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

Transcription

1 Analysis of Traffic Patterns for Large Scale Outdoor Events A Case Study of Vasaloppet Ski Event in Sweden By Parisa Ahmadi Examiner: Professor Haris Koutsopoulos Supervisor: Mahmood Rahmani Department of Transport and Logistic Royal Institute of Technology

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3 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 Dalarna 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 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. Malungsälen, Mora and Älvdalen are three municipalities in Dalarna County 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 PARISA AHMADI Abstract i

4 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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. PARISA AHMADI Acknowledgement ii

5 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Table of Contents Chapter 1: Introduction What Is a Planned Special Event? Event characteristics Event Impacts Mode choices Background and problem statement What is Vasaloppet? About the area Vasaloppet s official buses Parking in Berga Parking in Mora What is the problem Purpose of the Study Chapter 2: Literature review Definition of special event Modeling and Simulation Traffic Planning and Management Special events and ITS Environmental impacts of sport tourism activities Chapter 3: Methods Methodology Survey Survey Design Survey Constraints Survey Pilot Chapter 4: Results Socio-demographic characteristics of participants Traffic and travel pattern data Travel to the area Travel to the start point PARISA AHMADI Acknowledgement iii

6 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] a) Modal Split b) Departure from the Origin c) Arrival to the Destination During and after race trips a) During the race b) After race destinations Time Analysis Sälen Sälenfjällen Mora Flows and Average Speed Chapter 5: Suggestions Chapter 6: Conclusion Bibliography Appendices Appendix I Road profile between Berga and Mora Appendix II - Accommodation places in Malung-sälen Appendix III - Questionnaire PARISA AHMADI iv

7 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 Table 5 Gender Table 6 Age Distribution Table 7 Marital Status Table 8 Income Distribution Table 9 Participants from Counties in Sweden Table 10 Mode share Table 11 Origin of the trips on the day of the event Table 12 How long in the area Table 13 Mode share to the start Table 14 Departure from the origin Table 15 Arrival to the destination Table 16 Anderson-Darling values Table 17 During and after the race mode share for those who drove to Berga Table 18 Parking at control stations Table 19 Destination after the race Table 20 Mode share/sälen Table 21 Travel time summary statistics/sälen Table 22 Car and bus travel time distribution/sälen Table 23 - Travel time summary statistics from Lindvallen and Tandådalen Table 24 - Average travel speed from Lindvallen and Tandådalen Table 25 - Modal split from Mora Table 26 - Summary statistics for travel time and average speed/mora Table 27 - People-flow origin-destination matrix Table 28 - Vehicle occupancy rate Table 29 - Vehicle-flow origin-destination matrix Table 30 - Car-flow origin-destination matrix Table 31 - Bus-flow origin-destination matrix Table 32 - Club bus-flow origin-destination matrix Table 33 - Average speed from each origin Table 34 Traffic management plan component Table 35 Modal spit for trips to and within the area PARISA AHMADI List of Tables v

8 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 Figure 6 End place in Mora Figure 7 Main parts of the questionnaire Figure 8 Sweden's counties Figure 9 Percentage participants from different counties Figure 10 Origin of trips in Sweden Figure 11 Mode share Figure 12 Mode choice by gender, age and income Figure 13 Origin of the trips on the day of the event Figure 14 Mode share to the start Figure 15 Departure from origin Figure 16 Arrival to the destination Figure 17 Normality plot Figure 18 Departure time and arrival time distribution Figure 19 Vasaloppet road and race track Figure 20 Sälen-Mora during the race Figure 21 Destination after the race Figure 22 Malung-sälen Figure 23 Sälen area as considered by respondents Figure 24 - Car travel time distribution.48 Figure 25 - Bus travel time distribution Figure 26 - Departure time distribution for car and bus travelers from Sälen Figure 27 - Car travel time distribution/sälen 50 Figure 28 - Average car travel time by departure Figure 29 - Minimum travel times by car 50 Figure 30 - Maximum travel times by car Figure 31 - Bus travel time distribution from Sälen 51 Figure 32 - Average travel time by Bus from Sälen Figure 33 - Maximum and minimum travel times by bus from Sälen Figure 34 - Departure time distribution for walking Figure 35 - Lindvallen mode share 54 Figure 36 - Tandådalen mode share Figure 37 - Car travel time distribution Lindvallen.. 55 Figure 38 - Car travel time distribution Tandådalen..55 PARISA AHMADI List of Figures vi

9 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 Figure 42 - Overall travelers departure Figure 43 - Car travel time distribution from Lindvallen.. 58 Figure 44 - Average car travel time from Lindvallen Figure 45 - Bus travel time distribution/lindvallen Figure 46 - Car travel time distribution/ Tandådalen..59 Figure 47 - Average car travel time Figure 48 - Modal split from Mora Figure 49 Bus-club and bus travel time distribution/mora.61 Figure 50 - Car travel time distribution/mora Figure 51 - Car and bus travelers departure from Mora Figure 52 - Car travel time plot/mora.63 Figure 53 - Average travel time all modes/mora Figure 54 - Bus travel time plot/mora.. 63 Figure 55 - Club bus travel time plot/mora Figure 56 Speed parking PARISA AHMADI vii

