Reliability of Stockholm Subway - Index of

Reliability of Stockholm Subway
A N D R E Y E D E M S K I Y
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

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
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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 selfsupporting.
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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Passengers, thousand
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
1000
800
600
400
200
0
1012 1016 1016 1073 1094 1117 1117
2003 2004 2005 2006 2007 2008 2009
Figure 1.1 Number of passengers in Stockholm subway
Green line
Red line
Blue line
Total
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
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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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Percent
Spring
2004
Autumn
2004
Spring
2005
Autumn
2005
Spring
2006
Autumn
2006
Spring
2007
Autumn
2007
Spring
2008
Autumn
2008
Spring
2009
Autumn
2009
Percent
Spring
2004
Autumn
2004
Spring
2005
Autumn
2005
Spring
2006
Autumn
2006
Spring
2007
Autumn
2007
Spring
2008
Autumn
2008
Spring
2009
Autumn
2009
Percent
85
80
75
70
65
60
55
50
Green line
Red line
Blue line
Figure 1.2
Level of overall passengers‟ satisfaction in Stockholm subway
80
75
70
65
60
55
50
Green line
Red line
Blue line
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
90
Green line
Red line
85
Blue line
80
2003 2004 2005 2006 2007 2008 2009
Figure 1.4
Punctuality in Stockholm subway
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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
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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
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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
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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
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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 ontime
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
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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
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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
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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.
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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
Headway distribution
Travel times
Train operation analysis
On-time performance
Delay distribution
Headway adherence
Headway distribution
Waiting time
Dwell times distribution
Travel times
26

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.
27

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.
30

Table 4.1 Lines of Stockholm subway
Generalized name Line Destination
Blue Line
T10 Kungsträdgården - Hjulsta
T11 Kungsträdgården - Akalla
Red Line
T13 Norsborg - Ropsten
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
34

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

Leaving passengers
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.
Entering passengers
14000
12000
10000
8000
6000
4000
2000
Rådmansgatan
Skanstull
Fridhemsplan
Slussen (T)
Odenplan
Hötorget
Gullmarsplan (T)
T-centralen
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
12000
10000
8000
6000
Rådmansgatan
Odenplan
Skanstull
Hötorget
Fridhemsplan
Gullmarsplan (T)
Slussen (T)
T-centralen
4000
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
39

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/s 2 , and
designed headway of 90 seconds between trains during ordinary transport service.
40

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.
41

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.
42

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.
43

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.
45

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.
48

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
49

“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.
50

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, %
March NB SB NB SB NB SB
Average, %
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
51

The month contained 23 week days; however 3 days (the 2nd, 23rd and 30 th 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
52

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.
53

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
Average headway, s
Depar
ture
Table 5.4 Example of scheduled headway distribution at T-centralen
Headway,
min
T-centralen SB
Headway,
Depar
ture
min
Depar
ture
Headway,
min
Depar
ture
Headway,
min
T-centralen NB
Headway,
Depar
ture
min
Depar
ture
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
Headway,
min
Figure 5.1 shows the change of the average headways of 30 minutes intervals at the
station during the day.
240
220
South
North
200
180
160
140
120
100
Time period
Figure 5.1 Average headway in 30 minutes intervals during the period
from 6:30 till 19:00 at T-centralen
54

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
Line Direction Route
Travel time, min
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

HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Share of on-time
trains NB, %
HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Share of on-time
trains SB, %
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
60.0
40.0
20.0
0.0
100.0
80.0
60.0
40.0
20.0
0.0
Stations
Figure 5.2 The share of on-time trains at stations, SB
Figure 5.3 The share of on-time trains at stations, NB
57
Stations

HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Average deviation, s
HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Average deviation, s
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
0
240
Stations
Figure 5.4 Average deviation from scheduled departure at stations, SB
180
120
60
0
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

HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Coefficient of variation
HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Coefficient of variation
2.00
1.50
1.00
0.50
0.00
Stations
Figure 5.6 Coefficient of variation of deviation at stations, SB
2.00
1.50
1.00
0.50
0.00
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

HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
Coefficient of
variation
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
GLB
ENG
SOP
SVM
STB
BAH
HÖD
Coefficient of
variation
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
Dwell time, s
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
Dwell time, s
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
10
0
50
40
30
20
10
0
Stations
Figure 5.8 Average dwell times at stations, SB
0.60
Stations
Figure 5.9 Average dwell times at stations, NB
0.40
0.20
0.00
0.60
Stations
Figure 5.10 Coefficient of variation of dwell times at stations, SB
0.40
0.20
0.00
Figure 5.11 Coefficient of variation of dwell times at stations, NB
60
Stations

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
Travel time, s
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
2800
2700
2600
2500
2400
2300
2200
2100
Actual TT
Timetable TT
Average
85 Percentile
Time of day
Figure 5.12 Travel times of Line 17 Åkeshov – Skarpnäck, SB
61

