5 2 4 60 68 13 8 3 7 42 56 21 0 4 4 85 66 25 0 5 5 43 83 17 5 4 10 119 70 14 4 3 8 44 27 4 1 1 1 34 79 12 3 3 3 45 40 11 0 0 0 51 78 22 0 0 0 46 5 0 0 0 1 6 83 0 0 0 17 47 145 21 18 1 4 189 77 11 10 1 2 48 32 4 0 2 0 38 84 11 0 5 0 49 28 3 3 1 3 38 74 8 8 3 8 50 24 4 2 0 1 31 77 13 6 0 3 51 12 4 2 0 2 20 60 20 10 0 10 52 0 0 0 0 0 0 0 0 0 0 0 53 10 1 4 0 0 15 67 7 27 0 0 Table 4.1: Number of non-work trips by transport mode for each zone in Kungsbacka municipality. Source: Kungsbacka travel survey. C-Car, Cpass-Car as a passenger, PT-public transport, B-bicycle, W-walk.

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4.3. Percentage of non-work trips by PT-SAMPERS model

SAMPERS is the Swedish National Transportation Model. The first version of the model was developed in 1980. Over the years transportation models were successfully used to contribute to national strategic transport investment plans for example to evaluate effect of introducing a new train line on traffic.

The current version of SAMPERS 2.1 models all trips, more accurately all trips having at least origin or destination in Sweden. The three following models are distinguished:

 Regional model  Long distance model  International model

Due to different level of details required in forecasting system regional model operates with higher resolution than long distance or international model. Thus, Sweden is divided to:

 8500 zones for regional trips  670 zones for domestic long distance trips and international trips inside Sweden  180 zones for international trips outside of Sweden

To avoid problems resulting from running scenarios for 8500 zones in regional model the further division to 5 regions was done.

The main data source used for developing a model was the national Swedish travel survey between 1994 and 2001. The current SAMPERS 2.1. model utilizes 50 000 interviews done between 1994 and 2000. Each respondent was asked to list all trips done in particular day. The questionnaire asked also about trips over 100 km in last month and trips over 300 km made during last two months.

Generally all models use three types of variables to depict travel behavior:  Transportation supply variables (e.g. constant for public transport, travel costs in the region). Generally the variables includes travel costs and travel time.  Destination zone variables include the number of work places in a region. The variables consider also if destination zones belongs to municipality centre. Plenty of other variables determine if a trip to certain destination is possible or not. Such as unavailability of parking within the zone make it impossible for being destination zone.  Socio-economic variables mainly consider gender and driving licence holding. The model assumes that gender influence length of a trip and possibility to use a car.

There exist also other variables, which reflect differences in climate in Sweden or are related to topography such as dummy for Göta-Älv cut which assumes that trips across the cut would be rather rare. More in depth description of Sampers model is included in appendix 3.

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In current Sampers model the demand models, the databases and the Emme/2 system are integrated into one software. The supply data needed for the models to be run are created in the Emme/2 system. After running the various scenarios the results are stored in Emme/2 databank in form of O-D matrices.

Methods All data needed to create maps was taken from Emme/2. The work matrices were taken from scenario run before. The matrices in this database were divided according to the purpose for: work, business and other trips. This scenario couldn´t be used for getting matrices about other trips since they contained also school trips. The aim was to extract trips with the same purposes as from travel survey: different types of shopping or services, leisure time activities, visiting relatives and friends and driving or picking up children and others. For that reason the model was run again with different settings. Among all trips produced in regional model only trips in a purpose of social, recreation and other were chosen.

After running scenario the following matrices for the following travel modes were used: car, bicycle, walk and public transport. By using Emme tool “matrices calculation” the origin matrices were calculated for each mode. Then all have been exported form Emme and percentage of each transport mode was calculated.

The Emme gives results for “traffic areas”. The “sams areas” are almost the same. Only shape file of “sams areas” was available. In order to convert it to “traffic areas” the file containing information which “sams area” is equivalent to which “traffic area” was used. Some changes were found, e.g. two “sams areas” were merged into one “traffic area”. This shape file was used for all GIS analyses. The numbers of zones used is the same as for “sams areas”.

Data limitations Both Sampers model and travel survey have their limitations, what significantly influences the results. The most important limitation of Sampers, only with respect to this study, is that as a model it is already influenced by transportation supply or socio-economic variables. Meaning that, using Sampers data for analysis relationship between PT supply and demand is quite uncertain. Furthermore, Sampers as a model does not take into account personal attitudes towards different modes of transportation or self-decision towards residential choice. Therefore, some results can be overestimated. For example, in many cases even when trip destination and length favor the use of car, one can use other mode due to individual preferences. It is important to point that limitations mentioned above should be rather considered as data limitations, not Sampers model as itself. Nevertheless Sampers model served its role as support to travel survey.

Travel survey is uncertain on the zone level. After disaggregation to zone level number of responds for each one was too low to be statistically valuable.

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4.4. Public Transport Indicator

PTI is built based on four service measures: service coverage, travel time difference by car and PT, frequency and hours of service. Similar methodology as described in TCQSM was used for service measures selection and determination of grades for them. Thus this section describes methodology of determining service measures for whole Kungsbacka municipality and describes how grades were assigned to each zone within municipality. The final part presents the formula used to calculate PTI for each zone. The calculated values of PTI for each zone are used in further part of the thesis for developing relationship between PT supply and percentage of non- work trips by PT.

There are number of service measures deciding about the transit performance. Considering passengers point of view transit in order to be used must be simply accessible and available. In addition comfort may play substantial role when deciding to choose transit as an option for traveling. However while problems with one of the three primary concepts may completely preclude the chance for PT trip, low comfort make the trips less pleasant but still possible. This approach applied when developing PTI. TCQSM was used as an example for developing PTI. The main difference is that PTI doesn´t provide comments regarding level of services due to the fact that passengers attitude towards public transport in rural area is not known. The number of service measures was limited as well as the complexity of measuring each variable. I introduced the methodology incorporating the three important categories as mentioned before: trip coverage, spatial coverage and temporal coverage (Mamun and Lownes, 2011). Indicator is presented as a number for each zone which indicates the differences with public transport supply on a scale 1 to 6 where 1 indicates the lowest level of PT supply and 6 indicates the highest level of supply.

The following service measures were selected as the important for creation of the transit index. Service coverage:  Percentage of populated buildings within 400 m of walking distance from each Kungasbacka stations. Travel time difference:  Difference in travel time by car and public transport to Kungsbacka [min].  Difference in travel time by car and public transport to Göteborg [min]. Frequency:  Number of departures to Kungsbacka for weekdays and weekends. Hours of service:  Number of hours per day when public transport is provided for weekdays and weekends.

Before describing all service measures more in depth, it is important to mention that service coverage, frequency and hours of service were determined both for weekends and weekdays. Differences in travel time were performed just for the weekdays due to software limitations. All service measure results were disaggregated to the zone level and graded according to procedures presented below.

