Master Degree in Transport Planning and Operations
2014/2015
Performance analysis of a public transport interchange from the pedestrian
circulation perspective using microsimulation tools: the Colégio Militar case
study
André Filipe Ferreira Ramos*
* Student nr. 76819 – IST/UL
A R T I C L E I N F O A B S T R A C T
Keywords:
Pedestrian circulation
Public transport interchanges
Level of service
Microsimulation
Efficient alternatives to private transport are becoming truly necessary at a time when road traffic
congestion increases and vehicle emissions are a constant problem.
Public transport is, therefore, an essential part of the sustainable development of cities: network’s
organization is a major factor, as long as it allows efficient and comfortable transfers in interchanges,
providing to passengers a sense of continuity on their trips. However, these network nodes are often
associated with high pedestrian flows with constraints on pedestrian movement, which discourages
their use.
The analysis methods for the performance of public transport interchanges are usually based on
aggregate values, which may result in highly optimistic results. However, since the development of
microsimulation tools allows different ways of measuring these infrastructures’ performance, it
becomes necessary to establish appropriate guidelines, similar to those already in place for
conventional analysis.
With this in mind, and based on different studies regarding the use of microsimulation, a set of
“best practices" were compiled, and a complete methodology suggested, so that it can applied to both
existing interchanges and to the planning of future ones. One major change (compared with
conventional methods) involves considering 30-second periods in the performance analysis, instead of
considering the average value of the simulated period.
In order to apply this methodology, a microsimulation model of the Colégio Militar/Luz subway
station (in Lisbon) was developed, after which a diagnosis of the current situation was done and
different alternative scenarios tested, to both take advantage and demonstrate the potential of these
tools.
© 2015 IST/UL All rights reserved.
1. Introduction
Currently, public transport interchanges are crucial
points of the transport system, since they allow
passengers to transfer between different lines or transport
modes. However, the walking distances that are created
add some undesired uncertainty and inconvenience to
their trips (Ceder et al., 2013; de Abreu e Silva and
Bazrafshan, 2013; Guo and Wilson, 2011; Hadas and
Ranjitkar, 2012; Kalakou and Moura, 2014; McCord et al.,
2006; Nesheli and Ceder, 2014).
Simultaneously, microsimulation tools have been
evolving in the past years, and it is possible to simulate
the individual behavior of each pedestrian and the
interaction between them, as well as a reliable
representation of the surrounding environment
(Fernandes et al., 2013).
Yet, since the interchange analysis didn’t follow, to
date, any set of “guidelines” or “best practices” (Galiza et
al., 2009), this work’s objective was to define a
methodology that compiles the different items to collect
and analyze, so that it can be applied to other public
transport interchanges. At the same time, a major
improvement to current practices was considering periods
of 30 seconds in the performance analysis of the different
areas.
This paper starts with a summarization of the main
research developments on the pedestrian circulation on
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interchanges, as well as the main characteristics of the
microsimulation tools, particularly the chosen one for this
work, the PTV Vissim (Chapter 2). On Chapter 3, the
proposed methodology is described, and Chapter 4
presents the analysis of the case study, which is based on
the subway station of Colégio Militar/Luz, in Lisbon.
Finally, on Chapter 5 the main conclusions and
suggestions are summarized.
2. Literature review
2.1. Pedestrian circulation on interchanges
Most of the pedestrian transfers on a public transport
journey require a walking distance which adds an
additional uncertainty to the overall time, making the
quality of the connection between the two modes a main
aspect on the public transport passengers’ satisfaction.
For the same reason, passengers usually prefer direct
journeys, to avoid that uncertainty (Ceder et al., 2013;
Guo and Wilson, 2011; Hadas and Ranjitkar, 2012;
McCord et al., 2006).
Therefore, the interchange is a place that allows the
passengers to transfer between modes and aggregates
and redistributes the pedestrian flows in order to improve
the operational efficiency of the transport system
(Domingues, 2011; IMTT, 2011; Shah et al., 2013; Sun et
al., 2010; Terzis, 1998; Yang et al., 2010; Zhang et al.,
2009).
