Analysis of Traffic Patterns for Large Scale Outdoor Events A Case Study of Vasaloppet Ski Event in

By

Parisa Ahmadi

2011-09-12

Examiner: Professor Haris Koutsopoulos

Supervisor: Mahmood Rahmani

Department of Transport and Logistic

Royal Institute of Technology

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Abstract

Vasaloppet is a cross country ski event which has been held in Sweden for about 50 years. Now more than 50,000 people of different ages participate in various cross country ski races during the Vasaloppet winter week in County. This increasing demand needs good traffic and transportation planning to avoid congestion and provide safe, on time and environmentally friendly transportation for participants and visitors to the area. The key for a good event traffic planning is reliable and up-to-date traffic data which is not available for the Vasaloppet winter week.

This study is an attempt to collect traffic data in order to find the movement patterns in the area and estimate origin-destination matrices for the main event of Vasaloppet week. Based on resources and time limitation it was decided to use a web-based participants’ survey in order to collect traffic data. The link to the survey was sent to email address of a sample of 5000 participants.

About 64% of the participants drove from their home town to the area and about 31 percent travelled by bus. Train and airplane have a very small share in travel mode to the area. - sälen, Mora and Älvdalen are three municipalities in with the highest share in accommodating participants. On the day of the race, bus and car have approximately the same share in travel mode with 45% and 47% respectively.

Key words Vasaloppet, Data collection, Survey, Departure time, Arrival time, Travel time, Average speed, OD matrix

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Acknowledgement

I sincerely thank my examiner Professor Haris Koutsopoulos. It has been a great experience for me working with him and learning from him. I am grateful for his advice, inspiration and support throughout the project.

Special thanks to Jonas Bauer, managing director in Vasaloppet Hus who supported me throughout the project gathering data, providing the opportunity of field visit and helping me with the information I needed. I also would like to thank Tommy Höglund, Mats Skålander and Monika Eriksson from Vasaloppet Hus for their help.

Special thanks to my family for their motivation and support throughout the whole my life and special thanks to my husband for being patient and providing me with the opportunity to study and do research free from other Intellectual concerns.

I would also like to thank Bibbi and all my dear friends in Transport and Logistic department for the great time and cheerful activities I had with them.

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Table of Contents

Chapter 1: Introduction ...... 1 1.1 What Is a Planned Special Event? ...... 1 1.2 Event characteristics ...... 2 1.3 Event Impacts ...... 4 1.4 Mode choices ...... 6 1.5 Background and problem statement ...... 7 1.5.1 What is Vasaloppet? ...... 7 1.5.2 About the area ...... 7 1.5.3 Vasaloppet´s official buses ...... 9 1.5.4 Parking in Berga ...... 11 1.5.5 Parking in Mora ...... 12 1.5.6 What is the problem ...... 13 1.6 Purpose of the Study ...... 13 Chapter 2: Literature review ...... 14 2-1 Definition of special event ...... 14 2-2 Modeling and Simulation ...... 15 2-3 Traffic Planning and Management ...... 16 2-4 Special events and ITS ...... 17 2.5 Environmental impacts of sport tourism activities ...... 19 Chapter 3: Methods ...... 21 3-1 Methodology ...... 21 3-2 Survey ...... 21 3-2.1 Survey Design ...... 21 3-2-2 Survey Constraints ...... 26 3-2-3 Survey Pilot ...... 26 Chapter 4: Results ...... 27 4-1 Socio-demographic characteristics of participants ...... 27 4-2 Traffic and travel pattern data ...... 28 4-2-1 Travel to the area ...... 29 4-2-2 Travel to the start point ...... 34

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a) Modal Split ...... 34 b) Departure from the Origin ...... 35 c) Arrival to the Destination ...... 37 4-2-3 During and after race trips ...... 40 a) During the race ...... 40 b) After race destinations ...... 42 4-3 Time Analysis ...... 44 4-3-1 Sälen ...... 46 4-3-2 Sälenfjällen ...... 53 4-3-3 Mora ...... 59 4-4 Flows and Average Speed ...... 64 Chapter 5: Suggestions ...... 70 Chapter 6: Conclusion ...... 77 Bibliography ...... 79 Appendices ...... 81 Appendix I – Road profile between Berga and Mora ...... 82 Appendix II - Accommodation places in Malung-sälen ...... 84 Appendix III - Questionnaire ...... 85

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List of Tables Table 1 Categories of Planned Special Events ...... 2 Table 2 Event travel management Challenges and goals ...... 4 Table 3 Considerations in managing travel for rural events ...... 4 Table 4 Vasaloppet’s buses ...... 10 Table 5 Gender ...... 27 Table 6 Age Distribution ...... 27 Table 7 Marital Status ...... 28 Table 8 Income Distribution ...... 28 Table 9 Participants from Counties in Sweden ...... 29 Table 10 Mode share...... 31 Table 11 Origin of the trips on the day of the event ...... 33 Table 12 How long in the area ...... 34 Table 13 Mode share to the start ...... 35 Table 14 Departure from the origin ...... 36 Table 15 Arrival to the destination ...... 37 Table 16 Anderson-Darling values ...... 38 Table 17 During and after the race mode share for those who drove to Berga ...... 40 Table 18 Parking at control stations...... 42 Table 19 Destination after the race ...... 43 Table 20 Mode share/Sälen ...... 46 Table 21 Travel time summary statistics/Sälen ...... 47 Table 22 Car and bus travel time distribution/Sälen ...... 47 Table 23 - Travel time summary statistics from Lindvallen and Tandådalen ...... 54 Table 24 - Average travel speed from Lindvallen and Tandådalen ...... 55 Table 25 - Modal split from Mora ...... 60 Table 26 - Summary statistics for travel time and average speed/Mora ...... 61 Table 27 - People-flow origin-destination matrix ...... 64 Table 28 - Vehicle occupancy rate ...... 65 Table 29 - Vehicle-flow origin-destination matrix ...... 66 Table 30 - Car-flow origin-destination matrix ...... 67 Table 31 - Bus-flow origin-destination matrix ...... 67 Table 32 - Club bus-flow origin-destination matrix ...... 68 Table 33 - Average speed from each origin ...... 69 Table 34 Traffic management plan component ...... 71 Table 35 Modal spit for trips to and within the area ...... 77

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List of Figures Figure 1 Planned special event factors ...... 5 Figure 2 Planned special event mode combination schema ...... 6 Figure 3 Race track and road from Berga to Mora ...... 8 Figure 4 Road width and speed limit between Berga and Mora ...... 9 Figure 5 Start place in Berga ...... 11 Figure 6 End place in Mora ...... 12 Figure 7 Main parts of the questionnaire ...... 23 Figure 8 Sweden's counties ...... 24 Figure 9 Percentage participants from different counties ...... 30 Figure 10 Origin of trips in Sweden ...... 30 Figure 11 Mode share ...... 31 Figure 12 Mode choice by gender, age and income ...... 32 Figure 13 Origin of the trips on the day of the event ...... 33 Figure 14 Mode share to the start ...... 35 Figure 15 Departure from origin ...... 36 Figure 16 Arrival to the destination ...... 38 Figure 17 Normality plot ...... 39 Figure 18 Departure time and arrival time distribution ...... 39 Figure 19 Vasaloppet road and race track ...... 41 Figure 20 Sälen-Mora during the race ...... 41 Figure 21 Destination after the race ...... 43 Figure 22 Malung-sälen ...... 45 Figure 23 Sälen area as considered by respondents ...... 46 Figure 24 - Car travel time distribution……………………………………………………………………………………………………… .48 Figure 25 - Bus travel time distribution ...... 48 Figure 26 - Departure time distribution for car and bus travelers from Sälen ...... 49 Figure 27 - Car travel time distribution/Sälen…………………………………………………………………………………………… 50 Figure 28 - Average car travel time by departure ...... 50 Figure 29 - Minimum travel times by car…………………………………………………………………………………………………… 50 Figure 30 - Maximum travel times by car ...... 50 Figure 31 - Bus travel time distribution from Sälen…………………………………………………………………………………… 51 Figure 32 - Average travel time by Bus from Sälen ...... 51 Figure 33 - Maximum and minimum travel times by bus from Sälen ...... 52 Figure 34 - Departure time distribution for walking ...... 53 Figure 35 - Lindvallen mode share……………………………………………………………………………………………………………… 54 Figure 36 - Tandådalen mode share……………...... 54 Figure 37 - Car travel time distribution Lindvallen…………………………………………..………………………………………… 55 Figure 38 - Car travel time distribution Tandådalen …………………………………………………………………………………..55

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Figure 39 - Car and bus travelers departure from Lindvallen……………………………………………………………………….56 Figure 40 - Overall travelers departure……………………………………………………………………………………………………….56 Figure 41 - Car and bus travelers departure from Tandådalen .... ……………………………………………………………….57 Figure 42 - Overall travelers departure ...... 57 Figure 43 - Car travel time distribution from Lindvallen…………………………………………………………………………….. 58 Figure 44 - Average car travel time from Lindvallen ...... 58 Figure 45 - Bus travel time distribution/Lindvallen ...... 58 Figure 46 - Car travel time distribution/ Tandådalen…………………………………………………………………………………..59 Figure 47 - Average car travel time ...... 59 Figure 48 - Modal split from Mora ...... 60 Figure 49 – Bus-club and bus travel time distribution/Mora……………………………………………………………………….61 Figure 50 - Car travel time distribution/Mora ...... 61 Figure 51 - Car and bus travelers departure from Mora ...... 62 Figure 52 - Car travel time plot/Mora………………………………………………………………………………………………………….63 Figure 53 - Average travel time all modes/Mora ...... 63 Figure 54 - Bus travel time plot/Mora……………………………………………………………………………………………………….. 63 Figure 55 - Club bus travel time plot/Mora ...... 63 Figure 56 Speed parking ...... 75

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Chapter 1: Introduction Planned events have economic and tourism benefits for the society. The income generated by events is often used for development of the society. Often large scale events in cities lead to improvement in infrastructures like new roads, new stadiums and sporting complexes, better public transport systems, and up-to-date traffic management tools like ITS systems.

Successful events may result in increased tourism attraction for the community in future. Geta (2005) points out benefits of events for the area as to be: attract new tourists, increase visitors spending, stimulate business and trade, create/develop image or create animations.

1.1 What Is a Planned Special Event? The FHWA defines a planned special event as a public activity with a scheduled time, location and duration, which may impact the normal operation of the surface transportation system due to increased travel demand and/or reduced capacity attributed to event staging [1]. Planned special events include sporting events, concerts, festivals, and conventions occurring at permanent multi- use venues. Less frequent public events like parades, bicycle races, sporting games, motorcycle rallies, seasonal festivals at temporary venues like parks, streets and other open spaces with limited roadway and parking capacity are also under the definition for planned special events.

Four distinct classes of special event are identified by Transportation Management Centre (RTA) which focuses on:

disruption to traffic and transport systems, disruption to the non-event community.

Class 1: Events in this class impact major traffic and transport systems and the disruption to the non-event community is also significant.

Class 2: Events in this class impact local traffic and transport systems but the disruption to the non- event community is low-scale.

Class 3: Events in this class have minimal impact on local roads and the impact on the non-event community can be ignored.

Class 4: These kinds of events are conducted entirely under police control (but are not protest or demonstration).

Considering definition for these classes, Vasaloppet may be defined as a class 2 event that impacts major traffic and transport systems and there is significant disruption to the non-event community. In the Guide to Traffic and Transport Management for Special Events, the process for

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] traffic and transport management for special events is described in different steps based on classes defined for the event (Guide to Traffic and Transport Management for Special Events, 2006)

1.2 Event characteristics In the Handbook of Planned special Events (FHWA) 5 categories of planned special events are defined according to the operational characteristics and effects of the event on the community. These categories are listed in Table 1 below.

Table 1 Categories of Planned Special Events

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Source: ( Dunn & Walter, 2007)

Vasalopet lies under category of rural events, high attendance events attracting patrons from regional areas while the roadway capacity is limited and there is not a regular transit service. The start point is located in a village named Berga and the end point is a small town named Mora. The race route goes through forest and at some places passes very close to the road between Berga and Mora, a rural road.

Large scale planned special events create an increase in travel demand and generate additional trips thus impacting overall transportation system operations and may have significant impacts on travel safety, mobility, and travel time reliability across all surface transportation modes and roadway facilities while challenging the ability of transportation agencies to provide acceptable levels of mobility and safety. These events often require special traffic management and multiple agency support to meet the additional demand. Managing travel for large scale special events involves advanced operation planning, stakeholder coordination, developing a transportation management plan and raising the awareness of public and patrons of potential travel impacts. Major benefits which may arise from managing traffic for planned events include: 1) reduce delay, 2) reduce traffic demand, and 3) improve safety. Table 2 below lists major challenges and goals in managing travel for planned events. Predictability is one the important goals which can be achieved thorough techniques like: 1) A multimodal travel forecast, 2) Defining the area and components of transportation systems that may be impacted by the event, 3) Analysis of traffic demand and parking demand 4) Identifying and correcting roadway capacity deficiencies

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Table 2 Event travel management Challenges and goals

Challenges Goals

Need to manage intensive travel demand Achieve predictability Need to mitigate potential capacity constraints Ensure safety Need to influence attractiveness of alternative travel Maximize efficiency choices Minimize regional traffic effects from Need to accommodate potential for heavy pedestrian events flow and transit vehicles Meet public and event patrons expectations Source: ( Dunn & Walter, 2007)

An underlying challenge in managing travel for rural events is personal and equipment resource availability. Rural events may also fall under following categories: discrete/recurring event at a permanent venue, continuous event, or street use event. Table 3 presents considerations specific to managing travel for a rural event ( Dunn & Walter, 2007).

Table 3 Considerations in managing travel for rural events

Event Impact Factor Considerations

Travel Demand Travel demand characteristics of discrete/recurring event at permanent venue, continuous event, or street use event

Road/Site Capacity Limited road and parking capacity Lack of in-place transit service and fewer alternate routes to accommodate event/background traffic Limited or no permanent infrastructure for monitoring and managing traffic

Event Operation Generation of trips from a multi-county region Event operation characteristics of discrete/recurring event at permanent venue, continuous event, or street use event Source: (Dunn & Walter, 2007)

1.3 Event Impacts Figure 1 illustrates factors that affect the severity of event impacts. Three major factors that should be considered are travel demand, road/site capacity, and event operation.

Travel demand is referred to expected number of participants and spectators and the related arrival and departure rate. Key consideration in collecting travel demand data include 1) event attendance, 2) arrival and departure rate, 3) modal split, and 4) vehicle occupancy. Modal split influences the level of the event impact significantly and refers to the choice of travel mode by participants and spectators to reach to the event place, which includes personal vehicle, transit, PARISA AHMADI | Chapter 1: Introduction 4

2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] walking or a combination of these modes. Travel forecast for a planned special event involves estimating travel demand magnitude and rate, and modal split.

Figure 1 Planned special event factors Source: (Dunn & Walter, 2007)

Regarding road and site capacity, key considerations include (1) available parking lots and site access points, (2) available routes to accommodate event traffic, (3) roadway and parking area capacity, (4) background traffic and available transit conditions and, (5) site circulation. Event operation activities refer to any aspects of operating the event or the venue that impact spectators and participants travel to/from the event. Key considerations in this field include (1) expected attendance, (2) event location and venue configurations, (3) advance information provided to participants and spectators, and (4) pre and post event activities that affect the demand. Examples of external factors include construction activities on roads feeding the event area, other construction activities in the area and prevailing weather conditions. This study provides most items and information mentioned above like which affect severity of an event´s impacts. Measures like event attendance, arrival and departure rates, modal split, and vehicle occupancy. There is also information about available parking lots although the capacity is not known and demands further studies to collect data about the capacity of parking lots and their access and egress rate.

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1.4 Mode choices Generally common modes that participants and spectators use to reach the event site are:

Private cars where calls to consideration route and parking choice in planning the event Transit options including regular service and express/charter option. Alternative modes like walking and biking.

In case of rural evens like Vasaloppet where people travel from different parts of the country, mode choice can be divide into two parts; first mode choice to reach from other areas to the area of the event which include private cars, train, bus and plane, and second mode choice to travel from accommodation place to the start of the event which includes private car, transit options and walking.

Figure 2 illustrates various possible mode combinations that may serve a planned special event site. Each combination describes the inter-modal movements and transfer points from origin to the destination in event place. This mode combination schema may change slightly based on specification of each special event and is an important piece in traffic management plan. A successful traffic management plan should meet the service requirements of these estimated mode combination schemas. For example accommodating pedestrian trips connecting various modes of travel, shuttle bus operation to support public transit stations and satellite parking areas, and traveler information plans which cover all possible mode combinations.

Figure 2 Planned special event mode combination schema Source: (Dunn & Walter, 2007)

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1.5 Background and problem statement

1.5.1 What is Vasaloppet? Vasaloppet is a long distance (90 km) cross-country ski race which takes place in Dalarna County in Sweden annually on the first Sunday of March. It starts from Sälen and ends in Mora with total of 90 kilometers length. It is the longest and oldest cross-country ski race in the world1 and is included in Worldloppet Ski Federation. Worldloppet is an international sport federation of cross- country skiing marathons which was founded in 1978 in Sweden. Worldloppet includes 15 races from Europe, America, Asia and Australia.

The root for Vasaloppet goes back to 16th century when King Gustav Vasa tried to convince people of Mora to help him with his crusade against Danish King. When he failed to do so, he went on his ski from Mora to Sälen but then people of Dalarna changed mind and Vasa built his forces and after two and a half year, Sweden won its independence from Denmark. Vasaloppet runs the opposite direction of Vasa’s original journey and has been done since the first race in 1922. The race began in 1922 with 136 participants and over time has expanded to include 9 different types of race and over 50 000 participants per year. Since 1979 other types of ski race were added to the traditional one (Vasaloppet – 90 km), Öppet Spår (non-competitive 90 km) in 1979 and TjejVasan (ladies – 30 km) in 1988. Later other types of race were introduced such as KortVasan (short – 30 km), HalvVasan (half – 45km), SkejtVasan (free technique – 30 km and 45 km), StaffetVasan (relay – 90 km), UngdomsVasan (The teenagers’ vasalopp – 3,5,7 or 9 km) and Barnene Vasalopp (The children’s vasalopp – 900 meters). Different races have different start s but the end for all of them is in Mora.

Today the whole Vasaloppet week attracts more than 50 000 participants. Considering those accompany participants and those just visit the area, the number will be even higher. According to the results of a survey done by Rubin Research, visitors spend about t 187 million SEK in the area during the winter week 2011. The result of the survey also shows that about 78% of visitors and participants during the winter week took their car to reach to the area1

1.5.2 About the area Majority of people who stay in the area during Vasaloppet week reside in three municipalities which are known as Vasaloppet’s municipalities and are namely: Mora (the end of all races), Älvdalen and Malung-sälen. The main towns in these municipalities are small towns with low population. Rural roads connecting the towns and villages in the area are mainly two-lane 6.5 meters wide roads with speed limit of 80 km/hr.

The main race in Vasaloppet week with the highest number of participants (15,800) is Vasaloppet on the first Sunday of March which is the focus of this report. The start of the race is in Berga and

1 Vasaloppet official website PARISA AHMADI | Chapter 1: Introduction 7

2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] the end in Mora (see Figure 3). The race starts at 8:00 in the morning. There are several control stations on the race track. According to the rule of the race participants should reach control stations before specific time, otherwise they are not allowed to continue.

Road Race track

Figure 3 Race track and road from Berga to Mora

According to the results of the survey done in this project, majority of participants spent the night before the race in Malung-sälen (61%), Mora (27%) and Älvdalen (5.5%).

