Reliability of Stockholm Subway
ANDREY EDEMSKIY
Degree Project in Traffic and Transport Planning, Second Level 30.0 credits Master’s program Transport Systems Royal Institute of Technology, 2010 Supervisor Haris N. Koutsopoulos Examiner Albania Nissan
TEC-MT 10-009
Kungliga tekniska högskolan Skolan för Arkitektur och samhällsbyggnad
KTH ABE 100 44 Stockholm
URL: www.kth.se/abe
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Abstract
Economic growth causes the rapid process of urbanization which results in the considerable demand for transportation. Public transit is known to be one of the most effective and sustainable solutions to satisfy this demand. Among all the transportation modes subway is the most popular one having high capacity and being independent of road traffic. Expanding the subway capacity is usually expensive and the operator prefers to utilize the existing capacity more efficiently. Intensive use of the capacity may cause problems of service reliability which brings about less attraction of customers. That is why operators maximizing the capacity should guarantee the service reliability. Stockholm is a growing city and its subway also experiences difficulties in providing reliable operation. AB Storstockholm Lokaltrafik (SL), the owner of the subway network, evaluates the reliability with help of manual surveys that are costly and not comprehensive. Although SL has the system that automatically collects data on subway operations, the data are not widely applied at present. This research aims to introduce possible measures of reliability through statistical analysis of the dataset and the timetable. It includes evaluation of on-time performance; waiting and travel time; headway adherence; distribution of dwell time, delays and headways. In the case study the thesis examines the reliability of Green line in March, 2010. The results demonstrate the practical applicability of the proposed analysis which helps to detect the factors lowering reliability of the service.
Key words: reliability of subway, punctuality, regularity
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Acknowledgments
I am heartily thankful to my supervisor, Haris Koutsopoulos, whose guidance and support enabled me to develop the project. This work would not also have been possible without assistance and encouragement of Karl Kottenhoff of Kungliga Tekniska Högskolan. Anders Börjeson and Kée Tengblad of AB Storstockholms Lokaltrafik were very helpful in providing the information and the access to the database RUST. Special thanks to Anders Ulmestig for the excursions to traffic control center and his comprehensive explaining the operation of the system. Lastly, I would like to thank my friends, in particular Guineng Chen and Beakal Tadesse Alemu, for their considerable support and assistance.
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Contents
Chapter 1: Introduction ...... 9 1.1 Background and Motivation ...... 9 1.2 Problem Description ...... 11 1.3 Research Objectives...... 16 1.4 Thesis Content and Organization ...... 16 Chapter 2: Literature review ...... 17 Chapter 3: Methodology of data analysis ...... 21 3.1 Measures of punctuality ...... 21 3.1.1 On-time performance ...... 21 3.1.2 Deviation from scheduled departure ...... 21 3.1.3 Dwell times distribution ...... 22 3.1.4 Travel times ...... 22 3.1.5 Headway adherence ...... 23 3.2 Measures of regularity ...... 24 3.2.1 Headway distribution ...... 24 3.2.2 Waiting times ...... 25 3.3 Analysis ...... 26 3.4 Assumptions and limitations ...... 27 Chapter 4: Description of the Stockholm subway system ...... 29 4.1 Stockholm subway ...... 29 4.1.1 Stockholm ...... 29 4.1.2 History of Stockholm subway ...... 29 4.1.3 Stockholm subway nowadays ...... 30 4.2 Green Line ...... 32 4.2.1 Line description...... 32 4.2.2 Main terminals ...... 35 4.2.3 Peak hours ...... 38 4.2.4 Signaling system ...... 40 7
4.2.5 Rolling stock ...... 43 4.2.6 Information system ...... 44 4.2.7 Traffic control center ...... 46 4.2.8 Data collection ...... 46 4.3 RUST database ...... 48 4.3.1 Database inquiry...... 48 4.3.2 Data output ...... 49 Chapter 5: Study case: Green line ...... 51 5.1 Data ...... 51 5.2 Timetable analysis ...... 53 5.2.1 Headway distribution ...... 53 5.2.2 Travel times ...... 55 5.3 Train operation analysis ...... 57 5.3.1 On-time performance ...... 57 5.3.2 Deviation from scheduled departure ...... 58 5.3.3 Dwell times ...... 59 5.3.4 Travel times ...... 61 5.3.5 Headway adherence ...... 65 5.3.6 Headway distribution ...... 66 5.3.7 Waiting times ...... 71 5.4 Detailed analysis at stations ...... 73 5.4.1 T-centralen ...... 73 5.4.2 Slussen ...... 76 5.4.3 Skanstull ...... 78 Chapter 6: Conclusions ...... 81 6.1 Summary and conclusions ...... 81 6.2 Future research ...... 82 References ...... 85
Appendix ...... 87
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Chapter 1: Introduction
1.1 Background and Motivation Economic growth causes the rapid process of urbanization. The cities and their population constantly increase worldwide resulting in the considerable demand for transportation. Well-planned public transport system becomes the most efficient solution to satisfy the growing demand. It is a guarantee of sustainability and development for any modern city. Nowadays the effectively developing city is the one where everyday public transit is able to provide reliable, fast and comfortable commuting between different parts of the city.
One of the major problems that any city government faces is the increasing number of car owners. Car‟s high level of comfort and constantly lowering car prices make the car the most attractive transportation mode. To keep urban streets uncongested and manage the negative effects of private cars such as noise, pollution, and accidents, city has to provide a public transit service which could hold existing passengers loyal as well as attract the motorists. In order to reach the aim public transportation should be able to compete with private cars.
Another issue is that due to economical reasons public transit is not usually self- supporting. It often demands governmental subsidies and investments. Transit fare collection is the most popular way to partly compensate these subsidies. However, passengers are greatly sensitive to the ticket price. Thus the operator should provide the service the customers are willing to pay for.
According to many researches, for example Litman (2010), transit reliability is one of the most important characteristics of attractive public transportation. Besides, the advantage of the reliable transit service is that it attracts more passengers an as a result more fare money which, for example, can be used for public transport development.
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Transit service reliability is a wide term. According to TCQSM (Transit Capacity and Quality of Service Manual-2nd Edition, Transportation Research Board, Washington DC, 2003) reliability is “how often service is provided when promised”. It affects the waiting and travel time of passengers as well as influences the loading of rolling stocks. Unreliable service forces passengers to arrive to the stations or stops earlier and spend more time for the transportation. It also creates ground for uneven rolling stocks boarding decreasing the level of service and resulting in low passenger loyalty.
Reliability “can be defined as dependability in terms of time (waiting and riding), passenger load, vehicle quality, safety, amenities and information” (Ceder, 2007). It is also possible to specify the service reliability as “the invariability of service attributes that influence the decisions of travelers and transit providers” (Abkowitz et al., 1978).
“Transit related attributes that vary by time or space may be distributed. Therefore, the (statistical) characteristics of the distributions form the base for constructing measures of reliability” (Ceder, 2007). These characteristics are mean, variance, standard deviation and others. Analyzing the attributes with the characteristics it will be achievable to evaluate the service reliability of any public transit service during any specific time period as well as compare the results with other transit networks.
The transit attributes can be considered from points of view of passengers and operators. For example, schedule adherence, headway distribution, on-time arrival will be important to operators. While for passengers waiting, travel and dwelling times are more essential.
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1.2 Problem Description Subway network is one of the most efficient modes of mass transit systems in big cities. It has high capacity and it is independent of road traffic. Statistics shows that the amount of passengers in agglomerations constantly increases. To deal with the growing transportation demand transit operators may construct new infrastructure, which is usually extremely expensive, or enhance capacity of the current network by more effective operating. Intensive use of the capacity may cause problems of service reliability which brings about less attraction of customers. That is why operators maximizing the capacity should guarantee the service reliability.
Stockholm subway running by city-owned public transit company SL, AB Storstockholm Lokaltrafik, is not an exception. The number of passengers gradually but constantly grows. The tendency during last several years is presented on figure 1.1. The histogram shows the number of Stockholm subway passengers per one winter day (SL, 2009).
1200 1117 1117 1012 1016 1016 1073 1094 1000 800 Green line 600 Red line 400 Blue line
200 Total Passengers, thousand Passengers, 0 2003 2004 2005 2006 2007 2008 2009 Figure 1.1 Number of passengers in Stockholm subway
At peak hours subway experiences difficulties with increasing passenger flows on some segments of the network. To cope with the challenge the operator runs the service with the shortest possible headways. The minimal designed time difference between the pair of consecutive trains allowed by the system is 90 seconds. Theoretically it lets operating with up to 40 trains per hour. However in reality the
11 system operates 30 trains per hour at the most congested periods. To keep the system stable and prevent the possible postponements the trains should follow the timetable with high accuracy. SL considers the service punctual if a train arrives no more than 1 minute earlier or 3 minutes later. However, the delay of a train for three minutes during rush hours could cause a domino effect. This knock-on train delay provokes passenger overloading at platforms and on rolling stocks of the whole system. Under the conditions the timetable and service become unreliable: passengers‟ waiting and travel times increase, the level of service drops off and as a result – the line capacity declines. The mistakes of train drivers, maintenance staff and traffic controllers as well as unpredictable passengers‟ behavior also can cause unwarranted delays and disruption of the timetable.
Low level of service influences the loyalty of Stockholm inhabitants to choose the subway as a transportation mode. Unsatisfied passenger will hardly use the subway frequently and will try to change the mode if he/she has such a possibility. In order to watch over the customers loyalty SL carries out a survey every month on their satisfaction with the service. Satisfaction evaluation considers such parameters as regularity, punctuality, safety, cleanness of the rolling stocks and platforms, information, personal assistance and others. According to (SL, 2009) punctuality is the factor the most significantly influencing the level of passenger satisfaction.
Due to high variability of the results SL is used to combine the data for the period of six months and publishes the report on satisfaction with the service every half year. This data variability can be explained by season changes and casual circumstances such as heavy snowstorm, low temperature, and technical problems with the signal system and etc. Figures 1.2 and 1.3 demonstrate the change of commuter satisfaction with the overall subway service and with the provided punctuality during few past years (SL, 2009).
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85 80 75 70 65 Green line
Percent 60 Red line 55 Blue line
50
2004 2005 2006 2007 2008 2009
Spring Spring Spring Spring Spring Spring
2004 2005 2006 2007 2008 2009
Autumn Autumn Autumn Autumn Autumn Autumn
Figure 1.2 Level of overall passengers‟ satisfaction in Stockholm subway
80 75 70 65 Green line 60
Percent Red line 55 Blue line
50
2004 2005 2006 2007 2008 2009
Spring Spring Spring Spring Spring Spring
2004 2005 2006 2007 2008 2009
Autumn Autumn Autumn Autumn Autumn Autumn Figure 1.3 Level of passengers‟ satisfaction with punctuality in Stockholm subway
The SL report (SL, 2009) gives the statistics for subway punctuality during the same period presented on figure 1.4. It provides us with the information on the impact of a punctuality change into the level of satisfaction.
100
95 Green line 90
Red line Percent 85 Blue line
80 2003 2004 2005 2006 2007 2008 2009
Figure 1.4 Punctuality in Stockholm subway 13
The dip in punctuality level in 2006 and 2007 for the three lines corresponds to the dip in level of satisfaction for the same time period. It supports the idea that punctuality is important for customers.
One can notice the difference in punctuality of the lines and the level of satisfaction: the Blue line is the most punctual one nevertheless passengers of the line are the least satisfied with it. The difference can probably be explained with the different sensitivity of the passengers of the lines to punctuality. The Green line has more regular service than other lines; its headways are shorter therefore expected waiting time due to train delay will be on average shorter as well. Thus, the Green line commuters are supposed to be less sensitive to the train punctuality comparing to the Red and the Blue lines passengers. However, this is a suggestion which may more profoundly be studied in the future.
The aim of SL for the whole transit network including subway, busses and commuter trains, during the spring 2010 to have minimum 75% of passengers that are satisfied with the service and maximum 10% that are unsatisfied with it. Concerning passengers of Stockholm subway the results of the survey (SL, 2009) show that 78% passengers are satisfied with the service while the share of unsatisfied commuters reaches 8%. See the figure 1.5.
Figure 1.5 Level of passengers‟ satisfaction in subway (%), autumn 2009 14
The results of 2009 fit the SL aim of 2010. It means that passengers are satisfied enough according to the SL expectation. Nonetheless, it is necessary to wait for the results of the spring 2010 to define if it is a regular tendency or just a variability of the data.
Besides, SL uses an independent contractor to operate and maintain the subway system. According to the agreement between SL and the actual subway operator there are stipulated bonuses for the operator in case of a punctual train service. That is why the operator is interested in a reliable transit too. In November 2009 the Hong Kong MTR Corporation became the operator of Stockholm subway. It is a new player in the Swedish transportation market. They have definitely introduced new solutions in the subway operations which will certainly influence overall reliability of the system and the customers‟ satisfaction which are of interest to study.
SL has a source of data which can be widely used to examine the performance of the Stockholm subway. Data is electronically collected and stored in the database RUST. The database contains information on travel delays, travel and dwelling time. Applying computer analysis it is possible to investigate the changes of subway service performance on any date or during any time period. At the moment the company does not use the data widely and relies more on manually collected data of ÅF Group, AB. Next year there are plans in the company to begin more active RUST data using. To be confident in data reliability they carry out comparing it with manually collected data to reach as good concordance as possible.
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1.3 Research Objectives Concerning the problem description it is possible to formulate two main objectives of the thesis:
- To introduce possible measures of reliability basing on available information electronically collected in database RUST; - To study how reliable service of Green line is concerning its timetable basing on the introduced set of the reliability measures.
1.4 Thesis Content and Organization Chapter 2 presents a review of available literature studying the reliability of public transit.
Chapter 3 demonstrates the methodology applied in the thesis for data analysis. It discusses measures to evaluate reliability basing on the available data. The chapter states the assumptions of the methodology as well as its limitations.
Chapter 4 describes Stockholm subway and Green line in particular. It tells how the network was set up and how it is operated now. The chapter contains the description of the signaling system, control center and general information on subway operations.
Chapter 5 is a case study. It examines Green line with introducing statistical analysis of the schedule and actual data from SL database. The main parameters of the analysis are on-time performance, deviation from scheduled departure, travel times, headway, dwell times and passengers‟ waiting time.
Chapter 6 is a part of conclusions and recommendations as well as possible ideas for future researches.
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Chapter 2: Literature review
Early research on reliability initially was carried out for bus transit. Nowadays bus is the most studied mode concerning the question of reliability and reliability measures. The railway and bus transit systems have in set terms many similarities which creates a good basement for bench marketing in technology of performance evaluation between the modes. For example, Bertini and El-Geneidy (2003) discuss in their paper the advantages of data collected by a bus dispatch system relatively to manually data collection. They demonstrate the possibility to convert the data into potentially valuable transit performance measures proposed in TCQSM. They also develop the idea of systematic using of transit measures in order to improve the quality and reliability of transit agency service, leading to improvements to customers and operators alike.
Seung-Young Kho and et. (2005) develop punctuality indices of bus operation at bus stops in their paper using GPS data gathered for several bus routes in Seoul, South Korea. They offer three indices; the first one is modified index which indicates bus adherence. It is similar to on-time performance in TCQSM but considers data variance. The second index determinates regularity of the service and is analogous to the headway adherence in TCQSM. The third index is evenness of bus service. It reflects the magnitude of time gap between average headway of a day and the headway of successive buses which they propose to apply in order to evaluate service quality of the route as well as effects of service improvements.
Reliability is also a question of interest for performance analysis of heavy and light rail transportation. Carey (1999) studies the heuristic measures in his paper in order to estimate reliability and punctuality of train service. He reasons that there are various methods to measure reliability: analytical ones, but they are usually practical for very simple structured systems, then simulations, but they could be sometimes time
17 consuming and requires data that may not be available. Author suggests considering existing heuristic measures as well as proposes new ones to estimate service reliability. Most of the measures involve headway; some of the measures are based on the actual delays. The author focuses on measures which can be used in advance to estimate reliability of proposed schedules or changes in schedules. The proposed measures of reliability are especially recommended in cases of modes having possible knock-on delays as an important cause of unpunctuality or unreliability. As a conclusion Carey recommends to apply some of the measures into scheduling process to make timetable more reliable: the percentage of headways that is smaller than a certain size; the percentiles of the headway distribution; range, standard deviation, variance, or mean absolute deviation of the headways.