There are several critical points on an interchange,
and Transport for London (2009) identifies three main
zones: movement spaces, opportunity spaces and
decision spaces. One of those decision spaces are the
ticket barriers, which create an “intermediate step” on
passengers’ trip and increase their journey time. Ticket
barriers represent one of the most critical points of public
transport interchanges, not only for the presence of
crowds but also because for its complexity (Davidich et
al., 2013).
One way of predicting the capacity of an interchange
is using the “fundamental diagram”, which correlates the
three main characteristics of the pedestrian movements:
flow, density and speed (Daamen et al., 2005; Davidich
and Köster, 2012).
Indeed, the pedestrian speed is of vital importance
and it depends on several aspects, both individual
characteristics (age, sex, culture, body size, health
conditions, luggage, group size, trip motive, etc.) or from
the environment (temperature, location, type of floor,
presence of obstacles or bidirectional flows and
pedestrian density, for instance) (Chattaraj et al., 2009;
Cheng et al., 2014; Fruin, 1987; Galiza and Ferreira,
2013; Galiza et al., 2009; Hoogendoorn and Daamen,
2005; Johansson et al., 2008; Kholshevnikov et al., 2008;
Koh and Zhou, 2011; Lam et al., 2003; Löhner, 2010;
Miguel, 2013; Qu et al., 2014; Rotton et al., 1990; Shah et
al., 2015, 2013; Willis et al., 2004; Yuen et al., 2013).
According to Líbano Monteiro (1994), each stream of
pedestrians should be planned to avoid crossing with
other streams. However, since it is not always possible,
pedestrians usually organize themselves “efficiently”,
through the spontaneous formation of unidirectional
“stripes” (Helbing et al., 2005; Johansson et al., 2008;
Moussaïd et al., 2011; Singh et al., 2009). Alternatively,
several types of physical obstacles could be used as
dividers, like plants, barriers, columns or furniture
(Helbing et al., 2005; Seriani and Fernández, 2015).
2.2. Pedestrian circulation models
Explanatory models of human behavior during
circulation improved significantly during the second half of
the twentieth century. There are two main types of
models: the macroscopic models, that focus on the
analysis of the observable dimensions (like density and
flow), and the microscopic ones, focused on modeling the
individual behavior of each pedestrian (Brščić et al., 2014;
Zanlungo et al., 2011).
On the side of microscopic models, Helbing and
Molnár (1995) were responsible for the “social force
model”. Despite its relative simplicity, this model
reproduces quite accurately the dynamics of pedestrians
(Johansson et al., 2008) and corrected several
weaknesses of other models (Helbing and Molnár, 1998).
According to the “social force model”, the pedestrian
desired speed is similar to a “social force”, which
represents the effect of the surrounding environment
(other pedestrians or obstacles) on the pedestrian
behavior and will cause the motivation to act (Figure 1).
Figure 1 – Schematic representation of processes leading to
behavioral changes. Source: adapted from Helbing and Molnár (1995)
Much of the success of this model is due to its ability
to reproduce some of the “self-organization” phenomena
observed in a real context, as the formation of
unidirectional “stripes” (Davidich et al., 2013; Helbing et
al., 2007, 2005). Since its introduction, the “social force
model” has been adapted either by the authors
themselves or by other researchers, namely to include the
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effect of group walking, collision prediction or different
speeds (Helbing et al., 2000; Johansson et al., 2008;
Kretz, 2015; Kretz et al., 2011; Moussaïd et al., 2010;
Zanlungo et al., 2011).
2.3. Quality assessment and performance
analysis of interchanges
The assessment of the quality of interchanges have
been studied in the past twenty years, not only by several
academic researchers but also by the European
Commission, funding several research projects like
PIRATE (Promoting Interchange Rationale, Accessibility
and Transfer Efficiency), GUIDE (Group for Urban
Interchanges Development and Evaluation) or MIMIC
(Mobility, InterModality and InterChanges).
Regarding a quantitative performance analysis, the
“level of service” is still the most used indicator (Galiza et
al., 2009). Fruin (1987) was responsible for the used
reference values for sidewalks, corridors, stairways and
waiting areas, that depend on pedestrian space, density,
flow per unit width and average speed (TRB, 2003).
According to De Gersigny et al. (2010), the level of
service should be at least “C” at corridors and waiting
areas, while it should reach “D” at stairways.