Road 70, 1025, 1024 and 71 that connect these municipalities are two-lane roads which in most parts are 6.6 meters wide. The road between Mora and Berga is also a 6.5 meters wide two-lane road. The part of the road between the crossing with road 71 and the crossing with road 70 is known as Vasaloppet road since the race track is very close to the road. The speed limit on most parts of the road is 80 km/hr. On some parts like near to and inside residential areas the speed limit decreases to 70 and 50 km/hr. The horizontal and vertical geometry of the road in some segments is poor. There are several horizontal and vertical curves that do not meet the requirements of the acceptable standard in Sweden. Figure 4 shows road width and speed limit on one parts of the road between Fiskarheden and Mångsbodarna (for map of the other parts see Appendix I). The roadside along most parts of the road have low standards and is not clear from fixed obstacles.

Traffic measurements on road 1024 Fiskarheden-Evertsberg which was done in 2007 show that AADT was 790 vehicles per day with 13% heavy vehicles. In 2004 on road 1025 Evertsberg-Oxberg AADT was 590 with 12% heavy vehicles and on road 1012 between Oxberg and the crossing with road 70 AADT was 700 vehicles with 13% heavy vehicles (Brämerson, 2011).

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Figure 4 Road width and speed limit between Berga and Mora Source: (Brämerson, 2011)

1.5.3 Vasaloppet´s official buses

Each year Vasaloppet arranges buses from some origins in Dalarna to the start place on the morning and the opposite way in the afternoon on the day of the race. Origin and destination of buses varies with the race. For the main race which is the subject of this report destination is Berga and origins are Mora, Malung, Älvdalen and Sälen. Table 1 shows number of buses from each origin, departure time and average travel time. Travelers could buy ticket online and beforehand with 10 percent discount or buy it at place on the morning and before boarding.

After reaching Berga buses were not allowed to leave parking before the start of the race, so participants who travelled by bus could stay at bus before the start time to keep themselves warm.

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Table 4 Vasaloppet’s buses

Origin Number of buses First departure Average travel time Mora 49 3:45 am 135 min Sälen 4 6:00 am 10 min Malung 1 4:45 am 45 min

To facilitate boarding and make it faster in Mora a large area was devoted to bus terminal. To enter the area passengers had to pass through a tent where their ticket was checked then they were guided by personal to buses which were ready to board passengers. Four buses could simultaneously board and as soon as a bus was full it started the trip toward the start in Berga and another bus took its place in boarding area.

Last year all buses from Mora drove on the nearest route to Berga which is the most known route and approaches Berga from south. Some buses got stuck in traffic congestion; as a result some participants could not reach the start on time. For this reason a new route was planned for Vasaloppet’s buses which approached the start from north to avoid congestion on the southern access road. The new route is about 30 km longer than the old one which under free flow condition results in 35 min longer travel time between same origin and destination. This travel time even may become longer considering road and weather condition since this road is a local access road and not a main road (red color on map represents the old route and the blue color the new route). Forty nine buses from Mora took participants to the start and started departing at 3:45 am from Mora. Each bus left Mora as soon as it was full. Average travel time was about 2 hour and 15 minutes and buses did not experience traffic congestion as it was last year and then could reach the start on time. Buses were not allowed to leave parking before 8:00 am so participants could stay at bus before the start of the race to keep themselves warm.

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1.5.4 Parking in Berga Parking area for all buses that approached Berga from the north was on the north approach of road 71 and completely separated from the traffic approaching Berga from the south. This parking had a capacity of 60 buses and after the parking reached the full capacity, buses were allowed to park on the road. Parking lots on road 71 and road 1051 were planned for passenger cars and also private buses that approached Berga from north. When the last bus passed Åsen, road 71 was closed to traffic (Figure 5). Same traffic rules were regulated for parking lots on southern approach to Berga on road 71. Parking guides were instructed to fill out parking lots from the most near one to the start point to farther ones. After that just buses and vehicles with Vasaloppet’s sign were allowed to enter the area. There were several entries to parking lots while there was just one exit which made queues of vehicles waiting to exit. Two parking lots were dedicated to camper vans. During the night before the race other parking areas was closed to camper vans in order to keep them empty for those arrive on the morning to be able to guide them to parking as fast as possible in order to prevent traffic congestion caused by vehicles waiting to enter parking. Parking in Berga was free of charge. About 20 trucks which left Mora at about 2:30 am were in their place in Berga early in the morning before other traffic started moving to the start and without adding burden to the traffic. After they were loaded with participants’ personal equipments, trucks were first vehicles that were allowed to leave Berga starting from 8 am. Then five buses were allowed to leave Berga toward control station on the route to Mora to stay there and pick up participants who were not able to continue and left the race. After that passenger car were allowed to exit from parking lots. Other buses started departing from Berga at 9 am and headed back to Mora.

Figure 5 Start place in Berga

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1.5.5 Parking in Mora In Mora there were several small parking lots most of them had parking fee. A vast area beside rail station was prepared to be used as parking lot in addition to other existing parking lots. Although some parts were covered by snow and ice, still it was possible to park there (Figure 6). Following temporary yellow parking signs it was not difficult to find the location of the parking. According to the survey people was satisfied with finding parking place in Mora. The only thing they were unsatisfied was long walking distance to the Vasaloppet’s finish point and that some were not happy to pay for parking. The walkway from the parking lot to the finish point was slippery and hard to walk in some parts. The blue line on Figure 4 shows the walkway from the parking lot to the end point.

Figure 6 End place in Mora

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1.5.6 What is the problem In 2010 on the day of the main Vasaloppet race some participants were delayed and could not reach the start on time because of the congestion and long queues which were mainly on south access to Berga and also on the crossing of road 1024 and road 71 in Fiskarheden which caused the congestion to spill back on road 1024. Vasaloppet buses were also delayed because of the congestion. About 500 participants reached the start after 8:00 am and even many had to leave bus and car and take their skis and cloths and walk the remaining distance to the start. The problem was not just on the day of Vasaloppet but even on the day of other events in Vasaloppet week that congestion which caused some participants to be late to start.

The other problem is lack of traffic data. Although this event have been held for about 50 years but there is no traffic data. Movement patterns are not known and there is not data about origin- destination matrices. The main factors affecting the severity of an event which are also necessary for event traffic planning were not known in quantity before this study. Information about arrival and departure rate, modal split, vehicle occupancy, and origin-destination matrix are provided by this study. But still there are some missing parts which are mainly about parking capacity, parking access-egress rate and also data relating spectators.

1.6 Purpose of the Study The Vasaloppet ski event has been running for about 50 years, but no traffic data collection or analysis has been conducted up until now. Movement patterns in the area during Vasaloppet winter week are not known. Even there is no traffic information and this makes traffic planning very difficult. Main purpose of this study is to collect traffic data and find and analyze the movement patterns and travel behavior in the area during the main race on the first Sunday of March. To find out about socio-demographic specifications of participants, from where they take part in the event, how long they spend in the area and where do they accommodate during their stay in the area, which mode do they use to travel to the area and which mode do they choose to travel to the start point of the race and what happens during the race. Estimating origin- destination matrices is also intended which will be helpful for simulation and traffic planning in future. These data may be helpful to find out which routes in the area are mostly used and how congested they are. There is also some information which shows that how participants are eager to change from private car to bus for transportation in the region of the event.

The importance of this study even becomes more clear considering that traffic data is the core for a successful traffic and travel management and is a an important part in event planning.

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Chapter 2: Literature review

Relatively little research has been done on special and large scale event traffic analysis, planning and management. The National Cooperative Highway Research Program (NCHRP) Synthesis 309, Transportation Planning and Management for Special Events, was one of the first to focus on the state-of-the-practice for transportation planning and management for special events. The report noted the lack of special event related literature (Carson & Bylsma, 2003). The most comprehensive and covering document on special event transportation is the Managing Travel for Planned Special Events Handbook by Federal Highway Administration (FHWA). Another comprehensive approach was the first National Conference on Managing Travel for planned special Events held in New Orleans in December 2004. The aim was to raise public agency awareness about the importance and need to improve travel management for planned special events which significantly impact mobility and reliability of all surface transportation modes (Goodwill & Joslin, 2006).

Most existing researches are about mega events in large cities like which are different in nature from events hold in rural areas. Availability of traffic data in large cities make it possible to simulate the area affected by the event and try different ITS and demand management solutions to decrease the effect of the event in the area and provide safe and on time transportation. Situation is different in rural areas; collecting traffic data is challenging since there are no loop detectors or CCTVs. Installing ITS equipments and permanent infrastructure for monitoring and managing traffic is not a cost effective way. There are not enough alternate routes to accommodate event and background traffic and here is also the problem of lack of transit service near the venue.

2-1 Definition of special event ITS may be defined as integrated application of advanced sensors, electronics, communication technologies and computers and management strategies to increase safety and efficiency of the surface transportation system. ITS is applied for event traffic management in a variety of environments through the use of CCTV traffic surveillance cameras, vehicle detection systems, coordinated signal control systems, area-wide traveler information service, dynamic message signs, traveler’s advisory radio system and other technologies and systems. Most large cities in the US that host plenty of events each year enjoy the advantage of ITS system and technologies during special events to provide safe and convenient access to and from events while providing an acceptable level of service for other transportation system users (Intelligent Transportation Systems for Planned Special Events: A Cross-Cutting Study, 2008).

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2-2 Modeling and Simulation London is the host to 2012 Olympic and Paralympics Games. The Olympic Delivery Authority (ODA) has the responsibility to prepare and keep under review an Olympic transport plan for addressing transport matters relating to the Olympic. Forecasting the geographical distribution of spectators is done by a gravity model. The basic concept of the model is that the larger the population centre, the greater the demand from that specific location, and also the farther the location from London, the lower the demand. They have also forecasted the places that ticket holders will travel from on the day of the event. Survey data collected from different large events in UK and data from previous events was also used. A micro-simulation model supported by analytical and gravity models is used to forecast Games Family travel demand (Olympic Delivery Authority, 2008).

In 2008 Singapore was the host to Grand prix. The circuit passed through Central Business District including several congested arterials. PTV developed a VISUM network including all major land uses and junctions within the area affected by the event. Important public transport services that thousands of travelers use each day to travel to the city was also included in VISUM modeling. For simulation modeling, transport simulation was then imported from VISUM to VISSIM for more accurate results for signal timing, pedestrian behavior at crossings and traffic route choice. Connections between macroscopic modeling and micro-simulation made accurate scenario testing and evaluation of schemes possible. Trip generation and attraction surveys, a series of origin- destination surveys undertaken during the study and cordon traffic counts were the basis in developing models (Laufer, Fellows, Gopalakrishnan & Saifollah, 2010).

Olympic venues and their surrounding area were simulated by VISSIM for Beijing Olympic Games. Travel demand forecasting for each venue was made considering competition schedule, number of parking lots for the Olympic family members, and arrival and departure time distribution of Olympic family members. Experiences from previous Olympic Games and other large sports activities were also used in case of need for data. Different traffic operation plans were tested for each venue and corresponding recommendations were proposed which were proved by traffic operation during the Olympic Games to be correct and effective (Yu, Zhang, Wang, Huang, & Zhou, 2008).

Delphi project at the German Aerospace Center aims at developing a traffic prognosis at event situations for major Germany cities. It is shown that a traditional travel demand forecast combined with a simulation based approach can serve as a short-term forecast for the traffic situation. Soccer World cup 2006 in the city of Cologne provided the opportunity to develop and test the approach as a service for the action forces to react as fast as possible to developing aberrations. In the German cities Berlin, Cologne and Stuttgart, different systems had been set up to provide organizers and police with up-to-date traffic information and predictions. Data from loop detectors as well as data from airship have been used to correct the simulation results to be in line with

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] measurements. Data were sent any one minute from 781 detectors from 430 sites in Cologne. An open source traffic flow micro-simulation program SUMO was used. In addition to its normal car- following logic, SUMO has been extended with a so called mesoscopic traffic flow model formulated as a queuing model. It has been demonstrated that the combination of a transport system planning with a traffic simulation is helpful in providing information and is able to predict a future traffic state 30 minutes into the future. The average error was 330 veh/h (Behrisch, Krajzewicz, & Wagner, 2008).

2-3 Traffic Planning and Management For London Olympic Games in 2012 two different strategies is planned for Games Family clients (athletes, team officials, broadcast and other officials) and spectators. The strategy for Games Family clients includes dedicated lanes, alternations to traffic signal timings and free public transport. The other strategy is about spectators transport. This strategy is based on transporting all sports event ticket holders by free public transport, walking or cycling on the day of the event. No private car parking will be provided for spectators at any venue. The aim is achieve almost 100 percent of spectators travelling by public transport, walking or cycling to the competition venue. Managing the non-Games demand is another strategy that will be implemented to reduce travel on key routes during the game. The transport strategy for infrastructure is to make best use of existing infrastructure and services. Building a new infrastructure will be an alternative when it is essential and will have a strong benefit after the games (Olympic Delivery Authority, 2009).

City of Santa Monica which is host for several large events during a year has developed its own event traffic management and control plan. The critical component in this plan is estimate of attendance and traffic generation. The plan addresses issues like event area, traffic control, parking, shuttles and transit, traffic operations, pedestrians, bicycles, emergency access, city vehicle access, pre-event check list and event-day protocols. The level of details addressed in the transportation management plan varies depending on the size of the event. Their experience in the city of Santa Monica shows that by making parking rates more expensive near the event site and less expensive at more remote locations, they could reach better traffic and congestion management. Parking policy in this city also decreased car traffic near event venue while bicycle usage increased (Morrissey & Monica, 2010).

A report presented by Florida department of transportation and university of south Florida includes information and best practices that will be useful in the provision of any type of special event service. A survey was done in this project to define the degree of participations of private transit providers in special events. The result showed that while about one third of them indicated that they do not provide transit services in special events due to limited resources the prevailing factor was the perceived burden placed on agencies by Federal Transit Administration’s (FTA) charter regulations. Following the report suggests a step by step procedure for transit operators regarding planning policy, planning and operation of transit for special events (Goodwill & Joslin, 2006).

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There are several handbooks and toolkits regarding planning and management of special events. A national Cooperative Highway Research Program (NCHRP) synthesis report, Transportation Planning and Management for Special Events, addresses special event types, involved stakeholders, tools and techniques for managing travel demand and controlling traffic, operation guides, qualitative and quantitative assessment efforts, and funding source. A handbook by FHWA, Managing Travel for Planned Special Events, present and recommends policies, regulations, planning and operation processes, impact mitigation strategies, equipment and personnel resources, and technology applications used in the advanced planning, management, and monitoring of travel for planned special event (Latoski et al., 2003). An executive summery an updated version of the handbook is written to assist responsible agencies in managing the planned special events impacting transportation system operations in rural, urban, and metropolitan areas. It provides a step-by-step guidance through all phases of managing travel for planned special events. The handbook discusses aspects of planned special events including (1) characteristics and categories of planned special events, (2) regional and local coordination, (3) event operations planning, (5) day-of-event activities , and (60 post-event activities. It explains reasons to manage travel for planned special events and suggests 5 phases for special event travel management which are: (1) Regional planning and coordination, (2) Event operations planning, (3) Implementation activities, (4) Day-of-event activities, and (5) Post-event activities. A schedule for event operation planning can also be found in this handbook which provides a generic timeline. Successful event management ideas, resource applications, and best practices in US can be found through the handbook (Carson & Bylsma, 2003).

Most literature in the field of large event transportation planning and management emphasizes on the role of public transport for successful special event traffic management. Experience shows that for a significant change in modal split patterns and public behavior, public transport policies should be supported by reduction of automobile accessibility, mainly by well-enforced parking restrictions. The role of good cooperation between various stakeholders in organizing the event is also emphasized in literature (Guide to Traffic and Transport Management for Special Events, 2006)

2-4 Special events and ITS ITS include equipments to sense current traffic conditions, to control traffic flow and to inform travelers about the situation that they should expect, as well as centers that brings all these functions together. ITS can help meeting challenges related to special events and their effect on every day traffic by increasing the safety and efficiency of the surface transportation system.

The use of ITS technologies will bring some challenges for transport agencies. Many ITS systems need advanced communication or networking applications and thus trained operators. On the other hand some of these technologies are very costly for small communities and rural areas with more limited budget and not frequent yearly events.

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A cross-cutting study by FHWA has studied how ITS is used in six locations in five states in USA to reduce planned special event related congestion while reducing accidents, increasing travel time reliability , and reducing driver frustration. By interviewing transportation officials it was found that using ITS helps to ease the congestion and frustrations related to special events and transportation officials have recognized the importance and benefits of ITS in the success of planned special events. Locations were selected to represent wide range of size and scope of planned special events. Each site represents at least one of following characteristics:

Large urban or suburban areas with thousands of planned events each year Small urban or suburban areas with hundreds of planned events each year Non-urban areas with up to a hundred planned events each year with less developed infrastructure Rural areas with limited numbers of planned special events each year with less developed infrastructure An example selected for the case study is Montgomery County with a population of about 1 million. The county is home to a variety of traffic generating events annually in different areas of the county. Monitoring and controlling the traffic in and around the event location is centralized in TMC with representatives from various parties which play a role in event management like police and emergency operation centers. ITS technologies which are used at the TMC include: Portable dynamic message signs Traffic surveillance cameras Computerized traffic signal system Vehicle detection systems Regional Integrated Transportation System (RITIS). The RITIS collects, consolidate, and disseminate TMC data from Virginia, Maryland and the District of Colombia. Public agencies and travelling public have access to this information letting them to know about incidents or other transportation issues in the area of planned special events.

A comprehensive set of traveler information tools is used by the county to assist motorists with information about their trips to and from the host venues. These tools include cable TV that provides audio from the traveler’s advisory radio system, traveler’s advisory radio system, up-to-minute travel conditions are regularly updated on the Internet, the TMC media sharing concept which provide the media regularly with information. Aerial surveillance during special event is a unique opportunity to provide the planners and traffic management team with valuable real time input and accident information as soon as it happens. Another benefit of the surveillance plane is to assist parking management on the day of the event. Flying over the parking areas a visual estimate of the available parking capacity is provided which allows TMC staff to anticipate the time the main parking areas will become full and to begin redirecting traffic to satellite parking areas.

Another example is a Dutchess County with about 300,000 residents. The county is famous for hosting an annual agricultural fair that generates more than 500,000 visitors over a period of six days. Duutchess County is a rural county in nature with limited infrastructure and rural characterized roads and few heavily traveled two-lane state routes. The main road feeding the fair is a tow-lane road with two signalized

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] intersections north and south of the fairground which already operated near to capacity on most non-event days which was congested during the event days. It was decided to demonstrate the benefits of ITS technology to solve the problem in this area. The primary approach was to use portable ITS equipment together with a proactive traffic plan to remove the bottlenecks using traffic signal control and traveler information updates. The approach proved to be effective in traffic management with emphasis on communication and coordination between different stakeholders as keys to success for the Rural ITS demonstration project. In 2000 in order to manage traffic congestion at the exit points and reduce or omit the delay, computerized magnetic traffic counters were installed at the exit points. Traffic count data were downloaded each night to help planners to determine traffic volume. The project which started 1n 1999 concluded in 2003 due to the cost of deploying full range of ITS equipments which the county was not able to fund (Intelligent Transportation Systems for Planned Special Events: A Cross-Cutting Study, 2008).