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4.4.1. Service coverage

According to TCQSM the measure of service coverage area by itself is considered as not sufficient when it doesn´t take into account population density and employment density. For that reason TCQSM uses the concept of a transit-supportive area which provides minimum population and job densities that must support hourly transit service (Kittelson, 2003b). Population and job densities are not included in service coverage determination in the thesis. The main reason for this is that PTI represents individuals´ point of view, meaning that in individuals´ eyes population density not influence their possibility of taking PT trip. Surely the population number must be considered here since there is no reason to support uninhabited areas with PT, but no population or job densities are considered.

Regarding to the above discussion including a need of simplification of PTI, the service coverage was calculated as percentage of inhabited buildings within 400 m from each bus station. In that way the purpose of the PTI, to point the differences between service coverage for each zone is meet while taking into account differences in population number. Meaning that not populated areas are not included, thus they do not lower the service coverage value unnecessary.

For the service coverage calculation two GIS based methods were used: network analysis – service area tool and 400 m buffering from each bus station. The first method supposed to be more accurate since it measures real walking distance of 400 m, while the second measures air distances of 400 m. However the service area was calculated by use of the road network with lack of pedestrian one. It causes the problems with proper distances calculation since the distances may be both shorter and longer than the measured depending on not available in the networks walking paths, shortcuts or existence roads not accessible for walking. For the same reasons the second method doesn´t reflect the real walking distances as well. Both methods are presented in map 5.1.

The service coverage results were aggregated to the zone level. The results from these two methods were quite similar, differed approximately by couple of percent for each zone. For building the indicator the results from buffering method was used. For each zone the grade was assigned according to the table 4.2. Service area coverage was calculated separately for weekdays and weekends since number of stations supported by public transport differ significantly for weekdays and weekends.

Grades Service coverage [%] 6 85.0 – 100.0 5 70.0 – 84.9 4 55.0 – 69.9 3 40.0 – 54.9 2 25.0 – 39.9 1 < 25.0 and >0 Table 4.2: Service coverage - grades

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4.4.2. Travel time difference by car and public transport

Travel time is an important factor deciding about choice of transport mode. In PTI the differences in travel time to Kungsbacka and Göteborg city by car and PT were measured. Kungsbacka and Göteborgs cities were selected as the most common destinations for non-work trips in the region. For this calculation the accessibility tool developed at University of Gothenburg was used. This tool uses for analyses two GIS softwares: TransCAD and ArcGIS and additionally T500 for calculations. It measures travel time between 500x500 m squares. The squares extend over Västra Götaland Region. The tool calculates travel time from each square in the region to selected destination points. Calculation of travel time by car and by public transport differs. For travel time by car the national roads database (NVDB) showing roads conditions for 2009 is used. In the first step of calculation each start square is joined to the nearest road. The fictional line is done between center of each start square to the nearest road and travel time is determined based on length of the path. Then the tool search for the shortest route to the destination. Travel time is calculated on each segment of the road taking into account speed limits. Determination of travel time by PT is more complex. For calculation Västtrafik database (REBUS) presenting conditions for 20 September 2010 between 07:00 and 08:30 is used. Calculation of travel time by PT starts from measuring the travel time between square to the nearest bus station. Then Västtrafik data are used to calculate travel time to final station. Finally the travel time from final station to destination is determined. Calculation of travel time to and from station take into account length of the path and uses averaged speed of pedestrian and cyclists.

Before the differences in travel time by car and public transport were calculated, four maps presenting travel time to Kungsbacka and Göteborg by two modes were prepared. The maps are presented in Appendix 1.

Differences in travel time were aggregated to the zone level and grades were assigned for each zone according to the table 4.3. The travel time differences were determined just for weekdays conditions.

Grades Travel time difference, Trave time difference, Kungsbacka city [min] Göteborg city [min] 6 0-10 0-15 5 11-15 16-25 4 16-20 26-30 3 21-25 31-40 2 26-30 41-50 1 31-35 50-60 Table 4.3: Travel time difference by car and public transport to Kungsbacka and Göteborg city – grades

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4.4.3. Frequency

Frequency was read for each bus station within Kungsbacka municipality using Västtrafik web page as a data source. The list of stations was received also from Västtrafik. Some of the stations were not supported with the buses or supported with only school buses. In such a case the station was not included in analysis. In total 377 stations were classified as operating during the weekdays and 209 stations during weekends.

If a frequency in a certain station was low, or there were no departures from the station, the other stations within 5 minutes of walking time were checked in order to replace the station. When reading a timetable headway and there were more than one departure in the same time or within up to 5 minutes the trip was taken as a one. For example if a time schedule looks as follows: 14:10, 14:30, 14:34, 14:50, the frequency was set as a 3 departures per one hour. Frequency was determined separately for weekdays and weekends. For the weekdays the data were read for morning pick and the rest of a day separately and an average was calculated. For the weekends when morning pick hours doesn´t apply the headways were rather equal during the day.

The frequency was read in the way that for each of the headway presented below the value indicating number of departures per hour was assigned: 15 minutes – 4 20 minutes – 3 30 minutes – 2 45 minutes – 1.3 60 minutes – 1 90 minutes – 0.67 120 minutes – 0.5

As a final result for each zone average frequency value from the stations within the zone was calculated. Then grades were assigned for each zone according to the table 4.4.

Headway Grades Veh/h Comments (min) 6 <20 >3 More than 3 per hour 5 20-29 3 3 per hour, less than 2 per hour 4 30-44 2 2 per hour, less than each 45 minutes 3 45-89 1 About 1 per hour 2 90-119 1-0.5 About 1 per 1.5 hour 1 >=120 <= 0.5 Equal or less than each 2 hours Table 4.4: Frequency – grades

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4.4.4. Hours of service

In order to present more precise picture of possibilities to use public transport the hours of service provided must have been determined. It is particularly important when the PTI is built for the purpose of analyzing non-work trips, which quite often take place in the evenings or nights.

In the same way as for frequency the hours of service was read from Västtrafik webpage for each bus station in Kungsbacka. Hours of service was calculated at the minimum hourly level of frequency.

Hours of service was calculated separately for weekdays and weekends. As a final result an average value of each station was calculated for each zone. Since many stations in the weekends are not supplied by public transport some zones got very low values after average calculation. The grades for each zone were assigned according to table 4.5.

Grades Hours per day 6 19-24 5 17-18 4 14-16 3 12-13 2 4-11 1 0-3 Table 4.5: Hours of service - grades

Hours of service plays as important role as frequency and service coverage in determining the availability of transit service to potential users: if transit service is not provided at the time of day a potential passenger needs to take a trip, it does not matter where or how often transit service is provided the rest of the day.

4.4.5. Public Transport Indicator´s calculation

The results of all aggregated to zone level public transport service measures: service coverage, differences in travel time by car and public transport, frequency and hours of service are presented in appendix 2 in table 1.2. Appendix table 1.3 shows the results converted to grades according to the methodology introduced above.

Service coverage, frequency and hours of service are presented both for weekdays and weekends. Differences in travel time were possible to calculate with available tool just for weekdays. Travel time differences by car and PT are presented in final table as an average of two grades, for Kungsbacka and Göteborg.

Public Transport Indicator was calculated separately for weekdays and weekends as a sum of all service measures grades, according to formulas presented below. Thus the maximum value of

33 the indicator is 24. For developing the relationship between public transport supply and choice of transport mode for the purpose of non-work trips PTI was calculated as an average of PTI for weekdays and weekends.