Nie and Hansen (2005) apply system analysis approach to investigate relationship between scheduled and the real train operation at two major railway stations in The Hague through analyzing train detection data and determining its impact on punctuality, speed and track occupancy. In schedule analysis, they determine timetable margins and critical headways estimating the blocking times and track occupancy. To analyze train operation at stations standard statistical methods are applied. They study the delays, speed and buffer times as well as estimated the necessary buffer times in order to avoid knock-off delays and enable better reliability and punctuality of the service.
Niels van Oort and Rob van Nes (2009) consider two key measures of reliability: regularity and punctuality. In their article they propose the tool assessing the impact of network changes into service regularity and the level of transit demand. As a study case they analyze the performance of two tram lines which have one mutual segment. They consider two cases: both lines use the mutual segment; one tram line is a feeder for another, stopping the service at the merging station. Their research shows that regularity affects two aspects: appreciation (current commuters would appreciate
18 transit more because of shorter travel times and less crowded vehicles) and attractiveness (new costumers would be attracted with the service with better regularity). They also conclude that changes in the regularity also influence the capacity efficiency, which might affect the operational costs.
There are also papers concerning reliability of underground transit. Doyle (2000) in his study of New York City subway reliability analyzes data using NYC Transit‟s own measure of reliability. He calls it “Service regularity” which determines the proportion of headways falling within an acceptable range of the length they are scheduled to be. The measure “is the percentage of intervals between trips departing from all scheduled time points, not including terminals, which is within ± 50 percent of the scheduled interval (for all scheduled intervals less than ten minutes), or within ± 5 minutes of the scheduled interval (for scheduled intervals of 10 minutes or more” (Doyle, 2000).
Bylund and Lindholm (2004), studying the punctuality of Stockholm subway with passenger questionnaire, make a conclusion that commuters frequently using the subway are the most unsatisfied with the punctuality of the provided service. The most hard-to-please group is young people. Authors suppose that the reason is the young commuters have fewer possibilities to change the transportation mode and have to use subway more often than older commuters; therefore they are more sensitive to the service punctuality than other groups.
Dixon (2006) in his thesis proposes the idea of utilizing data stored in a rail transit operations controls system of Boston metro network in order to evaluate the subway performance. He develops a tool that extracts and uses information from the database for analyzing the operations of rail transit lines. Carrying out the research with the help of the tool Dixon reveals weaknesses of the current service and proposes reasonable changes in timetable which could improve the subway performance. In a
19 study case he also studies the impact of train drivers on travel and dwelling time as well as impact of power supply on acceleration of the trains departing the stations. He concludes that operators have statistically significant but small effect on travels times. He also reveals that trains experienced problems with acceleration at peak hours but the effect is not considerable.
The literature study demonstrates that there are a large number of papers considering the reliability. Some of them propose new measures of reliability but the most exploit old well-known measures applying them as they are or modifying them. Concerning the subway related papers it is difficult to reveal one methodology of reliability evaluation, as long as all the subway systems use absolutely different equipment such as signaling, control and information systems. It is the reason why collecting and storing data differs from one subway network to another. This thesis will try to employ the existing experience of subway data analyzing with regard to peculiarities of Stockholm subway.
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Chapter 3: Methodology of data analysis
Reliability as it was mentioned above is quite a vague term which is not able to provide us with a good specified and clear measure. In most cases under reliability researches mean two closely related concepts: punctuality and regularity. Punctuality of the service means how precise the operator adheres to the timetable. Regularity is how regular the service is. Regular service is usually characterized with its frequency and evenness of headways. Basing on the data stored in database of SL it is possible to calculate several measures pertaining to punctuality and regularity.
3.1 Measures of punctuality
3.1.1 On-time performance On-time performance measures the degree of trains‟ adherence to the timetable. The unpredictable delays make service unreliable and less attractive to commuters especially making time sensitive trips (e.g. to work, school, etc.). On-time performance can be demonstrated as the percentage of trains that have departed on- time or have been delayed. According to SL requirements, trains that depart in the range from 60 seconds earlier to 180 seconds later are on-time. Thus, it is possible to define three groups of data: trains that leave long before the departure time, trains that are on-time, and delayed trains.
3.1.2 Deviation from scheduled departure All the trains in Stockholm subway have to depart in accordance with timetable. However in reality different conditions, such as passenger flow, driver‟s behavior, technical problems, for example with signal and dispatching systems, weather and others cause train delays or create situations when trains have earlier departure. One of the parameters characterizing the schedule adherence of the service is the deviation from scheduled departure which is a difference between actual departure time and
21 scheduled one. The parameter can be positive when train is overdue and negative when it has early departure.
The analysis of the deviation distribution, its variability during different time of the day can reveal segments of the network experiencing problems and being bottle necks of the system. Considerable variability of the parameter means unreliable service.
3.1.3 Dwell times distribution Dwell times at stations influence the travel times and train delays. It is a recovery instrument that drivers use to decrease the delay and to catch up with timetable. Considerable variability of dwell times may mean the trains experience uneven passenger load as a reason of delays or uneven headways. The measure allows revealing the stations experienced problems with boarding and alighting passengers due to not uniform demand.
Dwell time is a difference between arrival and departure time from database. Strictly speaking it is not pure dwell time. This parameter also includes time for opening and closing door. Sometimes the driver can close the doors and waits for the signal to start the moving. Nonetheless the thesis considers this consolidated parameter as a dwell time. More detailed description of dwell times in the database is provided in Chapter 3.2.6.
It is also necessary to mention that dwell times at terminals are not considered in the analysis. The reason is that terminals are the starting or final points and trains usually wait there when they are out of operation for scheduled departure. In the case they could be available for boarding and alighting during the whole waiting time, which can reach several minutes.
3.1.4 Travel times Travel times for the same stretch vary considerably due to the traffic conditions and drivers‟ experience. The actual travel time is a difference between arrival time at final 22 terminal and departure time at the starting station. Big variability of the parameter can reveal problems with the network and signaling system.
To estimate actual travel times statistical measures such as mean and 85% percentile are usually applied by operators. The inconsistency of actual travel times to scheduled ones may be a ground for timetable reconsideration and improvement.
3.1.5 Headway adherence Headway adherence (or in other words deviation of headway) described in TCQSM can characterize service punctuality for the transit service operating at headways of 10 minutes or less. “The measure is based on the coefficient of variation of headways of transit vehicles serving a particular route arriving at a stop” (TCQSM). It is calculated as a difference between actual and scheduled headways at a studying time point. Coefficient of variation (CVH) of these differences informs about the level of service at the studying time point.
(3.1) where the headway deviation is the difference between the actual headway and the scheduled one. Level of service classification is presented in table 3.1.
Table 3.1 Level of service according to TCQSM LOS CVH P (hi > 0.5 h) Comments A 0.00-0.21 ≤1% Service provided like clockwork B 0.22-0.30 ≤10% Vehicles slightly off headway C 0.31-0.39 ≤20% Vehicles often off headway D 0.40-0.52 ≤33% Irregular headways, with some bunching E 0.53-0.74 ≤50% Frequent bunching F ≥0.75 >50% Most vehicles bunched NOTE: Applies to routes with headways of 10 minutes or less.
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3.2 Measures of regularity
3.2.1 Headway distribution The regular service is one which has the vehicles arriving with regular intervals. The considerable variation of headways means the service is irregular and unreliable.
The regular headways become important for passengers when service is frequent enough. High frequency service allows commuters not to remember the timetable and their arrivals to the station get random. It is possible to assume that they arrive uniformly over time. Under that assumption number of passengers gathering at platforms directly depends on the length of the time interval between the trains. As long as service is irregular the number of people arriving to the platform during longer intervals will exceed the number of people arriving during the short intervals in accordance with the rules of the probability theory. With uneven headways the probability for overcrowding at stations will increase.
Although overcrowding is more a factor of comfort and convenience of the trip it increases boarding and alighting times as well as train load. That creates prerequisites for trains delay and longer travel time. As a consequence it negatively affects reliability of the service and efficiency of the line capacity using.
There is a way to measure overcrowding at the subway platforms. First, it is necessary to assume that if the service follows the schedule with regular intervals passengers will not experience overcrowding. Second, knowing passenger demand at a station and scheduled time intervals between the successive trains it is possible to calculate the passengers‟ flow at the platform. If we take the number of passengers that arrive to the station during this regular interval as a basis we will be able to calculate the number of extra passengers that gather at the station due to increased waiting time caused, for example, by train delays. The durations of the actual service intervals can help to find out the number of passengers that experience overcrowding. In other
24 words, the share of commuters, which arrive during the intervals that are longer than regular one, will meet overcrowding at the platform according to our assumption.
3.2.2 Waiting times Talking about irregular headways causing crowding effect in the system it is necessary to mention that it also affects waiting time of commuters.
Let‟s suppose that passengers randomly arrive to the station. When the trains arrive at perfect and regular intervals the average waiting time of the passengers according to the Theory of Probability is equal to the half of the headway:
. (3.2)
However, in case of irregular service the average waiting time will increase. It can be explained with the example of famous Bus paradox, for instance, described in Gunther (2001).
Let‟s assume that buses arrive independently to the bus stop with average headway 10 minutes. The first case is that the waiting time will be equal 5 minutes, according to formula (3.2). Nonetheless, we know that buses arrive independently and intervals between them vary in length. The point is if the passengers arrive to the stop purely randomly – it is more likely that they will arrive during the longer intervals than during the short ones. The explanation is that the intervals with longer duration are more frequently represented than the short intervals in the scale of the total length of the studying period. The answer to that example is based on that the waiting time in case of irregular service depends on the variability of headways. The more headway varies the more considerable waiting time grows:
(3.3) where H – is an average headway during the studying period and CV is the coefficient of variation. 25
3.3 Analysis The analysis of Stockholm subway reliability is based on a study case of the Green line which is the most complicated one and has the newest signaling system.
Studying of the data starts with a choice of a sample size and particular time points on basis of availability and quality of data. The main data analysis in the study case consists of two important parts:
- timetable analysis; - train operation analysis.
Timetable analysis examines how well the timetable for Green line has been planned. Train operation analysis studies the actual work of the line. Both analyses use different sets of measures, which are presented in table 3.2.
Table 3.2 Measures of operation and timetable analyses Timetable analysis Train operation analysis Headway distribution On-time performance Travel times Delay distribution Headway adherence Headway distribution Waiting time Dwell times distribution Travel times
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3.4 Assumptions and limitations The main limitation of the analysis is that the database RUST will not be 100% accurate. Due to problems of different nature such as train misbehaving, disorder of central system and etc., there is always a possibility that data is missing or inaccurate. This factor limits wide data application, for example, it cannot be a basis for the bonus payments according to the commercial agreements between SL and operators. However available collected data is enough to evaluate subway performance for the company‟s internal needs in order to reveal problematic track sections. The thesis assumes that the data in RUST database is reliable, accurate and reflects the real service.
The performance of the subway constantly changes throughout the time. The thesis analyzes subway performance in March, 2010 during the most interesting and most problematic daytime period: from 6:30 till 19:00, which includes morning and evening peak hours as well as midday off-peak. All the results in the thesis indicate the performance of the subway basing on this chosen time period.
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Chapter 4: Description of the Stockholm subway system
4.1 Stockholm subway
4.1.1 Stockholm Stockholm is the biggest city of Sweden and is Swedish economical, political, industrial and cultural center. It is located in the central part of the country on its east cost. Due to historical reasons and its geographical location the city was spread out over numerous islands of Stockholm archipelago. The core of the city is the island Gamla stan. The other key parts of the Swedish capital are Norrmalm, Östermalm and Vasastan in the North; Kungsholmen in the West and Södermalm in the South. Population of the municipality is 0.8 million people (30 juni 2009). In the context of transportation it is necessary to talk about Stockholm as a metropolitan area, so-called Big Stockholm, which consists of 26 municipalities with population up to 2 million people. Geographical position, relief and geological characteristics of Big Stockholm make it complicated and expensive to build infrastructure for fast and convenient transportation in the city: over 30% of the city area is waterways. These natural limitations are the explanation why it is more effective and economically reasonable to improve available infrastructure of transit networks by increasing their capacity and improving level of service instead of building new one.
4.1.2 History of Stockholm subway The rail transit in Stockholm region was started with the trams with opening the first tram line in 1877. Since that the network was growing, getting more complicated as well as developing technically. In 1920 AB Stockholm Spårvägar combined all the tramways operators and the united network began being considered as a whole single system to be developed. The first underground link, which obtained the name “Katarinatunneln”, was built between Slussen and Skanstull in 1933. It was 1,4 km long and had two underground stations. The Traneberg Bridge erection in 1934 allowed continuing tramline from Kungsholmen to the Alvik area in the west 29
Stockholm and in 1944 a fully segregated tram link between Thorildspaln and Ängby was constructed. Erected in 1946 the new Skanstull Bridge let the tramlines reach directly southern suburbs from Slussen. The plans to build central city underground link were undertaken in 1941. As long as only one line was planned to be constructed the link was designed as a semicircle through Vasastan and Norrmalm in order to serve bigger area. After the Second World War the link was finished.
The developed tram network was chosen as the basis for the future Stockholm subway or “Stockholms Tunnelbana”. The tram route, which connected Slussen and Hökarängen, became the first subway line on October 1, 1950. In 1951 the tramline to Örby was also converted into new subway line. The south part of the future Green Line started being operated. The west part of the first subway line was opened in 1952, when the tramline between Ängby and Kungsgatan (Hötorget) was transformed. The cross-link between west and south parts of the subway line was constructed several years later, in 1957. This link connecting Slussen and Hötorget included a five-track bridge, which would allow operating two metro lines separately later. The Green line almost was completed. In 1964 the second, the Red line, was introduced and it linked T-centralen and Fruängen. The Blue Line was opened in 1975. The latest link in the system that is now a part of the Green Line and connects Bagarmossen – Skarpnäck was introduced in 1994.
4.1.3 Stockholm subway nowadays Nowadays Stockholm subway has a total length 105,7 km and 100 stations. It is the sixth largest network in Europe and one of the most extensive networks in the world as well. The subway is run by Hong Kong transport company MTR, which began to operate in November, 2009. The network consists of three lines and has 7 metro routes, see the table 4.1 and figure 4.1.
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Table 4.1 Lines of Stockholm subway
Generalized name Line Destination T10 Kungsträdgården - Hjulsta Blue Line T11 Kungsträdgården - Akalla T13 Norsborg - Ropsten Red Line T14 Fruängen – Mörby centrum T17 Åkeshov - Skarpnäck Green Line T18 Alvik - Farsta strand T19 Hässelby strand - Hagsätra
Figure 4.1 Map of the Stockholm subway with number of passengers boarding per winter day in 2008 (SL, 2008)
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4.2 Green Line
4.2.1 Line description It is the oldest and the longest line of Stockholm subway network. The Green line connects southern and western suburbs of Stockholm municipality with the city center. The line starts from Hässelby strand in the East then it goes through the Vällingby, Ängby, Bromma, Kungsholmen, passes along central districts Vasastan, Normalm, Gamla Stan, Södermalm and reaches the station Gullmarsplan in Johanneshov, where the line bifurcates into two branches: Skarpnäck/Farsta Strand and Hagsätra. At Skärmarbrink the branch Skarpnäck/Farsta strands splits again into the east branch to Skarpnäck and the west one to Farsta strand. The total length of the line is 41,3 km. It has three metro routes and 49 stations, where 12 of them are subterranean stations and 37 were constructed above ground. The underground part of the green line was mostly built with the method “cut and cover”, when the tracks were placed in the trench dug out in the inner city streets and then covered with protection shields. This as well as old technical requirements and regulations became the reason why curves of the line are nowadays tighter and have smaller radius. This caused the maximum speed limit of 70 km per hour on spans while the Red and Blue lines have the limit of 80 km per hour. At the platforms maximum allowed speed is 50 km per hour that is valid on all the subway lines in Stockholm. The rail traffic is left side. The rail gauge in Stockholm subway is standard and is 1435 mm. Trains of Green line are run on electricity via third rail located along the track with an unloaded voltage of 750 V DC.