1.5 Environmental impacts of sport tourism activities Green and Hounter (1995) have pointed to possible environmental impacts of tourism activities, which fit to sport events and specially the Vasaloppet ski event which is hold in heart of nature passing through forest and nature. Some of these impacts depending on the type of the event may be of high impact and some other may less important. Following is a list of possible types of impacts:

Floral and faunal impacts:

Disruption of breeding habits Inward and outward migration of animals Trampling and damage of vegetation by feet and vehicles Destruction of vegetation through gathering of wood and plants Change in extend and/or nature of vegetation cover through clearance or planting to accommodate tourist facilities Creation of wild life reserve/sanctuary or habit restoration

Pollution:

Water pollution through discharges of sewage, spillages of oil/petrol Air pollution from vehicle emissions, combustion of fuel for heating and lightning Noise pollution from tourist transportation and activities

Erosion:

Compaction of soil causing increased surface run-off and erosion Change in risk of occurrence and lad slips/slides Change in risk of avalanches occurrence

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Damage to geological features (e.g. tors, caves) Damage to river banks

Natural resources:

Depletion of ground and surface water supplies Depletion of fossil fuels to generate energy for tourists activities Change in risk of occurrence of fire Change in hydrological patterns Change in land used for primary productions

One important impact of ski resorts is soil degradation. According to Ries (1996) the establishment of the ski runs, the activity of skiing and ski run maintenance cause the impact. Loss of vegetation cover and top soil are the main impacts. Moreover the erosion of the soil can create flood effects after strong rains.

Environmental sustainability in sport tourism management is drawing more and more attention. Jageman (2004) has gathered all the requirements of sustainable sport tourism:

Promote and further develop forms of sports which are compatible with nature and environment Make sport-related infrastructure more environmentally compatible Reduce damage to vulnerable areas Secure and improve opportunities for sport and physical activities outside vulnerable areas Preserve and increase the recreational quality of countryside and its enjoyment value for those doing sport.

Some initiations are already taken in order to develop environmental plans for events to help event managers to plan for an environmentally sustainable event. As an example the department of Canadian Heritage and Sport has developed a specific guideline “Environmental Management and Monitoring for Sport Events and Facilities” in 1999 which can be used as a practical toolkit for managers. The environmental planning set up during the World ski championships of St Moritz in 2003 is another example for sustainable management of ski events. Ski event managers can be also referred to “Guidance document on the implementation of EMAS in sporting events” presented by the organization of the Winter Olympics in Torino in 2006 (Duclos, 2007).

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Chapter 3: Methods

3-1 Methodology The research methods used for this study consist of data collection and analysis. A participants’ survey was designed in order to collect traffic data from participants of the race. The overall intention is to use the results to find out movement patterns in the area and also to estimate OD matrices in the area. Distribution of departure times from origin, distribution of arrival times to the destination and also average travel speed from each origin is calculated according to the data from survey.

3-2 Survey When it was decided to conduct the study in late January there was not enough time for planning for a suitable kind of data collection system like video detectors or floating mobile data collection systems, so it was decided to use travel surveys to collect possible traffic data from the participants of the race. After discussion with Vasaloppets organizers it was decided to conduct a web-based survey and send the questionnaire to a sample of 5000 out of 15,800 participants by email. 1439 responses were received out of 5000. The email addresses was selected randomly from the organization’s database. The questionnaire is in Swedish and was send to participants one week after the race.

3-2.1 Survey Design The main objective of the survey was to collect travel data and find the movement pattern in the area and also to estimate origin-destination matrices. To capture this objective an interactive questionnaire was designed. Based on responses to specific questions, respondents were directed to different questions. Number of questions varies between 23 and 36 depending on response to the specific questions. Below you can see examples of questions that make the questionnaire interactive and based on response to these questions respondents were redirected to different questions.

Question 5, above, is the first interactive question that respondents are asked if they spend the night before the race in their home town or somewhere else. The objective is to catch possible mode changes on the way to the start point for those who started their trip from their home, without confusing other participants since there may be different patterns for those who spent the

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] night before the race at their home town compared to those traveled to the area before the race and spent several day in the area. If the answer to above mentioned question is yes the respondent will continue with:

Question 6 is about mode change on the way to the start on the day of the event. Giving “No” as an answer to this question, respondents were redirected to another series of questions which you can see two of them as an example below:

Following are other examples of interactive questions. As it can be seen these questions are about mode change travelling to the start and the mode which were used for traveling. Different questions were assigned to respondents according to the transport mode. Those who chose private car, car sharing or rental car were redirected to same series of questions. Those who selected bus or club bus are in same group with different questions from those who walked.

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Figure 7 below illustrates the main parts in the questionnaire. As it can be seen the questionnaire is designed in four main parts to catch the most important data required. These four parts consist of socio-demographic characteristics of the participants, travel to the area, travel to the start within the area, and trips during and after the race. Figure 7 shows main questions in each category which are necessary in order to reach the aims of the study.

•Gender Socio-demographic •Age characteristic •Marital status •Income

•Origin •Destination Travel to the area •How •When

•How •When left Travel to the start •When arrived •Congestion places

•Mode Trips during and after •Destination the race •When left the area •Congestion places

Figure 7 Main parts of the questionnaire

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The first part of the questionnaire is designed to collect data about socio-economic specifications of the participants; questions like gender, age, marital status and income. Main parts of the questionnaire is designed to capture the origins of the trips, transport mode from participants’ home town to the accommodation place and from the accommodation to the start of the race, departure time from the accommodation place, and arrival time to the destination.

The question in travel to the area part is about origins of the trip from all over the Sweden. Participants can select their origin from a drop-dawn menu consist of Sweden’s counties and big cities. Figure 8 below illustrates Sweden’s counties considered in this study. The event takes place in Dalarna. Those who gave Dalarna as origin of their trips were asked to state where exactly in Dalarna they started their trip.

Figure 8 Sweden's counties

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For those who drove from their accommodation place to the start of the race there are some questions with aim to capture average car occupancy.

Participants were also asked about options they would prefer instead of driving from accommodation to the start of the race. For those travelled by club buses there are some questions in order to find out average bus passenger occupancy.

Respondents were asked about congestion places and duration of congestion on routes leading to the star of the race. For more details and the complete questionnaire see Appendix III.

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3-2-2 Survey Constraints Since the Vasaloppet organization had plans for other surveys then a sample with size of 5000 participants were allocated for travel survey. It was decided to use web-based survey method. The link to the survey was sent to email address of participants. Web-based surveys have some disadvantages. One is that some of the participants who receive the questionnaire may ignore emails from unknown sources. On the other hand email addresses were retrieved from race registration database and there is the possibility that some may give email addresses that are not checked regularly.

The web service used for this research project has some limitation on design of the questionnaire, especially when the language of the survey is Swedish and the question is about date and time. Using better service may result in better design and more respondent friendly questionnaire.

The survey was just sent to participants in the race and there is data about visitors and spectators in the area during the event. Another simple questionnaire could be designed in the form of short interview with visitors in order to be able to collect more complete data.

3-2-3 Survey Pilot Before the main survey, a short pilot survey was performed to identify problems at an early stage. Respondents to the pilot survey were master and PhD students who participated in the race or had experience in doing web-based travel surveys.

Some suggestions incorporated to the design from feedbacks. In some parts, a map was added to the questionnaire in order to make the question more clear and help the respondents to recall the routes and places.

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Chapter 4: Results

4-1 Socio-demographic characteristics of participants As explained before, the survey’s first part consisted of several questions related to socio- demographic information of participants including gender, age, marital status, and income. Following is a summary of information from the first part of the questionnaire. No answer or blank fields have been excluded in percentage calculation and thus the total number in each table represents number of respondents who answered the question.

Table 2 summarizes the share of male and female participants in the main race. As it can be seen male is the dominant gender in this race with 85% share.

Table 5 Gender

Gender Number Percentage Share Female 210 15% Male 1215 85% Total 1425

Table 3 illustrates the age distribution of participants. Age group between 36 and 45 has the highest participation rate with about 32%. Majority of participants are between 26 and 55 years old. The age group of 76 years old and over just had 1 participant out of 1432 with about 0.1% share.

Table 6 Age Distribution

Age Group Count Percentage Share 19-25 86 6,0% 26-35 360 25,1% 36-45 466 32,5% 46-55 320 22,3% 56-75 199 13,9% 76- 1 0,1% Total 1432 100,0%

Table 4 summarizes marital status of participants in the main race with the highest rate for married participants with child. According to table 5 which shows the income distribution of participants, the highest share belongs to people with annual income between 301,000 and 400,000 SEK while people with the lowest income range has the lowest percentage share.

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Table 7 Marital Status

Marital Status Count Percentage Share Single no child 214 15% Single with child 63 4% Partner no child 226 16% Partner with child 211 15% Married no child 44 3% Married with child 666 47% Total 1424 100%

Table 8 Income Distribution

Income Range Count Percentage Share 0-150,000 96 7% 151,000 - 300,000 238 17% 301,000 - 400,000 438 31% 401,000 - 500,000 273 20% 501000- 352 25% Total 1397 100%

4-2 Traffic and travel pattern data The second part of questionnaire was intended to provide information about participants’ trips to the area and to the start point of the race in Berga. The first question in this part redirects participants to two different questionnaires depending on if they were at their home town the night before the race or not. This was in order to simplify the questionnaire for people with different travel pattern and prevent confusion of participants. According to the result, 3 percent of the participants spent the night before the race at their home town while 97% spent the night somewhere not at their home town but in an area near to the start point in Berga. In Sweden the first week of March is also winter sport week and since Sälen (about 6 km north of start point) is an attractive destination for skiers some people tend to spend several days there beside participating Vasaloppet. Participants were asked if they changed transport mode on the day of the race travelling from their home town to the start point in Berga (for those who were at their home town the night before the race, or from their accommodation to the start point for those who were not at their home town the night before the race). Depending on the answer, they were redirected automatically to two different parts of the questionnaire. According to the results, 13 percent of participants who were at their home town the night before the race changed transport mode from their home town to the start point and 87 percent did not change transport mode (change of transportation mode means for example they drove from their hometown to another town and then took bust to the start point in Berga). Coming to those who spent the night before the race PARISA AHMADI | Chapter 4: Results 28

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somewhere not at their home town, 10 percent changed transport mode travelling from their accommodation to the start point at the day of the race while 90% did not changed mode. By changing transport mode we mean inter-city mode changes. Totally 10% of participants changed transport mode on the day of the race travelling to the start point while 90% did not.

Following data is presented under three categories: travel to area, travel to start point in Berga and after race trips.

4-2-1 Travel to the area This section includes summary information concerning travel from the home town of the participants to the place where they stayed the night before the race. Main questions here are from which counties in Sweden they participated in the race, how they traveled to the area, where in the area they had accommodation in and how long they stayed there.

Table 6 and Figure 9 show from which Counties in Sweden people took part in the race. As it can be seen, County and Västra Götaland County have the highest percentage of participants. Dalarna, which hosts the event, is in the third place.

Table 9 Participants from Counties in Sweden

County Count Percentage share Blekinge län 9 1% Dalarnas län 82 6% Gävleborgs län 41 3% Gotlands län 8 1% Hallands län 40 3% Jämtlands län 38 3% Jönköpings län 58 4% Kalmar län 25 2% Kronobergs län 26 2% Norrbottens län 44 3% Skåne län 65 5% Södermanlands län 27 2% län 306 23% Uppsala län 68 5% Värmlands län 45 3% Västerbottens län 48 4% Västernorrlands län 30 2% Västmanlands län 29 2% Västra Götalands län 261 19% Örebro län 36 3% Östergötlands län 59 4% Total 1345 100%

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25%

20%

15%

10%

5%

0%

Figure 9 Percentage participants from different counties

Svealand which is referred to the central part of Sweden has the highest percentage of participants with 44%, followed by Götland which is the southern part with 41%. Norrland, referred to the northern part has the lowest rate with 15% of participants (see figure 10).

Figure 10 Origin of trips in Sweden

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An important question is how participants traveled to the area, or for those living in area but stayed somewhere other than their home town how they travelled there. Table 10 and Figure 11 illustrate the mode share for these trips. As it can be seen, the dominant mode here is car with total share of 64%. Bus has a 31% share. Train and airplane, with 4% and 1%, are the least favorable transport modes. SJ each year arranges special trains from Stockholm and Gutenberg to Mora. The trains arrive to the Mora station the day before the race. Travelers can stay the night in the train and take the bus from Mora to the start point on the day of the race, but it seems that it is not an attractive option for participants. From 31% of total bus share in transporting participants, buses arranged by ski clubs to transport their members has 54% share and the remaining 46% uses other bus services.

Figure 12 illustrates the mode share by gender, age and income. As it can be seen travel mode choice is the same for both female and male participants and also for different age and income groups: car is the dominant travel mode followed by bus and train and airplane respectively. Airplane is not a travel mode of choice for female participants. Data also indicates that female participants were more interested in train than male. The share of car as a travel mode for age groups 19-25 and 26-35 is higher compared to other age groups. Car share decreases slightly with increase in age while bus share increases instead. It seems that income does not have any specific effect on mode choice.

Table 10 Mode share 3.8% 0.6% Travel Mode Count Percentage share Own car Own car 508 39,1% 16.9% Car sharing Car sharing 281 21,6% 39.1% Rental car Rental car 45 3,5% 14.5% Bus 188 14,5% Bus Club bus 219 16,9% 21.6% Club bus Train 50 3,8% 3.5% Train Air plane 8 0,6% Air plane Grand Total 1299 100,0% Figure 11 Mode share

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Own car Car sharing Rental car Bus Club bus Train Air plane

Male 38.8% 21.5% 15.4% 17.0% Female 40.7% 22% 10% 16%

19-25 43.0% 27% 6% 15% 26-35 43.0% 27% 9% 12% 36-45 37.0% 23% 14% 16% 46-55 37.0% 17% 19% 19% 56-75 38.0% 14% 21% 23%

0-150 000 42.0% 24% 9% 14% 151 000-300 00 35.4% 22.20% 13.80% 18.30% 301 000-400 000 39.0% 23% 15% 16% 401 00- 500 000 41.6% 17.60% 16.10% 16.80% 501 000- 42.5% 21.10% 13.50% 14.60%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure 12 Mode choice by gender, age and income

The other question in the questionnaire was about place that participants spent the night before the race. This question was intended to catch the origin of trips in the area on the day of the event. Table 11 and Figure 13 below summarize the results for this question. As it can be seen Malung- sälen with about 61% and Mora with about 27% are two municipalities with highest percentage are main origins in the area compared to other municipalities in Dalarna. As mentioned before, Sälen in Malung-Sälen municipality, with several ski resorts is attractive for skiers during the winter sport week. These resorts, with plenty of accommodation facilities, are located near to the start point in Berga and thus attract the majority of participants. Just 0.5% of participants spent the night before the race somewhere out of Dalarna County, so it can be concluded that almost all participants spent the night in Dalarna County.

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Table 11 Origin of the trips on the day of the event

Count Average distance Municipality (kommun) Percentage (sample) from Berga (km) 10 0,7% 171 10 0,7% 142 3 0,2% 191 Malung-Sälen 826 61,4% Less than 1 km to 58 km Mora 367 27,3% 85 Orsa 30 2,2% 95 Rättvik 14 1,0% 122 1 0,1% 207 2 0,1% 104 Älvdalen 74 5,5% 50 Bollnäs 3 0,2% 210 Fagersta 2 0,1% 240 Hofors 1 0,1% 208 Norge 2 0,1% Total 1344 100%

Figure 13 Origin of the trips on the day of the event

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According to Table 11, the majority of participants were not at their home town the night before the race. About 61% stayed in area at least two nights and enjoyed the winter sport week in ski attraction sites in Dalarna County and especially in Malung-sälen. 39% of participants just spent 1 night there which is the night before the race.

Table 12 How long in the area

Response Count Percentage 1 night 505 38,5% 2 nights 420 32,1% 3 nights 257 19,6% 4 nights 43 3,3% 5 nights 9 0,7% 6 nights 8 0,6% 1 week 41 3,1% More 27 2,1% Total answered 1310 100,0%

4-2-2 Travel to the start point

This section includes the summary data concerning travel from the accommodation to the start point in Berga. The main issues here are how they traveled to the start point, departure time from accommodation, and arrival time at the start point.

a) Modal Split

Table 13 and Figure 14 illustrate how participants travelled to the start point in Berga on the day of the race regardless of how they travelled to the area. As it can be seen bus and car have approximately the same share in travel mode with 45% and 47% respectively. Table 10 includes more detailed categories of travel mode. Car as a travel mode is divided into three groups which are own car, car sharing, and rental car with 56%, 38%, and 6% respectively. Car sharing here means that some participants were in cars with other participants in the race. But also some participants reported traveling with their own car accompanied by other participants, either family members or friends. Average car occupancy, taking into account the number of participants in each car, is 2.64 persons per car. Ski clubs had their own buses travelling to the area and back to their home town.

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Table 13 Mode share to the start

Travel mode Count Percentage share Own car 322 25% Own car Car sharing 220 17% 8% 25% 17% Car sharing Rental car 36 3% Rental car Bus 380 30% 17% 30% Bus Club bus 223 17% Club bus Walking 104 8% Walking Total 1285 3%

Figure 14 Mode share to the start

b) Departure from the Origin

Table 14 and Figure 15 present the distribution of departure times toward the start point in 30 minutes intervals starting from 2 o’clock in the morning of the day of the race. This includes all modes. More detailed analysis separated by area and mode is presented later in section 4-3. As it can be seen the majority of participants departed between 4 and 6 am with the highest departure between 4:15 and 4:30. The distribution is skewed to right indicating that the majority departed after 4 o’clock. Some participants, 0.5% of the total response, reported departure periods after 8 am which can be either error in reporting or late departure because of some problems.

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Table 14 Departure from the origin

Time interval Frequency Percentage 2,00 4 0,3% 2,15 0 0,0% 2,30 5 0,4% 2,45 2 0,2% 3,00 8 0,7% 3,15 10 0,8% 3,30 34 2,8% 3,45 47 3,9% 4,00 150 12,4% 4,15 94 7,8% 4,30 168 13,9% 4,45 69 5,7% 5,00 118 9,8% 5,15 86 7,1% 5,30 103 8,5% 5,45 50 4,1% 6,00 94 7,8% 6,15 39 3,2% 6,30 44 3,6% 6,45 10 0,8% 7,00 33 2,7% 7,15 10 0,8% 7,30 17 1,4% 7,45 1 0,1% 8,00 6 0,5% 8,15 0 0,0% 8,30 1 0,1% 8,45 0 0,0% 9,00 3 0,2% 9,15 0 0,0% 9,30 2 0,2% 1208,00 100,0% 16.0%

14.0% 12.0% 10.0% 8.0% 6.0%

4.0%

Percentagefrequency 2.0% 0.0%

2.00 2.15 2.30 2.45 3.00 3.15 3.30 3.45 4.00 4.15 4.30 4.45 5.00 5.15 5.30 5.45 6.00 6.15 6.30 6.45 7.00 7.15 7.30 7.45 8.00 8.15 8.30 8.45 9.00 9.15 9.30

Departure time

Figure 15 Departure from origin

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] c) Arrival to the Destination

Table 15 and Figure 16 present the distribution of arrival times to the start point in Berga in 15 minutes intervals on the day of the event. According to the results, majority of the participants reached the start point in Berga between 5:00 and 6:30 am in the morning with the highest rate between 5:45 and 6:00 am (16%) and approximately symmetric distribution. The distribution of arrival times looks like a normal distribution. The symmetric shape of the arrival times distribution with the highest arrival percentage of 16% may indicate that no serious traffic congestion around the start point was expected.