2.4. Pedestrian microsimulation
Pedestrian micro-simulation has become a very
powerful tool in the past few years, although it requires
higher computational resources (Kretz et al., 2011). Its
success is due to the ability to simulate the behavior of
each individual agent of the system, thus the simulation is
influenced by the interaction of all the agents and results
in a more accurate and realistic representation (Bandini et
al., 2014; Fernandes et al., 2013; Helbing et al., 2005).
There are several software products that allow
pedestrian microsimulation, but Vissim stands out from
the others, as it was the first professional tool to enable
the simulation of pedestrians and vehicles simultaneously,
as well as the interactions between them (Bönisch and
Kretz, 2009; Cortés et al., 2010). Since 2008, Vissim
(through its ‘Viswalk’ module) uses the “social force
model” from Helbing e Molnár (1995), which allows the
definition of different types of pedestrian and their own
characteristics (speed, body size, etc.) but requires high
amounts of data, time and highly accurate calibration and
validation (Fellendorf and Vortisch, 2010; Fernandes et
al., 2013; Galiza and Ferreira, 2013; Galiza et al., 2009;
PTV, 2013).
3. A performance analysis methodology
using microsimulation
Even though different different performance analysis
of interchanges have been conducted for decades using
the work of Fruin (1987), there is still a lack of guidelines
regarding the use of microsimulation for this purpose
(Galiza et al., 2009).
Therefore, a methodology that could be replicated in
different transport interchanges where comparable results
were needed was established. For that, the
recommendations of many researchers of the subject
were followed, but there were also consideration for the
specifics of the Portuguese reality and the available data.
Galiza et al. (2009) suggest four key steps on these
types of analysis (Figure 2): first, the correct definition of
the scope of the work; secondly, the collection of all the
required data for the correct characterization of the “base
scenario”; as a third step, modeling that “base scenario”;
and, finally, studying of the desired changes in
infrastructure, demand or supply.
When choosing the scope of the work and its main
objectives, the necessary inputs for the correct
characterization and modeling of the problem should be
defined, as well as the methodology and the type of
output needed (Galiza et al., 2009).
It may be necessary to consider an extensive
collection of data in order to “feed” the microsimulation
model. For instance, to use the correct demand of the
infrastructure, the actual ticketing systems (which allow
access to the information in real time), video surveillance
cameras and passenger counts are some of the
possibilities.
To measure the pedestrian walking speed, the
tracking technique (Zacharias, 1993) can be used.
However, other elements like the number of passengers
in each ticket booth (and comparing it to the total number
of passengers), the time spent to pass the ticket barrier,
number of people waiting in the line to buy a ticket, the
number of passengers with bags or children and the
group composition are also useful to build the
microsimulation model (Fernández et al., 2010).
The modeling of the public transport supply is also
essential to ensure that the correct flows are modeled,
considering the real patterns of arrivals and departures.
It is important to remember that any kind of data
collection must consider “typical days”, which means that
days with severe seasonal effects, special events or
incidents should be discarded (Fruin, 1987).
It is also necessary to ensure that the calibration of
the microsimulation model is extremely accurate so that it
can be used to obtain reliable estimates (Galiza et al.,
2009).
4
Figure 2 – Schematic representation of the performance analysis methodology using microsimulation.
Fonte: adapted from Galiza et al. (2009)
The validation should ensure a good match between
the model and the modeled system (Teknomo et al.,
2006). To evaluate the similarity between the modeled
flows and the real flows, Galiza et al. (2009) recommend
a difference no higher than 5% regarding the sum of all
flows of the modeled interchange. The authors also
recommend the use of the GEH indicator, considering that
at least 85% of the values should be less than 5 and there
is no GEH higher than 10 (DMRB, 1991). According to the
rules of the Federal Highway Administration for road
microsimulation models (FHWA, 2004), GEH should also
be lower than 4 for the sum of all flows.
To validate the model, other aspects can be used:
time between two points in the interchange, percentage of
pedestrians in each ticket barrier, number of passengers
in queue, for instance.
Modeling an interchange with microsimulation tools
allows for a closer view of the pedestrians’ movement and
the perception of the constraints that really affect them.