It is possible to define five segments of the Green line:
1. The western (northern) segment (Hässelby strand – Alvik), figure 4.2. It is a section of 10,6 km long mostly on the surface. The only part of the section of 600 m long between Islandstorget and Blackeberg is in the tunnel. Alvik is a big transfer station of the section. Between subway tracks at Alvik platform 32
there are two tracks for Nockebybannan. Next to this station there is a train depot. Johannelund is the only station with side platforms. The stations Vällingby and Åkeshov have a third middle track to keep waiting trains there. The second train depot is located between Vällingby and Råcksta stations.
Figure 3.2 The western segment: Hässelby strand – Alvik
2. Central segment (Alvik – Gullmarsplan), figure 4.3. The section is partly underground and has length 10,3 km. The tunnel under Kungsholmen and Norrmalm is 5 km long, under Södermalm it is 1,4 km. There are four stations on the segment: Fridhemsplan, T-centralen, Gamla Stan and Slussen where it is possible to make a transfer to other lines of the subway network. According to the boarding data (SL, 2008) these stations are the most heavily loaded terminals of the subway network. This segment goes through the central business district of the city, which also explains big passenger flow on all the stations along the stretch. At Gullmarsplan the Green line bifurcates into two routes.
Figure 4.3 The central segment: Alvik – Gullmarsplan 33
3. Skarpnäck segment (Skärmasbrink – Skarpnäck) presented on figure 4.4 is 6,2 km long and is the eastern branch. It has 5 stations only, where two of them, Skarpnäck and Bagarmossen, are underground.
Figure 4.4 The Skarpnäck segment: Skärmasbrink – Skarpnäck
4. Farsta segment (Gullmarsplan – Farsta strand) on figure 4.5 has length of 8,1 km. The segment is above-ground and is geographically located in the south of Stockholm between Skarpnäck and Hagsätra branches. Skärmarbrink is the station where the Green line splits to Farsta and to Skarpnäck. There is the third train depot near the station.
Figure 4.5 The Farsta segment: Skärmasbrink – Skarpnäck
5. Hagsätra segment (Gullmarsplan – Hagsätra), figure 4.6. The western section has length of 7,7 km. It is an over ground line that goes across sparsely populated suburban area. Between Gullmarsplan and Gluben there is parallel
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tramline of Tvärbana. Globen is the area with big sport arena, many office building and big shopping mall. It generates big transport demands depending on the sport and cultural mass events. The other busiest station is Högdalen. It was constructed with the third track for terminating trains as well. At Högdalen station there is a link to the forth train depot. Hagsätra is the final station. In the future there are plans to continue the line construction up to Alvsjö station where the transfer to commuter train service will be possible.
Figure 4.6 The Hagsätra segment: Gullmarsplan – Hagsätra
4.2.2 Main terminals T-centralen is the most important transport node of the city transit system that is situated in the core of Stockholm central business district. Central Railway station and Bus terminal together with the multi-level subway station form the largest transit terminal in the city. All the three lines of the subway, both commuter train lines, all the intercity trains and bus lines start at, pass by or end at the terminal. All the transport infrastructures as well as numerous office and shopping areas around the station generate the huge passenger flows in the terminal. On weekdays there are up to 170000 passengers boarding the subway trains at T-centralen.
T-centralen is a three level station. Level “–1”is the platform for trains of the Green line with direction “Hässelby strand” and for the trains of the Red Line with
35 directions “Fruangen/Norsborg”. Level “-2” is the platform for the trains of the Green line with directions “Skarpnäck/Farsta strand/Hagsätra” and for the trains of the Red line with directions “Mörby centrum/Ropsten”. Level “-3” is a platform for the trains of the Blue line in both directions. Levels “-1” and “-2” are located one over another that makes transfer time of passengers between the levels short. It usually takes no more than 1 minute to change the platform. The situation is different when passengers want to transfer between the Blue and other lines. The platform of level “-3” is placed in a distance from the other platforms. To reduce the transfer time between the levels moving walkways were constructed. Nowadays it ordinary takes up to 3-5 minutes to change the line.
Fridhemsplan is another vital transport node of the transit network. It is located in the inner center of Kungsholmen, the big residential area, and surrounded by numerous malls and public institutes. The number of passengers boarding at the station reaches 54 000 on a weekday. Fridhemsplan station is also a transfer point between the Green and the Blue lines. The upper platform is used for the Green line trains, while the lower one is intended for the trains of Blue line. The transfer time varies from 2 to 4 minutes.
Slussen is the second important transport node of the city. It is located on Södermalm, the big dwelling district with popular shopping and leisure areas. Next to the station there are several ferry terminals, bus terminal and commuter train to Nacka. Passengers transfer here between the Red and the Green lines, between subway and commuter train, bus lines, or ferries. The number of boarding passengers reaches 84 000 people per day. The station consists of two platforms on the same level. One platform is used for the cross-platform transfers between the Green line with direction “Skarpnäck/Farsta strand/Hagsätra” and the Red line with direction “Fruangen/Norsborg”. The second platform is for the cross-platform transfer between the lines with directions “Mörby Centrum/Ropsten” and “Hässelby strand”. The
36 transfer between the platforms for commuters is not convenient and usually takes some efforts to complete it as long it is necessary to go up and then go down the stairs. Another feature of the station that negatively influences the uniform train boarding is the connection to bus and commuter train terminals. The sole entrance for the big flow of passengers transferring from busses and commuter trains is located at one end of the platform. That causes the uneven loading of the train cars stopping close by the entrance.
Gamla Stan is located in the historical part of the city. Near the station there located many governmental institutes and offices as well as many tourist spots. Stockholm residents are also fond of using this area for recreation: there is a big concentration of cafes and restaurants as well as favorite places to enjoy the weather next to the waters of Mälaren lake or the Baltic sea. The number of boarding people reaches the figure of 23 000 on week days. It is the fourth and the last interline transfer station of the subway network where passengers switch from the Red to the Green line or opposite. The station is build with the same patterns as one at Slussen. It has two platforms at one level, where passengers can make cross-platform transfer between the Red and the Green lines. One platform is used for the trains with the southern destinations and another one for trains going to the north of the city.
Gullmarsplan is a big living area situated right outside the inner city. Near the station the big office and leisure area Globen is located. The station is a transfer point among the Green line, tram and bus lines. Over the subway platforms there is a big bus terminal with buses of southern destinations. There are 35 000 people boarding at the station on weekdays. The terminal consists of two platforms at the same level. One platform is intended for trains following in southern direction, while another one is for northern direction trains. Every platform has two tracks. One track is used by trains of route T19 while another one is for routes T17 and T18. There is also an additional track for the waiting trains between these two platforms. This is the only
37 station where special electronic display located at the track for the north bound trains. This display shows the precise time in seconds elapsed since passage of previous train. One can call Gullmarsplan a key station of the Green line, because the line traffic control center is situated here as well.
Alvik is another station with a large number of boarding people. The figure reaches 17 000 passenger per regular business day. Alvik is also an important transfer node in East Stockholm. At the station passenger can change to tram line Tvärbanan and light rail line Nockebybanan. The station has two platforms. The first platform is for subway train going to Hässelby strand and for the train leaving for Nockeby. The second platform is used for the arriving train of Nockebybanan and subway trains of southern direction.
Skärmarbrink is a station where routes T17 and T18 split and take their own tracks. That is the reason why it has two platforms and four tracks to separate the trains of the different directions.
4.2.3 Peak hours During the period from 2008-03-11 till 2008-03-12 there were collected data on passengers entering and leaving the subway system with the help of turnstiles at a few stations. The data allow identifying the individual peak hour at every investigated station. Nonetheless, in the thesis we will consider the average peak hour periods peculiar to the whole network.
Turnstiles or fare gates control fare payment and are located at any station in the intermediate mezzanine levels between the street and platform levels. The fare gates allow entering the subway system after fare payment or leaving the station when the trip is over. Every fare gate opening is identified as one person entering or leaving the station. However, the results could not be precise because in some cases the construction of the gate allows several people to pass through it at once. There are
38 also some cases when some passengers just hop over the gate without payment. Thus, this group of passengers, so-called “stowaways”, is not included in the data either. However, the dataset can provide us with a general concept and allows determining the peak hour periods in the system.
14000 Rådmansgatan Odenplan 12000 Skanstull Hötorget Fridhemsplan Gullmarsplan (T) Slussen (T) T-centralen 10000
8000
passengers 6000
4000 Entering 2000
0 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Local time at stations, hour Figure 4.7 Number of passengers entering the stations
14000 Rådmansgatan Odenplan 12000 Skanstull Hötorget 10000 Fridhemsplan Gullmarsplan (T) 8000 Slussen (T) T-centralen 6000
4000 Leaving passengers Leaving
2000
0 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Local time at stations, hour Figure 4.8 Number of passengers leaving the station
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The charts on figures 4.7 and 4.8 show that there is a common tendency at the chosen station set. Number of commuters increases considerably after 6:00, reaches its peak at 8:00 and declines rapidly until 9:00. Then it continues to slightly decrease until 10:00. reaching its lowest value during the daytime. After that downfall number of passenger entering and leaving the terminals keeps on insignificantly growing until 14:00. At 14:00 the growth changes its character. The slopes get steeper. The graphs reach their evening peaks around 17:00. After that the number of passengers dramatically decreases until 19:00. Analyzing that variation of passenger flow at the presented stations one can identify three periods during the daytime, which will be interesting for reliability analysis: morning peak hour 6:30 – 9:00, midday off-peak 9:00 – 14:30, evening peak hour 14:30 – 19:00.
4.2.4 Signaling system In spring 1999 the old signaling system of the Green Line was completely replaced with the new one produced by Siemens. The new safety system is continuous with the automatic train control (ATC) feature. Continuous means that the signal continually is sent by the system through the rails wherever the train is.
The signal system was constructed with the following technical specification: the train length is three stock cars of C20 around 140 meters, maximum speed is 70 km/h and 50 km/h at the platforms, maximum acceleration/deceleration is 1,1 m/s2, and designed headway of 90 seconds between trains during ordinary transport service.
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Figure 4.9 Maximum speed change along the line
The signaling system is composed of seven interlocks and one control center at Gullmarsplan station. Every interlock controls one segment of the line consisting of a set of the blocks of different length which assist to define train location on the line. The length is depended on the necessity to have more precise data on the train position. The shortest blocks are situated along the platforms and at the points, while the longest ones are situated along the inter stations stretches.
Usually the blocks are physically isolated each one from another. The new technical solution of the system allows keeping the railway track unbroken. Each and every block of the Green line is a track circuit which consists of a sender and a reader connected to the interlock. The interlock sends a signal of particular frequency as well as a set of messages through the sender. Nowadays the system applies signals of 9 frequencies to differentiate the track circuits, which is enough to manage the railway traffic even on the complicated parts of the line. The reason for this number of frequencies is to have at least two different frequencies in between before the same frequency is used again.
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Figure 4.10 Net of tracks controlled with an interlock
The reader located on the opposite side of the block receives the signal and sends the information to the interlock that the block is unoccupied. The moment the train enters the block its first wheel axis connects both the rails preventing signal translation to the reader. In that case the reader does not send any signal and the interlock considers that the block is occupied. There will be no signal until the last train wheel axis will not have passed the block and the signal from the sender will not be interrupted any more.
Figure 4.11 Scheme of track circuit
Every block is isolated from others with the filters located on both sides of the block, which prevent the signal of one track circuit to be translated to the others. The filter is a wire put in „S‟-shape that connects one rail with another.
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The set of messages sending by interlock usually contain information on maximum allowed speed on the block, signals in front of the train, and condition of the track. They are obtained by train computer system and by driver with the help of two antennas (receivers) placed at the front of the train under its cabin closely to the rails.
The train control system is planned not to let the driver to ignore the speed limits and to pass stop signals. It would automatically stop the train if it seemed the driver did not follow to the sending restrictions, exceeded the speed limits or overlooked wayside signals.
4.2.5 Rolling stock On the Green line Stockholm subway uses rolling stock C20 or so called “vagn 2000”. The producer of the train type is Kalmar Verkstad, had been owned by Adtraz and now controlled by Bombardier. The car is double articulated and consists of three parts. It has four boggies. The length of the car is 46,5 m and the weight is around 70 tons. The total length of the regular train compiled of three cars is around 140 m, the short train consists of two cars and has length 94 m. It can reach the speed up to 105 km/h. Every car has 7 doors and can take 414 passengers. There are 126 seats and 288 standing places.
The cabin of C20 is equipped with speedometer, which shows two values: actual speed and maximum allowed speed. Driver controls the speed of the train with a handle in the front desk of the cabin. Pushing the handle driver increases the speed; dragging it back he/she breaks the train. If the driver has a higher than allowed speed and does not respond to the system warnings system forces the train to stop automatically. After the stop driver is allowed to take control over the train stirring again after some special procedures.
The trains also have an automatic control, so-called autopilot. Driver can apply this function by pressing the ATO-button (Automated train operation) on the cabin desk.
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The function is applied on one stretch of the line between two stations only. When the train reaches the next station the function will automatically get off. If a driver needs to continue stirring the train in autopilot mode he/she will suppose to press the button every time the train is leaving a station.
Another function of the drivers is to control train loading and unloading with passengers. The driver is not physically able to observe the boarding process from the cabin along all the train. That is why all the platforms are equipped with video cameras, which translate the behavior of the passengers at the end of the train. Driver has to leave the cabin and get convinced of that all the passengers has taken their places and doors are not blocked before closing them.
The driver has to keep in mind and stick with the timetable as long as he/she is restricted to be late or arrive earlier. He/she can control the adherence to timetable by knowing the timetable and reading electronic information on LED screens at the stations. The screens show information on two following trains and their calculated arrival time in minutes basing on the trains‟ actual location.
Every time the driver starts the trip he/she has to input the trip parameters and train identity, such as destination, number of the cars, line number, stopping code, and other. This information together with technical data on the train will be transmitted by antenna at the cabin and registered by sensors at the stations which are connected to the information system.
4.2.6 Information system All the stations are equipped with two sensors per track along platform fixing the train arrival and departure. First sensor is located in a distance around 100 -150 m before the platform up the line. It receives the signal from the train with the route information. The data consists of the route and train numbers, destination, number of cars, and others. The second sensor is located down the line, usually in a short
44 distance, about 10 m. It fixed the time when the train leaving the station. Both sensors send the information to the inductive data transfer system (IDTS) at every particular station.
Figure 4.12 Inductive data transfer system at a station
The IDTS is coupled to the public information system (TIS) and sends there the data received from the sensors. Backwards it obtains the real-time information on following trains that will arrive to the station in a while. Then the IDTS displays the travel information on the arriving and the following trains on special LED screens located over the platform. The LED screen consists of two red or orange rows. The upper row usually displays the information on arriving train, the lower row informs about the two following trains and/or traffic conditions.
It is important to keep in mind that the time of train arrival and departure at station is not real but approximate. This is because the sensors are located at different distances at any station. To calculate arriving time, which will be shown in the database, the systems adds 15 seconds to the time fixed by the first sensor before the platform. The departure time is calculated as the time fixed by the second sensor, when the train leaves the platform, minus 10 seconds.
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4.2.7 Traffic control center The train service of the Green line is monitored and regulated by a computer control center which is located at Gullmarsplan. The computer control works under VICOS (Vehicle and Infrastructure Control and Operating System), the operational system developed by Siemens. Together with the new signaling system that OS was applied in 1999.
The control is implemented automatically however under supervision of traffic controllers in order to timely respond to possible disturbances in operating. The center is equipped with four workstations for controllers and one workstation for an information person. Every controller can monitor the entire network but ordinary he/she watches his/her own part of the line. A controller observes the current situation in the monitored area on the three screens at his/her workstation. A giant electronic traffic board located in front of him/her also allows monitoring the line service in whole. The board reflects location of all the trains as well as trip information and signals along the line.
The controller can interfere in the automatic mode in case of an emergency. This case could be a passenger on the track, technical problem with a rolling stock, power failure, maintaining works etc. Using mouse and keyboard controller can easily stop the train by switching the signals or reroute it by changing the points. New command will go to the interlock which will complete the task and will also inform the train driver with the ATC messages. All the events are logged and stored in the system in order to restore the sources of the critical situations and the service disturbances.