Table 15 Arrival to the destination

Arrival interval Frequency Percentage 3,00 6 0,5% 3,15 1 0,1% 3,30 3 0,2% 3,45 0 0,0% 4,00 5 0,4% 4,15 6 0,5% 4,30 16 1,3% 4,45 33 2,6% 5,00 78 6,2% 5,15 115 9,2% 5,30 167 13,3% 5,45 112 8,9% 6,00 202 16,1% 6,15 128 10,2% 6,30 147 11,7% 6,45 66 5,3% 7,00 77 6,1% 7,15 35 2,8% 7,30 36 2,9% 7,45 17 1,4% 8,00 5 0,4% 1255 100,0%

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18% 16% 14% 12% 10% 8% 6% 4% Percentage frequency Percentage 2%

0%

3.00 3.15 3.30 3.45 4.00 4.15 4.30 4.45 5.00 5.15 5.30 5.45 6.00 6.15 6.30 6.45 7.00 7.15 7.30 7.45 8.00 Arrival time

Figure 16 Arrival to the destination

To test the hypothesis that if arrival times distribution follows a normal distribution Anderson-

Darling normality test is performed. The null hypothesis H0 is that the arrival times distribution follows a normal distribution. Following is the null and the alternative hypothesizes:

H0 : The arrival times distribution is a normal distribution

H1 : The arrival times distribution is not a normal distribution

Table 16 presents summary statistics from Anderson-Darling normality test. Figure 16 illustrates the normal probability plot. The p value resulted from the test is p = 0.000, so that the null hypothesis is rejected as the p value is less than any alpha level that might be chosen. The straight line on Figure 12 is null hypothesis of normality and the data should be as close to this line as possible in order to assume normality. As it can be seen from Figure 17 the data fluctuates above and behind the straight line and at both ends more points are farther from the line. This also rejects the null hypothesis. A-squared value also is not smaller than 95% critical value or smaller than 99% critical value so the null hypothesis that data is normally distributed is rejected.

Table 16 Anderson-Darling values

Anderson-Darling A-Squared 3.530 P 0.000 95% Critical Value 0.787 99% Critical Value 1.092

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3 y = 1.2527x - 7.432 2 R² = 0.9824

1

Z 0

-1

-2

-3 1.60 2.60 3.60 4.60 5.60 6.60 7.60 8.60

Figure 17 Normality plot

Figure 18 illustrates both the departure times from the origin and arrival times at the destination. The distribution of arrival times is similar to the departure times with a shift equal to 90 min to the right. Skewness of the departure times to the right and symmetry of the arrival times distribution indicate that considerable percentage of participants accommodated near to the start point.

18.0%

16.0% Departure 14.0% Arrival 12.0%

10.0%

8.0%

6.0% Percentage frequency Percentage

4.0%

2.0%

0.0%

4.00 8.00 2.00 2.15 2.30 2.45 3.00 3.15 3.30 3.45 4.15 4.30 4.45 5.00 5.15 5.30 5.45 6.00 6.15 6.30 6.45 7.00 7.15 7.30 7.45 8.15 8.30 8.45 9.00 9.15 9.30

Time interval

Figure 18 Departure time and arrival time distribution

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4-2-3 During and after race trips

a) During the race This section is about trips made during the race by people who accompanied participants and also trips after the race. Those participants who travelled by car to the start point in Berga either took a bus from Mora to Berga after the race to pick their car or had a family member or a friend driving the car to Mora and picking them up there. A part of the questionnaire was designed to catch these trips. For the small percentage that walked to the start there is no data about what they did after the race. The summary of the results and related observations are presented below.

From all participants who travelled by car to the start point in Berga about 20% took the bus from Mora to go back to Berga to pick up their car. A very low percentage took taxi and the majority of them had someone to drive the car from Berga to Mora. Table 17 shows the percentage share for each mode.

Table 17 During and after the race mode share for those who drove to Berga

Travel mode Count Percentage Bus 113 20.2% Taxi 2 0.4% Someone drove the car to Mora 442 79.4%

Total 557 100%

Since the race track and the road are near to each other on most parts of the route from Sälen to Mora (see Figure 19), family members or friends who drove participants to the start point and then drove the car to the end point in Mora could stop in control stations or other places on the way to watch the race and cheer-up the participants. At some control stations there were parking lots prepared for the event with a fixed entrance fee. Some other parking lots were free of charge. At some other control stations without parking lot and also at some sections of the route where the race track was very near to the road people parked on the roadside. This in some cases even resulted in lane closure and road narrowing. This, along with disturbance made by those trying to find a parking place or those trying to come out of parking caused speed reduction and stop-and- go traffic at some sections of the route. Although the road was one way toward Mora from Fiskarheden between 7:00 am and 6:00 pm but observed driving behavior indicated that most drivers did not know about it since they kept driving on right side (see Figure 20).

Those who had a family member or a friend driving car from Sälen to Mora were asked about congestion places. 28% responded on these questions. About 43% did not face congestion on the route. About 31% faced congestion at start place which may be the result of leaving parking lots. 25% experienced traffic congestion between Risberg and Evertsberg. About 20% faced congestion

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] on Berga-Risberg, Evertsberg and Mora. It seems that the first part of the route up to Evertsbeg was more congested. From Oxberg there are two routes to reach Mora which may be the reason for smooth traffic between Oxberg and Mora.

Figure 19 Vasaloppet road and race track

Figure 20 Sälen-Mora during the race

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Those who drove from Berga to Mora during the race were also asked at which control station they stopped and if they parked on roadside or in parking lot. From about 61% who answered this question 10% did not stopped at any control station. Mångsbodarna and Evertsberg are two stations where about 67% of respondents to this question stopped to watch the race or cheer up participants. Smågan and Eldris are two stations with the lowest percentage. Just 34% of respondents specified where at each station they parked their vehicle. Table 18 shows summary of results. As it can be seen Eldris is the control station with the highest percentage of roadside parking while Evertsberg is the one with the lowest percentage of roadside parking. Some possible reasons for parking on roadside at control stations may be:

There was no place in parking lots Unwillingness to pay for the entrance fee Easier to park on roadside

Table 18 Parking at control stations

Control stations Parking lot Roadside Smågan 80% 20% Mångsbodarna 85% 15% Risberg 85% 15% Evertsberg 93% 7% Oxberg 87% 13% Hökberg 90% 10% Eldris 60% 40%

b) After race destinations Participants were asked about their destination after the race. Table 19 and Figure 21 present the results. As it can be seen, Dalarna County has the highest percentage in accommodating participants after the race. Considering that 6% of participants live in Dalarna County (Table 9), it can be concluded that about 14% of the participants did not leave the area after the race and preferred to stay in Dalarna County for some more days. Stockholm county and Västra Götland county are second and third destinations with highest percentage respectively as they also had the highest percentage of participants.

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Table 19 Destination after the race

Response Count Percentage share Blekinge län 4 0.4% Dalarnas län 297 29.9% Gävleborgs län 34 3.4% Gotlands län 2 0.2% Hallands län 13 1.3% Jämtlands län 23 2.3% Jönköpings län 25 2.5% Kalmar län 6 0.6% Kronobergs län 14 1.4% Norrbottens län 18 1.8% Örebro län 25 2.5% Östergötlands län 34 3.4% Skåne län 32 3.2% Södermanlands län 19 1.9% Stockholms län 165 16.6% Uppsala län 47 4.7% Värmlands län 31 3.1% Västerbottens län 27 2.7% Västernorrlands län 23 2.3% Västmanlands län 23 2.3% Västra Götalands län 130 13.1% Total 992 100.00%

35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%

Figure 21 Destination after the race

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4-3 Time Analysis

In this section information about travel time, departure time from origin and arrival time to the start point by mode and place of accommodation are discussed. Major trip origins which are located in Dalarna County are selected for more detailed study. As presented in Table 8 major trip origins on the day of the race are Malung-sälen, Mora, and Älvdalen.

Malung-sälen is a municipality with area of 4106 km2 (statistiska centralbyrån 1 januari 2011) that attracted about 61% of participants. Berga as the start point for the race is located in the north part of the municipality. Existence of several ski facilities in Sälenfjällen (including Lindvallen, Högfjället, Tandådalen, Hundfjället and Stöten) has made the municipality an attractive destination during the winter sport week. These facilities are located near to the start of the race on North West of Berga. Sälen, including Sälen village and Sälenfjällen, accommodated about 40% of the participants. , 3 km south of Berga, and Lima, 17.5 km south of Berga, with about 12 and 10 percent respectively, are the second and third major trip origins in the Malung-sälen municipality. Berga, as the start point for the race, accommodated 6% of the participants with the remaining scattered in other places in the municipality. According to the data, places located in the north and south of Berga accommodated respectively about 44% and 38% of participants who choose Malung-sälen to reside. About 18% of respondents did not specify where in Malung-sälen they stayed. For detail information see table in Appendix II.

Following detail analysis of travel mode share, travel time and departure time for several major origins in the Malung-sälen municipality is presented.

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Figure 22 Malung-sälen

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4-3-1 Sälen

Sälen is a small densely built-up area in Malung-sälen located about 6 km north of Vasaloppet’s start in Berga (see Figure 23). The area of the village is about 1.33 km2 and the population is 6522. Regarding the big difference between the minimum and maximum reported travel time (Table 21) and also variation in reported travel time, it can be concluded that giving name of the area where participants spent the night before the race they have considered Sälen village, Sälenfjällen and even small villages on north and south of Sälen village as Sälen since not all participants know the area well. In Figure 23, the area marked with the red line shows what has been considered Sälen by respondents. The green line shows the route from Sälenfjälllen to Berga and the yellow route is part of the bus route from Mora.

Figure 23 Sälen area as considered by respondents

Table 20 bellow summarizes the mode share statistics considering Sälen as the origin of trips. Car, with 58% share, is the dominant mode for travelers to reach the start from Sälen. Bus is the second option with 27% share, followed by walking which has 15% share. Following, travel time and departure time distribution histogram is presented for each mode.

Table 20 Mode share/Sälen

Mode share Count Percentage share Car 90 58% Bus 18 12% Club bus 23 15% Walking 23 15% Total 154 100%

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Table 21 Travel time summary statistics/Sälen

Car Bus Mean 29 29 Median 30 30 Mode 30 30 Standard Deviation 14.3 17.9 Sample Variance 205.9 320.8 Minimum 5 5

Maximum 70 80 Maximum

Table 21 shows summary statistics for travel time by car and bus from Sälen to the start. As it can be seen, average travel time by car and bus is about 29 minutes. In free flow situations, according to Google map travel time from Sälen village is about 7 min. The difference could be because of two possible reasons: first, as mentioned before what is considered Sälen by participants is an area wider than just Sälen village, and second that all trips originated from north of Berga used the same road at the last part of the route from Sälen village to the start in Berga which may cause high volume and low speed. On the other hand, this year a different route for official buses from Mora to Berga was planned which may add to the traffic on the north link. The route is longer than the usual route and reaches Berga from the north. These altogether may result in higher travel times compared to travel times under free flow conditions. It is also important to notice that the travel time suggested by Google map is not precise.

Median and mode are the same for both alternatives while the standard deviation for travel time by bus is higher indicating that bus travel time has higher variability.

Considering the average travel time from Table 21, the average speed is about 31 km/hr (assuming that average travel distance from the area is about 15 km) while average speed under free flow situations is about 51 km/hr. This means 31% reduction in average travel speed.

Table 22 Car and bus travel time distribution/Sälen

Car Bus Range(min) Frequency Percentage Range(min) Frequency Percentage 0-15 18 21% 0-15 10 29% 15-30 44 51% 15-30 18 53% 30-45 16 19% 30-45 1 3% 45-60 6 7% 45-60 2 6% 60-75 2 2% 60-75 2 6% 75-90 1 3% Total 86 100% 34 100%

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60% 60% 51% 53% 50% 50%

40% 40% 29% 30% 30% 21% 19%

20% 20%

Percentagetravlers Percentagefrequency 10% 7% 10% 6% 6% 2% 3% 3% 0% 0% 15 30 45 60 75 15 30 45 60 75 90 Travel time (min) Travel time (min)

Figure 24 - Car travel time distribution Figure 25 - Bus travel time distribution

Figure 24 and 25 illustrate the distribution of car and bus travel time respectively. As it can be seen, the car travel time is skewed to the right. The majority of travel times fall in the range of 15 to 30 minutes for both modes.

Figure 26 illustrates the distribution of departure times for car and bus travelers. The X axis is departure time in 30 minutes intervals and The Y axis is the percentage frequency for each departure interval. Peak departure period for bus travelers appears between 4:30 am and 5:00 am while peak departure for car travelers appears about 90 minutes later between 6 and 6:30 am. Percentage departure for bus travelers increases slightly for the next time period and remains constant between 5:30 and 6:30 then shows a fall by 5%. Departure percentage for car travelers increases gradually to reach the peak at around 6:30 am then decrease sharply by about 18% at next time interval. At around 6 am percentage departure is same for both modes while before that departure rate is higher for bus travelers both car travelers and bus travelers and after that time is the opposite way with higher departure rate for car travelers.

Considering overall departure for both modes it can be seen that the departure rate increases by 25% from around 4:30 am to reach the highest departure rate at 6:30 am with two peaks at 5 and 6:30. The first peak is resulted from bus departure peak and the second peak at 6:30 am which has higher departure rate represents car departure peak.

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35% Car 30% Bus Overall 25%

20%

15%

10%

5%

0%

Figure 26 - Departure time distribution for car and bus travelers from Sälen

Figure 27 illustrates the distribution of travel times by departure time for those participants who traveled by car, either car sharing or own car. The Y axis presents travel time in minutes while the X axis is the departure time from the origin. Figure 28 shows the average travel time between 4:00 and 7:30 am in 30 minutes intervals. As it can be seen the average travel time increases between 4:00 and 7:00 am, although there is a slight decrease between 5:30 and 6: 00 am. At the last time interval the average travel time decreases to about 20 minutes but still is higher than the minimum travel time which is about 11 minutes in the first time interval. The second lowest average travel time from Sälen is observed between 7 and 7:30.The maximum average travel time is about 33 minutes and occurs between 6 and 7 am.

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80 80

70 70

60 60

50 50 (min) 40 40

30 30

Traveltime 20 20

10 10

0 0

Figure 27 - Car travel time distribution/Sälen Figure 28 - Average car travel time by departure

Figure 29 and 30 illustrate the minimum and maximum travel times by departure time for 30 minutes intervals. Maximum travel time fluctuates between 20 and 70 minutes while minimum travel time change between 5 and 20 minutes with an either constant or increasing trend. As it can be seen, the minimum travel times are more consistent compared to maximum travel times that vary a lot.

80 80 70 70

60 60

50 50 40 40 30 30

Travel time Travel (min) 20 20 10 10 0 0

Figure 29 - Minimum travel times by car Figure 30 - Maximum travel times by car

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Figure 31 illustrates the distribution of bus travel times including club buses and other bus services. The X axis is travel time in minutes and the Y axis is the departure time from the Sälen area toward the start point in Berga. Figure 32 shows the average travel time during 15 minutes intervals. The lowest travel time occurs in the first time interval and is higher than the average car travel time in the same time period. The highest average travel time is about 40 minutes and occurs between 5:00 and 5:30 am with an increase of 17 minutes from the first time interval. During the remaining time intervals the average travel time is more consistent between 23 and 30 minutes.

90 90 80 80

70 70

60 60 50 50 40 40 30

Travel time Travel (min) 30 20 20 10 10 0 0

Figure 31 - Bus travel time distribution from Sälen Figure 32 - Average travel time by Bus from Sälen

The maximum average travel time by bus is about 40 minutes and occurs between 5:00 am and 5:30. Figure 33 illustrates the minimum and maximum travel times by bus respectively. The minimum travel time changes between 5 and 30 minutes with an increasing trend except the last period where the travel time decreases slightly. The lowest value occurs at the first time period and most likely corresponds to those trips originated from villages near to the start point which have been considered as Sälen by participants. Maximum travel times here also show wide variation from 30 to 80 minutes. The highest value for maximum travel time by car is about 70 minutes and occurs around 5:30 and also 6:30. For bus trips this value is 80 minutes for bus and it also occurs around 5:30.

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90 80 70 60 50 Max Travel Times 40 Min Travel Times 30

Travel time time (minutes)Travel 20 10 0

Figure 33 - Maximum and minimum travel times by bus from Sälen

Since the Sälen area as considered by the survey respondents is a very wide area travel time data from this survey may not be a good representative of congestion on routes connecting the area to the start point. For more definitive conclusions about magnitude of traffic congestion and its effect on average travel time detailed traffic studies are needed.

In general, travel time variability during each departure time period may be caused by a combination of factors:

a) Driver variability,qualitative effect of other vehicles. Number and distribution of slower vehicles on the road influence travel times on rural roads since overtaking can be performed just in dedicated lanes or when sight distance is sufficient and there is no oncoming vehicle on opposite direction (Dutschke & Woolley, 2009). b) Distance variability c) Stops for non traffic reasons d) Traffic congestion which depends on variability of departure time e) Data errors.

Considering these analysis about travel times and departure times it can be concluded that for those selected to stay in the Sälen area, bus is as good alternative as car.

For those travelers who resided in walking distance from the start point the average travel time is about 16 minutes. Apart from the time period between 5 and 5:30 which has the highest departure rate the other time periods have similar departure rate (See Figure 34). These are probably participants who resided within a 1.5 km from the start in so called Sälen area (considering 5 km/hr average walking speed for pedestrian and average travel time of 16 min).

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45% 40% 35% 30% 25% 20% 15% 10%

Percentagefrequency 5% 0% 5.3 6 6.3 7 7.3 Departure time

Figure 34 - Departure time distribution for walking

4-3-2 Sälenfjällen

Sälenfjällen is an area located north west of Sälen in Malung-sälen municipality, with high mountains and several ski facilities. Access from the area to Berga is through road 71. Major ski destinations are Lindvallen, Högfjället, Tandådalen, Hundfjället and Stöten. The nearest one to Berga is Lindvallen, 10 km from Berga, and the furthest is Stöten, 43 km from Berga. Sälenfjällen resided about 20% of participants who chose to stay in Malung-sälen municipality the night before the race. 33% of those participants who stayed in Sälenfjällen resided in Lindvallen and 34% in Tandådalen.

2-4.1.1 Lindvallen and Tandådalen Lindvallen and Tandådalen are two ski areas in Sälenfjällen which are located 10 and 23 km from Berga on northwest of the start. Figure 35 and 36 illustrate the mode share for travelers from these two areas to the start point of the race. As it can be seen the mode share is almost the same for both areas. Car with 84% and 85% is the dominant mode while bus has just 16% and 15% share.

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Buss Car

16% 15%

84% 85%

Figure 35 - Lindvallen mode share Figure 36 - Tandådalen mode share

Table 23 shows the car and bus travel times summary statistics for both areas. Considering Lindvallen as origin of the trips, both modes have approximately the same average travel time which is equal to 29 minutes. The most frequent travel time for both modes is 30 min with higher standard deviation for bus compared to car. Average travel time with under free flow conditions based on what Google map suggests is about 13 minutes. As it can be seen average travel time at the day of the race have increase almost twofold compared to free flow situation. But it is important to notice that the travel time retrieved from Google map is not the exact travel time for this specific day. On the other hand, road condition in a winter day may cause lower speed and higher travel time compared with the one suggested by Google map.

Table 23 - Travel time summary statistics from Lindvallen and Tandådalen

Lindvallen Tandådalen Summary statistics Car Bus Car Bus Mean 28.7 29.3 42.9 45 Median 30 30 35 45 Mode 30 30 30 30 Standard Deviation 12.2 15.7 18.8 14.9 Sample Variance 149.5 245.2 352.9 221.4 Minimum 15 15 20 30 Maximum 60 60 90 70

When Tandådalen is the origin of trips, average travel time by car is about 43 minutes while average travel time by bus is just 2 minutes more and is about 45 minutes. Here also the most frequent travel time experienced is 30 min for both modes. Average travel time from Tandådalen is about 1.5 times more than the average travel time from Lindvallen while the distance from Tandådalen to Berga is twice the distance from Lindvallen to Berga. PARISA AHMADI | Chapter 4: Results 54

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Table 24 shows average travel speed from Lindvallen and Tandådalen and also the travel speed in free flow conditions. Average travel speed from Lindvallen has decreased to less than half compared to free flow conditions and about 38% reduction in average travel speed from Tandådalen.