10 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 multiuse 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 nonevent 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 PARISA AHMADI Chapter 1: Introduction 1

11 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 PARISA AHMADI Chapter 1: Introduction 2

12 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 PARISA AHMADI Chapter 1: Introduction 3

13 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Table 2 Event travel management Challenges and goals Challenges Need to manage intensive travel demand Need to mitigate potential capacity constraints Need to influence attractiveness of alternative travel choices Need to accommodate potential for heavy pedestrian flow and transit vehicles Goals Achieve predictability Ensure safety Maximize efficiency Minimize regional traffic effects from events 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 Travel Demand Road/Site Capacity Considerations Travel demand characteristics of discrete/recurring event at permanent venue, continuous event, or street use event 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 Source: (Dunn & Walter, 2007) Generation of trips from a multi-county region Event operation characteristics of discrete/recurring event at permanent venue, continuous event, or street use event 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

14 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. PARISA AHMADI Chapter 1: Introduction 5

15 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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) PARISA AHMADI Chapter 1: Introduction 6

16 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 1.5 Background and problem statement 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 world 1 and is included in Worldloppet Ski Federation. Worldloppet is an international sport federation of crosscountry 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 The race began in 1922 with 136 participants and over time has expanded to include 9 different types of race and over 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 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 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 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 area 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

17 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). PARISA AHMADI Chapter 1: Introduction 8

18 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Figure 4 Road width and speed limit between Berga and Mora Source: (Brämerson, 2011) 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. PARISA AHMADI Chapter 1: Introduction 9

19 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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. PARISA AHMADI Chapter 1: Introduction 10

20 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 PARISA AHMADI Chapter 1: Introduction 11

21 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 PARISA AHMADI Chapter 1: Introduction 12

22 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 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 origindestination 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 origindestination 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. PARISA AHMADI 13

23 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 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 Olympic Games 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). PARISA AHMADI Chapter 2: Literature review 14

24 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 origindestination 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 PARISA AHMADI Chapter 2: Literature review 15

25 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 carfollowing 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). PARISA AHMADI Chapter 2: Literature review 16

26 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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. PARISA AHMADI Chapter 2: Literature review 17

27 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 PARISA AHMADI Chapter 2: Literature review 18

28 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: Pollution: Erosion: 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 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 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 PARISA AHMADI Chapter 2: Literature review 19

29 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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). PARISA AHMADI Chapter 2: Literature review 20

30 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 responses were received out of The addresses was selected randomly from the organization s database. The questionnaire is in Swedish and was send to participants one week after the race 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 PARISA AHMADI Chapter 3: Methods 21

31 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. PARISA AHMADI Chapter 3: Methods 22

32 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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. Socio-demographic characteristic Travel to the area Travel to the start Trips during and after the race Gender Age Marital status Income Origin Destination How When How When left When arrived Congestion places Mode Destination When left the area Congestion places Figure 7 Main parts of the questionnaire PARISA AHMADI Chapter 3: Methods 23

33 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 PARISA AHMADI Chapter 3: Methods 24

34 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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. PARISA AHMADI Chapter 3: Methods 25

35 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 address of participants. Web-based surveys have some disadvantages. One is that some of the participants who receive the questionnaire may ignore s from unknown sources. On the other hand addresses were retrieved from race registration database and there is the possibility that some may give 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 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. PARISA AHMADI Chapter 3: Methods 26

36 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Chapter 4: Results 4-1 Socio-demographic characteristics of participants As explained before, the survey s first part consisted of several questions related to sociodemographic 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 % Male % 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 ,0% ,1% ,5% ,3% ,9% ,1% Total ,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. PARISA AHMADI Chapter 4: Results 27

37 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Table 7 Marital Status Marital Status Count Percentage Share Single no child % Single with child 63 4% Partner no child % Partner with child % Married no child 44 3% Married with child % Total % Table 8 Income Distribution Income Range Count Percentage Share 0-150, % 151, , % 301, , % 401, , % % Total % 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

38 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 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, Stockholm 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% Stockholms län % Uppsala län 68 5% Värmlands län 45 3% Västerbottens län 48 4% Västernorrlands län 30 2% Västmanlands län 29 2% Västra Götalands län % Örebro län 36 3% Östergötlands län 59 4% Total % PARISA AHMADI Chapter 4: Results 29

39 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 PARISA AHMADI Chapter 4: Results 30

40 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 and 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 Travel Mode Count Percentage share Own car ,1% Car sharing ,6% Rental car 45 3,5% Bus ,5% Club bus ,9% Train 50 3,8% Air plane 8 0,6% Grand Total ,0% 3.8% 0.6% 16.9% 39.1% 14.5% 21.6% 3.5% Figure 11 Mode share Own car Car sharing Rental car Bus Club bus Train Air plane PARISA AHMADI Chapter 4: Results 31

41 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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% % 27% 6% 15% % 27% 9% 12% % 23% 14% 16% % 17% 19% 19% % 14% 21% 23% % 24% 9% 14% % 22.20% 13.80% 18.30% % 23% 15% 16% % 17.60% 16.10% 16.80% % 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 Malungsälen 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. PARISA AHMADI Chapter 4: Results 32