SC – service coverage, weekdays SCW – service coverage, weekends F – Frequency, weekdays FW – Frequency, weekends H – Hours of service weekdays HW – Hours of service weekends TT – average travel time difference for Kungsbacka and Göteborg PTI weekdays = SC+F+H+TT PTI weekends = SCW+FW+HW+TT PTI = average PTI weekdays and PTI weekends

All service measures used to build PTI have been evaluated as equally important. From passenger´s point of view, in order a trip to be taken all four service measures must be available on the satisfying level for customers. Public transport must be accessible (service coverage), available (frequency and hours of service) and trip must be done in reasonable time (difference in travel time). All four PTI components are dependent form each other in the way that when just one of it is not satisfying the trip is precluded. It can be thought that long travel time does not make trip impossible. However in Kungsbacka, considered to be a rural area, the travel time by bus from some areas is irrational.

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5. Building the Public Transport Indicator

The methodology part provided description of methods of four service measures. This chapter aims to show how the four service measures were interpreted to build PTI. Firstly the results of all service measures are described together with limitations of the methods. Additionally the maps of service coverage and differences in travel time to Kungsbacka and Göteborg city are presented. The final part of this chapter refers to issue of building the index for rural area.

Why four service measures were selected The indicator was developed with use of four service measures: service coverage, frequency, hours of service and travel time. These measures are commonly used for measuring PT performance, what was confirmed in literature review. There are other service measures which reflect passengers´ perception towards PT, such as costs or quality. However, due to the need of study limitations, just the most important measures were selected. Precisely, the measures necessary for the trip to be taken. The issue to discuss may be why travel time difference was selected as significant measures among three others. The main reason is that there was a need to include access to common destinations in the study. This is particularly important for the purpose of analyzing non-work related trips since the majority of shopping places or activities are located in Göteborg or Kungsbacka city. Short distance to public transport stops, satisfying frequency or hours of service do not guarantee that desirable destination can be reached, also in reasonable time. This issue is addressed by difference in travel time by PT and car to Kungsbacka and Göteborg city.

Service coverage The results of service coverage are presented in map 5.1 below. The map shows number of residential buildings within distance of 400 m from bus stations and the one located out of this area. It shows just a part of the area covering Kungsbacka city center. The map presents weekdays conditions. Percentages of service area differ much among the zones. In Kungsbacka city area 95 % of residential have less than 400 m to the nearest bus stations. The lower percentage of service area, less than 50 % is measured rather for zones sparsely populated. All results for each zone are presented in appendix 2-table appendix 1.2.

Limitations Service area was calculated as buffers around each bus station in Kungsbacka with awareness of uncertainties associated with this method. This method measures air distance of 400 m instead of walking distance. The calculation was performed both using buffers and network analysis. However the lack of pedestrian network made unable to get accurate results from this measure also. Surprisingly results of these two methods after disaggregation to the zone level gave similar results. Service area was calculated as a percentage of residential. Counting number of people living in would be more accurate. The reason for that was need for simplification and availability of

35 data. Furthermore, counting number of people may be also not precise when doesn´t consider the age.

Figure 5.1: Service coverage – 400 m. Number of residential buildings within 400 m distance from bus stations. Weekday’s condition.

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Frequency Frequency of PT in municipality varies from around 4 departures per hour around Kungsbacka city to less than one departure per two hours in eastern part of municipality. Generally in more populated zones, such as: 2, 3, 4, 6, 19, 23, 25, 26, 41 the PT is available more frequent than one departure per half an hour. Exception is noted for zones number 37 and 38. In these zones the headways is equal to around 45 minutes. Frequency in weekends is generally lower. The most commonly used is equal headway of 30 or 60 during hours of service operating. The final averaged results show that most of higher populated areas are supported with averaged one departure per hour. In turn, less populated areas, such as zones 42-53 are not provided with PT during weekends (see table appendix 1.2).

Limitations Frequency was calculated for each zone as an average value from each bus station within the zone. The zones differ quite much in size as well as in number of stations within. For that reason calculated averages of frequency and service measures may not be entirely representative when comparing the results between zones. For example within big zone some stations have really good frequency, while some quite bad. The result for zone is averaged so it doesn´t capture the capacity of the zone perfectly.

Hours of service Hours of service operating varies between 15 and 18 for almost all zones from number 2 to 32. It is significantly lower for most of the zones from number 33 to 53, with average 4 to 10 hours of service for zones 42-53. Particularly worth of attentions with respect to population number are again zones 37 and 38. Hours of service in these zones are equal to around 10 and 14 respectively, both for weekdays and weekends. In contradiction to frequency measure, for some zones PT is operating longer during weekends. Nevertheless, more zones than for weekdays are not supported with PT, such as zones number 42-53. (see table appendix 1.2).

Limitations of hours of service methods calculation is the same as for frequency and described above.

Difference between travel time by car and public transport Based on the data about travel time by car and PT, the differences in travel time by these to modes were counted. Figure 5.2 below shows difference in travel time by car and PT to Kungsbacka city and figure 5.3 to Göteborg city. Both maps show bigger differences in travel time by two modes from areas located further from the city center. Difference in travel time by two modes to Kungsbacka city differs between around one minute to 50 minutes. Very little areas show higher than 50 minutes difference. Difference in travel time by car and PT to Göteborg varies between 2 to 100 minutes.

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Figure 5.2: Difference in travel time by car and public transport to Kungsbacka city

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Figure 5.3: Difference in travel time by car and public transport to Göteborg city

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Limitations While frequency, hours of service and service area were determined separately for weekends and weekdays the travel time component is determined just for weekdays. That was due to the limitation of the software used. Thus travel time difference was used to build PTI for both weekdays and weekends. The reason why the travel time component was not skipped in weekends PT index calculation was that the two indexes would not be comparable. When analyzing differences in travel time to Kungsbacka and Göteborg cities, the other issue raised. Namely if difference between traveling by car and public transport increases with length of trip. According to the results, difference in travel time to Kungsbacka ranges from about 6 to 32 minutes, while to Göteborg from 12 to 53 minutes. The increasing difference in travel time with length may be explained by increasing number of transfers needed with distance and waiting time associated with transfers.

Specific of Kungsbacka as a rural area Kungsbacka is a rural municipality, unevenly populated which fosters car orientated transport. Hence it is difficult to support whole area with effective public transport with high frequency and long daily time of service.

This study attempts to create Public Transport Indicator, reflecting passengers´ point of view. However, due to lack of information about passengers´ perception towards public transport, it was impossible to assign the comments to each group of service measures. As for example, Transit Capacity and Quality of Service Manual for each particular service measure, assigns a LOS from “A” – best to “F” – worst, with the proper description of each LOS based on passenger´s perception of a particular aspect of transit service (Kittelson, 2003 b). In the thesis each service measure was divided on 6 groups and then grades were assigned. However, grouping was not done based on passengers´ perception on PT. It was performed in the way that best capture differences in each service measure.

Rural transportation conditions encourage a use of cars rather than public transport for short distances trips. In not urban conditions when taking short trip by public transport walking to stations and waiting time may take more than time spent in vehicle. For that reason difference in travel time by car and public transport is usually significantly higher in rural conditions, compare to urban.