Table 24 - Average travel speed from Lindvallen and Tandådalen

Lindvallen Tandådalen Average speed (km/hr) Car Bus Car Bus Day of the race 20.7 20.7 32.17 30.7 Free flow conditions 46.6 55.2

Figures 36 and 37 illustrate the distribution of travel times from Lindvallen and Tandådalen respectively when car is the travel mode. Travel time distribution for bus is skipped because of low share and not sufficient data. As the graph shows for those travelling from Lindvallen more than 70% experienced travel time between 10 to 30 minutes and just 5% reported travel time between 50 and 60 minutes. It can be concluded that for participants who choose to stay in Lindvallen the night before the race the most probable travel time is between 15 and 30 minutes and in worst conditions and with a very low probability between 50 and 60 minutes. Starting from Tandådalen, around 70% of travelers experienced travel time between 25 and 50 minutes. The most frequent travel time is between 25 and 30 minutes while at worst conditions it will increase to 90 minutes with a very low probability (2.5%).

45% 45% 40% 40% 35% 35% 30% 30% 25% 25% 20% 20%

15% 15% Percentagetraveler 10% 10% 5% 5% 0% 0% 20 30 40 50 60 20 30 40 50 60 70 80 90 Travel time (min) Travel time (min)

Figure 37 - Car travel time distribution Figure 38 - Car travel time distribution Lindvallen Tandådalen

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Figure 39 illustrates the distribution of car and bus departure times from Lindvallen regardless of overall departure and Figure 40 illustrates the overall departure times and share of each mode in overaal departure. Although the percentage of bus travelers is low, it is clear that majority of bus travelers (78%) departed from Lindvallen between 4:30 and 5 am while in the same time period less than 10% of the car travelers departed. The peak departure percentage for car travelers happens between 6 and 6:30. Participants who drive their car to Berga start departing from Lindvallen at 4:00 am with the peak in the period between 6:30 and 7:00 am. The only disadvantage to travel by bus from Lindvallen compared with traveling by car is the earlier departure time. While Figure 39 illustrates percentage departure for each mode just with regard to that mode, Figure 40 illustrates the percentage departure in overall. As it can be seen the highest departure rate is about 30% and occurs between 6:00 and 6:30 am. The second highest departure occurs between 4:30 and 5:00 am and is slightly more than 20%.

80% 80% Bus Car Bus 70% 70% Car Overall 60% 60% 50% 50% 40% 40% 30% 30%

20% 20% Percentage frequencyPercentage 10% 10%

0% 0%

Figure 39 - Car and bus travelers departure from Figure 40 - Overall travelers departure Lindvallen

Figure 41 illustrates percentage departure form Tandådalen for each mode just with regard to that mode. Figure 42 illustrates the percentage departure in overall. The peak departure period for car travelers from Tandådalen is between 5:00 and 5:30 with 33% departures. More than half departed between 4:30 and 5:30. All bus travelers departed between 4:30 and 5:30 with equal rate in the two time periods (see Figure 41). Considering departure time distribution and average travel time for both modes, the only advantage for car travelers is freedom in deciding their departure time. Figure 42 illustrates overall departure percentage from Tandådalen. Car and bus travelers departure percentage is calculated with regard to overall departure from Tandådalen. As it can be seen the highest departure percentage occurs between 5:00 and 5:30 am and is about 30%.

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80% 80% Bus Car Bus 70% 70% Car Overall 60% 60%

50% 50%

40% 40%

30% 30%

20% 20% Percentagefrequency 10% 10%

0% 0%

Figure 41 - Car and bus travelers departure Figure 42 - Overall travelers departure from Tandådalen

Comparing the departure percentage of car travelers from both origins it can be seen that while just slightly less than 30% of car travelers departed from Lindvallen before 5:30, more than twice this amount departed from Tandådalen in the same time period. Peak departure in Tandådalen appears one hour earlier compared to peak departure from Lindvallen. This is related to differences in distance from the start point which is twice farther for Tandådalen so participants departed sooner to be sure to reach the start on time.

Figure 43 shows travel times by car from Lindvallen as a function of departure time. The minimum travel time is constant and independent from departure time but maximum travel time and average travel time change by departure time. Average travel time increases by time until it reaches the peak at 6 am (39 minutes) then decreases but still higher than what it was at beginning . Participants departed before 5:30 experienced lower average travel time compared to those departed later (see Figure 44).

By comparing the departure time distribution (Figure 39) and travel time(Figure 44) it can be concluded that travel time not only is affected by departure rate from this specific origin but also by traffic from other parts of the network upstream and downstream. It seems that people know when to depart in order to avoid probable congestion since the peak departure happens in earlier time slot than the period that has maximum average travel time.

Since number of bus travelers from Lindvallen in this sample is not enough to be analyzed for travel time, just the scatter plot is presented in Figure 45.

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70 70

60 60

50 50 40 40 30 30

20 Travel time Travel (min) 20

10 10

0 0

Figure 43 - Car travel time distribution Figure 44 - Average car travel time from Lindvallen from Lindvallen

80 70 60 50 40 30 20

Travel time Travel (min) 10 0

Figure 45 - Bus travel time distribution/Lindvallen

Figure 46 illustrates travel time by car from Tandådalen with regard to departure time. As it can be seen travel times are distributed in wide range from 15 to 90 minutes. From Table 23 average travel time from Tandådalen to Berga is about 43 min for car and 45 min for bus. In free flow conditions travel time is about 25 minutes3 and considering the distance from the start point which is about 23 km, speed under free flow conditions would be 55 km/hr while on the day of the race average speed is about 32 km/hr for car and 30 km/hr for bus. Figure 47 illustrates average travel time from Tandådalen in 30 minutes intervals. Those who departed in first interval faced high

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average travel time of about 46 min which then decreased to 35 min for the next interval but increased again gradually to reach the peak (55 min) between 6 and 6:30. Average travel time decreased by 7 km/hr for the last interval. A comparison between departure time distribution (Figure 40) and average travel time (Figure 46) confirm that in addition to departure rate from specific origin interaction between different parts of the road network affects average travel time. Here also people based on previous experience about probable congestion in the network depart earlier to avoid high travel time which occurs between 6 and 6:30.

100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0

Figure 46 - Car travel time distribution Figure 47 - Average car travel time from Tandådalen from Tandådalen

4-3-3 Mora

Mora is a municipality in Dalarna County, located west of Malung-sälen with area of 3129 km2 and population of 20133. Central town in this county is Mora with area equal to 12.35 km2 and population of 10940. Mora is the end point to the main race. Vasaloppet Hus which is the main office organizing the event is also located here. Mora is the second municipality in Dalarna with highest percentage of accommodating participants (27.3%).

Analysis for mode share, travel times and departure times for Mora as the origin of trips on the day of the race and for all modes is presented following.

Table 25 and Figure 48 show the mode share for participants departed from Mora. Bus, with 82% share is the dominant mode for travelers from Mora to the start, 19% of which belong to various clubs and the remaining 63% is mainly Vasaloppet’s official buses arranged to transport participants from Mora to the start. Tickets should be bought in advance and the cost is 200 SEK.

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Car is the second option with 18% share. As it can be seen bus is a popular mode to travel from Mora probably because participants can sleep during the trip to the start point and then after the race they do not have to go back to Berga to pick up their car. Also those who travel by train or bus to Mora take bus to reach the start point. In 2011, 49 buses were arranged from Mora departing from 4 am. A gate system was designed in the central bus station to facilitate boarding and also to make it faster. As mentioned before, a different bus route was planned for official buses in order to avoid traffic congestion. In previous years the Vasaloppet’s buses drove the most known route from Mora to Berga which is used by most drivers to reach Sälen and other northern areas and were delayed in traffic in the southern access to Berga. As a result, some participants reached the start late. For this reason people in charge decided on different route which is longer and reach the start from North. It was reported that in 2011 participants could reach the start on time and without problem. This route is via Älvdalen and Lövnäs with driving distance equal to 115 km which is about 30 km longer than the usual route and free flow travel time equal to 2 hour.

Table 25 - Modal split from Mora

Travel mode Share 19% 18% Car Car 18% Bus Bus 63% Club bus Club bus 19% 62% Total 100%

Figure 48 - Modal split from Mora

Table 26 shows summary statistics for travel times from Mora to the start. Since official Vasaloppet buses and club buses might follow different routes they are treated as two separate modes. As it can be seen from table 26 the lowest average travel time belongs to car with about 87 minutes, followed by club bus with average travel time equal to 101 min and bus which is mostly official buses has the highest travel time equal to 124 minutes. The longer route for bus resulted in 37 minutes longer travel time on average. The most frequent travel time is 90 min for car and 120 min for bus. While in the other origins presented before difference between the average travel time by car and by bus was negligible, from Mora the difference is considerable which is certainly related to the longer route for bus.

Average travel speed by car is 59 km/hr which is the highest compared to the average speed of bus and club bus. Comparing average speed of bus and club bus it can be seen that bus has higher average speed which might be an indication of less congestion on the longer route compared to the usual route that club buses drove.

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Table 26 - Summary statistics for travel time and average speed/Mora

Car Bus Club bus Mean 87 124 101 Median 85 120 95 Mode 90 120 90 Standard Deviation 19.07 24.56 23.47 Sample Variance 363.97 603.39 551.21 Average speed (km/hr) 59 55.6 50.8 Travel times are in minute

Figure 49 and 50 illustrate the distribution of travel times for bus, club bus and car. The highest frequency for travel time by club bus falls between 86 and 100 minutes (39%) while same percentage of bus travelers experienced travel times between 116 and 130 minutes. The minimum travel time for club bus is lower than minimum travel time by bus. For car travelers the most frequent travel times are between 81 and 90 minutes (30%), about 30 min less than what most bus travelers experienced.

45% 45% 40% Bus 40% 35% Club bus 35%

30% 30% 25% 25% 20% 20% 15% 15% Frequency 10% 10% 5% 5% 0% 0% 70 85 100 115 130 145 160 175 190 More 50 60 70 80 90 100 110 120 130 140 Travel time (min)

Figure 49 – Bus-club and bus travel time distribution Figure 50 - Car travel time distribution from Mora from Mora

Figure 51 illustrates the distribution of departure time for car, bus and club bus. Percentage departure for each mode is calculated independent from other modes. Bus and club bus have similar departure pattern with the highest rate between 4:00 and 4:30 am. Car departures also have similar pattern with highest departure rate at the same time slot as bus and club bus. The main difference between car and bus departures is that for bus and club bus the departure rate becomes approximately three fold between 4 and 4:30 compared to the previous time slot and falls sharply during the next time slot while changes for car are very small.

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Unlike other origins described before, where car departures had the highest departure rate later than bus, in this area all modes experience the peak rate at the same time period. The reason may be that the experience from previous year, with congestion and high travel times caused participants to depart earlier this year to avoid congestion and be at the start on time.

70%

60% Car Bus 50% Club bus

40%

30% Frequency 20%

10%

0%

Departure

Figure 51 - Car and bus travelers departure from Mora

Figure 52 illustrates car travel times by departure time for Mora as the origin of trips. The Y axis is travel time in minutes and the X axis is departure time in 30 minutes intervals. Departures for car are scattered between 3:00 and 5:30 am. Some very low travel times are reported which may probably are experienced by participants departed from areas in Mora municipality, like Oxberg, with shorter distance to the start point.

Figure 53 illustrates average travel times in 30 minutes intervals for car, bus and club bus travelers staring from Mora. Bus is the mode with the highest average travel time, about 50 minutes longer compared to the travel time by car. Travel times are decreasing closer to the start time. Car has the lowest average travel time in all time periods while bus has the highest average travel time. Both car and club bus that share same route show similar pattern which is decreasing by time although there is an increase in average travel time in the time period between 4:30 and 5:00 am. This is more obvious for car than club bus. The highest average travel time for all modes occurs in the first time slot between 3:00 and 3:30 am.

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160 160 Car Bus 140 140

Club bus

120 120

100 100

80 80

60 time Travel (min) 60 Travel time Travel (min)

40 40

Departure Figure 52 - Car travel time plot/Mora Figure 53 - Average travel time all modes/Mora

Figures 54 and 55 illustrate travel times by departure time for those travelled by bus and club bus from Mora. Higher departure density in Figure 54 can be recognized in time period between 3:30 and 4:30 am which seems reasonable as the result of scheduled departure for official Vasaloppet’s buses. Buses started departing from Mora at 3:45 am and left Mora as they were full.

250 250

200 200

150 150 100

100 50

50 0

Figure 54 - Bus travel time plot/Mora Figure 55 - Club bus travel time plot/Mora

Considering the freedom in departure time and the lower average travel time car is the best mode to travel from Mora to Berga. However, then there are also two disadvantages, first not all car passengers can sleep during the trip while taking bus all can take a nap and rest. Second car users need to go back to Berga after the race to pick up their car. These two reasons more likely led most

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4-4 Flows and Average Speed

This section contains information about flows from different origins in the area to the start place in Berga on the day of the event, March 6th. Flow is divided to people-flow and vehicle-flow. Most trip origins are municipalities in Dalarna County but there are some from neighbor counties and also from with a very small share.

As described before, percentage of participants who started their trip from each origin is known in the sample .Having this share and also the total number of participants which is 15800, people- flow from each origin is calculated and presented in Table 27. To calculate vehicle-flow average passenger occupancy for car is 2.64 (from questionnaire) and average passenger occupancy for bus is assumed 50 (from the bus company responsible for Vasaloppet’s buses). For club buses average passenger occupancy is calculated to be 18 persons per bus.

Table 27 below shows origin-destination matrix for people-flow considering Berga as the destination of the trips for total number of participants in 30 minutes intervals. Those highlighted with darker color are origins out of Dalarna County while others are from Dalarna. Those who walked from their accommodation to the start are not included in this matrix.

Table 27 - People-flow origin-destination matrix

AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM

Origin

30 00 30 00 30 00 30 00 30 00 30 00 30 00 30

Total

: : : : : : :

2: 3 3: 4 4: 5 5: 6 6: 7 7: 8 8: 9 9: Falun 13 26 39 39 118 Leksand 26 26 54 14 120 Ludvika 12 12 12 36 Malung-Sälen 13 83 742 1515 2018 1672 1597 483 184 26 67 13 13 8426 Mora 115 1062 2327 681 66 70 4321 Orsa 12 26 65 156 78 13 350 Rättvik 36 71 59 166 Vansbro 12 12 24 Älvdalen 13 13 38 169 426 137 61 13 870 Bollnäs 12 12 12 36 Fagersta 24 24 Hofors 12 12 Norge 12 12 24 Total 86 63 291 1369 3559 2726 2234 1803 1610 483 184 26 67 13 13 14527

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As it can be seen, the major origin is Malung-Sälen with highest share in trip production. Total number of people who originated from this area is about 8426 out of 15800 which is more than the half (58%), excluding the walking percent from the population. Second and third Counties with highest share are Mora and Älvdalen respectively with about 30 and 6 percent of total population who chose motorized modes to travel to the start from their accommodation.

Although start time of the race was 8:00 in the morning but data shows that some participants departed after 8:00 AM from origin. Part of this may be error in reporting departure time which has become even more magnificent by transferring from sample to population. Another possible explanation may be that some participants refused to participate in the race or other non-traffic related problems prohibited them from earlier departure.

The average occupancy for car, bus and club bus considering data from survey is presented in Table 28. To calculate traffic-flow, passenger car flow is considered as the base flow and bus and truck flow is converted to passenger car flow by means of passenger car equivalent factor which is assumed 2.5 and 3 for car and bus respectively.

Table 28 - Vehicle occupancy rate

Vehicle type Average vehicle occupancy rate Car 2.64 Bus 50 Club bus 18

Table 29 illustrates OD matrix for vehicle-flow in 30 minutes intervals considering Berga as destination of trips. As it can be seen Malung-sälen, Mora and Älvdalen are major trip producers and other counties have a very low share. Malung-Sälen County is the main vehicle trip generator in the area which produces about 71 percent of vehicle trips (including car, bus and club bus). Mora with about 18 percent share in vehicle trip generation has the second place followed by Älvdalen with about 5 percent trip generation. Comparing Malung-sälen and Mora as origin of participants (Table 27), it can be seen that Malung-sälen has slightly less than twice share while considering vehicle trip generation, Malung-sälen produces about five times more vehicle trips compared to Mora. The big difference between Malung-sälen and Mora is because Malung-sälen has the biggest share in producing car trip among other origins while Mora has the biggest share in bus trip generation (Table 30 and Table 31).

Looking at vehicle flows originated from Malung-sälen it can be seen that most vehicle started departing between 4:00 am to 7:00 am, with highest departure between 6:00 am and 6:30 am. Considering Mora as origin most vehicles departed between 3:30 am and 5:00 am with highest number of departures between 4:00 and 4:30 am.

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Table 30 and 31 show car flow and bus flow origin-destination respectively. As it can be seen and also mentioned before, Malung-sälen is the main car trip generator in the area with about 79% share followed by Mora and Älvdalen with 11 and 5 percent share respectively. Considering bus as travel mode, 50% of bus trips originate from Mora which is because of high number of Vasaloppet’s official buses that depart from Mora. The number of buses departed from some origins like Orsa may be more than what it was in real. The main reason for this is low number of responses from these areas which being divided into 30 minutes time intervals results in data which is not statistically significant. According to data from the bus company that arranged Vasaloppet´s official buses, they had 5 official buses from Malung-sälen. What the data from the survey shows are 29 buses. The operator of these 24 additional buses could not be captured from data.

Table 33 presents club bus-flow origin-destination matrix. As it can be seen Malung-sälen, Mora and Älvdalen are only counties as origin for club buses. The main reason for this is distance of these counties from Berga. Malung-sälen was origin for about 60 percent of club buses and just half of this amount started trip from Mora while Älvdalen has the lowest share with 11 percent.