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
Travel time, s
6:00
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
Travel time, s
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 85 th
percentile times and the times, considered in the timetable, reaches 100-150 seconds.
2900
2800
2700
2600
Actual TT
Timetable TT
Average
85 Percentile
2500
2400
2300
2200
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
2500
2400
2300
Actual TT
Timetable TT
Average
85 Percentile
2200
2100
2000
Time of day
Figure 5.14 Travel times of Line 18 Alvik – Farsta strand, SB
62

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
Travel time, s
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
Travel time, s
2600
2500
2400
2300
Actual TT
Timetable TT
Average
85 Percentile
2200
2100
2000
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 85 th percentile times
exceed the schedule by up to 200 seconds.
3600
3500
3400
3300
Actual TT
Timetable TT
Average
85 Percentile
3200
3100
3000
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 85 th percentile times do by more
than 200 seconds.
63

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
Travel time, s
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
Travel time, s
3600
3500
3400
3300
Actual TT
Timetable TT
Average
85 Percentile
3200
3100
3000
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 85 th percentile times and scheduled times
varies from around 120 seconds in the morning to almost 300 seconds in the late
afternoon.
3800
3700
3600
3500
3400
3300
3200
3100
Actual TT
Timetable TT
Average
85 Percentile
Time of day
Figure 5.18 Travel times of Line 19 Hässelby strand - Hagsätra, SB
64

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
Travel time, s
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 85 th percentile times differ from
schedule by around 200 seconds reaching 250-300 during the peak hours.
3900
3800
3700
3600
3500
3400
3300
3200
Actual TT
Timetable TT
Average
85 Percentile
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 85 th percentile travel time. In case of the line 18 and 19 the difference
between the scheduled travel times and the 85 th 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

HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Coefficient of variation (LOS)
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
0.50
0.40
0.30
0.20
Direction "South" Direction "North" LOS E
LOS D
LOS C
LOS B
0.10
LOS A
0.00
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

HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Coefficient of variation
Coefficient of variation
HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
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
0.6
0.5
0.4
0.3
0.2
0.1
0
Stations
Figure 5.21 Coefficient of variation of headways at stations, SB
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

Relative frequency
Relative frequency
Relative frequency
Relative frequency
30%
Odenplan
Rådmansgatan
25%
Hötorget
20%
15%
10%
5%
0%
-80 -40 0 40 80 120 160 200 240 280
Deviation, s
16.0%
14.0%
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
0.0%
Odenplan
Rådmansgatan
Hötorget
70 100 130 160 190 220 250 280 310 340
Headway, s
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
T-centralen
Gamla Stan
Slussen
Medborgarplatsen
-80 -40 0 40 80 120 160 200 240 280
Deviation, s
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
T-centralen
Gamla Stan
Slussen
Medborgarplatsen
70 100 130 160 190 220 250 280 310 340
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

Relative frequency
Relative frequency
Relative frequency
Relative frequency
Odenplan to Medborgarplatsen the stations with less regular service are Odenplan,
Rådmansgatan, Hötorget and Medborgarplatsen.
25.0%
20.0%
15.0%
10.0%
Gullmarsplan
Skanstull
Medborgarplatsen
Slussen
T-centralen
12.00%
10.00%
8.00%
6.00%
4.00%
Gullmarsplan
Skanstull
Medborgarplatsen
Slussen
Gamla Stan
5.0%
2.00%
0.0%
-80 -40 0 40 80 120 160 200 240 280
Deviation, s
0.00%
70 100 130 160 190 220 250 280 310 340
Headway, s
18.0%
16.0%
14.0%
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
0.0%
T-centralen
Hötorget
Rådmansgatan
Odenplan
St. Eriksplan
-80 -40 0 40 80 120 160 200 240 280
Deviation, s
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
T-centralen
Hötorget
Rådmansgatan
Odenplan
St. Eriksplan
70 100 130 160 190 220 250 280 310 340
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
the time between successive trains
93 104 112 112 112 112 108 97
Percent of people experiencing
overcrowding during the studying period, %
0 51 65 50 52 67 60 46
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
the time between successive trains
117 127 127 122 117 112 108 122
Percent of people experiencing
overcrowding during the studying period, %
40 42 50 36 35 38 25 45
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.
71

HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Increase of waiting time,
%
HÄS
HÄG
JOL
VBY
RÅC
BLB
ILT
ÄBP
ÅKH
BMP
ABB
SMO
ALV
KRB
THP
FHP
SEP
ODP
RMG
HÖT
TCE
GAS
SLU
MBP
SKT
GUP
SKB
HYÖ
BJH
KÄT
BAM
SNK
BLU
SAB
SKY
TAK
GUÄ
HÖÄ
FAR
FAS
GLB
ENG
SOP
SVM
STB
BAH
HÖD
RÅG
HAG
Increase of waiting time,
%
40
30
20
10
0
Stations
Figure 5.25 The increase of waiting time at stations, SB
40
30
20
10
0
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.
72