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6. The context of the case study area

In this chapter the study area is described in terms of travel behavior what serves as a background for analysis of the thesis results. Description of the study area and authorities transportation plans must be considered from a broader perspective than just Kungsbacka municipality. Thus, the general travel pattern characteristics and transportation plans are presented for the whole GR. Similarly, the maps showing travel behavior with respect to trips purpose include the whole GR.

6.1. Regional transportation plans

Regional development is concentrated around Göteborg city which is treated as core of the region and planned to be easily accessible for entire region. In the region´s core five main corridors meet, forms by structure of the main rail and roads. The new work and residential places are planned to be developed along the main corridors.

One of the main goals for the GR is to achieve sustainable transportation mainly by increasing public transport trips. Project called K2020 aims to increase the trips by public transport to 40 % by 2025 (K2020). Currently around 20 % of trips are made using public transportation.

In order to achieve the goal of K2020 project several initiatives are on-going. Among them “The West Swedish Agreement” is a crucial one. This project aims to provide possibilities of fast and more sustainable commuting, decreasing car traffic and reduce negative impact of transport on environment. Among many investments in public transport the project involves: rail tunnel (Västlänken) with underground stations in Göteborg center, Korsvägen and Haga or road tunnel under river Göta “Marieholmstunneln” (Västsvenska paketet).

6.2. Travel behavior in the study area

Table 6.1 shows modal split for municipalities with more than 70 000 travelers during weekdays in 2005 according to travel survey made for Göteborg Region (Resvanor i Göteborgs-regionen, 2007). In turn table 6.2 shows travel patterns according to a survey made for Göteborg and ten other municipalities belonging to GR. The study attempted to present travel patterns, rather than daily modal split by asking in the survey about travel habits (Så reste Göteborgarna våren, 2012).

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Public Car Bicycle Walk Municipality transport % Alingsås 70 12 9 9 Göteborg 50 9 26 14 Kungsbacka 75 6 9 7 Kungälv 69 9 10 10 Mölndal 68 9 11 10 Partille 65 5 17 12 Table 6.1: Modal split for GRs municipalities with more than 70 000 travelers for weekdays in 2005 (Resvanor i Göteborgs-regionen, 2007)

Employed Students Transport Mode % Public transport 20 67 Bicycle 7 3 Car 51 4 Walk 6 7 Table 6.2: Travel habits of students and employed in Göteborg and ten other GRs municipalities in 2012. (Så reste Göteborgarna våren, 2012).

The maps presented on figures 6.1 and 6.2 show percentage of trips by public transport and by private cars respectively for work and non-work purposes. They attempt to introduce the travel pattern in Göteborg Region but also to point out differences between modal split for work and non-work purposes. Both maps were prepared with use of data received from Sampers model.

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Figure 6.1: Comparison of PT trips percentages for work and non-work purposes

Figure 6.2: Comparison of private car trips percentages for work and non-work purposes

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7. Analysis and discussion

In this chapter the results together with analysis are presented in relation to the research questions and aim of the thesis raised in the first chapter-“Introduction, aim and research questions”. Thus, the chapter is structured in the way that follows research questions and aim. In the first section public transport demand for non-work trips is presented, both for data from Sampers model and travel survey. Distribution of different transport mode for different non- work trips is discussed and modal split for non-work trips is compared to work trips. In second section data derived from Sampers model and travel survey are compared. The third section is focused on public transport supply in Kungsbacka municipality. The level of supply is discussed in relation to service measures used to build PTI. The differences between PTI for weekdays and weekends are discussed and presented on maps. Finally the aim of the thesis-relationship between PT supply (PTI) and choice of transport mode for non-work trips is analyzed.

7.1. Public transport demand for non-work trips in Kungsbacka municipality

Sampers The figure 7.1 shows map of percentage of trips by public transport among all non-work trips, for each zone within Kungsbacka municipality. The map was prepared with use of data from Sampers model. According to Sampers Kungsbacka´s inhabitants use public transport rather rarely. Percentages of PT trips don´t differ much between zones and vary between 3.14 % and 7.73 %. The map shows that people living closer to city center tend to use public transport a little bit more. However the correlation is very weak.

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Figure 7.1: Percentage of non-work trips by public transport in Kungsbacka municipality based on data from Sampers model

Travel survey Percentage of non-work trips by public transport for each zone in Kungsbacka municipality based on data from travel survey is presented at map 7.2. According to travel survey people don´t travel much by PT for non-work trip purposes. The percentages for each zone differ more than for Sampers data and vary from 0% to 16.22 %. Due to low number of responds, the amount of zones showed percentages statistically not valuable. These zones were excluded from further relationship analyses. In the map this zones are marked in white color. The pink colored zones are excluded because there is no one living there.

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A lot of research confirmed that more densely developed areas promote the use of public transport. This dependency has limited visibility in the results for Kungsbacka city, precisely for zones number 2 and 3 (for location of zones see figure 4.2). According to travel survey 6.5 % of non-work trips are made by public transport. However, the results show increased number of walk and bike trips in this area, in comparison to rest of the municipality, less densely populated. There is 20 % and 13 % of non-work trips made by walk and bike in zone number 2 and 3 respectively (see table 4.1). Even though the pattern is not clear, some of the very sparsely developed areas show rather low percentage of trips by public transport. For example zones number 42 and 45 do not show any PT trips and from zone 44 only 2.94 % is made by PT.

Figure 7.2: Percentage of non-work trips by public transport in Kungsbacka municipality based on data from travel survey.

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7.1.1. Distribution of different transport modes for different non-work purposes

Travel survey made for Kungsbacka municipality was accurate enough to categorized non-work trips for different trip purposes as presented in table 7.1. The table shows also modal split for different purposes. Kungsbackas inhabitants almost always use cars for the purpose of driving and picking up children and others. Trips for purpose of shopping groceries and other tend to be done by car also. These trips show also low percentage for PT trips. Public transport tends to be used a bit more for the purpose of leisure or visiting trips.

Percentage of Percentage Purpose all trips by purpose Car PT Bike Walk Groceries 16 86 3 5 7 shopping Other 11 90 5 1 4 shopping Leisure 24 79 9 4 8 Visiting relatives/ 12 85 8 3 5 friends Service 11 83 6 3 8 Drive/ pick 17 92 3 1 4 up children Drive/pick 4 97 2 0 1 up others Other 5 86 5 4 4 Table 7.1: Modal splits of non-work trips by different purposes

7.1.2. Comparison of modal split of non-work trips to work trips

The final travel survey report showed modal split of work trips for whole municipality (figure 7.4). Based on travel survey database the modal split for all non-work trips was calculated (figure 7.3). Travel survey shows that people tend to travel more by car for other than work purposes. Moreover people use less public transport for this type of trips.

3%4% 3%6% Car 6% Car 16% PT PT Bike Bike Walk Walk 77% 85%

Figure 1 FigureFigure 2 7.3: Modal split for non-work trips, Figure 7.4: Modal split for work trips, Kungsbacka travel survey Kungsbacka travel survey

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Travel survey shows that Kungsbacka inhabitants tend to use cars more for non-work than work purposes. 85 % of all non-work trips are made by car, while percentage of car work trips is equal to 77%.