4.2.8 Data collection One of the VICOS‟s functions is to collect and manage all the data received from interlocks. TIS is a part of VICOS OS, that collects data from IDTS and collates it with interlocks data. This module is also connected to timetable manager which provides the schedules for all the train operations starting and finishing in depot. In 46 the case when the information on following trains is not yet available the TIS module sends the arriving time according to relevant timetable to IDTS.
Figure 4.13 Process of data collection
Database of VICOS contains all the genuine, unchanged and untransformed data. In the case of a need it is always possible to get the playback data to investigate the real situation with traffic conditions at a particular moment. Everyday raw data from VICOS‟s database is recorded and sent to FTP server of SL, where it gets available for processing by SL traffic planners or the subway operator.
RUST application fits raw data to planned timetable data. If the data are consistent they fed into the database. If there is any inconsistency or data has any formal errors it will be stored in a dump file where it is also available to be inspected. Another function of RUST is to transform raw data into Excel format to simplify the process of analysis.
The system of data recording and transferring has one negative aspect – there could be data losses on every step. Data will never be 100% accurate. That is why the data kept in RUST could have some limitations to be used broadly for commercial purposes. For example collected data will not become a good ground for efficiency estimation 47 of the subway operator in order to calculate the award. Nevertheless it can be applied as an inner efficiency estimator, which will be able to help planners to disclose the bottle necks and drawbacks of the system. Basing on the data it is possible to take timely decisions and adequately correspond to the grave situations.
4.3 RUST database RUST is a database, which is available to SL employees to trace and analyze the performance of the subway. The database can be used through intranet in the SL office or via VPN protected internet connection from any computer worldwide when you are allowed.
4.3.1 Database inquiry Excel file inquiry allows receiving access to the database. The file contains the menu where the request can be carried out. To show the results the menu for input parameters presented on the figure 4.14 offers to choose: time period (dates and time), line, route, direction, required stations, number of cars and type of timetable. It is possible to choose any moment and any station during the period of recorded data. User can also make request for two different time periods.
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Figure 4.14 Input table to select required data set
4.3.2 Data output As an output the user will receive the excel sheet presented on figure 4.15.
Figure 4.15 Example of selected data set
Column A “Datum” represents the date of the record, column B provides the departure time of the train at the first station. Column C gives information on destination of the train. Column D “Tur” gives the number of the route. Column E
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“Linje” and column F “Riktning” provide information on line number and direction of the train. Column G “Vagnar” informs about number of cars in the train. “8” means the length of the regular train of 3 cars of C20 type or 8 cars of Cx type. “6” is 6 Cx cars or 2 C20 while “4” is 4 Cx or 1 C20. Column H “Tidtabell” tells the number of the timetable that was carried out by the train. Then five following columns I-M represents the data for train at chosen station. Columns I “Ank” reports the time of arriving to the station, column J “Plan” does the departure time according to schedule derived from SL timetable database, column K “Avg” does the actual departure time. Column L “Försenat” calculates the deviation from scheduled departure and column M “Uppehåll” computes the dwell time. In the example the first line is the train T19 leaving for Hässelby strand from Hagsätra station that departed on time and had dwell time 1 minute 44 seconds. This long boarding time is because Hagsätra was the final station and the train waited to depart according to timetable while was allowing passengers to board.
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Chapter 5: Study case: Green line
5.1 Data SL provided an opportunity to use their data collected from November 2009 when MTR had started to operate the subway. In order to demonstrate the data analysis it was decided to choose performance of the subway during week days of one month. Month was picked up according to the following considerations: as long as November was the first month of MTR operation, their service probably experienced some disturbances. In December and January there were many holidays so these months would be not representative enough. In February the systems went through a week of transport collapse due to extreme weather conditions. Thus, week days of March 2010 were chosen for the data analysis.
Table 5.1 Percent of recorded trains of Green line in March 2010
Date 17 Skarpnäck, % 18 Farsta strand, % 19 Hagsätra, % Average, % March NB SB NB SB NB SB 1 97.2 97.1 94.3 90.1 91.6 95.0 94.2 2 93.4 90.4 89.3 83.5 87.4 79.3 87.2 3 100.0 97.1 94.3 95.0 97.5 94.2 96.4 4 96.2 95.2 89.3 88.4 91.6 89.3 91.7 5 96.2 94.2 92.6 92.6 92.4 91.7 93.3 8 98.1 96.2 92.6 90.1 94.1 96.7 94.6 9 98.1 94.2 97.5 97.5 96.6 95.0 96.5 10 97.2 98.1 96.7 97.5 97.5 95.9 97.1 11 99.1 98.1 97.5 96.7 97.5 96.7 97.6 12 99.1 96.2 93.4 99.2 97.5 96.7 97.0 15 99.1 96.2 96.7 95.0 100.0 96.7 97.3 16 96.2 96.2 86.1 86.8 95.0 93.4 92.3 17 98.1 96.2 96.7 96.7 96.6 95.0 96.6 18 99.1 97.1 96.7 100.0 100.0 96.7 98.3 19 100.0 97.1 95.9 98.3 98.3 95.9 97.6 22 99.1 96.2 99.2 97.5 97.5 95.0 97.4 23 34.0 73.1 41.0 39.7 31.1 35.5 42.4 24 100.0 97.1 86.9 90.1 99.2 95.0 94.7 25 98.1 97.1 97.5 98.3 96.6 95.9 97.3 26 96.2 95.2 96.7 96.7 95.0 96.7 96.1 29 99.1 94.2 97.5 98.3 97.5 98.3 97.5 30 85.8 90.4 86.1 85.1 74.8 72.7 82.5 31 98.1 96.2 93.4 95.9 99.2 99.2 97.0
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The month contained 23 week days; however 3 days (the 2nd, 23rd and 30th of March) had bad performance because of reported technical problems or incidents. Green line daily performance in March is represented by percentage of trains recorded in database relative to the schedule for all the lines in both directions. The results are demonstrated in the table 5.1.
The data set still contains a big amount of information that is complicated to process. Therefore it would be reasonable to reduce the sample size. In order to do that it would be useful to range the data and receive three groups of days depending on the lines‟ performance. These groups are presented in table 5.2.
Table 5.2 Groups of day according to line performance
Range, % Number of days Share, % Sample share, days 98.5-96 14 66.7 5 96-93.5 3 14.3 1 93.5-91 3 14.3 1
Random sample of 7 days from March 2010 was selected. The dates are presented in table 5.3.
Table 5.3 Sample
Sample choice 9 March 10 March 26 March 12 March 25 March 8 March 4 March
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5.2 Timetable analysis Stockholm subway is operated from 5 am to 1 am on weekdays and has clock around service at weekends. Timetable provides information on departure time at all the stations. However it does not contain any information on arrival time and dwell time. According to SL traffic planners the boarding and alighting time for passengers was considered being around 30 seconds in most cases when the timetable was compiled. Arrival time might be eliminated due to practical reasons. For example, to perform a safer service driver will not try to drive riskily the train with maximum allowed speed to get the station on time or catch up the timetable in case of the train delay. Instead he/she will prefer to adjust the dwell time at station in order to depart in accordance with schedule.
5.2.1 Headway distribution The regular headway of all three lines (T17, T18, T19) is 10 minutes during off-peak hours. It makes the shared sections have headway of 3-4 minutes at the period. In the rush hour the operator increases the frequency and the traffic reaches 5 trains every 10 minutes in the central part of the line. In accordance with the timetable the morning rush hour approximately lasts from 7:30 till 8:30 and the evening one does from 16:00 till 18:00.
Due to variability of travel times the headways slightly differ from each other at different stations. Table 5.4 demonstrates the example of headway distribution based on the timetable for T-centralen during three time intervals. The example shows that the intervals are not perfectly regular. The minimal value of headway stipulated by the timetable is 2 minutes during the studying period.
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Table 5.4 Example of scheduled headway distribution at T-centralen T-centralen SB T-centralen NB Head- Head- Head- Head- Head- Head- Depar Depar Depar Depar Depar Depar way, way, way, way, way, way, ture ture ture ture ture ture min min min min min min 7:30 2 12:00 4 17:00 2 7:32 2 12:03 3 17:03 2 7:32 2 12:04 3 17:02 2 7:34 2 12:06 3 17:05 2 7:34 2 12:07 3 17:04 2 7:36 3 12:09 4 17:07 2 7:36 2 12:10 4 17:06 2 7:39 3 12:13 3 17:09 4 7:38 2 12:14 3 17:08 2 7:42 2 12:16 3 17:13 2 7:40 2 12:17 3 17:10 2 7:44 2 12:19 4 17:15 2 7:42 2 12:20 4 17:12 2 7:46 2 12:23 3 17:17 2 7:44 2 12:24 3 17:14 2 7:48 2 12:26 3 17:19 4 7:46 4 12:27 3 17:16 2 7:50 2 12:29 4 17:23 2 7:50 4 17:18 2 7:52 2 17:25 2 7:54 2 17:20 2 7:54 2 17:27 2 7:56 3 17:22 2 7:56 2 17:29 4 7:59 2 17:24 2 7:58 2 17:26 2 17:28 2
Figure 5.1 shows the change of the average headways of 30 minutes intervals at the station during the day.
240 South North 220
200
180
160
140 Average headway, Average s 120
100
7:00 6:30 7:30 8:00 8:30 9:00 9:30
18:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00
Time period Figure 5.1 Average headway in 30 minutes intervals during the period from 6:30 till 19:00 at T-centralen
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One can notice that average headways for both directions are similar and equal around 3 minutes during the period from 9:30 till 14:30. There are differences between the directions during morning and evening hours only. By the timetable shorter headways are proposed for northern trains in the morning and for southern trains in the evening. The pattern can be sensibly explained with the schedule which considers the large passenger demand in the southern suburbs of Stockholm. The three branches of Green line pass along dense areas which create big number of commuters going to the city center to work or study in the morning and heading home from the center in the evening.
5.2.2 Travel times Timetable of all the three lines T17, T18 and T19 does not imply that there is one route for each line. The timetable was compiled to serve the needs of more effective train using along the lines. In order to fulfill the aim there are trains starting at one terminal and ending at different terminals. One train can serve several lines changing the direction at those terminals. Nonetheless, it is possible to define the several routes with the common reiteration during the studying period from 6:30 till 19:00.
Table 5.5 Routes of Green line from 6:30 till 19:00 Line Direction Route Number of trips 17 SB Råcksta - Skarpnäck 3 Åkeshov - Skarpnäck 75 Alvik - Skarpnäck 1 NB Skarpnäck - Alvik 1 Skarpnäck - Åkeshov 72 Skarpnäck - Råcksta 1 Skarpnäck - Vällingby 1 Skarpnäck - Hässelby strand 3 18 SB Hässelby strand - Hökarängen 1 Hässelby strand - Farsta strand 3 Vällingby - Farsta strand 38 Åkeshov - Farsta strand 1 Alvik - Farsta strand 49 Gullmarsplan - Farsta strand 1
55
Table 5.5 Routes of Green line from 6:30 till 19:00 (continue) Line Direction Route Number of trips 18 NB Farsta strand - Alvik 48 Farsta strand - Åkeshov 1 Farsta strand - Råcksta 5 Fartsa strand - Vällingby 36 Fartsa strand - Hässelby strand 3 Hökarängen - Alvik 1 19 SB Hässelby strand - Högdalen 7 Hässelby strand - Hagsätra 77 Åkeshov - Högdalen 5 Alvik - Högdalen 5 NB Hagsätra - Åkeshov 2 Hagsätra - Råcksta 1 Hagsätra - Vällingby 2 Hagsätra - Hässelby strand 73 Högdalen - Alvik 8 Högdalen - Vällingby 2 Högdalen - Hässelby strand 2
The more widespread routes and the travel time along them are presented in table 5.6. The timetable as it is seen in the table contains different travel times for the same route depending on the time of day. During peak hours the timetable provides additional time to cope with possible delays due to the high passenger demand. That additional time is usually equal to 1 minute according to the timetable analysis.
Table 5.6 Main routes of Green line and their scheduled travel times Travel time, min Line Direction Route Off-peak hours Peak hours 17 SB Åkeshov - Skarpnäck 39 40 NB Skarpnäck - Åkeshov 39 40 18 SB Alvik – Fasta strand 37 38 SB Vällingby – Farsta strand 52 53 NB Farsta - Alvik 37 38 NB Farsta - Vällingby 51 52 19 SB Hässelby strand - Hagsätra 55 56 NB Hagsätra - Hässelby strand 55 56
56
5.3 Train operation analysis
5.3.1 On-time performance Looking at figure 5.3 and the table A.1 in Appendix one can see a tendency for the trains going to the North: the percentage of delayed trains considerably increases as far as trains reach the northern bound. At Johanneslund and Hässelby gård the delayed trains exceed 50%. In addition the station St. Eriksplan displays significant part of delayed trains as well, 37%.
There is the same tendency for southern trains on figure 5.2 but it is less considerable. The share of delayed trains at southern bounds reaches up to 25-30%. The worse result was observed at station Skogskyrkogården where almost 40% of trains were delayed.
It is also possible to notice a characteristic of several stations located in the central segment of the Green line: a certain part of trains, less than 1%, departs earlier than 60 seconds before the scheduled departure time, which may also be considered as a precondition for unreliable service.
100.0
80.0 time - 60.0 40.0
20.0 trains SB, % SB, trains
Share ofShareon 0.0
ILT
JOL
SKT SKY
SEP
BJH
STB
FAS
BLB
TCE
SLU SKB
ALV
TAK KÄT SAB FAR
GLB
VBY FHP
KRB BLU
THP SNK
SOP
ÄBP
RÅC
HÄS
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG HAG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.2 The share of on-time trains at stations, SB 100.0
80.0 time - 60.0 40.0
20.0 trains NB,% trains
Share ofShareon 0.0
ILT
JOL
SKT SKY
SEP
BJH
STB
FAS
BLB
TCE
SLU SKB
ALV
KÄT SAB TAK FAR
GLB
VBY FHP
KRB BLU
SNK
THP
SOP
ÄBP
RÅC
HÄS
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG HAG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.3 The share of on-time trains at stations, NB 57
5.3.2 Deviation from scheduled departure The summary statistics of the deviation at stations throughout the chosen daytime period is demonstrated in table A.2. The results for both directions are also illustrated on figures 5.4 – 5.7.
240
180
120
60
Average deviation, Average s 0
ILT
JOL
SKT SKY
SEP
BJH
STB
FAS
BLB TCE
SLU
SKB
ALV
SAB FAR
KÄT TAK
GLB
VBY FHP
KRB BLU
THP SNK
ÄBP SOP
RÅC
HÄS
ABB GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG HAG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.4 Average deviation from scheduled departure at stations, SB
240
180
120
60
Average deviation, Average s 0
ILT
JOL
SEP SKT SKY
BJH
STB
FAS
TCE BLB
SLU
SKB
ALV
SAB FAR
KÄT TAK
GLB
VBY
FHP
KRB BLU
SNK
THP
ÄBP SOP
RÅC
HÄS
ABB GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG HAG
GUÄ
HÖÄ
SVM
HÖD
MBP BMP
SMO BAM RMG Stations Figure 5.5 Average deviation from scheduled departure at stations, NB
Figures 5.6 and 5.7 show that the deviation for the trains heading to the North propagates more considerably comparing to the southern trains. Passing by the southern suburbs the trains experience average deviation around 1 minute. When they reach the inner city the deviation gets twice as big as it was and continues to accumulate approaching inadmissible values at the end of the line. For example, the average deviation at Hässelby gård reaches 253 second.