Additionally, it is possible to obtain from the model the
pedestrian density (in pedestrians/m²), and, consequently,
the level of service. However, the usual analysis often
focus on the hourly average level of service, which will
lead to more favorable results and eliminates many of the
advantages of using a microsimulation model.
For that reason, it is suggested that the assessment
should be based on two different criteria:
Hourly average level of service;
Number of 30-second periods during which the
level of service is worse than the acceptable
minimum.
The second point is based on the one used to
calculate the design hourly volume (DHV) of a designed
road. The volume used is not the “busiest” hour, but the
30th most loaded of the year (the volume that is exceeded
29 times during the year). Above this value, the volume of
traffic rises exponentially (the slope of the curve changes
noticeably), so the DHV represents a kind of compromise
between economic and operational aspects (AASHTO,
2001; CCDR-N, 2008).
Depending on the place on the station and on the
station itself, the number of periods for which it is
“affordable” to have worse levels of service should be
different. For instance, within the zone of the ticket
barriers, when the level of service is worse than “C”
during more than 30 seconds, it is likely to exist an
inefficient situation.
An alternative way of assessing the efficiency of the
interchange should be the measurement of the delay
experienced by the passengers, based on the
measurement of the unconstrained travel time and the
real travel time inside the interchange; this should be a
more useful comparison between alternative scenarios.
Finally, alternatives to the “base scenario” should be
tested further, particularly when its performance is not
good. However, even with a satisfactory result, the
response of the infrastructure to demand growths should
be assessed.
The alternative scenarios can also be related to
changes on the supply side or changes in the
infrastructure, like a change in the location or the number
of ticket barriers, kiosks or shops, vending machines or
customer service centers. There also might be the
opportunity to test physical obstacles to guide passenger
flows more efficiently (Nai et al., 2012) or the creation of
escalators or moving walkways.
4. Case study analysis: the Colégio Militar
interchange
To apply the suggested methodology and test different
solutions, the interchange of Colégio Militar, in Lisbon,
was chosen as case study.
This is an intermodal interchange where the blue line
of Metropolitano de Lisboa (the local subway system)
connects to Santa Apolónia rail station and Amadora Este
(outside Lisbon), as seen on Figure 3, and it has also
several urban and suburban bus operators services
(Carris, Rodoviária de Lisboa and Lisboa Transportes).
Figure 3 – The location of Colégio Militar interchange.
Source: Metropolitano de Lisboa
The subway operates approximately between
6:30 a.m. and 1:00 a.m. the next day, with intervals of 5
minutes (during peak periods) to 12 minutes (at the
5
evening). The three bus operators have a total number of
21 lines at this station.
It has nine access points, four of them from the bus
station and one with internal connection to Colombo
shopping center. On the inside, the station has a central
lobby that allows the access to the boarding platforms
through 22 ticket barriers (Figure 4): the “base schema”
consists in 8 barriers dedicated to incoming passengers
and 14 dedicated to exiting passengers (which include the
two “special” barriers, dedicated to passengers with
reduced mobility or with large volumes, which are
automatically reversible).
Figure 4 – Ticket barriers in Colégio Militar/Luz subway station.
4.1. Data collection
To help modeling the infrastructure, the subway
company (Metropolitano de Lisboa) provided the detailed
blueprints of the station. Additionally, a few other
dimensions that were not available were collected that
were not available yet (e.g., stairs’ steps and ticket
barriers dimensions). Additionally, different elements that
could become obstacles – like ticket booths, shops,
kiosks, vending machines and hawkers – were identified.
The ticketing data of a normal week of 2014 was also
requested to the Metropolitan Transport Authority of
Lisbon (AMTL, Autoridade Metropolitana de Transportes
de Lisboa). In other words, it corresponds to a week of
ticket validations on the subway station of Colégio
Militar/Luz, as well as on the other stations of the subway
network and on the buses that pass by the station.
In order to represent the passengers’ movement
inside the station and attend to the demand oscillations
inside the rush hour, the peak hours were refined to 15-
minutes periods, which allows to more clearly identify the
busiest periods. Thus, according to the ticketing data, the
morning peak hour takes place between 8:30 a.m. and
9:30 a.m., while the afternoon peak hours occurs between
6:00 p.m. and 7:00 p.m. (considering both incoming and
exiting passengers).