Specific of other than work trips As stated in literature, transport mode for non-work trips may be more influenced by subjective variables such as attitudes and individual´s perception. Trips for other purposes than work may be considered as more burdensome when using public transport, such as shopping or travelling with family. Moreover these types of trips are frequently not associated with public transport, meaning that public transport may not be even considered as an option for travel. Thus, the potential in switching the mode from car to public transport lies not only in available, accessible and convenient public transport system but at high extend in changing attitude. For that reason and due to high percentage of these trips by car it may be concluded that non-work trips have high potential for switching the mode from car to public transport.

7.2. Sampers vs travel survey

Data on modal split for work and non-work trips were received from Sampers model and travel survey for Kungsbacka municipality. Tables 7.2 and 7.3 shows modal splits for work and other trips in Kungsbacka municipality according to Sampers and travel survey respectively. The table 7.4 summarizes the similarities and differences between this two data source.

Considering differences between work and non-work trips Sampers models both higher percentages of car trips for work and PT trips for work. This pattern was also visible at figures 6.1 and 6.2. In turn number of bike and walk trips for non-work purposes is rather high. Both data sources indicate that people don´t prefer to take public transport for other than work purposes.

Sampers - Kungsbacka Travel survey - Kungsbacka Work [%] Non-work [%] Work [%] Non-work [%] Car 83 Car 71 Car 77 Car 85 95 94 PT 9 Bike/walk 24 PT 16 Bike/walk 9 Bike/walk 8 PT 5 Bike/walk 7 PT 6 Table 7.2: Modal split for work and non-work Table 7.3: Modal split for work and non-work trips, Sampers trips, Travel survey

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Sampers Travel survey The same sequence of modes, both for work and non-work purposes Non-work trips are less likely to be done by public transport Relatively high percentage of bike/walk trips for non-work purposes Balanced with high amount of car trips Relatively low percentage of car trips for non- work purposes Not big difference between percentage of PT Not big difference between percentage of trips by purpose bike/walk trips by purpose Big difference between percentage of Big difference between percentage of PT trips bike/walk trips by purpose by purpose Table 7.4: Similarities and differences between modal split in Kungsbacka municipality according two source of data: Sampers and travel survey

7.3. Public Transport Supply-PTI

The figures 7.5 and 7.6 show maps presenting results of PTI for weekdays and weekends respectively. The results are presented at the maps in order to better capture the differences in supply level between weekdays and weekends. Results of PTI for weekdays, weekends and averaged, are presented also in appendix table 1.1. The level of PT supply differs significantly between the municipalities regions both during weekdays and weekends. In weekday the lowest PT supply level is observed for zones 20, 27, 29 and 46. Central part of municipality is characterized by high supply level with PTI around 20. Western part of municipality has relatively good supply level and values of PTI vary between 11 and 17. In turn eastern part of municipality is characterized by lower PT supply level. PTI in these areas differs from 7 to 15. Particularly low value of PTI is observed for zones: 42, 45, 50, 51. During weekends the trend in PT supply is similar to the weekdays. The highest level of supply is shown for Kungsbacka city area. However the extend of the area with PTI around 20 is smaller. The eastern part of the municipality has relatively good PT supply, at the level similar to weekdays conditions. In eastern part of municipality PT supply level is very low and value of PTI is lower than 11 for the most of the area. Especially low level of PT supply is shown for the zones 42, 44, 45, 49, 50 and 51.

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Figure 7.5: Public Transport Indicator-weekdays

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Figure 7.6: Public Transport Indicator-weekdays

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Differences in PT supply between weekdays and weekends PTI is show similar trend in level of PT supply for weekdays and weekends. Meaning that, the area around Kungsbacka city is characterized by high level of PT supply. Western part of municipality has relatively good PT supply and the lowest level of PT supply is observed for eastern part of municipality. In central and eastern part of municipality the difference in PT supply between weekday and weekends is moderate. The biggest difference is in extent of area characterized by low PT supply level in eastern part of municipality. Number of bus stations operating there is significantly lower.

Differences in supply level are significant also between different day times. Frequency and hours of service measures indicates that PT favor peak hours commuting in the region. Even during the weekdays public transport may not be an option for traveling in the evenings for some areas where hours of service operating is rather short. Travel survey shows that trips for the purpose of driving and picking up children and others constitutes for 21 % of all non-work trips. Even though it includes driving to kinder gardens, the number is high and may be a consequence of low supply of PT during evenings and weekends.

As in many other systems Kungsbacka´s public transport system is designed in the way that promote peak hour commuting and not to support the trips other than work.

Passengers´ point of view It is important to keep in mind that PTI represents passengers´ point of view, especially when looking at maps 7.5 and 7.6. Thus, population density and job density are not included in the indicator developing. For that reason the differences in PTI for eastern part of Kungsbacka are so significant. If population density would be applied in PTI calculation the differences would be less visible. Nevertheless the PTI plays its role of showing differences in PT supply from passengers´ point of view.

7.4. Relationship between public transport supply and choice of transport mode

The relationship between public transport supply and choice of transport mode was developed on a zone level separately with use of Sampers and travel survey data. Figure 7.7 shows relationship between PTI and percentage of non-work trips by PT according to Sampers data. Figure 7.8 presents relationship between PTI and percentage of non-work trips by PT according to travel survey.

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10.00 8.00 SAMPERS: 6.00 Correlation: 0.34

4.00 Regression:

Trips by PT PT by Trips [%] 2.00  R square: 0.11 0.00  P value: 0.02 0 5 10 15 20 25 PT Index

Figure 7.7: Correlation between PTI and percentage of PT trips (Sampers)

20.00

15.00 Travel survey: 10.00 Correlation: 0.24 Regression:

5.00  R square: 0.06 Trips by PT PT by Trips [%]  P value: 0.20 0.00 0 5 10 15 20 25 PT Index

Figure 7.8: Correlation between PTI and percentage of PT trips (travel survey)

The relationship between public transport supply and demand is rather weak. The main reason for that is relatively low level of public transport supply in Kungsbacka municipality, especially during evenings and weekends, when majority of non-work time activities happen. The strongest relationship may be expected in the areas supported with public transport in a way which encourages taking a trip by public transport. In Kungsbacka public transport undoubtedly plays its role, as for rural conditions. However, level of public transport supply can be consider rather as providing option for travel, not as a factor which encourages public transport trip.

The other issue which may partially explain the low relationship is specific of non-work trips. As stated in literature part, the choice of transport mode for this trip may be particularly influenced by subjective variables such as attitude. This theory may be confirmed by the differences between modal split received from Sampers model and travel survey. Sampers, as a model shows results which are already at some point influenced by built environment and socio-economic factors. On the other hand travel survey which reflects peoples´ attitude shows different results. The difference is more visible for non-work trips than for work what may suggest that individuals´ perception on public transport, attitude or lifestyle are particularly important when deciding about transport mode for non-work trips.

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8. Conclusions

The results of this thesis confirmed that Kungsbacka municipality transportation is dominated by cars. As an average only 6 % of Kungsbacka´s inhabitants use public transport for non-work trip purposes. Car is mostly used for the purposes of driving and picking up children and different types of shopping. Public transport tends to be used a bit more for the purposes of leisure or visiting trips. In Kungsbacka city percentage of non-work trips by PT does not differ much from the whole municipality. Thus, the results do not contribute to the research indicating that more densely developed areas promote the use of public transport. Below the most substantial findings are summarized.