The average deviation from scheduled departure of the trains heading to the South varies from 60 to 120 seconds. The exception is the Farsta branch, where the deviation reaches its upper limits for punctual train service of 180 seconds and even exceeds the limit at Skogskyrkogården. 58
2.00
1.50
1.00
0.50
0.00
Coefficient Coefficient ofvariation
ILT
JOL
SKT SKY
SEP
BJH
STB
FAS
BLB
TCE
SLU
SKB
ALV
KÄT SAB TAK FAR
VBY FHP GLB
BLU KRB
SNK THP
ÄBP SOP
RÅC
HÄS
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG HAG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.6 Coefficient of variation of deviation at stations, SB
2.00
1.50
1.00
0.50
0.00
Coefficient Coefficient ofvariation
ILT
JOL
SKY SKT
SEP
BJH
STB
FAS
BLB TCE
SLU
SKB
ALV
SAB FAR
KÄT TAK
GLB
VBY FHP
KRB BLU
THP SNK
ÄBP SOP
RÅC
HÄS
ABB GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG HAG
GUÄ
HÖÄ
SVM
HÖD
MBP BMP
SMO BAM RMG Stations Figure 5.7 Coefficient of variation of deviation at stations, NB Considerable coefficient of the deviation variation is mostly observed at starting and ending terminals for the trains of both directions. Greater variation of the deviation is also a characteristic of the trains going to the South through the stations of Farsta segment: Blåsut, Sandsborg and Skogskyrkogården. The results can give us a hint that at the stations there might be technical problems that affect on-time departure from the stations and they should be studied in detail.
5.3.3 Dwell times Table A.3 in Appendix shows summary statistics of dwell times at the stations of the Green line. The inspection of the data presented in the table A.3 demonstrates that in most cases dwell times vary between 20-30 seconds. At the same time one can see that at the stations experiencing heavy loading the considerable part of trains can have dwell times more than 30 seconds. The majority of these stations are located in the downtown area. Especially this tendency is noticeable at T-centralen and
59
Gullmarsplan, where dwell times more than 30 seconds are observed in 80% cases or even more. The average dwell times and their coefficient of variation for all the stations are presented on figures 5.8 – 5.11. 50 40 30 20
Dwell time, Dwell s 10
0
ILT
JOL
SEP SKT SKY
BJH
STB
BLB TCE
SLU SKB
ALV
SAB FAR
KÄT TAK
GLB
VBY
FHP
KRB BLU
THP
ÄBP SOP
RÅC
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO
BAM RMG Stations Figure 5.8 Average dwell times at stations, SB
50 40 30 20
10 Dwell time, Dwell s
0
ILT
JOL
SKT SKY
SEP
BJH
STB
BLB
TCE
SLU
SKB
ALV
SAB FAR
KÄT TAK
VBY FHP GLB
KRB BLU
THP
ÄBP SOP
RÅC
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.9 Average dwell times at stations, NB
0.60
0.40
variation 0.20 Coefficient Coefficient of
0.00
ILT
JOL
SKY SKT
SEP
BJH
STB
BLB TCE
SLU SKB
ALV
FAR
KÄT SAB TAK
GLB
VBY FHP
KRB BLU
THP
SOP
ÄBP
RÅC
ABB
GAS
ÅKH
BAH
ENG
HÖT HYÖ
GUP
ODP
HÄG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.10 Coefficient of variation of dwell times at stations, SB
0.60
0.40
variation 0.20 Coefficient Coefficient of
0.00
ILT
JOL
SEP SKT SKY
BJH
STB
BLB TCE
SLU SKB
ALV
FAR
KÄT SAB TAK
GLB
VBY
FHP
KRB BLU
THP
SOP
ÄBP
RÅC
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.11 Coefficient of variation of dwell times at stations, NB
60
Bar charts of coefficient of variation show that dwell times do not considerably vary at most of the station. At the same time one can see that for the Green line southbound dwell times at several stations, like Johannelund, Åkeshov, Alvik, Gullmarsplan, Gubbängen and Bandhagen, is twice as big as at other stations. The same tendency one can notice at few stations of the Green line northbound, such as Alvik, Kristineberg, Thorildsplan, Gullmarsplan and Skärmarbrink.
5.3.4 Travel times In the analysis of travel times the thesis considers four routes presented in table 5.6. Figures 5.12 through 5.19 show the distribution of actual travel times, the average and the 85th percentile times as well as the scheduled travel times for the chosen four routes in both directions during the period from 6:30 till 19:00.
Figure 5.12 demonstrates travel times as a function of time for the Line 17 Åkeshov - Skarpnäck. Graphically, one can see that actual travel times of the line fit the scheduled ones until midday. Then average travel times slightly increase during evening peak hour and the maximal difference between scheduled and average actual travel time reaches about 100 seconds from 16:00 till 18:00. The difference with the 85th percentile distribution of actual time achieves almost 250 seconds in the period.
2900 Actual TT 2800 Timetable TT 2700 Average 85 Percentile 2600 2500
2400 Travel time, s Travel 2300 2200
2100
7:00 7:30 8:00 8:30 9:00 9:30
10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 Time of day Figure 5.12 Travel times of Line 17 Åkeshov – Skarpnäck, SB
61
The plot of travel times for the opposite direction of the same line 17 on figure 5.15 informs us that during all the studied time period travel time exceeds the scheduled one by around 50 seconds on average. The average difference between the 85th percentile times and the times, considered in the timetable, reaches 100-150 seconds.
2900 Actual TT 2800 Timetable TT Average 2700 85 Percentile 2600 2500
Travel time, s Travel 2400 2300
2200
7:30 6:00 6:30 7:00 8:00 8:30 9:00 9:30
17:00 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:30 18:00 18:30 19:00 Time of day Figure 5.13 Travel times of Line 17 Skarpnäck – Åkeshov, NB
The figures 5.14 and 5.15 show that travel times of the line 18 from Alvik to Farsta strand and back almost do not differ from the scheduled ones. It means the trains of the line successfully adhere to the timetable. The noticeable difference is obvious for line 18 southbound during the evening peak, when the 85th percentile times are bigger by about 150 seconds relatively to the scheduled travel times.
2600 Actual TT 2500 Timetable TT Average 2400 85 Percentile 2300
2200 Traveltime, s 2100
2000
9:30 9:00
13:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 Time of day Figure 5.14 Travel times of Line 18 Alvik – Farsta strand, SB
62
2600 Actual TT 2500 Timetable TT Average 2400 85 Percentile 2300
2200 Travel time, s Travel 2100
2000
8:00 8:30 9:00 9:30
17:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 18:00 18:30 19:00 Time of day Figure 5.15 Travel times of Line 18 Farsta strand – Alvik, NB
The route Vällingby – Farsta strand of the line 18 is implemented during morning and evening peak hours only. Travel times for the south direction during morning peak hour fit the timetable. Nonetheless, throughout late afternoon hours the travel times are larger than the scheduled times by about 100 seconds. The 85th percentile times exceed the schedule by up to 200 seconds.
3600 Actual TT 3500 Timetable TT 3400 Average 85 Percentile 3300
3200 Travel time, s Travel 3100
3000
6:30 7:00 7:30 8:00 8:30 9:00 9:30
12:00 10:00 10:30 11:00 11:30 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 Time of day
Figure 5.16 Travel times of Line 18 Vällingby – Farsta strand, SB
Northern direction of the line also experience longer travel times than they are stipulated by the timetable. One can see that the average travel times differ from the scheduled ones by about 100-150 seconds, while the 85th percentile times do by more than 200 seconds.
63
3600 Actual TT 3500 Timetable TT Average 3400 85 Percentile 3300
3200 Travel time, s Travel 3100
3000
6:30 7:00 7:30 8:00 8:30 9:00 9:30
13:00 10:00 10:30 11:00 11:30 12:00 12:30 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 Time of day Figure 5.17 Travel times of Line 18 Farsta strand - Vällingby, NB
Travel times of the line 19 Hässelby strand – Hagsätra are presented on figures 5.18 and 5.19. A quick look at the graphs informs us that the actual travel times exceed the scheduled ones for the both directions.
During morning hours the trip along the line 19 southbound takes on average 50-60 seconds longer than it is proposed by the timetable. After midday the difference grows and reaches its maximal value of 150 seconds around 15:00 and then it slightly decreases. The difference between the 85th percentile times and scheduled times varies from around 120 seconds in the morning to almost 300 seconds in the late afternoon.
3800 Actual TT 3700 Timetable TT Average 3600 85 Percentile 3500 3400
Travel time, s Travel 3300 3200
3100
6:30 7:00 7:30 8:00 8:30 9:00 9:30
10:00 18:30 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 19:00 Time of day Figure 5.18 Travel times of Line 19 Hässelby strand - Hagsätra, SB 64
Concerning the northern direction of the line 19 one can notice that the difference between scheduled times and actual ones is almost constant and varies from 100 to 150 seconds during the studying period. The 85th percentile times differ from schedule by around 200 seconds reaching 250-300 during the peak hours.
3900 Actual TT Timetable TT 3800 Average 3700 85 Percentile 3600 3500
Travel time, s Travel 3400 3300
3200
6:30 7:00 7:30 8:00 8:30 9:00 9:30
10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 Time of day Figure 5.19 Travel times of Line 19 Hagsätra - Hässelby strand, NB
The analysis of travel times reveals that the trips along almost all the lines take on average more time than it is stipulated by the timetable. The biggest difference is being observed during peak hours, especially during evening ones. At the same time the difference between the average travel times and the scheduled ones, which varies from 1 to 3 minutes, may be considered as the punctual service according to SL standards. However, in order to develop more reliable and robust timetable operators usually use the 85th percentile travel time. In case of the line 18 and 19 the difference between the scheduled travel times and the 85th percentile ones exceed 3 minutes, the maximum value of punctual service, being the driving force of service unreliability.
5.3.5 Headway adherence Analysis of the table A.5 of Appendix and the graph on figure 5.20 report us on different level of service at the Green line stations. All the stations located in the downtown experience irregular service with some “bunching” according to TCQSM. “Bunching” of subway trains is not exactly the same as bunching of buses. Bunching 65 of trains is prohibited by signaling system which requires having buffer time between consecutive trains. The short intervals between the “bunching” trains make the signaling system to break the following trains. But concerning irregular headways and their influence on to service reliability the subway bunching is analogous to the bus bunching. The rest of the Green line stations operate under A and B level of service. Service is provided like clockwork there or the trains depart slightly off headway.
0.60 Direction "South" Direction "North" LOS E 0.50 LOS D 0.40 LOS C 0.30 LOS B 0.20
0.10 LOS A Coefficient Coefficient ofvariation (LOS)
0.00
ILT
JOL
SEP SKT SKY
BJH
STB
FAS
BLB TCE
SLU SKB
ALV
FAR
KÄT SAB TAK
GLB
VBY
FHP
KRB BLU
SNK
THP
ÄBP SOP
RÅC
HÄS
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG HAG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.20 Coefficient of variation in headways and Level of service
The difference in the level of service also can be explained by the length of headways. The downtown section has bigger number of passing trains and as result shorter intervals between them comparing to the stations located at the ends of the line. The variability of the short headways more considerably affects the level of service.
5.3.6 Headway distribution Summary statistics on headways is presented in table A.4. Coefficient of headway variation for the stations of both Green line directions is shown on figures 5.21 and 5.22. The coefficient is almost constant along all the segments of the line except the central one where it gradually accumulates as trains traverse through the stations of 66 the segment. The biggest value of the coefficient, more than 0.5, is observed at the stretch from Vällingby to Stora mossen and is characteristic for both directions. The lowest level of variation is typical for the Skarpnäck segment.
0.60 0.50 0.40 0.30 0.20 0.10
0.00
ILT
JOL
SEP SKT SKY
BJH
STB
FAS
BLB TCE
SLU SKB
ALV
SAB FAR
KÄT TAK
GLB
VBY
FHP
KRB BLU
SNK
THP
ÄBP SOP
Coefficient Coefficient ofvariation
RÅC
HÄS
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG HAG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.21 Coefficient of variation of headways at stations, SB 0.6 0.5 0.4 0.3 0.2 0.1
0
Coefficient Coefficient ofvariation
ILT
JOL
SKT SKY
SEP
BJH
STB
FAS
BLB
TCE
SLU SKB
ALV
KÄT SAB TAK FAR
GLB
VBY FHP
KRB BLU
THP SNK
SOP
ÄBP
RÅC
HÄS
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HAG HÄG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
BAM SMO RMG Stations Figure 5.22 Coefficient of variation of headways at stations, NB
The figures 5.23 and 5.24 demonstrate the distribution of headways as well as deviation from scheduled departures at stations of the Green line central segment from 6:30 till 19:00.
67
30% Odenplan 16.0% Odenplan Rådmansgatan Rådmansgatan 14.0% 25% Hötorget Hötorget 12.0% 20% 10.0% 15% 8.0% 6.0%
10% Relative frequency Relative Relative frequency Relative 4.0% 5% 2.0% 0% 0.0% -80 -40 0 40 80 120 160 200 240 280 70 100 130 160 190 220 250 280 310 340 Deviation, s Headway, s
20% T-centralen 25.0% T-centralen 18% Gamla Stan Gamla Stan Slussen Slussen 16% 20.0% Medborgarplatsen Medborgarplatsen 14% 12% 15.0% 10% 8% 10.0%
6% Relative frequency Relative Relative frequency Relative 4% 5.0% 2% 0% 0.0% -80 -40 0 40 80 120 160 200 240 280 70 100 130 160 190 220 250 280 310 340 Deviation, s Headway, s Figure 5.23 Deviation and headway distributions at central stations, SB
One can see that the shifting of the distribution curves of deviation from scheduled departure is characteristic for the Green line southbound. The curves shift to the right as train traverse through the stations. The exceptions for the chosen stations are T- centralen and Slussen. Timetable includes additional time at the central segment. Drivers use the time to catch up with the schedule that is why distribution curves for those stations shift to the left or stay the same as the previous station‟s deviation distribution. At the same time the curves of headway distribution have the same pattern and differ from each other at their peaks. The stations with lower curves are supposed to have more problems with regularity of headways. At the stretch from
68
Odenplan to Medborgarplatsen the stations with less regular service are Odenplan, Rådmansgatan, Hötorget and Medborgarplatsen.
Gullmarsplan 12.00% Gullmarsplan 25.0% Skanstull Skanstull Medborgarplatsen Medborgarplatsen 10.00% 20.0% Slussen Slussen T-centralen Gamla Stan 8.00% 15.0% 6.00% 10.0%
4.00%
Relative frequency Relative Relative frequency 5.0% 2.00%
0.0% 0.00% -80 -40 0 40 80 120 160 200 240 280 70 100 130 160 190 220 250 280 310 340 Deviation, s Headway, s
T-centralen 12.00% T-centralen 18.0% Hötorget Hötorget Rådmansgatan Rådmansgatan 16.0% 10.00% Odenplan Odenplan 14.0% St. Eriksplan St. Eriksplan 12.0% 8.00% 10.0% 6.00% 8.0%
6.0% 4.00% Relative frequency Relative Relative frequency Relative 4.0% 2.00% 2.0% 0.0% 0.00% -80 -40 0 40 80 120 160 200 240 280 70 100 130 160 190 220 250 280 310 340 Deviation, s Headway, s
Figure 5.24 Deviation and headway distributions at central stations, NB Figure 5.24 shows the same pattern for the distribution of deviation from scheduled departure at stations along the central section of Green line northbound. The shifting of the curves informs us that train delay accumulates at each next following station apart from Medborgarplatsen, Gamla Stan, T-centralen and Odenplan. It also can be explained by additional time stipulated by timetable for the stretches of central segment. The headway distribution curves tell us that Gullmarsplan and Skanstull have less regular service among the other stations of the Green line northbound central segment. 69
Irregular service is a key factor of the overcrowding effect. For example, looking at headway distribution at T-centralen one can see how irregular service affects overcrowding at the platforms of the station and as result on the rolling stocks.
This example considers the service at the station from 17:00 till 18:00. According to the data on the number of people entering T-centralen this period is the most crowded one during the day when around 16000 passengers enter the station. Basing on data (SL, 2009) the Green line share of passengers among the three lines is around 35%. Thus, during the chosen time period around 5600 passengers travel with Green line from T-centralen. The example uses the assumption, that the number of people traveling to the North and to the South is equal and is 2800 passengers. Another assumption is that if the headway is less or equal to the scheduled one platform is not overcrowded. Basing on this information one can calculate the number of commuters that gather on the platform waiting for the train on the days of the sample considered by the thesis as well as the number of people experiencing overcrowding.
The table 5.7 provides information on average headway and number of people on the platform for the trains of southern direction. The results show that due to irregular headways overcrowding always takes place on the platform during the studying period. On average more than 50% of people regularly experience overcrowding at the station.