42 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Municipality (kommun) Table 11 Origin of the trips on the day of the event Count (sample) Percentage Average distance from Berga (km) Falun 10 0,7% 171 Leksand 10 0,7% 142 Ludvika 3 0,2% 191 Malung-Sälen ,4% Less than 1 km to 58 km Mora ,3% 85 Orsa 30 2,2% 95 Rättvik 14 1,0% 122 Smedjebacken 1 0,1% 207 Vansbro 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 % Figure 13 Origin of the trips on the day of the event PARISA AHMADI Chapter 4: Results 33

43 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 ,5% 2 nights ,1% 3 nights ,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 ,0% 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. PARISA AHMADI Chapter 4: Results 34

44 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Table 13 Mode share to the start Travel mode Count Percentage Own car 322 share 25% Car sharing % Rental car 36 3% Bus % Club bus % Walking 104 8% Total % 30% 8% 25% 3% 17% Own car Car sharing Rental car Bus Club bus Walking 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. PARISA AHMADI Chapter 4: Results 35

45 Percentage frequency 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Table 14 Departure from the origin 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 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, ,8% 3, ,8% 3, ,9% 4, ,4% 4, ,8% 4, ,9% 4, ,7% 5, ,8% 5, ,1% 5, ,5% 5, ,1% 6, ,8% 6, ,2% 6, ,6% 6, ,8% 7, ,7% 7, ,8% 7, ,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% Departure time Figure 15 Departure from origin PARISA AHMADI Chapter 4: Results 36

46 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, ,3% 4, ,6% 5, ,2% 5, ,2% 5, ,3% 5, ,9% 6, ,1% 6, ,2% 6, ,7% 6, ,3% 7, ,1% 7, ,8% 7, ,9% 7, ,4% 8,00 5 0,4% ,0% PARISA AHMADI Chapter 4: Results 37

47 Percentage frequency 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 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 H 0 is that the arrival times distribution follows a normal distribution. Following is the null and the alternative hypothesizes: H 0 : The arrival times distribution is a normal distribution H 1 : 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 P % Critical Value % Critical Value PARISA AHMADI Chapter 4: Results 38

48 Percentage frequency 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 3 2 y = x R² = Z 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% 14.0% 12.0% Departure Arrival 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Time interval Figure 18 Departure time and arrival time distribution PARISA AHMADI Chapter 4: Results 39

49 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 % Taxi 2 0.4% Someone drove the car to Mora % Total % 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-andgo 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 PARISA AHMADI Chapter 4: Results 40

50 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 PARISA AHMADI Chapter 4: Results 41

51 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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. PARISA AHMADI Chapter 4: Results 42

52 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Table 19 Destination after the race Response Count Percentage share Blekinge län 4 0.4% Dalarnas län % Gävleborgs län % Gotlands län 2 0.2% Hallands län % Jämtlands län % Jönköpings län % Kalmar län 6 0.6% Kronobergs län % Norrbottens län % Örebro län % Östergötlands län % Skåne län % Södermanlands län % Stockholms län % Uppsala län % Värmlands län % Västerbottens län % Västernorrlands län % Västmanlands län % Västra Götalands län % Total % 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Figure 21 Destination after the race PARISA AHMADI Chapter 4: Results 43

53 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 km 2 (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. Transtrand, 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. PARISA AHMADI Chapter 4: Results 44

54 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Figure 22 Malung-sälen PARISA AHMADI Chapter 4: Results 45

55 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 km 2 and the population is 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 % 2 Statistika centralbyrån Tätorternas landareal, folkmängd och invånartäthet 2005 och 2010 PARISA AHMADI Chapter 4: Results 46

56 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Table 21 Travel time summary statistics/sälen Car Bus Mean Median Mode Standard Deviation Sample Variance Minimum 5 5 Maximum 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 % % % % % % % % % % % Total % % PARISA AHMADI Chapter 4: Results 47

57 Percentage travlers Percentage frequency 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 60% 50% 51% 60% 50% 53% 40% 40% 30% 20% 21% 19% 30% 20% 29% 10% 7% 2% 10% 3% 6% 6% 3% 0% % 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. PARISA AHMADI Chapter 4: Results 48

58 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 35% 30% 25% Car Bus Overall 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. PARISA AHMADI Chapter 4: Results 49

59 Travel time (min) Travel time (min) 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 Figure 29 - Minimum travel times by car Figure 30 - Maximum travel times by car PARISA AHMADI Chapter 4: Results 50

60 Travel time (min) 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 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. PARISA AHMADI Chapter 4: Results 51

61 Travel time (minutes) 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] Max Travel Times Min Travel Times 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). PARISA AHMADI Chapter 4: Results 52

62 Percentage frequency 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Departure time Figure 34 - Departure time distribution for walking 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 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. PARISA AHMADI Chapter 4: Results 53

63 2011 [ANALYSIS OF TRAFFIC PATTERNS FOR LARGE SCALE OUTDOOR EVENTS ] 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 Median Mode Standard Deviation Sample Variance Minimum Maximum 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