Table 29 - Vehicle-flow origin-destination matrix

AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM

Origin

30 00 30 00 30 00 30 00 30 00 30 00 30 00 30

: : : : : : :

2: 3 3: 4 4: 5 5: 6 6: 7 7: 8 8: 9 9: Total Falun 5 8 13 3 28

Leksand 8 8 3 3 20

Ludvika 5 5 5 15

Malung-Sälen 5 18 177 372 528 505 547 178 50 8 23 5 5 2418

Mora 604 15 141 251 114 19 23 622

Orsa 5 3 8 18 10 3 45

Rättvik 12 12 5 28

Vansbro 5 5 10

Älvdalen 5 5 10 25 75 41 24 3 187

Bollnäs 5 3 5 13

Fagersta 9 9

Hofors 5 5

Norge 5 5 10

Total 34 80 69 205 485 578 590 552 549 178 50 8 23 5 5 3348

(The unit is vehicle/30 minutes)

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Table 30 - Car-flow origin-destination matrix

AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM

30 00 30 00 30 00 30 00 30 00 30 00 30 00 30

: : : : : : :

2: 3 3: 4 4: 5 5: 6 6: 7 7: 8 8: 9 9: Total Falun 5 5 10 20 Leksand 5 5 10 Ludvika 5 5 5 15 Malung-Sälen 5 15 134 282 455 465 519 173 40 5 20 5 5 2123 Mora 7 81 101 81 14 20 304 Orsa 5 5 10 5 25 Rättvik 9 9 18 Vansbro 5 5 10 Älvdalen 5 5 5 10 42 33 24 124 Bollnäs 5 5 10 Fagersta 9 9 Hofors 5 5 Norge 5 5 10 Total 34 15 51 130 260 415 502 509 519 173 40 5 20 5 5 2683

Table 31 - Bus-flow origin-destination matrix

AM AM AM AM AM AM AM AM AM AM AM AM AM AM

AM

Origin

30 00 30 00 30 00 30 00 30 00 30 00 30 00 30

: : : : : : :

2: 3 3: 4 4: 5 5: 6 6: 7 7: 8 8: 9 9: Total Falun 1 1 1 3 Leksand 1 1 1 1 4 Ludvika 0 Malung-Sälen 1 3 5 10 6 2 1 1 29 Mora 2 14 32 7 1 1 57 Orsa 1 1 3 2 1 8 Rättvik 1 1 2 4 Vansbro 0 Älvdalen 1 2 4 1 1 9 Bollnäs 1 1 Fagersta 0 Hofors 0 Norge 0 Total 0 2 6 19 44 19 13 7 3 0 1 0 1 0 0 115 (The unit is vehicle/30 minutes)

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Table 32 - Club bus-flow origin-destination matrix

AM AM AM AM AM AM AM AM AM AM AM AM AM

AM AM

Origin

30 00 30 00 30 00 30 00 30 00 30 00 30 00 30

: : : : : : :

Total 2: 3 3: 4 4: 5 5: 6 6: 7 7: 8 8: 9 9: Falun 0 Leksand 0 Ludvika 0 Malung-Sälen 14 31 19 10 9 2 3 1 89 Mora 1 10 28 6 1 46 Orsa 0 Rättvik 0 Vansbro 0 Älvdalen 1 4 9 2 16 Bollnäs 0 Fagersta 0 Hofors 0 Norge 0 Total 0 0 1 11 46 46 22 10 9 2 3 1 0 0 0 151 (The unit is vehicle/30 minutes)

Table 33 shows average speeds (km/hr) by mode, from each origin in 30 minutes time interval and also the average speed in free flow situations from those origins. Calculating the average speed from each origin, the distance between the main town of the municipality and Berga has been used as an estimation of the average distance between origin and destination. Since Malung-sälen municipality, where the start of the race is located, is a vast area and have several towns and villages very close to the start of the race in Berga, calculating the average speed for the whole municipality using the average distance from the main town of the municipality (Malung) will not be a good estimation for the average speed.

Origins like Ludvika, Hofors, Vansbro and Fagersta which are farther from the destination have the highest average speeds. The reason is related to less demand and higher speed on parts of the route before reaching areas with high demand and congestion. Älvdalen has the lowest average travel speed for all modes.

For most of the origins the difference between the average speed of car and bus is not considerable. Originating from Mora and Älvdalen from where club buses and Vasaloppets’ official buses took different routes toward the start of the race, the overall average speed of bus is higher than the average speed of club bus which took the most usual route to the start.

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In most cases the overall average speed is even higher than the average speed in free flow conditions. From Älvdalen all modes have overall average speed lower than the average speed in free flow conditions but still the difference is not too much.

Still there is not enough evidence to take conclusion about existence of serious congestions on some parts of the route, which most probably would be areas around the start in Berga.

Table 33 - Average speed from each origin

AM AM AM AM AM AM AM AM

AM

Mode

00

30 00 30 30 00 30 00 30

:

: :

Origin :

Overall Overall

4

average

2: 3 3: 4: 5 5: 6 6: Freeflow

Car 68 52 68 76 66 60 Falun Bus 85 49 49 86 67 Car 52 59 55 59 Leksand Bus 50 65 64 57 59 Ludvika Car 64 64 75 67 62 Malung-Sälen ------Car 51 67 59 59 63 67 61 57 Mora Bus 47 58 60 62 76 61 Club bus 73 51 54 56 59 Car 68 64 47 52 58 53 Orsa Bus 50 43 55 45 49 Car 65 61 63 59 Rättvik Bus 36 65 61 54 Vansbro Car 58 81 69 Car 38 57 36 55 59 56 50 56 Älvdalen Bus 47 49 60 52 Club bus 62 56 49 36 51 Car 47 70 58, 56 Bollnäs Bus 50 50 Fagersta Car 68 68 63 Hofors Car 70 70 Norge Car 44 44

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Chapter 5: Suggestions There are many tools available to help transportation agencies in development, implementation and after action of planned special events. Variety of ITS and traffic and demand management tools help planners in efficient event traffic planning in urban areas, while for rural events limited tools can be used by planners. Simulation and prediction tools allow planners to assess the effectiveness of different available tools beforehand. Availability of traffic data in urban areas facilitates the use of traffic modeling and simulation for event planning and decision making.

A traffic management plan which includes operational strategies is necessary for managing event generated and background traffic on the day of the event within the impacted area. A traffic management plan

Indicates how traffic, parking, and pedestrian operations will be managed on the day-of- event Coordinates and mitigates transportation impacts, Adapts to traffic demand scenarios, demand management plan, and contingencies.

Key components of traffic management plan development include:

Traffic flow route planning, Site access and parking planning, Pedestrian access planning, Traffic control planning, Travel demand management (TDM) and transit service planning, Incident management and traveler information planning.

An event traffic management plan could be considered successful if can satisfy requirements of all transportation users while personnel and equipment resources assigned to day-of-event operations is optimized. A good traffic management plan also provides flexibility and proactive strategies for responding to real-time conditions and events (Dunn & Walter, 2007).

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Table 34 presents components of traffic management plan.

Table 34 Traffic management plan component

Traffic Flow Route Planning Site Access and Parking Planning Pedestrian Access Planning

Freeway/arterial and local traffic Lot assignment Pedestrian control through flow routes Vehicle access and circulation routing and crossing Alternate routes Parking area design and operation Disabled accessibility Emergency access routes Parking occupancy monitoring Shuttle bus service, station Background traffic accommodation Parking regulations design, management, and Transit accommodation Traveler information cost

Travel Demand Management Incident Management Traffic Control Planning (TDM), Transit Planning and Traveler Information Freeway traffic control-traveler TDM Best driving/transit routes information and interchange Use of alternate travel modes from specific origins operation Shift in arrival and departure times Venue parking area locations Street traffic control-alternative lane Increase in vehicle occupancy and fees operations, route guidance, and Diversion of background traffic Recommended event ingress conditions monitoring around impacted area and egress routes Intersection traffic control-traffic Up-to-the-minute travel flow control and traffic signal Transit Services conditions information operations Public transit services expansion- Estimated travel time by additional vehicle hours and/or different travel modes route modifications Advisories and restrictions Express transit services-direct service from park and ride facility and event venue Charter service-direct (contract) service from outlying area Source: (Dunn & Walter, 2007)

What are benefits of managing travel for special events? Following are several important benefits of managing travel for planned special events: Reducing delay for motorists attending the planned special events through more active information dissemination, traffic management, and alternate mode use, Reducing delay for motorists not attending the special event through active promotion of alternate route or modes, Reducing overall traffic demand at or near the special event site through ctive promotion of alternate routes or modes or dissemination of information, resulting in the cancellation or delay of unnecessary trips,

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Improving safety through more active traffic management and reduced motorist frustration, Maintaining the attractiveness of the event to encourage people to return in the future.

At a time when public and private agencies recognize the importance of sustainable development, the environmental impacts of large scale events are commanding increasing attention. The core impacts common to most major events include emissions from traffic and transportation, energy consumption, volume of waste generated and GHG emission. The main goal in traffic field is that transport arrangements for events should be adapted to environmental requirements, as many journey as possible should be made by environmentally friendly modes.

There are successful models of event planning which demonstrated that major events could be environmentally friendly. Nordic Ski World Championship 1999 in Ramsua is a successful planning example which could reduce the car share in traveling to the competition area to 30% and maintained the health spa-air quality during the event (Transport and Exceptional Public Events, 2003). Environmental impacts of the 2006 Torino Winter Games, Melbourne Commonwealth Games and Soccer World cup in Germany was assessed and minimized as part of the organizing of the events. Environmental achievements of such events have resulted in changing of international expectations about delivery of major sporting events.

The 2008 Beijing Olympic Games, 2010 Vancouver Olympic and Paralympics and 2012 London Olympic Games began to include environmental management in their bids to host the games. Based on current trend it may be expected that over time public opinion of major events will change from commending major events for addressing environmental sustainability to criticizing them for inadequately addressing environment impacts.

As it can be seen environment issues are getting more and more attention in planning for large scale events. So it could be of high value to promote more environmentally friendly transport modes for Vasaloppet as well and provide incentives to promote people to switch from their cars to bus for transportation to the area and in the area and try to reduce the car share from 47% and 67% for trips in the area and to the area respectively.

There are several reasons why it is a good idea to promote bus and train for trips to the area and bus for trips in the area and to the start point. As mentioned before this will help to make the traffic generated by the event environmentally friendly and will reduce the damage to the environment. On the other hand by reducing number of car trips it would be possible to use the same route for bus and car from Mora and Älvdalen to Berga as before which is shorter in distance and time. The other benefit in reducing car traffic is the opportunity for later departure times than what it was in 2011 which Participants started to depart from 3:00 am from their accommodation to be sure that they would reach the start on time. Considering time needed for preparation

PARISA AHMADI | Chapter 5: Suggestions 72

2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] before leaving the accommodation this means very short time for relaxing and resting the night before the race.

One possible way to increase the bus share is to increase number of buses from major origins in the area and encourage participants to take bus instead of driving to the start. Participants were asked about options that will encourage them to change from car to bus. About 53% did not respond to this question. 30% resisted on driving to the start. About 17% mentioned that they will change to bus provided availability of bus service from the town they stayed in, lower ticket price and integration between race registrations and buying bus ticket.

Some participants mentioned that it is cheaper for them to drive car than traveling by bus since the only out of pocket cost for travelling by car is the fuel cost. Free parking near to the start of the race is the main reason for low cost for traveling by car. Parking fee in Berga will increase the out of pocket cost for car travelers and may decrease the percentage of car trips in the area. To shift participants from their car to bus it should be supported by appropriate enforcement strategies like providing limited number of parking places near to the start and then provide park-and-ride facilities. The availability and location of parking facilities is a significant factor in mode choice between car and bus. But these all need to be supported by good enforcement and providing participants with information.

One of the important and simplest elements of the traffic management plan for rural events is public information. Experience from other events shows the importance of availability of good and on time information on improvement of traffic situation on the day of the event. Some evidence from the questionnaire and also observation of driving behavior indicates that more effort is needed in field of dissemination of information. For example participants were asked about options that would encourage them to shift from car to bus; some of them commented that if they could stay in bus until the start of the race they would change from car to bus. They were not informed that buses were not allowed to leave the site before the start of the race providing the opportunity for participants to stay in bus until the race start. The other evidence is the driving behavior between Berga and Mora during the race which showed that majority of drivers did not know that the road was one directional toward Mora so they kept driving on right side which resulted in stop-and-go driving condition.

There are several ways to spread traffic information, like local newspaper, local radio and event’s website but this is not enough to be sure that most participants are informed about traffic regulations during the race. Regarding Vasaloppet, one good way to be sure that all participants will be informed about traffic rules on the day of the event is to include a printed version of the traffic information in registration package that participants should pick up at least the day before the race from Mora or Berga. To make the printed version available at major accommodation places like hotel and schools and also information offices in Mora and Berga is also recommended.

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

It would be very helpful if a map is attached to traffic information since most people do not know area well. Temporary traffic signs on roads during the event are essential and would help derivers to become aware of temporary traffic rules.

Some changes in traffic planning in 2011 compared to 2010 like different bus route from Mora to Berga in the morning, more parking places and better parking operation in Berga resulted in better traffic conditions and less congestion in 2011 compared to last year. The passing traffic to and from the ski sites in Dalarna were recommended to use alternative route instead of main routes between Mora and Sälen. Although changing bus route for a longer route made improvement in traffic but still there may be other solutions that may have same or better effects without need to change the bus route, since this longer route and travel time may result in less demand for bus in future. On the other hand more and free parking place near the start may result in demand growth for car trips in the future.

Efficient parking lot management and parking lot operational techniques would also be helpful in reducing congestions near to the start place. Several entrances to the main parking lot near to the start of the race was one of the improvements in field of parking management in 2011 which proved to be efficient in reducing congestion. But conflicts between pedestrians and car in parking area made parking process a little bit slower. A good solution for this could be to fill the parking lot from the end closest to the venue first, and then fill sequentially backward. Speed parking has also been proved to be one of the most efficient parking lot management and operational techniques. Speed parking is defined as organizing and parking two vehicles in tandem, one behind the other, in pre-striped rows, moving from left to right and then to left until the lot is filled in a sequential manner. Figure 56 illustrates the layout for Texas stadium speed parking (Brooks & Torrance, 2010).

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

Figure 56 Speed parking

Source: (Brooks & Torrance, 2010)

Along with attempts to make the bus a better option and encourage participants to shift from car to bus, some changes in traffic regulations would make the condition better for those who resist on driving to the start. One possible option is to consider the possibility of making the road one directional from Mora to Brga in the morning for several hours before the start of the race. There are several small residential areas across roads 70, 1024, 1025 and 71 between Mora and Berga but since these villages have small population and the day of the event is Sunday it may not cause problems for local residents. There is also alternative route for those who want to travel from north of the area where ski attraction are located toward the south.

All these strategies in demand and traffic management area may be helpful in reducing traffic congestion around the start and making the event more environmentally friendly by reducing the car share and providing safe and on time transportation for participants. But the best way to find out how effective is each solution is to simulate the area and try each of the suggested alternatives and combination of them. Simulation needs traffic data for calibration and validation process. 2011 was the first year in last 50 years that traffic data was collected by participants’ questionnaire, but still there is not enough data for simulation purpose.

Since the event has been growing during past years, a good event traffic planning is essential and a good and efficient planning needs data. Among data collections methods, floating car data along

PARISA AHMADI | Chapter 5: Suggestions 75

2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] with a simple questionnaire are recommended for future data collection work. Participants’ questionnaire can be also used but then the precision of data would be lower compared to FCD.

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

Chapter 6: Conclusion

In 2010 some participants of the main race in Vasaloppet winter week were late to the start because of traffic congestion and long queue mainly on the south access of Berga, where the race starts. Since there was no traffic data from previous years, it was decided to gather traffic data during Vasaloppet in 2011. Considering time period before the race and expenses of different data collection method, participants’ questionnaire was decided as the appropriate method for data collection.

Table 31 shows modal split for trips to the area and trips within the area. While car is the dominant mode for trips to the area, car and bus have approximately the same share for trips within the area on the day of the race. According to the results of data analysis, the main vehicle flow on the day of the race originated from Malung-sälen municipality, where the start of the race is, with about 72 percent of the total vehicular flow. From that about 44% originated from the north of the start and the remaining 36 percent from the south. 55% of participants who originated from Malung- sälen used car to reach the start and 27% used bus and club bus, the remaining walked to the start.

Table 35 Modal spit for trips to and within the area

Travel mode Travel mode to the area Travel mode within the area Car 64.20% 45% Bus 31.40% 47% Train 3.80% 0 Air plane 0.60% 0 Walking 0 8%

Considering the total vehicular traffic, 66 percent used the south access road to reach to Berga and 34 percent approached from north. This 34 percent includes also bus traffic from Mora that drove on different route from other vehicular traffic from Mora and reached the start from the north instead of south. About two third of the traffic used the south access and just one third used the north access.

The average speed from different origins on the day of the event does not differ considerably from the average speed in free flow situation. This supports police’s report that there was not serious traffic congestion on routes leading to the start in Berga.

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

Considering the average travel time from different origins, bus and car have approximately the same travel time. This make bus as good travel mode as car. The only disadvantage for bus travelers is the early departure time compared to car and limited departure time. When Mora is the origin for bus traveler, average travel time is about 37 minutes longer by bus compare to average travel time by car. The reason for this is the longer route that Vasaloppet buses took from Mora toward the start to avoid probable congestion on the main route.

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

Bibliography

Behrisch, M., Krajzewicz, D., & Wagner, P. (2008). Event traffic forecast for metropolitan areas based on microscopic simulation. Traffic, 1-10.

Brooks, R. D., & Torrance, K. (2010). Speed Parking for Large Scale Events. ITE 2010 Annual Meeting and Exhibit. Vacouver. Retrieved from http://trid.trb.org/view.aspx?id=1083535

Brämerson, H. (Trafikverket). (2011). Väg 1024/1025/1012, Fiskarheden – Evertsberg – Oxberg, ” Vasaloppsvägen”. Retrieved from http://www.trafikverket.se/PageFiles/48161/FSv1024_1025l%C3%A5g.pdf

Carson, J., & Bylsma, R. (2003). Transportation Planning and Management for Special Events; A Synthesis of Highway Practice. Environmental Protection. Washington, DC: Transportation Research Board.

Duclos, C. (2007). Environmental concerns and Environmental Management Systems in ski events. Management. Bournemouth University, United Kingdom. Retrieved from http://www.du.se/PageFiles/5054/Duclos.pdf

Dutschke, J. K., & Woolley, J. E. (2009). Simulation of rural travel times to quantify the impact of lower speed limits. Proceedings of the 2009 Australasian Road Safety Research , Policing and Education Conference 10 -13 November 2009 , Sydney , New South Wales (pp. 246-260). RTA New South Wales.

Goodwill, J. A., & Joslin, A. (2006). Special Event Transportation Service Planning and Operations Strategies for Transit. Security. Retrieved from http://www.dot.state.fl.us/research- center/Completed_Proj/Summary_PTO/FDOT_BD549_09_rpt.pdf

Guide to Traffic and Transport Management for Special Events. (2006). Management. Retrieved from http://www.rta.nsw.gov.au/trafficinformation/downloads/tmc_specialevents_dl1.html

Intelligent Transportation Systems for Planned Special Events: A Cross-Cutting Study. (2008). Administrator. Retrieved from http://www.ieee.org/documents/ieeecitationref.pdf

Latoski, S. P., Dunn Jr, W. M., Wagenblast, B., Randall, J., & Walker, M. D. (2003). Managing travel for planned special events. Organization (Vol. 1). Retrieved from http://tmcpfs.ops.fhwa.dot.gov/cfprojects/uploaded_files/Front Matter.doc

Laufer, J., & Fellows, N. (2010). Special Event Planning and Management : Micro Operations in The Macro Landscape . A Case Study of The Singapore Grand Prix. Proceeding of the 2010 IPENZ Transportation Group Conference. Christchurch.

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Morrissey, S., & Monica, S. (2010). Event Traffic Management in Santa Monica. City.

Olympic Delivery Authority. (2008). Olympic Delivery Authority Transport update Issue 1: Travel demand forecasting. Network. London.

Olympic Delivery Authority. (2009). Transport Plan for the London 2012 Olympic and Paralympic Games. Transport. The Stationery Office. Retrieved from http://books.google.com/books?hl=en&lr=&id=HearymHcdyQC&oi=fnd& pg=PT1&dq=Transport+plan+for+the+London+2012+Olympic+and+paraolympic+games &ots=3qwV_-9yUr&sig=TLGCTzvv8JNoa0cw6IjSDRMa3pc

Transport and Exceptional Public Events. (2003). Transport and Exceptional Public Events, Report on the. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:TRANSPORT+AND+EXCEPTI ONAL+PUBLIC+EVENTS#0

Walter Dunn. (2007). Managing Travel for Planned Special Events Handbook : Executive Summary.