Trips for non-work purposes tend to be done by cars more than work trips Travel survey shows that Kungsbacka inhabitants tend to use cars more for non-work than for work purposes. 85 % of all non-work trips are made by car, while percentage of car work trips is equal to 77%. Only 6 % of non-work trips is made by public transport, while percentage of work trips by public transport is equal to 16%.

Public transport system favors peak hours commuting The difference in PT supply between weekdays and weekends is particularly visible in eastern part of municipality. The extent of the area characterized by low PTI is significantly higher during weekends and number of bus stations operating there is much lower. Furthermore, the hours of service measure indicates that level of PT supply is weaker during evenings. As in many other systems Kungsbacka´s public transport system is designed in the way that promote peak hour commuting and not to support the trips other than work.

Relationship between PT supply and choice of transport mode The relationship between public transport supply and demand is rather weak. The main reason for that is too low level of public transport supply in Kungsbacka municipality during evenings and weekends, when majority of non-work time activities happen. The level of public transport supply in Kungsbacka municipality during the weekends and evenings is rather low and does not encourage taking a trip. Relative to cars, PT in the region does not promote itself. The higher correlation between PT supply and choice of transport mode for non-work trips may be expected for the regions better supplied with PT during evenings and weekends. However, further investigation of the issue is needed.

Further investigation This thesis pointed out that non-work trips have high potential for switching mode from car to public transport. However, demand for non-work trips is significantly less investigated. The number of research and travel surveys tends to focus on work trips. The results of this thesis contributed to understanding PT supply in rural areas by developing Public Transport Indicator. However, individual´s perception towards public transport in rural areas is not well investigated. That knowledge may contribute to the effort of finding the

54 solution for effective PT supply in the areas characterized by low population density. Moreover, increased attention towards relation between PT supply and non-work trips may contribute to solving the problem of high amount of these trips by car.

55

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Appendix 1 – Travel time to Kungsbacka city center and Göteborg city center by car and PT-maps

Figure appendix 1.1: Travel time to Kungsbacka city center by car

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Figure appendix 1.2: Travel time to Kungsbacka city center by public transport

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Figure appendix 1.3: Travel time to Göteborg city center by car

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Figure appendix 1.4: Travel time to Göteborg city center by public transport

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Appendix 2 – Results of service mesures and PTI

Zone PTI weekdays PTI weekends PTI number 2 21.00 20.00 20.50 3 20.50 17.50 19.00 4 17.00 16.00 16.50 5 16.50 14.50 15.50 6 15.50 15.50 15.50 7 15.00 15.00 15.00 8 13.50 13.50 13.50 9 15.00 15.00 15.00 10 16.50 16.50 16.5.0 11 16.50 16.50 16.50 12 0.00 0.00 0.00 13 15.50 13.50 14.50 14 15.00 13.00 14.00 15 13.00 10.00 11.50 16 16.00 16.00 16.00 17 16.00 16.00 16.00 18 18.50 17.50 18.00 19 22.00 21.00 21.5.0 20 4.00 4.00 4.00 21 15.50 16.50 16.00 22 16.00 15.00 15.50 23 22.00 21.00 21.50 24 15.00 15.00 15.00 25 17.50 12.50 15.00 26 19.00 20.50 19.75 27 1.50 1.50 1.50 28 19.00 19.00 19.00 29 3.00 3.00 3.00 30 20.00 19.00 19.50 31 17.50 14.50 16.00 32 16.00 15.00 15.50 33 13.00 9.00 11.00 34 13.00 8.00 10.50 35 20.00 19.00 19.50 36 13.00 6.00 9.50 37 14.00 12.00 13.00 38 17.00 16.00 16.50 39 12.00 9.00 10.50 40 10.50 5.50 8.00 41 14.50 13.50 14.00 42 8.00 2.00 5.00 43 11.50 6.50 9.00 44 10.00 3.00 6.50 45 8.00 2.00 5.00 46 4.00 4.00 4.00 47 13.50 7.50 10.50 48 12.50 5.50 9.00 49 11.00 4.00 7.50 50 8.50 2.50 5.50 51 7.50 3.50 5.50 52 0.00 0.00 0.00 53 14.00 6.00 10.00 Table appendix 1.1: Results of Public Transport Indicator: for weekdays-PTI weekdays, for weekends-PTI weekends, and averaged-PTI.

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Zone Service Service Travel time Travel time Frequency Frequency Hours of Hours of number area area difference difference weekdays weekends service service weekdays weekends Kungsbaca Göteborg weekdays weekends [%] [%] [min] [min] [h] [h] 2 95.33 95.33 5.92 11.86 2.72 1.55 16.15 16.16 3 94.12 94.12 6.44 15.75 2.34 1.05 14.73 12.13 4 42.70 42.70 23.75 24.09 2.40 1.00 17.50 19.80 5 47.73 37.28 25.36 22.69 2.21 1.00 18.30 21.00 6 52.13 52.13 23.76 29.14 2.00 1.00 17.75 20.00 7 40.76 40.76 20.82 32.25 2.00 1.00 18.00 20.00 8 15.38 15.38 15.37 34.34 2.00 1.00 17.50 19.50 9 29.63 29.63 13.97 34.84 2.00 1.00 17.50 19.50 10 54.33 54.33 13.42 29.29 2.00 1.00 17.50 19.50 11 44.35 50.10 11.87 29.11 2.00 1.00 17.50 19.50 12 ------13 58.53 58.53 17.90 31.24 1.10 0.88 17.50 11.55 14 51.18 51.18 20.49 34.10 1.56 1.17 17.50 16.19 15 36.44 36.44 23.68 36.38 1.00 0.75 17.50 10.50 16 46.34 46.34 22.11 34.63 2.13 1.41 17.63 19.38 17 50.93 50.93 21.22 33.93 3.00 1.50 18.00 20.50 18 46.31 46.31 11.80 25.19 3.20 1.50 18.00 20.50 19 74.21 74.21 5.61 13.46 3.50 1.50 18.00 20.50 20 8.64 8.64 20.93 33.92 0.00 0.00 0.00 0.00 21 30.96 30.96 19.94 31.09 2.75 2.13 17.75 20.50 22 38.21 4.48 20.58 31.65 3.50 3.00 18.00 20.50 23 70.11 70.11 7.77 14.92 3.50 1.50 18.00 20.50 24 9.03 3.87 22.98 30.51 3.50 3.00 18.00 20.50 25 60.35 52.50 16.09 24.32 2.67 0.83 15.33 11.39 26 73.54 72.53 9.22 17.07 3.50 1.50 17.70 20.50 27 0.00 0.00 31.68 49.04 0.00 0.00 0.00 0.00 28 60.61 60.61 12.64 19.61 3.00 1.50 18.00 20.50 29 0.00 0.00 23.67 35.12 0.00 0.00 0.00 0.00 30 76.40 70.27 8.12 11.82 2.17 1.00 16.42 18.00 31 31.77 16.15 15.44 18.93 3.57 1.92 17.21 18.50 32 36.40 36.40 11.03 17.93 2.00 1.00 17.50 18.50 33 43.81 27.94 11.77 22.80 0.85 0.32 6.00 3.00 34 57.48 22.99 14.31 22.64 1.04 0.11 8.54 1.45 35 46.81 46.81 9.46 13.93 4.00 1.50 17.50 19.00 36 89.74 1.49 20.35 34.40 0.67 0.15 4.61 1.90 37 79.43 41.14 15.49 28.58 1.18 0.68 9.59 8.64 38 58.83 57.63 11.11 23.91 1.63 1.13 13.88 14.25 39 52.16 5.56 18.25 28.93 1.00 0.50 9.00 6.33 40 45.71 17.87 22.22 43.75 0.73 0.11 5.32 1.35 41 35.54 33.37 14.33 29.26 1.70 1.20 14.80 15.20 42 37.85 0.00 26.88 43.50 0.57 0.00 4.23 0.00 43 41.75 1.29 18.31 33.72 0.68 0.04 5.23 0.30 44 40.96 0.00 23.54 33.19 0.59 0.00 6.00 0.00 45 23.71 0.00 23.13 53.20 0.92 0.00 5.00 0.00 46 3.51 3.51 21.97 32.25 0.00 0.00 0.00 0.00 47 61.29 6.69 16.73 18.43 1.10 0.13 7.93 1.53 48 51.35 10.36 12.49 26.44 1.22 0.00 9.67 0.00 49 57.69 0.00 22.71 23.17 0.50 0.00 8.00 0.00 50 48.24 0.00 25.26 33.15 0.50 0.00 10.00 0.00 51 35.10 4.64 29.53 34.89 0.50 0.00 10.00 0.00 52 ------53 55.98 22.83 13.58 18.04 1.00 0.00 7.00 0.00 Table appendix 1.2: Results of all service measures for each zone: service coverage, difference in travel time, frequency, hours of service. For weekdays and weekends