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%.
73

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
Average deviation, s
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
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
Percent of on-time trains
Percent of on-time trains
100%
100%
80%
60%
40%
20%
0%
80%
60%
40%
20%
0%
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
150
SB
NB
100
50
0
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

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
Coefficient of variation
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
Average dwell times, s
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
55
50
45
40
35
30
SB
NB
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”.
Time
1.00
0.80
0.60
0.40
0.20
SB
NB
LOS F
LOS E
LOS D
LOS C
LOS B
LOS A
0.00
Time
Figure 5.30 Level of service at T-centralen
75

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
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
Percent of on-time trains
Percent of on-time trains
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%
60%
40%
20%
0%
80%
60%
40%
20%
0%
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.
76

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
Average dwell times, s
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
Average deviation, s
250
200
SB
NB
150
100
50
0
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
45
40
35
30
25
20
SB
NB
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

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
Coefficient of variation
is typical for both directions. The worst headway adherence is observed for the trains
departing to the North around 18:00.
1.00
0.80
SB
NB
LOS F
0.60
LOS E
0.40
0.20
LOS D
LOS C
LOS B
LOS A
0.00
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%.
78

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
Average deviation, s
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
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
Percent of on-time trains
Percent of on-time trains
100%
100%
80%
60%
40%
20%
0%
80%
60%
40%
20%
0%
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
200
SB
NB
150
100
50
0
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
Time

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
Coefficient of variation
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
Average dwell times, s
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
40
35
30
25
20
15
SB
NB
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
0.80
0.60
0.40
0.20
0.00
SB
NB
LOS F
LOS E
LOS D
LOS C
LOS B
LOS A
Time
Figure 5.38 Level of service at Skanstull
80

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.
82

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.
83

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.
85

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.
86

Appendix
87

Table A.1 On-time performance at stations of the Green line
Station
Deviation "South", % Deviation "North", %
Deviation "South", % Deviation "North", %
Station
+180 s +180 s +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
88

Sample
mean, s
st.
deviation,
s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
Sample
mean, s
st.
deviation,
s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
Table A.2 Average values of deviation from scheduled departure and its variability at stations
Delay “South”
Delay “North”
Station
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

Sample
mean, s
st. deviation,
s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
Sample
mean, s
st. deviation,
s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
Table A.2 Average values of deviation from scheduled departure and its variability at stations (continuation)
Delay “South”
Delay “North”
Station
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

N
mean, s
st. deviation,
s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
N
mean, s
st. deviation,
s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
Table A.3 Average values of dwell times and their variability at stations
Dwell time "South"
Dwell time "North"
Station
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

N
mean, s
st. deviation,
s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
N
mean, s
st. deviation,
s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
Table A.3 Average values of dwell times and their variability at stations (continuation)
Dwell time "South"
Dwell time "North"
Station
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

mean, s
standard
deviation
, s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
mean, s
standard
deviation
, s2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
Table A.4 Average values of headway and its variability at stations
Headway "South"
Headway "North"
Station
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

mean, s
Standard
deviation, s 2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
mean, s
Standard
deviation, s 2
CV
max, s
upper
quartile, s
median, s
lower
quartile, s
min, s
Table A.4 Average values of headway and its variability at stations (continuation)
Headway "South"
Headway "North"
Station
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
Station
Direction "South" Direction "North"
Direction "South" Direction "North"
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
Station
Waiting time Actual
Waiting time Actual
Average
Average
CV with regular waiting % increase
CV with regular waiting
headway, s
headway, s
headway, s time, s
headway, s time, s
% increase
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

mean, s
CV
Waiting time
with regular
headway, s
Actual waiting
time, s
% increase
mean, s
CV
Waiting time
with regular
headway, s
Actual waiting
time, s
% increase
Table A.6 Waiting time at stations (continuation)
SB
NB
Station
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
Station
Number of
boarding
pasengers, more
than 10000 per day
Transfer
station
On-time
performance,
less than 80%
Delay, more
than 180 s
Station time,
more than 30
s
Station time
CV, more
than 0.25
Headway CV,
more than
0.5
Waiting time,
more than
20%
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
LOS, D
Summary
index
98

Table A.7 Table of the stations with summary index (continuation)
Station
Number of
boarding
pasengers, more
than 10000 per day
Transfer
station
On-time
performance,
less than 80%
Delay, more
than 180 s
Station time,
more than 30
s
Station time
CV, more
than 0.25
Headway CV,
more than
0.5
Waiting time,
more than
20%
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
LOS, D
Summary
index
99