Some additional corrections were made to the
ticketing volumes, namely an increase of 1.2% on the
demand between 2014 and 2015 and an additional
volume of 8.1% due to the estimated fraud (both values
taken from Metropolitano de Lisboa official documents)
(ML, 2015). Some assumptions have also been made,
namely a maximum transfer time (time interval between
ticket validations) of 60 minutes and some compatibility
regarding a group of tickets that were not included in the
data received.
The total number of passengers considering their
previous or following journey leg is shown on Table 1.
Table 1 – Estimated demand at the station on morning and afternoon peak hours.
Movement 8:30 – 9:30 a.m. 6:00 – 7:00 p.m.
288 180
16 25
23 75
572 1.480
169 329
25 107
45 150
960 1.338
Source: Autoridade Metropolitana de Transportes de Lisboa
The passengers’ transfers were distributed through
the entries and exits nearest to the bus stops. However, in
addition to the ticketing data received, a plan of data
collection in the station was necessary, since most of the
passengers did not have a previous or subsequent leg in
their trips. The data collection was carried out in May
2015 and included:
a) Passenger counts on the “decision points”
(junctions) of the station;
b) Pedestrian speeds measurement;
c) Pedestrian passing times through the ticket
barriers;
d) Pedestrian waiting times at the ticket booths and
ATM.
The sampling counts were carried out for 10 to 20
minutes at each point during the two peak hours, and
proved the strong relationship of the station with the
Colombo shopping center, both in the morning peak hour
and the afternoon peak hour. The calculated distributions
were later applied to the volumes for which the entry or
exit was unknown.
The walking speed was measured from
96 passengers in free flow, using the tracking technique in
a straight path (with about 32 meters long), recording the
sex and apparent age of the observed passenger. The
result of this measurement revealed an average speed of
1.38 m/s (Figure 5), not much far from Fruin (1987), Willis
et al. (2004) or Costa (2010) observations. The time spent
to cross the ticket barriers was measured for 224
passengers, which resulted in an average time 8% lower
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on exit movements comparing to entry movements
(Figure 6).
Additionally, the time spent on the ticket booths and
ATM machines and the number of passengers in the
ticket booth line were also measured, as well as all the
“intermediate movements” inside the station (e.g., going
to the coffee or to the ticket booth) and the dwell time of
the trains.
Figure 5 – Measured walking speed of the passengers.
Figure 6 – Measured time spent to cross the ticket barriers.
4.2. “Base scenario” model
The “base scenario” model consists of 134 areas
(corridors or waiting areas), 208 obstacles (walls, columns
or barriers, as seen on Figure 7) and 21 stairways
(between the lobby and the platform, and between the
station and the surface). The modeling of the station was
limited to the entrance/exit of the subway station, but it
included all the “intermediate movements” of the
passengers in an attempt to ensure the highest possible
realism.
Based on the ticketing data, the flows for each
movement in the 15-minutes periods of the peak hours
were modeled, while the subway services were created
with the frequency of arrivals described by Metropolitano
de Lisboa (with a random variation to ensure the
existence of subways arriving to the station in both
directions). The measured walking speeds were
introduced in the software for the circulation in corridors,
while for the stairs the values obtained by Kretz et al.
(2008) were used.
Regarding some specific aspects of modeling, the
flows at the station were represented in two different
ways: the distributions obtained from the data collection
and the ticketing data were introduced as “pedestrian
static routes”, while the choice of the ticket barrier to
enter/exit the station was modeled as “pedestrian partial
routes”, letting the model decide which was the path (or
barrier) with lower delay in each moment. In each barrier
it was included also the time spent by the passengers to
cross it.
For the calibration and validation tasks, several values
for the main parameters of the “social force model” were
tested, and the final solution consists on smaller values of
ReactToN, Asocial, Bsocial, Asocial,Iso and Bsocial,Iso (Table
2), the ones that directly influence the distance between
pedestrians, which seemed to be high with the original
parameters. All the modeled routes have their “dynamic
potential” activated, with the default Vissim parameters.
Additionally, the time step of the model was switched to
0.2 s.