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Travel time Hours of Hours of Zone Service area Service area difference Kbacka Frequency Frequency service service number weekdays weekends & Göteborg weekdays weekends weekdays weekends average 2 6 6 6 5 4 4 4 3 6 6 6 5 3 4 3 4 3 3 4 5 3 5 6 5 3 2 4 5 3 5 6 6 3 3 4 4 3 5 6 7 3 3 3 4 3 5 6 8 1 1 4 4 3 5 6 9 2 2 4 4 3 5 6 10 3 3 5 4 3 5 6 11 3 3 5 4 3 5 6 12 ------13 4 4 4 3 3 5 3 14 3 3 3 4 3 5 4 15 2 2 3 3 3 5 2 16 3 3 3 5 4 5 6 17 3 3 3 5 4 5 6 18 3 3 5 6 4 5 6 19 5 5 6 6 4 5 6 20 1 1 3 0 0 0 0 21 2 2 4 5 5 5 6 22 2 1 3 6 5 5 6 23 5 5 6 6 4 5 6 24 1 1 3 6 5 5 6 25 4 3 5 5 3 4 2 26 5 5 6 4 4 5 6 27 0 0 2 0 0 0 0 28 4 4 5 5 4 5 6 29 0 0 3 0 0 0 0 30 5 5 6 5 3 4 5 31 2 1 5 6 4 5 5 32 2 2 5 4 3 5 5 33 3 2 5 3 1 2 1 34 4 1 5 2 1 2 1 35 3 3 6 6 4 5 6 36 6 1 3 2 1 2 1 37 5 3 4 3 3 2 2 38 4 4 5 4 3 4 4 39 3 1 4 3 2 2 2 40 3 1 3 3 1 2 1 41 2 2 5 4 3 4 4 42 2 0 2 2 0 2 0 43 3 1 4 3 1 2 1 44 3 0 3 2 0 2 0 45 1 0 2 3 0 2 0 46 1 1 3 0 0 0 0 47 4 1 5 3 1 2 1 48 3 1 5 3 0 2 0 49 4 0 4 1 0 2 0 50 3 0 3 1 0 2 0 51 2 1 3 1 0 2 0 52 ------53 4 1 5 3 0 2 0 Table appendix 1.3: Results of all service measures after grading for each zone: service coverage, difference in travel time, frequency and hours of service. For weekdays and weekends

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Appendix 3 – Sampers model

1. Overview

SAMPERS is the Swedish National Transportation Model. The first version of the model was developed in 1980. Since the time several improved versions were developed. Over the years transportation models were successfully used as part of national strategic transport investment plans for example to evaluate effect of an introduction a new train line on traffic.

Currently used model is called SAMPERS 2.1. The previous version was quite comprehensive but several things needed improvement:

 Model was not integrated into one single system which was the consequence of model developing by different organisations over the years  User unfriendliness  Need for inclusion model for international trips base on a new national travel survey

Therefore the current version of SAMPERS 2.1 models all trips, more accurately all trips having at least origin or destination in Sweden. The three following models are distinguished:

 Regional model  Long distance model  International model

Due to different level of details required in forecasting system regional model operates with higher resolution than long distance or international model. Thus, Sweden is divided to:

 8500 zones for regional trips  670 zones for domestic long distance trips and international trips inside Sweden  180 zones for international trips outside of Sweden

Huge number of zones for regional trips results with long matrices, difficult to operate. To avoid problems resulting from running scenarios for 8500 zones in regional model further breakdown to 5 regions was made.

In order to make the system user friendly, the demand models, the databases and the EMME/2 system had to be integrated into one software under the Windows NT operating system.

The main data source used for developing a model was the national Swedish travel survey between 1994 and 2001. The current SAMPERS 2.1. model utilizes 50 000 interviews done between 1994 and 2000. Each respondent was asked to list all trips done in particular day. The questionnaire asked also about trips over 100 km in last month and trips over 300 km made during last two months.

Generally all models use three types of variables to depict travel behaviour:

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 Transportation supply variables  Destination zone variables  Socio – economic variables

2. Regional model

Regional model is explained more in details since the thesis considers mainly non-work and for some extend work trips, which are modelled by regional model.

The regional models consider trips for the following modes: car as a driver, car as a passenger, bus, commuter train, bicycle and walk. Since models work on tour basis only one mode can be assign to one trip. In case when the trips is made by more than one mode the following hierarchy applies: train, bus, car as a driver, car as a passenger, bicycle and walk, where the first in the hierarchy is the one used as the main for a tour.

Regional models produce trips in the following purposes:

 Work  Business  School  Social  Recreation  Other

All work trips are modelled as a home based trips. Regional model aims to capture also so called “trip chaining” which considers trips made as secondary destination or other work based trips. For example shopping trips can be considered as secondary destination trip, made on the way home from work. Because of the complexity of modelling this type of trips the necessary simplifications were made:

 Sampers looks into only work and home based trips,  The secondary destination trips and work place based trips are restricted to the most important ones.  The secondary destination trips were modelled only as “other” trips purpose and work based trips were modelled only as a business trips.

Regional models are divided for three major parts: Regional home based models, regional models with secondary destinations and work based tours and access/egress tours.