Yu, C., Zhang, X., Wang, J., Huang, D., & Zhou, Y. (2008). Traffic Organization with Simulation for Vehicles on Beijing Olympic Venues. Journal of Transportation Systems Engineering and Information Technology, 8(6), 25-31. China Association for Science and Technology. doi:10.1016/S1570-6672(09)60002-7

PARISA AHMADI | Bibliography 80

2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

Appendices

PARISA AHMADI | Appendices 81

2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

Appendix I – Road profile between Berga and Mora

Figure I1 Road width and speed limit between Mångsbodarna and Skepphussjön

PARISA AHMADI | 82

2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

Figure I2 Road width and speed limit between Skepphussjön and Oxberg

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

Appendix II - Accommodation places in Malung-sälen The night before the race Count Percentage Berga by 48 5,9% Fiskarheden 12 1,5% Fulunäs 1 0,1% Garpsätra 1 0,1% gubbmyren 5 0,6% Hammarsbyn 1 0,1% Hemfjällsbyn 1 0,1% Hemfjällstangen 6 0,7% Horrmunden 3 0,4% hundfjället 19 2,3% Högfjället 14 1,7% Högstrand 1 0,1% Kläppen 39 4,8% Köarskär 2 0,2% Lima 82 10,0% 4 0,5% Lindvallen 54 6,6% Malung 38 4,7% 4 0,5% Malung-Sälen 98 12,0% Nornäs 5 0,6% Norra Brändan 1 0,1% Näsfjället 5 0,6% Risätra 3 0,4% Stöten 8 1,0% Sälen 157 19,2% Sälen by 7 0,9% Sälenfjället 9 1,1% sörnäs 1 0,1% Sörsjön 4 0,5% Tandådalen 56 6,9% Tandö 1 0,1% Torgås 19 2,3% Transtrand 101 12,4% Trångsund 1 0,1% Vallerås 1 0,1% Östra färdkällan 3 0,4% Östra Lillmon 1 0,1% Östra Långstrand 1 0,1% Grand Total 817 100,0%

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2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ]

Appendix III - Questionnaire

PARISA AHMADI | 85

Välkommen till Vasaloppets trafikundersökning! Denna undersökning är en del av ett samarbete mellan KTH och Vasaloppet. Vi skulle vilja ställa frågor om resan till Vasaloppet den 6:e mars. Det kommer att ta mindre än 15 minuter att fylla i enkäten och dina svar behandlas anonymt. Dina kommentarer hjälper oss att ta reda på efterfrågan och rörelsemönster i området. Med hjälp av dessa uppgifter skulle vi kunna planera för bättre trafiktillstånd som innebär mindre trängsel, kortare restid och säker resa. Tack för ditt deltagande!

Page 1 1. Kön :

nmlkj Man

nmlkj kvinna

2. Ålder:

nmlkj 19­25 nmlkj 36­45 nmlkj 56­75

nmlkj 26­35 nmlkj 46­55 nmlkj 76­

3. Civilstånd :

nmlkj Singel utan barn nmlkj Sambo utan barn nmlkj Gift utan barn

nmlkj Singel med barn nmlkj Sambo med barn nmlkj Gift med barn

4. Personlig inkomst per år före skatt :

nmlkj 0­150,000 nmlkj 301,000 ­ 400,000 nmlkj 501,000­

nmlkj 151,000 ­ 300,000 nmlkj 401,000 ­ 500,000

*5. Har du tillbringat natten före loppet i din hemstad? ( Med hemstad menar vi den stad du bor i )

nmlkj Ja

nmlkj Nej

Page 2 *6. På väg till startplatsen, bytte du transportsätt? (Vi är intresserade av ändringar du gjort att resa mellan två städer till exempel om du reste med bil till en stad och tog sedan buss till startplatsen )

nmlkj Ja

nmlkj Nej

Page 3 7. Var bor du ? 6

Annat

8. Om nånstansi Malung­Sälen kommun vilken ort ?

9. När började du resan mot startplatsen i Sälen?

TT MM FM/EM

Tid : : 6

10. När kom du fram till startplatsen ?

TT MM FM/EM

Tid: : 6

*11. Hur reste du till startplatsen ?

nmlkj Egen bil nmlkj Bil ( samåkning ) nmlkj Klubbbuss

nmlkj Hyrbil nmlkj Buss nmlkj Promenad

Page 4 12. Var du föraren ?

nmlkj Ja

nmlkj Nej

13. Hur många var ni i bilen ?

nmlkj 1 nmlkj 4 nmlkj mer

nmlkj 2 nmlkj 5

nmlkj 3 nmlkj 6

14. Hur många av er deltog i loppet ?

nmlkj 1 nmlkj 3 nmlkj 5

nmlkj 2 nmlkj 4 nmlkj 6

Om mer hur många?

Page 5 15. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockning på din rutt från där du började din resa tills du kom framtill startplatsen i Sälen. (Du kan välja flera alternativ) A B C D E F G H I J K L Inget Vet inte Platser jag upplevde gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc trängsel:

Page 6 16. För varje av dessa områden som du valde i frågan ovan , skriv hur länge du fastnade i trafikstockningar? (För de områden utan trängsel lämna fältet tomt)

I området A tog det (i minuter):

I området B tog det (i minuter):

I området C tog det (i minuter):

I området D tog det (i minuter):

I området E tog det (i minuter):

I området F tog det (i minuter):

I området G tog det (i minuter):

I området H tog det (i minuter):

I området I tog det (i minuter):

I området J tog det (i minuter):

I området K tog det (i minuter):

I området L tog det (i minuter):

17. Hade du svårt att hitta parkeringsplats i startplatsen i Sälen?

nmlkj Ja

nmlkj Nej

18. Om svaret på ovanstående fråga var "Ja" gärna meddela oss vilka av följande alternativ du var missnöjd med: (Du kan välja flera alternativ)

gfedc Det tog tid att hitta parkeringsplats

gfedc Långa gångavstånd till startpunkten

gfedc Jag var tvungen att parkera på parkeringförbjudet område

gfedc Väntade i kö för att komma in på parkeringen

gfedc Väntade i kö för att lämna parkeringen

Annat

*19. Hur gick du tillbaka till startplatsen i Sälen för att hämta bilen efter loppet?

nmlkj Buss

nmlkj Taxi

nmlkj Någon körde bilen till Mora och hämtade mig

Annat :

Page 7 20. När lämnade den person som körde bilen från Sälen till Mora, Sälen?

TT MM FM/EM

Tid: : 6

21. När han/hon kom framtill Mora ?

TT MM FM/EM

Tid: : 6

Page 8 På vägen från startplatsen i Sälen till Mora Om hon / han stannade på kontrollstationer var vänlig besvara följande frågor annars tryck Nästa för att fortsätta.

22. Vilken kontrollstationer hon/han stannade i ? (Du kan välja flera alternativ)

gfedc Kontrollen i Smågan

gfedc Kontrollen i Mångsbodarna

gfedc Kontrollen i Risberg

gfedc Kontrollen i Evertsberg

gfedc Kontrollen i Oxberg

gfedc Kontrollen i Hökberg

gfedc Kontrollen i Eldris

gfedc Ingen

23. Var hon/han parkerade bilen vid varje kontrollstation ? Parkeringsplats Kantstenparkering Smågan : nmlkj nmlkj

Mångsbodarna : nmlkj nmlkj

Risberg : nmlkj nmlkj

Evertsberg : nmlkj nmlkj

Oxberg : nmlkj nmlkj

Hökberg : nmlkj nmlkj

Eldris : nmlkj nmlkj

Page 9 24. På vägen till Mora från startplatsen om det fanns trängsel, vänligen välj trängsel platser. (du kan markera flera alternativ)

gfedc Start place in Sälen gfedc Risberg­Evertsberg gfedc Hökberg

gfedc Start place­Smågan gfedc Evertsberg gfedc Hökberg­Eldris

gfedc Smågan gfedc Evertsberg­Oxberg gfedc Eldris

gfedc Smågan­Mångsbodarna gfedc Evertsberg­Älvdalen gfedc Eldris­Mora

gfedc Mångsbodarna gfedc Älvdalen­Mora gfedc Mora

gfedc Mångsbodarna­Risberg gfedc Oxberg gfedc Ingen trängsel

gfedc Risberg gfedc Oxberg­Hökberg

25. När kom hon/han framtill Mora, var det svårt att hitta parkeringsplats i Mora?

nmlkj Ja

nmlkj Nej

26. Om svaret på ovanstående fråga var "Ja" gärna meddela oss vilka av följande alternativ du var missnöjd med: ( du kan välja flera alternativ )

gfedc Det tog tid att hitta parkeringsplats

gfedc Lång gångavstånd

gfedc Jag var tvungen att parkera på parkeringförbjudet område

gfedc Jag letade efter gratis parkering

Annat

Page 10 27. Vart tog du vägen från Mora efter loppet ? 6

Annat

28. Om till Malung­Sälen, vilken ort ?

29. När började du din resa från Mora till den plats du valde i föregående fråga?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid: / / : 6

Page 11 Titta på kartan nedan. Om du körde på märkta rutter till startplatsen i Sälen var vänlig besvara följande frågor, annars gå till den sista frågan på denna sida.

30. Vad skulle uppmuntra dig att byta från bil till buss ( bara på dessa linjer )?

gfedc Kort gångavstånd till busstationen

gfedc Fri parkering i närheten av bussterminalen i Mora, Älvdalen och Malung

gfedc Tätare busstrafik

gfedc Billigare biljettpris

gfedc Integrerad biljettförsäljning vid lopp registrering

gfedc Jag föredrar bil ändå

Annat

Page 12 31. Skulle du byta till samåkning om vi ger möjlighet på Vasaloppets hemsida?

nmlkj Ja

nmlkj Nej

32. Vilket alternativ skulle du föredra om bilen inte var ett alternativ från Mora och Älvdalen till Sälen ?

nmlkj Buss

nmlkj Jag föredrar att köra en längre väg än att byta till annat

nmlkj Kommer inte att delta i tävlingen

nmlkj Jag kör redan annan väg

Annat

*33. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 13 34. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockning på din rutt från där du började din resa tills du kom till startplatsen i Sälen. ( du kan välja flera alternativ ) A B C D E F G H I J K L Inget Vet inte Platser jag gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc upplevde trängsel:

Page 14 35. För varje av dessa områden du har valt i ovanstående fråga, skriv hur länge du fastnade i trafikstockningar ? ( För de områden utan trängsel lämna fältet tomt )

I området A tog det ( i minuter ):

I området B tog det ( i minuter ):

I området C tog det ( i minuter ):

I området D tog det ( i minuter ):

I området E tog det ( i minuter ):

I området F tog det ( i minuter ):

I området G tog det ( i minuter ):

I området H tog det ( i minuter ):

I området I tog det ( i minuter ):

I området J tog det ( i minuter ):

I området K tog det ( i minuter ):

I området L tog det ( i minuter ):

36. När lämnade du Mora efter loppet ?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid : / / : 6

37. När du lämnade Mora efter loppet, vad var din destination ? 6

Annat

38. Om någonstans i Malung­Sälen kommun vilken ort ?

39. Hur kom du dit ?

nmlkj Bil nmlkj Tåg

nmlkj Taxi nmlkj Promenad

nmlkj Buss

Annat

40. Var du nöjd med Vasaloppet bussen ?

nmlkj Ja

nmlkj Nej

nmlkj Jag använde en annan buss servive

Page 15 41. Om ditt svar på föregående fråga var Nej meddela gärna oss vilka av följande alternativ gjorde dig missnöjd : ( du kan välja flera alternativ )

gfedc Hög biljettpris

gfedc Lång gångavstånd till busstationen

gfedc Lång gångavstånd från busshållplats

gfedc Lång väntetid

gfedc Lång restid

gfedc Det var inte punklig

gfedc Trafikstockningar

gfedc Jag kom inte till destinationen i tid

gfedc Det var obekväm

Annat

*42. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 16 43. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockning på din rutt från där du började din resa tills du kom till start plats i Sälen . ( du kan välja flera alternativ ) A B C D E F G H I J K L Inget Vet inte Platser jag gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc upplevde trängsel:

Page 17 44. För varje av dessa områden du har valt i ovanstående fråga, skriv hur länge du fastnade i trafikstockningar? ( för de områden utan trängsel lämna fältet tomt )

I området A tog det ( i minuter ) :

I området B tog det ( i minuter ) :

I området C tog det ( i minuter ) :

I området D tog det ( i minuter ) :

I området E tog det ( i minuter ) :

I området F tog det ( i minuter ) :

I området G tog det ( i minuter ) :

I området H tog det ( i minuter ) :

I området I tog det ( i minuter ) :

I området J tog det ( i minuter ) :

I området K tog det ( i minuter ) :

I området L tog det ( i minuter ) :

45. När lämnade du Mora efter loppet?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid : / / : 6

46. När du lämnade Mora efter loppet, vad var din destination ? 6

Annat

47. Om någonstans i Malung­Sälen kommun vilken ort ?

48. Hur reste du dit ?

nmlkj Bil nmlkj Klubbbuss

nmlkj Taxi nmlkj Tåg

nmlkj Buss nmlkj Promenad

Annat

49. Vad är namnet på din klubb ?

50. Hur många bussar från klubben tog deltagarna till startplatsen och sedan tillbaka till hemmstaden ?

Page 18 *51. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 19 52. När lämnade du Mora efter loppet?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid : / / : 6

53. När du lämnade Mora efter loppet, vad var din destination ? 6

Annat

54. Om någonstans i Malung­Sälen kommun vilken ort ?

55. Hur reste du dit ?

nmlkj Bil nmlkj Klubbbuss

nmlkj Taxi nmlkj Tåg

nmlkj Buss nmlkj Promenad

Annat

*56. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 20 Att svara på följande frågor, överväga bara resor du gjorde mellan städer.

57. Var bor du ? 6

Annat :

58. Om någonstans i Malung­Sälen kommun vilken ort ?

59. Var har du byta transportsätt? ( platsen du väljer här kommer att bli kallad "transit punkt" i följande frågor ) 6

Annat :

60. När lämnade du hem till transit punkten ?

TT MM FM/EM

Tid : : 6

61. När kom du framtill transit punkten?

TT MM FM/EM

Tid : : 6

*62. Hur reste du dit ?

nmlkj Egen bil nmlkj Bil nmlkj Tåg ( samåkning ) nmlkj Hyrbil

nmlkj Buss

Page 21 63. Var du föraren ?

nmlkj Ja

nmlkj Nej

64. Hur många var ni i bilen ?

nmlkj 1 nmlkj 4 nmlkj mer

nmlkj 2 nmlkj 5

nmlkj 3 nmlkj 6

65. Hur många av er deltog i loppet ?

nmlkj 1 nmlkj 3 nmlkj 5

nmlkj 2 nmlkj 4 nmlkj 6

Om mer, hur många?

66. Hade du svårt att hitta parkeringsplats i transit punkten ?

nmlkj Ja

nmlkj Nej

67. Om svaret på ovanstående fråga var Ja meddela gärna oss vilka av följande alternativ du var missnöjd med: ( du kan välja mer än ett alternativ )

gfedc Det tog tid att hitta parkeringsplats

gfedc Långa gångavstånd till transitterminal

gfedc Jag var tvungen att parkera på parkeringförbjudet område

gfedc Jag letade efter gratis parkering

Annat :

68. Hur långt gick du från parkering till transitering terminalen?

nmlkj mindre än 500 meter

nmlkj 500 meter till 1 km

nmlkj fler än 1 kilometer

Page 22 69. Hur reste du från transit punkten till stratplatsen i Sälen ?

nmlkj Buss

nmlkj Klubbbuss

nmlkj Taxi

Annat :

70. När lämnade du transit punkten ?

TT MM FM/EM

Tid : : 6

71. När kom du framtill startenplatsen i Sälen?

TT MM FM/EM

Tid : : 6

Page 23 72. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockning på din rutt från där du började din resa tills du kom till startplatsen i Sälen. ( du kan välja flera alternativd ) A B C D E F G H I J K L Inget Vet inte Platser jag gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc upplevde trängsel :

Page 24 73. För varje av dessa områden du markerat i ovanstående fråga, skriv gärna hur lång tid tog det ? ( för de områden utan trängsel lämna fältet tomt )

I området A det tog ( i minuter ):

I området B det tog ( i minuter ):

I området C det tog ( i minuter ):

I området D det tog ( i minuter ):

I området E det tog ( i minuter ):

I området F det tog ( i minuter ):

I området G det tog ( i minuter ):

I området H det tog ( i minuter ):

I området I det tog ( i minuter ):

I området J det tog ( i minuter ):

I området K det tog ( i minuter ):

I området L det tog ( i minuter ):

74. När lämnade du Mora efter loppet ?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid : / / : 6

75. När du lämnade Mora efter loppet, vad var din destination ? 6

Annat :

76. Om någonstans i Malung­Sälen kommun vilken ort ?

77. Hur kom du dit från Mora ?

nmlkj Bil nmlkj Tåg

nmlkj Buss nmlkj Promenad

Annat :

78. Var du nöjd med Vasaloppet bussen ?

nmlkj ja

nmlkj Nej

nmlkj Jag användade en annan buss service

Page 25 79. Om ditt svar var Nej på ovanstående fråga gärna meddela oss vilka av följande alternativ gjorde dig missnöjd : ( du kan välja flera alternativ )

gfedc Hög biljettpris

gfedc Långa gångavstånd till busstationen

gfedc Lång promenad från busstationen

gfedc Lång väntetid

gfedc Lång restid

gfedc Det var inte punktlig

gfedc Trafikstockningar

gfedc Jag kom inte fram till destinationen i tid

gfedc Det var obekväm

Annat

*80. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 26 81. När lämnade du transitpunkten ?

TT MM FM/EM

Tid : : 6

82. När kom du framtill startplatsen i Sälen?

TT MM FM/EM

Tid : : 6

83. Hur reste du från transit punkten till startplatsen in Sälen ?

nmlkj Samåkning

nmlkj Taxi

nmlkj Buss

Annat :

Page 27 84. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockning på din rutt från där du började din resa tills du kom till startplatsen i Sälen. ( du kan välja flera alternativ ) A B C D E F G H I J K L Inget Vet inte Platser jag gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc upplevde trängsel :

Page 28 85. För varje av dessa områden du markerat i ovanstående fråga, skriv gärna hur länge du fastnade i trafikstockningar? ( för de områden utan trängsel lämna fältet tomt )

I området A tog det( i minuter ) :

I området B tog det( i minuter ) :

I området C tog det( i minuter ) :

I området D tog det( i minuter ) :

I området E tog det( i minuter ) :

I området F tog det( i minuter ) :

I området G tog det( i minuter ) :

I området H tog det( i minuter ) :

I området I tog det( i minuter ) :

I området J tog det( i minuter ) :

I området K tog det( i minuter ) :

I området L tog det( i minuter ) :

86. När lämnade du Mora efter loppet ?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid : / / : 6

87. När du lämnade Mora efter loppet, vad var din destination? 6

Annat :

88. Om någonstans i Malung­Sälen kommun vilken ort ?

89. Hur kom du dit ?

nmlkj Bil nmlkj Tåg

nmlkj Taxi nmlkj Promenad

nmlkj Buss

Annat :

90. Var du nöjd med Vasaloppet bussen ?

nmlkj Ja

nmlkj Nej

nmlkj Jag användade en annan buss service

Page 29 91. Om ditt svar på föregående fråga var Nej meddela gärna oss vilka av följande alternativ gjorde dig missnöjd: ( du kan välja flera alternativ )

gfedc Hög biljettpris

gfedc Long walking distance to bus station

gfedc Långa gångavstånd till busstationen

gfedc Lång väntetid

gfedc Lång restid

gfedc Det var inte punktlig

gfedc Trafikstockningar

gfedc Jag kom inte fram till destinationen i tid

gfedc Det var obekväm

Annat

*92. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 30 93. Åkte du buss tillbaka till startplatsen i Sälen, var du nöjd med bussen ?

nmlkj Ja

nmlkj Nej

94. Om ditt svar på föregående fråga var Nej meddela gärna oss skälet: (du kan välja flera alternativ )

gfedc Långa gångavstånd till busstationen

gfedc Lång promenad från busstationen

gfedc Lång väntetid

gfedc Lång restid

gfedc Trafikstockningar

gfedc Det var obekväm

Annat

95. När du gick till din bil efter loppet, vad var nästa destination? 6

Annat :

96. Om någonstans i Malung­Sälen kommun vilken ort ?

97. När började du din resa från Sälen mot ditt mål?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid: / / : 6

Page 31 Titta på kartan nedan. Om du körde på märkta rutter till startplatsen i Sälen var vänlig besvara följande frågor, annars gå till den sista frågan på denna sida.