Table 5.7 Crowding at T-centralen for the chosen sample from 17:00 till 18:00, SB Date (from the sample) Parameters Timetable 4 8 9 10 12 25 26 March March March March March March March Average headway, s 120 133 144 144 144 144 138 124 Coefficient of headway variation 0 0.5 0.49 0.59 0.65 0.42 0.54 0.34 Average number of people gathering during 93 104 112 112 112 112 108 97 the time between successive trains Percent of people experiencing 0 51 65 50 52 67 60 46 overcrowding during the studying period, %
70
For the platform of the northern direction the timetable already contains irregularity of the service, presupposing that 40% of passengers may experience overcrowding. The actual service due to not perfect adherence varies in terms of regularity. As a result, as demonstrated in table 5.8, there are days with even more regular service than it is stipulated by the timetable. Nonetheless, there are still on average around 40% of passengers that wait for the train in overcrowding conditions.
Table 5.8 Crowding at T-centralen for the chosen sample from 17:00 till 18:00, NB Date (from the sample) Parameters Timetable 4 8 9 10 12 25 26 March March March March March March March Average headway, s 150 164 164 157 150 144 138 157 Coefficient of headway variation 0.35 0.59 0.48 0.43 0.63 0.64 0.34 0.5 Average number of people gathering during 117 127 127 122 117 112 108 122 the time between successive trains Percent of people experiencing 40 42 50 36 35 38 25 45 overcrowding during the studying period, %
The overcrowding on the platform also causes overcrowding on the train which decreases the comfort of the trip as well as delays the train due to longer boarding time. Another negative side of the overcrowding is that not all the passengers are able to take the train due to the lack of places. The situation is even more severe for handicapped passengers. The failed boarding increases commuters‟ waiting time as well as lowering their level of satisfaction with the service.
5.3.7 Waiting times Irregular service increases waiting times for passengers. Figures 5.25 and 5.26 present the excess of the time passengers spend at stations of the Green line waiting for trains due to service irregularity. The bar charts show that passengers at all the stations of the line have to wait longer time than in the case when the service is regular. The minimal waiting time increasing is characteristic of the Skarpnäck segment.
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40
30
20 %
10
0
Increase ofwaiting time,
ILT
JOL
SKT SKY
SEP
BJH
STB
FAS
BLB
TCE
SLU SKB
ALV
KÄT SAB TAK FAR
GLB
VBY FHP
KRB BLU
THP SNK
SOP
ÄBP
RÅC
HÄS
ABB
GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HÄG HAG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.25 The increase of waiting time at stations, SB
40
30
20 %
10
0
Increase ofwaiting time,
ILT
JOL
SKT SKY
SEP
BJH
STB
FAS
BLB TCE
SLU
SKB
ALV
FAR SAB
KÄT TAK
GLB
VBY FHP
KRB BLU
THP SNK
ÄBP SOP
RÅC
HÄS
ABB GAS
ÅKH
BAH
RÅG
ENG
HÖT HYÖ
GUP
ODP
HAG HÄG
GUÄ
HÖÄ
SVM
HÖD
BMP MBP
SMO BAM RMG Stations Figure 5.26 The increase of waiting time at stations, NB
One can see that travelers that start their trip from the stations of the western segment in both directions have to wait on average greater time than commuters starting from other stations. For the southern direction the difference in waiting times of the regular service and irregular one at the station is around 25%, while for the northern direction the difference reaches almost 35%. For the both direction there is also a common pattern that waiting time grows gradually on the central segment getting the peak at ends of the segment. The waiting times at Alvik for passengers going to the North is 31% longer than if there is a regular service. The waiting times for passengers going to the South also exceed the waiting times of a regular service by around 30% at Skanstull, Medborgarplatsen and Skärmarbrink.
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5.4 Detailed analysis at stations Analyzing the line it is possible to define the stations experiencing more difficulties than the others. In table A.7 there is a list of stations with their summary indexes. The summary index shows the number of the reliability measures exceeding the limiting values subjectively proposed by the author in order to choose stations for the detailed analysis. The limits are: the number of boarding passengers more than 10000 per day; possibility to transfer to another line; the share of on-time trains is less than 80%; average delay at station is more than 180 seconds; average dwell time exceeds 30 seconds; coefficient of variation of the dwell times is more than 0.25; coefficient of headway variation is greater than 0.50; the level of service concerning the headway adherence is equal or lower than “D”; the waiting time is longer by 20% than the waiting time of the regular service.
According to the table two stations Slussen and Skanstull were chosen due to bigger number of extreme values of the studying parameters. The third station is T- centralen. It is not the station with considerably severe results but it is the central and the most crowded one which makes the station interesting to study in detail during day time service.
5.4.1 T-centralen Figure 5.27 demonstrates the on-time performance at the station throughout the period from 6:30 to 19:00. One can see the decreasing the percentage of on-time departure of the trains of the southern direction during morning and evening peaks. The considerable dip is observed at 17:00 when the share of on-time trains drops to about 64%. The morning dip in on-time service on the bar chart is less considerable and varies from 82% to 88%. For the trains departing in the northern direction the opposite tendency can be noticed. During the morning the amount of on-time trains decreases to 65% while in the evening peak hours the share of the trains is above 80%.
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100% 100%
80% 80% timetrains
60% time trains 60%
- -
40% 40%
20% 20%
Percent ofon Percent Percentof on
0% 0%
6:30 7:30 8:30 9:30
6:30 7:30 8:30 9:30
10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30
10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30 Time Time Figure 5.27 On-time performance at T-centralen, a) SB, b)NB
The average deviation from scheduled departure at T-centralen is shown on the figure 5.28. It is obvious that the trains of southern direction experience more considerable delay comparing to the southern trains. The exception is the morning peak hour, when southern trains‟ average deviation exceeds the deviation of the northern trains. The graphs show that the peak of delayed southern trains takes place from 8:00 till 9:00 in the morning, while the peak of the delayed northern trains occurs from at around 17:00.
200 SB NB 150
100
50
Average deviation, Average s 0
6:30 7:00 7:30 8:00 8:30 9:00 9:30
12:30 10:00 10:30 11:00 11:30 12:00 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 Time Figure 5.28 Average deviation from scheduled departure at T-centralen
Figure 5.29 demonstrates the average dwell times. The dwell times of the southern trains does not change considerably during the day varying from 45 to 50 seconds. The exception is the morning peak hour when the dwell times grow at 7:30 and reach 74 their maximum 55 seconds at 8:30. The increase ends at 9:00. The dwell times of the northern trains remain almost constant during the morning hours and are about 35 seconds. After 12:00 they gradually grow and reach the flat peak at 15:00 which lasts until 18:00. The dwell times vary between 45 and 50 seconds during the period.
60 SB NB 55 50 45 40 35
Average dwell times, Average s 30
7:30 6:30 7:00 8:00 8:30 9:00 9:30
10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 Time Figure 5.29 Average dwell times at T-centralen
The level of service based on the headway adherence at the station varies considerably during the day. On average the service can be characterized with irregular headway with some train bunching. The irregularity is slightly severe for the southern direction. During the evening peak the northern trains experience considerable irregularity when level of service reaches the level “F”.
1.00 SB NB LOS F 0.80 LOS E 0.60 LOS D 0.40 LOS C LOS B 0.20
Coefficient Coefficient ofvariation LOS A
0.00
6:30 7:00 7:30 8:00 8:30 9:00 9:30
10:00 15:30 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 16:00 16:30 17:00 17:30 18:00 18:30 Time Figure 5.30 Level of service at T-centralen
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5.4.2 Slussen The graph on figure 5.31 displays the same pattern for on-time departure at Slussen as at T-centralen. Southern direction experiences more considerable decrease in on time service during evening peak when the share of on time trains departing from the station drops to 57%. For the northern direction the significant fall is typical during morning hours when the percentage of on-time trains reaches 58-60%.
100% 100%
80% 80% timetrains
60% time trains 60%
- -
40% 40%
20% 20% Percent ofon Percent
0% Percentof on 0%
6:30 7:30 8:30 9:30
6:30 7:30 8:30 9:30
10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30
12:30 10:30 11:30 13:30 14:30 15:30 16:30 17:30 18:30 Time Time Figure 5.31 On-time performance at Slussen, a) SB, b)NB
Average deviation presented on figure 5.32 is similar for both directions during midday off-peak and is around 100 seconds. The difference between the directions is observed during peak hours. In the morning trains heading to the North experience greater deviation comparing to the southern trains. The deviation peaks at 8:00 and has a value of around 190 seconds. In the evening the situation with the deviation is opposite. The trains going to the South are delayed more and the average deviation reaches almost 200 seconds at 17:00.
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250 SB NB 200 150 100 50
Average deviation, Average s 0
9:00 6:30 7:00 7:30 8:00 8:30 9:30
10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 Time Figure 5.32 Average deviation from scheduled departure at Slussen
Dwell times change for both directions at Slussen has almost the same pattern as the average deviation graphs. In the morning the average dwell times of trains heading to the North are greater than the dwell times of southern trains. They reach their summit at 8:00 and are more than 45 seconds. In the evening average dwell times of the trains departing to the North remain almost constant and are equal to 30-35 seconds. In contrast the dwell times of the southern trains grow and have a flat peak which continues from 14:30 until 18:00. The dwell times vary between 40 and 45 seconds during the period.
50 SB NB 45 40 35 30 25
20
Average dwell times, Average s
9:30 6:30 7:00 7:30 8:00 8:30 9:00
12:00 14:30 17:00 10:00 10:30 11:00 11:30 12:30 13:00 13:30 14:00 15:00 15:30 16:00 16:30 17:30 18:00 18:30 Time Figure 5.33 Average dwell times at Slussen
Headway adherence at the station varies considerably and on average corresponds to the level D for both directions. In the morning the southern direction experiences the low level of service which is accompanied with frequent bunching. For the evening it
77 is typical for both directions. The worst headway adherence is observed for the trains departing to the North around 18:00. 1.00 SB NB LOS F 0.80 LOS E 0.60 LOS D 0.40 LOS C LOS B 0.20 Coefficient Coefficient ofvariation LOS A
0.00
6:30 7:00 7:30 8:00 8:30 9:00 9:30
13:00 16:30 10:00 10:30 11:00 11:30 12:00 12:30 13:30 14:00 14:30 15:00 15:30 16:00 17:00 17:30 18:00 18:30 Time Figure 5.34 Level of service at Slussen
5.4.3 Skanstull At Skanstull the on-time performance of the trains is different for two directions. Trains heading to the North depart mostly on-time experiencing some slight difficulties during peak hours. The lowest value of on-time service for the direction is observed at 18:00 when it is equal to 84%. The share of the on-time departed trains of southern direction is significantly lower. In the morning it drops to the value of 72% while in the evening hours the drop is more considerable and has longer duration. The lowest on-time service is typical for the period from 17:00 till 18:00 when the share of on-time departed trains is around 50-55%.
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100% 100%
80% 80% timetrains
60% timetrains 60%
- -
40% 40%
20% 20%
Percent ofon Percent Percent ofon Percent
0% 0%
6:30 7:30 8:30 9:30
6:30 7:30 8:30 9:30
11:30 10:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30
10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30 Time Time Figure 5.35 On-time performance at Skanstull, a) SB, b)NB
Average deviations from scheduled departures at Skanstull for the northern trains as one can see on the figure 5.36 are nearly constant sticking to the value of around 100 seconds. The peaky character of the graph is more obvious for the trains departing to the South. The trains experience delay of up to 150 seconds in the morning and up to 200 seconds in the evening.
250 SB NB 200 150 100
50 Average deviation, Average s
0
6:30 7:00 7:30 8:00 8:30 9:00 9:30
12:00 13:00 14:00 15:00 16:00 17:00 18:00 10:00 10:30 11:00 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30 Time Figure 5.36 Average deviation from scheduled departure at Skanstull
Looking at the figure 5.37 one can see that in the morning hours the dwell times of the trains heading to the North exceed the dwell times of the southern trains when they reach 35 seconds and the peak lasts from 7:30 until 9:00. For the northern direction the average dwell times are about 25-27 seconds during the time period. After the midday off-peak the average dwell times are almost the same for both
79 directions at Skansen slightly differing in the evening. For the southern trains the values are almost 35 seconds while for the northern ones they are about 32 seconds.
45 SB NB 40 35 30 25 20
Average dwell times, Average s 15
8:30 6:30 7:00 7:30 8:00 9:00 9:30
18:00 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:30 Time Figure 5.37 Average dwell times at Skanstull
The headway adherence at Skanstull shown on figure 5.42 is different for two directions during the day. For the southern trains it is on average the level of service “D” while for the northern trains it varies from “B” to “C”. The evening peak hours demonstrate the same pattern as the station discussed above. One can notice the low level of service concerning headway adherence for the both directions from 17:00 till 18:00 when it reaches the levels “E” and “F” which are characterized with frequent bunching of the trains. 1.00 SB NB LOS F 0.80 LOS E 0.60 LOS D 0.40 LOS C LOS B 0.20 Coefficient Coefficient ofvariation LOS A
0.00
9:30 6:30 7:00 7:30 8:00 8:30 9:00
13:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 Time Figure 5.38 Level of service at Skanstull
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Chapter 6: Conclusions
6.1 Summary and conclusions The thesis showed that the data collected and stored in SL‟s database RUST is sufficient to calculate different reliability measures which can be applied to evaluate the service performance at any moment and during different time periods. Comparing to manual data collection it is a fast and less costly method to examine the system and reveal the stretches and stations experiencing difficulties. The greater use of the automatically collected data will assist in better understanding of the background reasons of the problems and let to deal with them timely.
The case study analysis brought out conclusions concerning punctuality and the regularity of the service on the Green line during March, 2010. Besides, the thesis demonstrated the feasibility of the timetable and how the actual operations fitted it.
Data analysis revealed decreasing of punctuality along the line. The delay accumulated when train traverses through the stations and share of on-time departing decline correspondently. The least satisfactory results of on-time service were observed for the Green line northbound, on the stretch between Råcksta and Hässelby strand, where the share of on-time trains dropped lower 60%. The other problematic stretch concerning the train delays is the northern part of Farsta segment for the trains heading to the South. At stations Blåsut, Sandsborg and Skogskyrkogården considerable positive deviation from scheduled departure can be noticed. That provides us with a hint of possible technical problems along the stretch which should be studied in detail.
The analysis of travel times showed that they on average exceeded the times proposed by the timetable. The biggest difference was associated with peak hours, especially evening ones. Inconsistency between the average actual travel time and the scheduled
81 one varied from 1 to 3 minutes. This fits the SL‟s requirements of punctuality but it may contribute to the unreliability of the service during peak hours.
The longer dwell times and their variability were typical at the transfer stations and big terminals.
Irregular headways due to the timetable and the fluctuation in the actual service together contribute not only to increase of waiting times but also to overcrowding on the platforms. For example, at T-centralen during 1 hour of service, 40-50% of the passengers experienced overcrowding conditions on the platforms. Overcrowding affects dwell times and causes train delay. The other negative side of the phenomenon is the lowering of customers‟ level of satisfaction.
Analysis of the stations revealed the difference in transport demand between two directions during the day. It showed that the trains heading to the North experienced more difficulties with reliability carrying out the service during morning peak hour while the southern trains usually had problems with reliable operations during the evening.
6.2 Future research Future research can concern two important areas to study: data processing and data using.
Improving data processing will help to simplify the evaluation of subway operations. SL is interested in a convenient evaluation process of the subway performance. Development of software, which is automatically able to calculate reliability parameters for any time period, is a question of interest. The software should provide the planners and the operator with actual information on the subway performance, which could help to quickly determine problematic track sections and the bottlenecks of the system as well as remove those defects as early as they appear.
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The other topic of data processing is to create the possibility to get the data on-line from the data storage system in the traffic control center and monitor the situation in real time. That will allow early problem detection and early intervention. For example, SL collects electronic data from fare payment system about the number of entering and leaving the system passengers. Combining the information together with actual train data will help to determine actual overcrowding at platforms due to irregular service and take a decision aiming to improve the situation.
Concerning the question of data using there could be next interesting topic: modeling of the line service. The collected data can be used to build a model which analyzes the system‟s capacity and performance under different extreme conditions. This analysis will help to detect weak elements of the system as well as propose possible solutions to escape the breakdowns of the system.