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2.1. Regional home based models

Regional home based model distinguish work trips and non-work trips. Model structures of these trips are presented in figures 1:5 and 1:6 below.

Trip No trip

Card Carp Publ . t. Walk Bicycle

Dest1…n

Figure appendix 1.5: Model structure for work tours

Trip No trip

Home based Secondary destination Work place based

Card Cp PT W BC Card Cp PT W BC Card Cp PT W BC

...... Dest1…n Dest1…n Dest1…n

Figure appendix 1.6: Model structure for non-work tours

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2.1.1. Work tours

Travel costs

To a quite high degree the costs of work trips is dependent on tax deductions possibility. Travel survey provides some information regarding number of people using tax deductions for different time categories (every day, twice a week, 2-3 times a week, occasionally or never). Even the data was limited there was possible to conclude that approximately half of the drivers count on making a tax deduction in this year´s income tax return. Running costs have been estimated to 13 SEK per 10 km. The cost of work trip by company car was set to be zero. In case when household has both company and private cars the average was estimated. Cost of a trip by public transportation was determined under the assumption that monthly pass is used.

Variables used

As for all models three types of variables was used:

 Transportation supply variables (e.g. constant for public transport, travel costs in the region). Generally the variables includes travel costs and travel time.  Destination zone variables include the number of work places in a region. The variables consider also if destination zones belongs to municipality centre. Plenty of other variables determine if a trip to certain destination is possible or not. Such as unavailability of parking within the zone make it impossible for being destination zone.  Socio-economic variables mainly consider gender and driving licence holding. The model assumes that gender influence length of a trip and possibility to use a car.

 There exist also other variables, which reflect differences in climate in Sweden or are related to topography such as dummy for Göta-Älv cut which assumes that trips across the cut would be rather rare.

For each variable the model parameter is established. Each variable is assign to specific choice level: F – frequency, M – mode, D – destination. Just general description of variables deciding about travel behaviour is presented here. The number of variables used is too big. The other reason is the complexity of them.

2.1.2. Non-work tours

Travel costs

Car costs were estimated similarly as for work tours, but no tax deductions were taken into account. For public transportation it was assumed that if person report using of monthly pass in travel survey the costs was equal to zero. Otherwise the cost was set to the single travel price.

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Segmentation

Travel survey hasn´t provided enough data to model all non-work categories of tours. Also due to the need for decreasing time of models running the aggregation was performed. Base on priori considerations and empirical tests the following trip purposes was distinguished: school, business, social, recreation and other.

Estimation data

The same destination alternative samplings scheme as for work tour was used. However the model structure differs from work trips models; destination choice is at the lowest level, mode choice at the middle level and frequency at the highest level. In addition, the models have been established separately for mode and destination choice and for frequency choice.

Variables used

Similarly to work trips three general types of variables were applied: transportation supply, destination zone, and socio-economic variables. In the same way also choice level: frequency, mode and destination was assigned to each variable.

Business trips

Mode and destination model

 The number of observations is very low; just 554 observations in SAMPERS 2.1.  Logsum parameters are not significant  The cost variable is significant only for low – income group  From time component parameter only in – vehicle time play a role

Frequency model

 Man are more likely to have business trips than women  Logsum parameters are significant  Person with low education are not likely to have business trips  Person with own business are more likely to travel  The probability to travel decrease for economic sector SNI3 which is manufacturing  Lower activity in summer time and December

School trips

Mode and destination model

 Cost and some of the time components are significant  Wait time is not significant (school buses are not coded into networks).

Frequency model

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 No logsum since school trips are mandatory  Persons up to 18 are more likely to travel than higher education students  Lower activity during summer time

Social trips

Mode and destination model

 Cost and time components are significant  Summer houses are significant  Age variables play substantial role: e.g. person under 15 is more likely to travel by bicycle

Frequency model

 Accessibility and income are significant factors  “The trip frequency is adjusted downwards for the densely populated areas, and upwards for the regions with low density”

Recreation trips

Mode and destination model

 Time and cost are significant  Summer houses are significant

Frequency model:

 Shows similar depending as frequency model for social trips

Other trips

Mode and destination model

 Variables refer to many different trip purposes  Availability of supermarkets and tourists attraction are significant

Frequency model  Accessibility and income are important

IMPLEMENTATION

The probabilities are calculated for each zone and for different socio-economic categories. Each model is run separately for each socio-economic group in each zone. Due to long running time for each subgroup, which model defines, the probabilities are aggregated to number of trips by number of persons in each category such as age group, gender, occupational status, car ownership and income.

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Implementation based on mode choice

Using car mode is restricted by an age and existence of a car in the household. Public-transport-mode is restricted by the appearance of in-vehicle time. If there is no proper supply between O-D pair, in-vehicle time is equal to zero and there is no trip.

Walk and bicycle is always an alternative to car and public transport trip in each OD-pair but it is only favour when the distance is short.

CALIBRATIONS

There are two types of correction made in the implementation: the one aims to compensate for aggregation errors such as driving licence taken as an average; the second one-calibration aims to eliminate differences between model results and travel survey.

There exist different types of a calibration adjusted to the regions.

2.2. Regional model: secondary destination and work place based trips

The secondary destination and work place based trips included in so called “trip chaining” involves many destinations possibilities and thus are not easy to compute. This was the reason to restrict the modelling of home-based secondary destination to the “other” trips purpose only and work place based to business trips only.

Probability to choose secondary destination is calculated for each o-d pair of home-work relations however, with the limitation of the number of destination zones (secondary destinations trip is not likely to be longer than primary trip).

The current implementation is a full implementation, but averaged with respect to socio- economic categories and includes:

 Implementations of other trip for secondary destination model  Implementation of business trips for work based model.

Limitations:

The sampers model is calibrated in the way that results fit data from travel survey at an aggregated level. The deviations are possible on more detailed level.

In reality change in one choice dimensions cause change in another. In the model the dependencies between the choice dimensions are modelled by the nested model structure, except for car ownership model. As a consequence car ownership model is not influenced by transport system variables. Thus development of public transport facilities will not influence car ownership model. This may partially explain low number of PT trips to work compare to travel survey for Kungsbacka.

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Another limitation is that time of day choice is not included in the regional models. That means that effects of time of day are analyzed without consideration of the fact that car users may shift departure time instead of choosing another destination, another mode or not to travel at all.

Sampers take walk and bicycle trip always as an alternative to car and public transport trip. But it is only possible to model when the distance is short. Trips for other purposes tend to be shorter than for work or business what may be the reason why Sampers showed higher amount of other trips by bike/walk than travel survey. In reality frequently short distances tend to be done by car. Common attitude towards using public transportation is that walking to the station and waiting time are too long with comparison to in-vehicle time.

Other limitations:

 Inner zone trips  Kilometrage, especially for inner zone trips  Secondary destination, are considered for each o-d pair. They may be overestimated in number and underestimated in distance. This is partially due to the fact that many trips take place within the same zone. In that case zero distance is calculated, and partially to the fact that the calculation has been restricted to a maximum of 3 km extra trip length.  Long distances matrices are not reliable, (extremely high values for some zones (truck matrices). Many destinations for long distance travel are randomly chosen.  To high aggregation in terms of purposes and zones for long distances.

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Appendix 4 – Travel survey diary

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