98. Vad skulle uppmuntra dig att byta från bil till buss ( bara på dessa linjer )?

gfedc Kort gångavstånd till busstationen

gfedc Infartparkering i närheten av bussterminalen i Mora, Älvdalen och Malung

gfedc Tätare busstrafik

gfedc Billigare biljettpris

gfedc Integrerad biljettförsäljning vid lopp registrering

gfedc Jag föredrar bil ändå

Annat :

Page 32 99. Skulle du byta till samåkning om vi ger möjlighet på Vasaloppets hemsida ?

nmlkj Ja

nmlkj Nej

100. Vilket alternativ skulle du föredra om bilen inte var ett alternativ från Mora och Älvdalen till Sälen ?

nmlkj Buss

nmlkj Jag föredrar att köra en längre väg än att byta till annat

nmlkj Kommer inte att delta i tävlingen

nmlkj Jag kör redan annan väg

Annat

*101. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 33 102. Var bor du ? 6

103. Var har du tillbringat natten före loppet? 6

Annat :

104. Om någonstans i Malung­Sälen kommun vilken ort ?

105. När kom du fram dit ?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid : / / : 6

106. Hur reste du dit ?

nmlkj Egenbil nmlkj Buss nmlkj Flyg

nmlkj Samåkning nmlkj klubbbuss

nmlkj Hyrbil nmlkj Tåg

Annat :

107. Hur länge stannade du där ?

nmlkj 1 natt nmlkj 4 nätter nmlkj 1 vecka

nmlkj 2 nätter nmlkj 5 nätter nmlkj Mer

nmlkj 3 nätter nmlkj 6 nätter

*108. På väg från ditt boende till startplatsen i Sälen fick du byta transportsätt ?

nmlkj Ja

nmlkj Nej

Page 34 109. Var bytte du transportsätt ? ( den plats som du väljer här kommer att kallas "transitpunkt" i följande frågor) 6

Annat :

110. När lämnade du din boende mot transitpunkten ?

TT MM FM/EM

Tid : : 6

111. När kom du framtill transitpunkten ?

TT MM FM/EM

Tid : : 6

*112. Hur reste du dit ?

nmlkj Egenbil nmlkj Samåkning nmlkj Tåg

nmlkj Hyrbil nmlkj Buss

Page 35 113. Var du föraren ?

nmlkj ja

nmlkj Nej

114. Hur många var ni i bilen ?

nmlkj 1 nmlkj 4 nmlkj mer

nmlkj 2 nmlkj 5

nmlkj 3 nmlkj 6

115. Hur många av er deltog i loppet ?

nmlkj 1 nmlkj 3 nmlkj 5

nmlkj 2 nmlkj 4 nmlkj 6

Om mer, hur många?

116. Hade du svårt att hitta parkeringsplats i transitpunkten ?

nmlkj Ja

nmlkj Nej

117. Om ditt svar på ovanstående fråga var "Ja" meddela gärna oss vilka av följande alternativ du var missnöjd med: ( du kan välja mer än ett alternativ )

gfedc Det tog tid att hitta parkeringsplats

gfedc Långa gångavstånd till transitterminal

gfedc Jag var tvungen att parkera på parkeringförbjudet område

gfedc Jag letade efter gratis parkering

Annat :

118. Hur långt gick du från parkeringen till transitterminalen ?

nmlkj mindre än 500 meter

nmlkj 500 meter till 1 km

nmlkj fler än 1 kilometer

119. Hur reste du från transitpunkten till startpltsen i Sälen ?

nmlkj Buss

nmlkj Taxi

Annat :

Page 36 120. När lämnade du transitpunkten mot startplatsen i Sälen?

TT MM FM/EM

Tid : : 6

121. När kom du framtill startplatssn i Sälen?

TT MM FM/EM

Tid : : 6

122. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockning på din rutt från ditt boende till startplatsen i Sälen. ( du kan välja flera alternativ ) A B C D E F G H I J K L Inget Vet inte Platser jag upplevde gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc trängsel:

Page 37 123. För varje av dessa områden du har valt i frågan ovan, skriv gärna hur länge du fastnade i trafikstockningar ? ( för de områden utan trängsel lämna fältet tomt )

I området A tog det (i minuter):

I området B tog det (i minuter):

I området C tog det (i minuter):

I området D tog det (i minuter):

I området E tog det (i minuter):

I området F tog det (i minuter):

I området G tog det (i minuter):

I området H tog det (i minuter):

I området I tog det (i minuter):

I området J tog det (i minuter):

I området K tog det (i minuter):

I området L tog det (i minuter):

124. När lämnade du Mora efter loppet ?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid : / / : 6

125. När du lämnade Mora efter loppet vad var din destination? 6

Annat :

126. Om någonstans i Dalarns län vilken kommun ?

127. Hur kom du dit ?

nmlkj Bil nmlkj Tåg

nmlkj Buss nmlkj Promenad

Annat :

*128. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 38 129. När lämnade du transitpunkten ?

TT MM FM/EM

Tid : : 6

130. När kom du framtill startplatsen i Sälen ?

TT MM FM/EM

Tid : : 6

131. Hur reste du till startplatsen i Sälen ?

nmlkj Samåkninh

nmlkj Hyrbil

nmlkj Taxi

nmlkj Buss

Annat :

Page 39 132. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockning på din rutt från ditt boende till startplatsen i Sälen. ( du kan välja flera alternativ ) A B C D E F G H I J K L Inget Vet inte Platser jag upplevde gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc trängsel:

Page 40 133. För varje av dessa områden du har valt i frågan ovan, skriv gärna hur länge du fastnade i trafikstockningar ? ( för de områden utan trängsel lämna fältet tomt )

I området A tog det (i minuter):

I området B tog det (i minuter):

I området C tog det (i minuter):

I området D tog det (i minuter):

I området E tog det (i minuter):

I området F tog det (i minuter):

I området G tog det (i minuter):

I området H tog det (i minuter):

I området I tog det (i minuter):

I området J tog det (i minuter):

I området K tog det (i minuter):

I området L tog det (i minuter):

134. När lämnade du Mora efter loppet ?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid : / / : 6

135. När du lämnade Mora efter loppet, vad var din destination ? ( om någonstans i Dalarnas län välj vilken kommun ) 6

Annat :

136. Om någonstans i Dalarnas län vilken kommun ?

137. Hur reste du dit ?

nmlkj Bil nmlkj Train

nmlkj Taxi nmlkj Promenad

nmlkj Buss

Annat :

138. Var du nöjd med Vasaloppet bussen ?

nmlkj Ja

nmlkj Nej

nmlkj Jag användade en annan buss service

Page 41 139. Om ditt svar var Nej för att tidigare fråga vänligen meddela oss vilka av följande alternativ gjorde dig missnöjd : ( du kan välja flera alternativ )

gfedc Hög biljettpris

gfedc Långa gångavstånd till busstationen

gfedc Lång promenad från busstationen

gfedc Lång väntetid

gfedc Lång restid

gfedc Det var inte punktlig

gfedc Trafikstockningar

gfedc Jag har inte nått destinationen i tid

gfedc Det var obekväm

Annat

*140. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 42 141. När började du din resa från ditt boende mot startplatsen i Sälen ?

TT MM FM/EM

Tid : : 6

142. När kom du framtill startplatsen i Sälen ?

TT MM FM/EM

Tid : 6

*143. Hur reste du till startplatsen i Sälen ?

nmlkj Egenbil nmlkj Buss

nmlkj Hyrbil nmlkj Klubbuss

nmlkj Samåkning nmlkj Promenad

Page 43 144. Var du föraren ?

nmlkj Ja

nmlkj Nej

145. Hur många var ni i bilen ?

nmlkj 1 nmlkj 4 nmlkj mer

nmlkj 2 nmlkj 5

nmlkj 3 nmlkj 6

146. Hur många av er deltog i loppet ?

nmlkj 1 nmlkj 3 nmlkj 5

nmlkj 2 nmlkj 4 nmlkj 6

Om mer, hur många?

Page 44 147. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockningar på vägen från ditt boende till startplatsen i Sälen. ( du kan välja flera alternativ ) A B C D E F G H I J K L Inget Vet inte Platser jag upplevde gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc trängsel:

Page 45 148. För varje av dessa områden du har valt i frågan ovan, skriv gärna hur länge du fastnade i trafikstockningar ? ( för de områden utan trängsel lämna fältet tomt )

I området A tog det (i minuter):

I området B tog det (i minuter):

I området C tog det (i minuter):

I området D tog det (i minuter):

I området E tog det (i minuter):

I området F tog det (i minuter):

I området G tog det (i minuter):

I området H tog det (i minuter):

I området I tog det (i minuter):

I området J tog det (i minuter):

I området K tog det (i minuter):

I området L tog det (i minuter):

149. Hade du svårt att hitta parkeringsplats i startplatsen i Sälen ?

nmlkj Ja

nmlkj Nej

150. Om ditt svar på föregående fråga var "Ja" meddela gärna oss vilka av följande alternativ du var missnöjd med: ( du kan välja mer än ett alternativ )

gfedc Det tog tid att hitta parkeringsplats

gfedc Långa gångavstånd till startpunkten

gfedc Jag var tvungen att parkera på parkeringförbjudet område

gfedc Väntade i kö för att komma in på parkeringen

gfedc Väntade i kö för att lämna parkeringen

Annat

*151. Efter loppet, hur gick du tillbaka från Mora till startplatsen i Sälen för att hämta din bil ?

nmlkj Buss

nmlkj Taxi

nmlkj Någon körde bilen till Mora och hämtade mig

Page 46 152. När den person som körde bilen från Sälen till Mora lämnade Sälen ?

TT MM FM/EM

Tid : : 6

153. När hon/han komm framtill Mora ?

TT MM FM/EM

Tid : : 6

Page 47 På vägen från startplatsen till Mora Om hon/han stannade på kontrollstationer var vänlig besvara följande frågor annars tryck Nästa för att fortsätta.

154. Vilka kontrollstationer satannade hon/han i ? ( du kan välja flera alternativ )

gfedc Kontrollen i Smågan

gfedc Kontrollen i Mångsbodarna

gfedc Kontrollen i Risberg

gfedc Kontrollen i Evertsberg

gfedc Kontrollen i Oxberg

gfedc Kontrollen i Hökberg

gfedc Kontrollen i Eldri

gfedc Ingen

155. Var hon/han parkerade bilen vid varje kontrollstation ? Parkeringsplats Kantstenparkering Smågan : nmlkj nmlkj

Mångsbodarna : nmlkj nmlkj

Risberg : nmlkj nmlkj

Evertsberg : nmlkj nmlkj

Oxberg : nmlkj nmlkj

Hökberg : nmlkj nmlkj

Eldris : nmlkj nmlkj

Page 48 156. På vägen till Mora från startplatsen om det fanns trängsel, vänligen välj trängsel platser. ( du kan markera flera alternativ )

gfedc Startplatsen i Sälen gfedc Risberg­Evertsberg gfedc Hökberg

gfedc Startplatsen­Smågan gfedc Evertsberg gfedc Hökberg­Eldris

gfedc Smågan gfedc Evertsberg­Oxberg gfedc Eldris

gfedc Smågan­Mångsbodarna gfedc Evertsberg­Älvdalen gfedc Eldris­Mora

gfedc Mångsbodarna gfedc Älvdalen­Mora gfedc Mora

gfedc Mångsbodarna­Risberg gfedc Oxberg gfedc Ingen trängsel

gfedc Risberg gfedc Oxberg­Hökberg

157. Var det svårt att hitta parkeringsplats i Mora ?

nmlkj Ja

nmlkj Nej

Page 49 158. Om ditt svar på föregående fråga var "Ja" meddela gärna oss vilka av följande alternativ du var missnöjd med: ( du kan välja mer än ett alternativ )

gfedc Det tog tid att hitta parkeringsplats

gfedc Lång gångavstånd

gfedc Jag var tvungen att parkera på parkeringförbjudet område

gfedc Jag letade efter gratis parkering

gfedc Väntade i kö för att komma in på parkeringen

gfedc Väntade i kö för att lämna parkeringen

Annat

159. Efter loppet, vart gick du från Mora? 6

160. Om i Dalarnas län vänligen ange kommun :

161. När började du din resa dit ?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid / / : 6

Page 50 Titta på kartan nedan. Om du körde på märkta rutter till startplatsen i Sälen Var vänlig besvara följande frågor, annars gå till den sista frågan på denna sida.

162. Vad skulle uppmuntra dig att byta från bil till buss ( bara på dessa linjer )?

gfedc Kort gångavstånd till busstationen

gfedc Infartparkering i närheten av bussterminalen i Mora, Älvdalen och Malung

gfedc Tätare busstrafik

gfedc Billigare biljettpris

gfedc Integrerad biljettförsäljning vid lopp registrering

gfedc Jag föredrar bil ändå

Annat

Page 51 163. Skulle du byta till samåkning om vi ger möjlighet på Vasaloppets hemsida ?

nmlkj Ja

nmlkj Nej

164. Vilket alternativ skulle du föredra om bilen inte var ett alternativ från Mora och Älvdalen till Sälen ?

nmlkj Jag kör redan annan väg

nmlkj Bus

nmlkj Taxi

nmlkj Jag föredrar att köra en längre väg än att byta till annat

nmlkj Kommer inte att delta i tävlingen

Annat

*165. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 52 166. Åkte du buss tillbaka till startplatsen i Sälen, var du nöjd med bussen ?

nmlkj Ja

nmlkj Nej

167. Om ditt svar var Nej för att tidigare fråga vänligen meddela oss skälet : ( du kan välja flera alternativ

gfedc Långa gångavstånd till busstationen

gfedc Lång promenad från busstationen

gfedc Lång väntetid

gfedc Lång restid

gfedc Trafikstockningar

gfedc Det var obekväm

Annat

168. När lämnade du Mora mot startplatsen för att hämta din bil?

TT MM FM/EM

Tid : : 6

169. När kom du framtill startplatsen i Sälen för att hämta din bil ?

TT MM FM/EM

Tid : : 6

170. Vad var din nästa destination när du gick till bilen ? 6

171. När började du din resa från Sälen mot den destinationen ?

TT MM FM/EM

Datum/Tid : 6

Page 53 Titta på kartan nedan. Om du körde på märkta rutter till startplatsen i Sälen var vänlig besvara följande frågor, annars gå till den sista frågan på denna sida.

172. Vad skulle uppmuntra dig att byta från bil till buss ( bara på dessa linjer )?

gfedc Kort gångavstånd till busstationen

gfedc Infartparkering i närheten av bussterminalen i Mora, Älvdalen och Malung

gfedc Billigare biljettpris

gfedc Integrerad biljettförsäljning med lopp registrering

gfedc Jag föredrar bil ändå

Annat :

Page 54 173. Skulle du byta till samåkning om vi ger möjlighet på Vasaloppets hemsida ?

nmlkj Ja

nmlkj Nej

174. Vilket alternativ skulle du föredra om bilen inte var ett alternativ från Mora och Älvdalen till Sälen ?

nmlkj Jag kör redan annan väg

nmlkj Buss

nmlkj Jag föredrar att köra en längre väg än att byta till annat

nmlkj Kommer inte att delta i tävlingen

Annat

*175. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 55 176. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockning på din rutt från ditt boende till startplatsen i Sälen. ( du kan välja flera alternativ ) A B C D E F G H I J K L Inget Vet inte Platser jag upplevde gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc trängsel:

Page 56 177. För varje av dessa områden du har valt i ovanstående fråga, skriv hur länge du fastnade i trafikstockningar ? ( för de områden utan trängsel lämna fältet tomt )

I området A tog det (i minuter):

I området B tog det (i minuter):

I området C tog det (i minuter):

I området D tog det (i minuter):

I området E tog det (i minuter):

I området F tog det (i minuter):

I området G tog det (i minuter):

I området H tog det (i minuter):

I området I tog det (i minuter):

I området J tog det (i minuter):

I området K tog det (i minuter):

I området L tog det (i minuter):

178. Efter loppet, vart gick du från Mora ? 6

179. När började du din resa dit ?

TT MM FM/EM

Tid : 6

180. Hur reste du dit ?

nmlkj Egenbil nmlkj Buss nmlkj Tåg

nmlkj Samåkning nmlkj Klubbbuss nmlkj Flyg

Annat :

181. Var du nöjd med Vasaloppet bussen ?

nmlkj Ja

nmlkj Nej

nmlkj Jag använde en annan buss Servive

Page 57 182. Om ditt svar på föregående fråga var Nej meddela gärna oss vilka av följande alternativ gjorde dig missnöjd : ( du kan välja flera alternativ )

gfedc Hög biljettpris

gfedc Långa gångavstånd till busstationen

gfedc Lång promenad från busstationen

gfedc Lång väntetid

gfedc Lång restid

gfedc Det var inte punktlig

gfedc Trafikstockningar

gfedc Jag har inte nåt destinationen i tid

gfedc Det var obekväm

Annat

*183. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 58 184. Med tanke på den områdesindelning på kartan, välj områden du upplevt trafikstockning på din rutt från din boende till startplatsen i Sälen . ( du kan välja flera alternativ ) A B C D E F G H I J K L Inget Vet inte Platser jag upplevde gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc gfedc trängsel:

Page 59 185. För varje av dessa områden du har valt i frågan ovan, skriv gärna hur länge du fastnade i trafikstockningar ? ( för de områden utan trängsel lämna fältet tomt )

I området A tog det (i minuter):

I området B tog det (i minuter):

I området C tog det (i minuter):

I området D tog det (i minuter):

I området E tog det (i minuter):

I området F tog det (i minuter):

I området G tog det (i minuter):

I området H tog det (i minuter):

I området I tog det (i minuter):

I området J tog det (i minuter):

I området K tog det (i minuter):

I området L tog det (i minuter):

186. När lämnade du Mora efter loppet?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid : / / : 6

187. Efter loppet, vart gick du från Mora ? 6

188. Hur kom du dit ?

nmlkj Bil nmlkj Klubbuss

nmlkj Taxi nmlkj Tåg

nmlkj Buss nmlkj Promenad

Annat :

189. Vad är namnet på din klubb ?

190. Hur många bussar från klubben tog deltagarna till startplatsen och sedan tillbaka till hemstad ?

*191. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 60 192. När lämnade du Mora efter loppet?

DD MM ÅÅÅÅ TT MM FM/EM

Datum/Tid / / : 6

193. Efter loppet, vart gick du från Mora ? 6

194. Hur kom du dit ?

nmlkj Bil nmlkj Klubbbuss

nmlkj Taxi nmlkj Tåg

nmlkj Buss nmlkj Promenad

Annat :

*195. Nu är du färdig att svara på enkäten, välj Klar och tryck sedan på Nästa för att avsluta undersökningen.

nmlkj Klar

Page 61 Vi på Kungliga tekniska högskolan och Vasaloppet skulle vilja ta tillfället att tacka dig för din tid och medverkan. Vänligen kontakta oss om du har några frågor. Kontaktpersonen för denna förskning heter Parisa Ahmadi, [email protected]

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