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References
Abkowitz, M.; Slavin, H.; Waksman, R.; Englisher, L.; Wilson, N. H. M., Transit Service Reliability, Technical Report UMTA-MA-06-0049-78-1, US DOT Transportation Systems Center, Cambridge, MA, 1978. Bertini Robert L., El-Geneidy Ahmed, Using archived data to gtenerate transit performance measures, Washington D.C., 2003 Boström Martin, Se upp för dörrarna! Dörrarna stängs, Stockholm Universitet, 1982. Bylund Andreas and Lindholm Fredrik, Punktlig kollektivtrafik, Examensarbete, Kungliga Tehniska Högskolan, Stockholm, 2004. Carey Malachy, Ex ante heuristic measures of schedule reliability, Transportation Research Part B: Methodological, Elsevier, vol. 33(7), pages 473-494, September 1999. Ceder Avishai, Public Transit Planning and Operation: Theory, Modeling and Practice, UK, 2007 Dixon Matthew C., Analysis of a subway operations system database: the MBTA operations control system, thesis, Northeastern University, Boston, Massachusetts, 2006 Doyle Michael T., A Field Study of Subway Service Reliability, New York City Transit Riders Council, August 2000 Dr. Neil Gunther, Of Buses and Bunching: Strangeness in the Queue, 2001 Fakta om SL och länet 2008 (SL, 2008). SL rapport, AB Storstockholms Lokaltrafik, 2009 Karimian Maria, Operatörsgränssnitt för manöversystem, Examensarbete inom kurs i datalogi, Stockholms universitet, Stockholm, 2004. Kittelson & Associates, Inc., KFH Group, Inc., Parsons Brinckerhoff Quade & Douglass, Inc., Katherine Hunter-Zaworski (2003) Transit Capacity and Quality of Service Manual-2nd Edition, Transportation Research Board, National Academy Press, Washington, DC.
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Litman Todd, Valuing Transit Service Quality Improvements, Victoria Transport Policy Institute, 2010 Nie Lei and Hansen Ingo A., System analysis of train operations and track occupancy at railway stations, EJTIR, 5, no. 1 (2005), pp. 31-54 Niels van Oort and Rob van Nes, Regularuty analysis for optimizing urban transit, Public Transport 2009 1 (155-168) Schwandl Robert, U-bahnen in Skandinavien, Berlin, 2004. Seung-Young Kho, Jun-Sik Park, Young-Ho Kim, Eun-Ho Kim, A development of punctuality index for bus operation, Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 492 - 504, 2005 SL och Framtiden, en specialtidning från Nordisk Infrastruktur, Redaktör: Christian Hillbom, Malmö, 2007. SL Trafiken i sifror 2009 (SL, 2009). SL‟s sammanställning av av de gångna årens trafik i samarbete med ÅF-Infrastruktur.
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Appendix
87
Table A.1 On-time performance at stations of the Green line
Deviation "South", % Deviation "North", % Deviation "South", % Deviation "North", % Station Station <-60 s -60-+180 s >+180 s <-60 s -60-+180 s >+180 s <-60 s -60-+180 s >+180 s <-60 s -60-+180 s >+180 s HÄS 0 97.3 2.7 0 65.6 34.4 GUP 0.6 88.2 11.1 0.1 96.8 3.0 HÄG 0 95.9 4.1 0 22.7 77.3 SKB 0 84.4 15.6 0 97.3 2.7 JOL 0 94.5 5.5 0 35.4 64.6 HYÖ 0 81.9 18.1 0 99.6 0.4 VBY 0 73.9 26.1 0.1 58.9 41.0 BJH 0 84.2 15.8 0 99.4 0.6 RÅC 0 90.0 10.0 0.0 58.0 42.0 KÄT 0.4 88.9 10.7 0 100 0 BLB 0 91.8 8.2 0.1 72.0 27.9 BAM 0 86.5 13.5 0 100 0 ILT 0 91.9 8.1 0.1 70.0 29.9 SNK 0.8 89.9 9.4 0.2 99.8 0.0 ÄBP 0 91.4 8.6 0.1 74.1 25.8 BLU 0 74.4 25.6 0 96.4 3.6 ÅKH 0.4 96.7 2.9 0.5 85.4 14.1 SAB 0 68.5 31.5 0.2 96.8 3.1 BMP 0 93.6 6.4 0.1 81.1 18.8 SKY 0 58.8 41.2 0 96.0 4.0 ABB 0 93.7 6.3 0.1 75.4 24.4 TAK 0 66.7 33.3 0 95.0 5.0 SMO 0 95.1 4.9 0.1 72.0 27.9 GUÄ 0 70.3 29.7 0 95.5 4.5 ALV 0.2 96.2 3.6 0.7 86.1 13.2 HÖÄ 0.2 79.1 20.8 0 96.0 4.0 KRB 0 92.7 7.3 0 68.3 31.7 FAR 0 79.4 20.6 0 95.1 4.9 THP 0 89.3 10.7 0 76.0 24.0 FAS 3.3 84.9 11.8 0 95.9 4.1 FHP 0 89.2 10.8 0 68.6 31.4 GLB 0.2 77.1 22.7 0 98.2 1.8 SEP 0.1 90.9 9.0 0 60.2 39.8 ENG 0.2 68.8 31.0 0 97.7 2.3 ODP 0.6 95.7 3.7 0 79.6 20.4 SOP 0.2 77.0 22.9 0 98.7 1.3 RMG 0.1 92.8 7.1 0 71.0 29.0 SVM 0.2 70.6 29.2 0 98.5 1.5 HÖT 0.1 85.8 14.1 0 89.0 11.0 STB 0.2 76.3 23.5 0 98.5 1.5 TCE 0.2 88.1 11.7 0.3 93.4 6.3 BAH 0.2 69.8 30.1 0 99.5 0.5 GAS 0.1 83.0 16.9 0 87.4 12.6 HÖD 0.2 76.4 23.5 0 99.7 0.3 SLU 0.1 82.1 17.8 0 88.1 11.9 RÅG 0 77.5 22.5 0 99.8 0.2 MBP 0.1 77.5 22.4 0 93.6 6.3 HAG 0.2 78.8 21.1 0 99.6 0.4 SKT 0.1 77.5 22.5 0 94.8 5.0
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Table A.2 Average values of deviation from scheduled departure and its variability at stations
Delay “South” Delay “North”
tile, s tile,
s2 s2
st. st. st.
CV CV
lower lower lower
upper min,s upper min,s
max,s max,s
Sample Sample
mean,s mean,s median,s
Station median,s
quartile,s quartile,s quartile,s quar
deviation, deviation, HÄS 585 25 54 2.12 590 21 7 2 -29 540 149 99 0.67 726 209 136 81 -44 HÄG 585 67 55 0.81 649 70 52 41 8 542 253 101 0.40 838 317 241 185 59 JOL 585 89 50 0.56 411 93 74 63 35 542 221 99 0.45 775 281 209 155 15 VBY 839 111 143 1.28 624 213 29 24 1 800 175 108 0.61 963 235 161 101 -82 RÅC 858 120 54 0.45 447 136 108 89 0 843 178 111 0.62 1019 234 160 108 -60 BLB 858 106 55 0.52 436 123 95 74 -14 849 144 106 0.73 981 197 128 78 -97 ILT 865 104 56 0.54 432 121 92 70 -21 849 150 101 0.68 848 201 134 84 -94 ÄBP 865 106 57 0.54 436 124 94 71 -22 849 140 99 0.71 824 192 124 75 -106 ÅKH 1386 38 54 1.42 407 53 21 5 -148 1352 98 93 0.95 784 145 86 37 -147 BMP 1386 93 54 0.59 454 113 77 61 -31 1359 119 92 0.77 773 167 106 60 -127 ABB 1386 89 56 0.62 455 110 74 56 -37 1359 141 90 0.64 782 187 128 83 -103 SMO 1392 77 57 0.74 448 98 61 42 -48 1356 150 89 0.60 761 195 137 92 -96 ALV 1755 52 62 1.21 475 78 35 8 -115 1720 94 90 0.96 706 141 81 34 -127 KRB 1755 89 65 0.73 530 119 73 45 -50 1733 156 90 0.58 792 203 143 93 -28 THP 1755 106 66 0.62 548 140 91 61 -35 1733 139 88 0.63 770 186 127 79 -41 FHP 1748 101 68 0.67 548 136 85 54 -45 1733 156 86 0.55 789 202 144 97 -18 SEP 1748 89 69 0.78 532 127 75 40 -65 1733 174 84 0.48 798 221 163 117 0 ODP 1741 43 66 1.54 486 74 22 1 -125 1737 133 83 0.62 756 177 121 77 -40 RMG 1748 74 69 0.93 527 108 54 28 -100 1744 154 80 0.52 788 198 141 99 -14 HÖT 1748 107 76 0.71 620 144 85 56 -81 1744 103 78 0.75 752 143 89 50 -54 TCE 1748 89 82 0.93 642 128 66 32 -110 1751 65 75 1.16 714 101 51 13 -84 GAS 1748 110 87 0.79 684 151 87 51 -84 1751 121 70 0.58 733 156 111 75 -21 SLU 1748 111 92 0.82 727 157 87 48 -88 1751 119 69 0.58 736 153 109 74 -17 MBP 1741 129 95 0.73 751 179 104 63 -75 1758 93 64 0.69 718 121 84 53 -30 SKT 1734 127 96 0.75 742 177 102 61 -74 1758 90 60 0.67 715 116 81 54 -34 89
Table A.2 Average values of deviation from scheduled departure and its variability at stations (continuation)
Delay “South” Delay “North”
iation,
s2 s2
CV CV
lower lower lower
upper min,s upper min,s
max,s max,s
Sample Sample
mean,s mean,s
median,s median,s
quartile,s quartile,s quartile,s quartile,s st. deviation, st. Station dev st. GUP 1741 69 92 1.33 688 108 35 4 -169 1758 53 56 1.06 670 74 44 20 -62 SKB 1129 105 87 0.83 735 143 73 47 -50 1156 42 53 1.26 532 58 35 14 -57 HYÖ 513 129 81 0.62 514 158 98 76 0 535 71 23 0.32 236 85 68 57 0 BJH 513 111 83 0.74 506 145 81 56 -28 535 98 20 0.21 260 111 96 86 29 KÄT 513 74 84 1.13 470 107 44 17 -65 535 71 16 0.23 152 82 69 61 6 BAM 513 93 87 0.93 495 133 63 36 -56 535 60 15 0.25 130 69 58 50 0 SNK 513 53 88 1.68 458 91 22 -5 -96 535 9 12 1.43 81 12 5 0 -63 BLU 616 147 93 0.63 772 189 115 82 -21 614 35 60 1.71 485 47 21 1 -53 SAB 616 163 94 0.58 789 210 130 99 0 621 19 58 2.99 395 30 5 -13 -63 SKY 609 192 95 0.49 823 241 160 127 16 621 48 57 1.20 427 58 33 17 -26 TAK 609 168 96 0.57 805 217 136 102 -14 621 71 56 0.80 452 80 55 41 0 GUÄ 616 153 98 0.64 813 206 121 87 -27 621 61 56 0.92 440 67 45 32 0 HÖÄ 611 118 99 0.84 778 171 87 50 -63 621 37 55 1.50 413 40 20 10 -19 FAR 617 114 101 0.88 780 167 85 45 -60 608 75 54 0.71 442 76 58 51 31 FAS 610 60 103 1.72 722 105 33 -10 -120 615 25 54 2.17 391 21 6 1 -20 GLB 612 125 102 0.82 728 178 94 51 -128 602 70 38 0.54 308 90 64 45 -16 ENG 612 155 103 0.67 755 210 124 80 -100 602 98 35 0.35 333 118 92 76 9 SOP 612 124 104 0.84 730 182 93 50 -130 602 67 32 0.48 302 83 62 46 -13 SVM 612 147 106 0.72 771 205 115 71 -113 602 79 30 0.37 317 95 74 61 8 STB 612 125 107 0.86 751 185 93 46 -136 602 71 28 0.39 306 84 66 55 13 BAH 612 147 109 0.74 790 211 117 69 -115 602 47 26 0.55 287 58 42 33 0 HÖD 605 119 112 0.94 771 182 91 39 -166 602 19 24 1.30 265 25 13 6 -20 RÅG 506 118 109 0.93 774 181 91 38 -49 531 47 21 0.45 286 54 42 37 15 HAG 513 109 114 1.04 761 170 83 25 -60 531 11 21 1.88 254 12 6 2 -28 90
Table A.3 Average values of dwell times and their variability at stations
Dwell time "South" Dwell time "North"
Station
N N
s2 s2
CV CV
lower lower lower
upper min,s upper min,s
max,s max,s
mean,s mean,s
median,s median,s
quartile,s quartile,s quartile,s quartile,s
st. deviation, st. deviation, st. HÄG 585 27 5 0.19 65 29 26 23 17 542 27 6 0.22 74 30 26 23 16 JOL 585 19 9 0.45 192 20 18 16 10 542 24 4 0.18 60 26 23 21 16 VBY 585 27 8 0.29 109 30 25 23 14 542 30 7 0.24 110 33 29 26 17 RÅC 839 28 5 0.17 85 29 27 25 19 800 28 11 0.38 156 29 26 24 18 BLB 858 24 5 0.19 57 26 23 21 16 849 27 10 0.38 206 28 25 23 15 ILT 865 23 4 0.17 52 24 22 20 14 849 25 5 0.20 78 27 24 22 16 ÄBP 865 22 4 0.19 54 24 22 20 13 849 22 5 0.25 129 24 22 20 13 ÅKH 865 23 10 0.44 134 24 21 19 11 849 24 8 0.35 203 25 23 21 15 BMP 1386 27 8 0.29 178 30 26 23 14 1359 31 6 0.18 65 33 30 27 17 ABB 1386 21 4 0.20 67 23 20 18 12 1359 22 4 0.19 58 24 21 19 1 SMO 1392 22 6 0.26 166 23 21 19 14 1356 23 4 0.16 52 25 23 21 14 ALV 1385 35 19 0.53 377 41 30 25 16 1356 38 15 0.39 126 44 34 28 11 KRB 1755 25 9 0.35 335 26 24 22 16 1733 22 12 0.53 353 23 21 19 1 THP 1755 26 5 0.21 106 28 25 23 16 1733 25 10 0.39 323 26 24 22 1 FHP 1748 28 6 0.22 89 31 27 24 15 1733 27 7 0.25 163 29 26 23 1 SEP 1748 26 8 0.29 234 28 25 22 14 1733 32 5 0.16 108 34 31 29 1 ODP 1741 39 14 0.37 111 41 34 30 20 1737 29 7 0.24 211 31 28 25 17 RMG 1748 29 7 0.24 145 32 28 25 14 1744 28 5 0.18 111 30 28 25 1 HÖT 1748 28 9 0.33 200 31 27 23 14 1744 32 6 0.19 117 34 31 28 1 TCE 1748 40 11 0.27 172 45 38 33 14 1751 47 12 0.25 150 52 45 40 6 GAS 1748 30 7 0.25 98 33 29 25 17 1751 28 5 0.19 72 30 27 25 1 SLU 1748 37 10 0.26 116 42 35 31 19 1751 34 9 0.27 179 38 32 29 1 MBP 1741 39 12 0.29 142 47 37 30 6 1758 30 7 0.23 118 33 29 26 1 SKT 1734 30 8 0.25 68 34 29 25 15 1758 31 8 0.26 231 34 30 27 1 91
Table A.3 Average values of dwell times and their variability at stations (continuation)
Dwell time "South" Dwell time "North"
Station
N N
s2 s2
CV CV
lower lower lower
upper min,s upper min,s
max,s max,s
mean,s mean,s
median,s median,s
quartile,s quartile,s quartile,s quartile,s
st. deviation, st. deviation, st. GUP 1734 49 22 0.44 211 59 43 34 18 1758 45 23 0.51 379 52 39 31 18 SKB 1129 25 5 0.22 90 27 24 22 15 1156 27 14 0.51 230 28 24 21 14 HYÖ 513 28 5 0.18 56 30 27 25 19 535 27 5 0.17 57 29 26 24 18 BJH 513 30 11 0.37 230 31 28 26 20 535 27 9 0.35 195 28 26 23 15 KÄT 513 29 6 0.21 78 31 28 25 16 535 31 5 0.16 67 34 30 28 21 BAM 513 29 5 0.17 54 32 29 25 18 535 26 5 0.19 51 29 25 23 17 BLU 616 29 6 0.19 73 31 28 26 19 614 24 6 0.24 106 26 23 21 13 SAB 616 24 5 0.20 63 26 24 21 14 621 28 5 0.19 54 32 27 24 15 SKY 609 20 4 0.20 56 22 20 18 13 621 23 5 0.22 70 24 22 20 14 TAK 609 22 4 0.18 53 24 21 19 1 621 21 4 0.20 46 23 21 19 10 GUÄ 616 25 10 0.40 193 26 23 21 15 621 28 5 0.20 70 30 27 25 17 HÖÄ 611 26 6 0.22 87 27 25 22 16 615 24 5 0.21 59 26 23 21 14 FAR 617 30 9 0.32 142 32 28 25 17 608 26 8 0.32 125 28 25 22 13 GLB 612 28 8 0.28 152 30 26 24 16 602 28 10 0.37 163 30 26 24 17 ENG 612 24 4 0.17 69 26 24 22 1 602 25 9 0.38 233 26 24 22 17 SOP 612 24 4 0.19 52 25 23 21 16 602 21 6 0.28 104 23 20 18 11 SVM 612 30 5 0.17 65 32 29 27 18 602 25 4 0.15 45 27 24 22 16 STB 612 26 8 0.31 195 27 25 23 16 602 24 4 0.16 69 26 24 22 16 BAH 612 26 10 0.40 246 27 24 22 17 602 25 4 0.16 48 27 24 22 16 HÖD 506 31 8 0.25 83 34 29 26 17 531 33 7 0.20 98 35 32 29 21 RÅG 506 27 6 0.22 90 30 26 24 17 531 26 6 0.21 89 28 25 23 17
92
Table A.4 Average values of headway and its variability at stations
Headway "South" Headway "North"
Station
CV CV
,s2 ,s2
lower lower lower
upper min,s upper min,s
max,s max,s
tandard
mean,s mean,s
standard s
deviation deviation
median,s median,s
quartile,s quartile,s quartile,s quartile,s HÄS 539 182 0.34 1238 607 595 504 75 HÄG 539 185 0.34 1244 613 591 507 79 577 193 0.33 1860 675 588 488 84 JOL 540 185 0.34 1250 614 589 504 80 577 191 0.33 1855 676 588 490 84 VBY 377 185 0.49 1207 578 314 254 63 578 190 0.33 1851 675 588 495 90 RÅC 368 187 0.51 1133 573 309 236 73 389 219 0.56 1427 550 379 189 84 BLB 368 187 0.51 1144 570 310 233 81 368 218 0.59 1431 533 343 176 76 ILT 368 187 0.51 1149 564 313 235 76 368 217 0.59 1415 533 347 178 74 ÄBP 368 188 0.51 1152 560 313 236 81 368 216 0.59 1409 537 347 177 81 ÅKH 228 119 0.52 932 284 205 134 9 368 215 0.58 1399 532 345 176 86 BMP 228 116 0.51 860 286 211 132 77 232 139 0.60 950 301 199 118 77 ABB 228 116 0.51 831 284 212 133 79 232 137 0.59 943 300 201 118 79 SMO 228 117 0.51 838 285 209 133 78 228 128 0.56 838 299 201 118 75 ALV 179 77 0.43 769 229 169 116 70 228 127 0.56 826 301 200 119 85 KRB 179 78 0.43 762 227 168 113 77 182 88 0.48 685 232 166 110 70 THP 179 79 0.44 763 229 168 113 75 182 86 0.47 676 231 166 111 72 FHP 179 81 0.45 760 230 165 110 75 182 84 0.46 667 231 168 112 72 SEP 180 82 0.46 753 229 164 108 75 181 80 0.44 642 229 168 114 76 ODP 180 81 0.45 757 228 168 112 74 181 77 0.43 624 227 169 115 80 RMG 180 83 0.46 761 230 166 110 77 181 75 0.41 603 226 169 117 83 HÖT 180 86 0.47 759 230 163 107 77 181 72 0.40 561 225 171 119 76 TCE 180 89 0.49 743 231 162 105 73 180 70 0.39 540 223 170 119 87 GAS 180 90 0.50 715 231 160 105 76 180 69 0.39 545 223 170 120 81 SLU 180 92 0.51 709 231 156 105 79 180 68 0.38 569 223 173 120 84 MBP 180 96 0.53 722 231 155 102 75 180 66 0.37 562 222 175 123 82 SKT 181 99 0.55 737 232 152 100 77 179 65 0.36 560 221 176 124 79 93
Table A.4 Average values of headway and its variability at stations (continuation)
Headway "South" Headway "North"
2 2
Station er
CV CV
lower lower lower
upper min,s upp min,s
max,s max,s
mean,s mean,s
Standard Standard
median,s median,s
quartile,s quartile,s quartile,s quartile,s
deviation,s deviation,s GUP 181 93 0.51 750 228 158 114 13 179 64 0.36 554 220 179 129 45 SKB 277 158 0.57 1016 414 225 132 81 273 109 0.40 643 362 256 192 71 HYÖ 608 144 0.24 1773 643 598 552 204 589 99 0.17 1253 616 597 574 285 BJH 608 146 0.24 1776 645 598 546 203 589 98 0.17 1241 614 598 578 286 KÄT 614 151 0.25 1776 650 601 547 201 590 97 0.16 1239 613 598 581 292 BAM 614 153 0.25 1776 652 603 545 185 590 97 0.16 1224 611 598 583 280 SNK 590 96 0.16 1217 606 599 589 297 BLU 510 205 0.40 2177 621 558 342 104 507 200 0.40 1802 613 575 394 79 SAB 510 205 0.40 2167 623 557 341 100 507 199 0.39 1800 611 577 399 81 SKY 509 206 0.40 2167 625 554 335 98 507 197 0.39 1798 610 577 400 91 TAK 509 207 0.41 2160 626 553 338 96 508 198 0.39 1793 609 581 395 79 GUÄ 514 212 0.41 2152 633 556 339 95 508 197 0.39 1799 608 582 394 85 HÖÄ 515 214 0.42 2150 635 553 336 96 508 196 0.39 1792 607 584 397 80 FAR 516 215 0.42 2133 638 554 339 98 511 202 0.40 2444 605 586 404 76 FAS 511 201 0.39 2452 602 592 401 72 GLB 514 222 0.43 1361 641 560 397 82 523 172 0.33 1818 617 579 390 93 ENG 514 223 0.43 1367 643 561 394 89 523 170 0.33 1804 615 581 385 95 SOP 514 224 0.44 1378 642 560 393 78 523 169 0.32 1803 615 581 383 95 SVM 519 228 0.44 1395 647 562 389 95 523 168 0.32 1805 612 583 380 100 STB 514 225 0.44 1401 643 553 385 79 523 168 0.32 1809 610 586 379 111 BAH 514 228 0.44 1408 645 555 381 83 523 167 0.32 1820 608 587 378 110 HÖD 614 171 0.28 1411 691 610 515 92 523 167 0.32 1826 608 589 370 104 RÅG 614 173 0.28 1418 696 608 515 86 593 115 0.19 1835 611 598 586 294 HAG 593 114 0.19 1832 606 599 590 235 94
Table A.5 Level of service at stations
Direction "South" Direction "North" Direction "South" Direction "North" Station Station CV LOS CV LOS CV LOS CV LOS HÄS 0.12 A - - GUP 0.49 D 0.39 C HÄG 0.12 A 0.22 B SKB 0.30 B 0.25 B JOL 0.12 A 0.22 B HYÖ 0.16 A 0.05 A VBY 0.20 A 0.21 A BJH 0.16 A 0.05 A RÅC 0.20 A 0.30 B KÄT 0.16 A 0.04 A BLB 0.20 A 0.31 C BAM 0.17 A 0.04 A ILT 0.21 A 0.31 C SNK - - 0.03 A ÄBP 0.21 A 0.30 B BLU 0.19 A 0.15 A ÅKH 0.32 C 0.30 B SAB 0.19 A 0.15 A BMP 0.30 B 0.43 D SKY 0.19 A 0.14 A ABB 0.31 C 0.41 D TAK 0.20 A 0.14 A SMO 0.31 C 0.42 D GUÄ 0.20 A 0.14 A ALV 0.41 D 0.41 D HÖÄ 0.20 A 0.14 A KRB 0.42 D 0.50 D FAR 0.21 A 0.13 A THP 0.43 D 0.49 D FAS - - 0.13 A FHP 0.44 D 0.48 D GLB 0.24 B 0.10 A SEP 0.44 D 0.47 D ENG 0.24 B 0.09 A ODP 0.42 D 0.46 D SOP 0.24 B 0.08 A RMG 0.43 D 0.45 D SVM 0.24 B 0.08 A HÖT 0.45 D 0.44 D STB 0.25 B 0.07 A TCE 0.47 D 0.43 D BAH 0.25 B 0.07 A GAS 0.49 D 0.44 D HÖD 0.22 B 0.07 A SLU 0.50 D 0.43 D RÅG 0.22 B 0.05 A MBP 0.51 D 0.41 D HAG - - 0.05 A SKT 0.52 D 0.41 D 95
Table A.6 Waiting time at stations SB NB Waiting time Actual Waiting time Actual Station Average Average CV with regular waiting % increase CV with regular waiting % increase headway, s headway, s headway, s time, s headway, s time, s HÄS 539 0.34 269.6 300.3 11.4 HÄG 539 0.34 269.6 301.3 11.8 577 0.33 288.4 320.6 11.2 JOL 540 0.34 270.0 301.5 11.7 577 0.33 288.4 320.0 11.0 VBY 377 0.49 188.3 233.5 24.0 578 0.33 289.2 320.3 10.8 RÅC 368 0.51 184.1 231.5 25.7 389 0.56 194.3 255.9 31.7 BLB 368 0.51 184.1 231.7 25.8 368 0.59 184.0 248.4 35.0 ILT 368 0.51 184.0 231.6 25.9 368 0.59 184.0 248.1 34.8 ÄBP 368 0.51 184.0 231.9 26.0 368 0.59 184.0 247.4 34.5 ÅKH 228 0.52 114.1 145.1 27.3 368 0.58 184.0 246.7 34.1 BMP 228 0.51 114.1 143.7 25.9 232 0.60 115.8 157.3 35.8 ABB 228 0.51 114.1 143.7 25.9 232 0.59 115.8 156.2 34.8 SMO 228 0.51 113.9 144.0 26.4 228 0.56 114.2 150.2 31.5 ALV 179 0.43 89.7 106.3 18.4 228 0.56 114.2 149.4 30.8 KRB 179 0.43 89.7 106.6 18.9 182 0.48 90.9 112.0 23.2 THP 179 0.44 89.7 107.2 19.5 182 0.47 90.9 111.1 22.2 FHP 179 0.45 89.6 107.9 20.4 182 0.46 90.9 110.2 21.1 SEP 180 0.46 89.9 108.6 20.8 181 0.44 90.4 108.0 19.4 ODP 180 0.45 89.9 108.1 20.3 181 0.43 90.4 106.9 18.3 RMG 180 0.46 90.2 109.2 21.2 181 0.41 90.3 105.7 17.1 HÖT 180 0.47 90.2 110.5 22.5 181 0.40 90.3 104.8 16.1 TCE 180 0.49 90.2 112.0 24.1 180 0.39 90.2 103.7 15.0 GAS 180 0.50 90.2 112.6 24.8 180 0.39 89.9 103.3 14.9 SLU 180 0.51 90.2 113.7 26.1 180 0.38 89.9 102.6 14.2 MBP 180 0.53 90.2 116.0 28.6 180 0.37 89.8 101.9 13.4 SKT 181 0.55 90.3 117.2 29.8 179 0.36 89.7 101.5 13.2 96
Table A.6 Waiting time at stations (continuation)
SB NB
Station
CV CV
dway,s
time, s time, s time,
mean,s mean,s
% increase % increase %
headway,s hea
withregular withregular
Waiting time Waiting time
Actual waiting Actual waiting GUP 181 0.51 90.3 114.0 26.3 179 0.36 89.7 101.2 12.8 SKB 277 0.57 138.7 184.0 32.6 273 0.40 136.4 158.3 16.0 HYÖ 608 0.24 304.1 321.2 5.6 589 0.17 294.7 303.1 2.8 BJH 608 0.24 304.1 321.7 5.8 589 0.17 294.7 303.0 2.8 KÄT 614 0.25 306.9 325.5 6.1 590 0.16 294.8 302.8 2.7 BAM 614 0.25 306.9 326.0 6.2 590 0.16 294.8 302.8 2.7 SNK 590 0.16 294.8 302.6 2.7 BLU 510 0.40 255.2 296.2 16.1 507 0.40 253.3 292.9 15.6 SAB 510 0.40 255.2 296.5 16.2 507 0.39 253.7 292.6 15.3 SKY 509 0.40 254.7 296.5 16.4 507 0.39 253.7 292.1 15.1 TAK 509 0.41 254.7 297.0 16.6 508 0.39 253.8 292.2 15.2 GUÄ 514 0.41 257.2 301.1 17.1 508 0.39 253.8 292.0 15.0 HÖÄ 515 0.42 257.3 301.6 17.2 508 0.39 253.8 291.6 14.9 FAR 516 0.42 257.9 302.8 17.4 511 0.40 255.4 295.5 15.7 FAS 511 0.39 255.4 294.8 15.4 GLB 514 0.43 256.9 304.8 18.7 523 0.33 261.5 289.8 10.8 ENG 514 0.43 256.9 305.1 18.8 523 0.33 261.5 289.2 10.6 SOP 514 0.44 256.9 305.7 19.0 523 0.32 261.5 288.7 10.4 SVM 519 0.44 259.3 309.2 19.3 523 0.32 261.5 288.5 10.3 STB 514 0.44 257.0 306.4 19.2 523 0.32 261.5 288.4 10.3 BAH 514 0.44 257.0 307.4 19.6 523 0.32 261.5 288.3 10.2 HÖD 614 0.28 307.2 330.9 7.7 523 0.32 261.5 288.0 10.1 RÅG 614 0.28 307.2 331.7 8.0 593 0.19 296.7 307.8 3.8 HAG 593 0.19 296.7 307.7 3.7 97
Table A.7 Table of the stations with summary index Number of On-time Station time, Station time Headway CV, Waiting time, Delay, more boarding Transfer performance, more than 30 CV, more more than more than LOS, D Summary Station than 180 s pasengers, more station less than 80% s than 0.25 0.5 20% index than 10000 per day SB NB SB NB SB NB SB NB SB NB SB NB SB NB HÄS + 1
HÄG + + 2
JOL + + + 3
VBY + + + + + 5
RÅC + + + + + + 6
BLB + + + + + + 6
ILT + + + + + 5
ÄBP + + + + + + 6
ÅKH + + + + + + 6
BMP + + + + + + + + 8
ABB + + + + + + 6
SMO + + + + + + + 7
ALV + + + + + + + + + 9
KRB + + + + + 5
THP + + + + 4
FHP + + + + + + 6
SEP + + + + + + 6
ODP + + + + + + 6
RMG + + + + 4
HÖT + + + + + 5
TCE + + + + + + + + 8
GAS + + + + + + + 7
SLU + + + + + + + + + + 10
MBP + + + + + + + + + 9
98
Table A.7 Table of the stations with summary index (continuation) Number of On-time Station time, Station time Headway CV, Waiting time, Delay, more boarding Transfer performance, more than 30 CV, more more than more than LOS, D Summary Station than 180 s pasengers, more station less than 80% s than 0.25 0.5 20% index than 10000 per day SB NB SB NB SB NB SB NB SB NB SB NB SB NB SKT + + + + + + + + + + 10
GUP + + + + + + + + 8
SKB + + + 3
HYÖ 0
BJH + + + 3
KÄT + 1
BAM 0
SNK 0
BLU + 1
SAB + 1
SKY + + 2
TAK + 1
GUÄ + 1
HÖÄ + 1
FAR + + + + + 5
FAS 0
GLB + + + 3
ENG + + 2
SOP + + 2
SVM + + 2
STB + + 2
BAH + + 2
HÖD + + + + 4
RÅG + 1
HAG + 1
99