To validate the model, four key indicators have been
chosen:
Passengers flows in each entrance and exit of
the station;
Walking speed in the measured section;
Passenger “share” in each group of ticket
barriers;
Visual comparison between the observed
situation and the microsimulation model (namely
the reproduction of some pedestrian behaviors).
Since the model requires a warm up period (during
which the station is not yet with the real flows), the
analysis period was chosen to be the intermediate 30-
minute period of the simulation hour (between the
seconds 900 and 2,700 of the simulation).
Table 2 – Parameters of the social force model used.
Parameter Value
𝛕 0,4 s
𝛌 0,176
𝐑𝐞𝐚𝐜𝐭𝐓𝐨𝐍 5 pedestrians
𝐀𝐬𝐨𝐜𝐢𝐚𝐥 0,2 m/s²
𝐁𝐬𝐨𝐜𝐢𝐚𝐥 2,0 m
𝐀𝐬𝐨𝐜𝐢𝐚𝐥,𝐈𝐬𝐨 2,0 m/s²
𝐁𝐬𝐨𝐜𝐢𝐚𝐥,𝐈𝐬𝐨 0,1 m
𝐕𝐃 3 s
The comparison between the pedestrian flows
modeled and the real ones revealed a GEH lower than 5
in all the paths, as well as a GEH lower than 4 in the total
volume. With regard to the walking speed, the individual
values get from each pedestrian showed that
approximately 98% of the pedestrians in the morning
peak hour and 89% in the afternoon peak hour revealed a
walking speed 5% smaller than the “desired speed”.
Passengers’ “choices” in the ticket barriers zones
were also compared with the ticketing data, with good
results for several groups of barriers in the morning peak
hour. In the afternoon peak hour, where the “dynamic
potential” is more “requested”, passengers do greater
detours, due to a more congested situation.
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Finally, some visual comparisons were also taken out,
for both modeled periods. Some of the intended behaviors
were, in fact, observed in the model, including the queue
formation around the ticket barriers and the number of
passengers in ticket booths.
For the performance analysis, the level of service was
calculated for a set of areas (circulation areas or waiting
areas), presented in Figure 8. The circulation areas never
reach a level of service lower than “C” in any moment of
the two peak periods, even though there are some
congested moments due to the “platooned’ exit of the
passengers; the only occurrence of a level of service “C”
is during the afternoon peak hour, and only in the
corridors that lead to the Colombo shopping center.
In the waiting areas, and during the morning peak
hour, only one point reaches an average level of service
of “D”, even if during some moments the level of service
reaches the levels “E” or “F”. In the afternoon peak hour,
several waiting areas near the ticket barriers present an
average level of service of “D”, which reveals a higher
probability of longer queues.
The model data was grouped in periods of 30
consecutive seconds and ordered by the growth of
pedestrian density from one period to the other (60
periods of 30 seconds). According to Figure 9, after the
55th most loaded period the growth of densities is
exponentially higher; for this reason, it was set as limit the
existence of more than 5 periods of (at least) 30 seconds
in the analyzed 30 minutes in which the level of service
was “D” or worst.
With this criteria, and within the afternoon peak hour,
the existence of at least 5 periods of 30 seconds with
level of service of “D” or worse were identified, located in
the entry ticket barriers nearer to the Colombo shopping
center. In the exit direction, there were also several
barriers that presented a level of service lower than “C”. In
the morning peak hour, the clearance time is always
smaller and no serious problems were found.
Figure 7 – Ticket barriers in the microsimulation model.
Figure 8 – Circulation areas and waiting areas analyzed in the “base scenario”.
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Figure 9 – 30-second periods ordered by the growth of the pedestrian density.
4.3. Scenario analysis
Since this is an interchange with more than 25 years, the
ability to change the infrastructure is very small. For that
reason, 4 alternative scenarios regarding other type of
changes were chosen, based on the “base scenario”:
Scenarios A1/A2: changes in the layout of the ticket
barriers zones;
Scenario B: creation of new kiosks in the corridors;
Scenario C: creation of routing systems that ensure
unidirectional flows.
Additionally, it was also tested an increase in the
demand in the “base scenario” and in the other four
alternative scenarios.
Changes in the layout of the ticket barriers
zones
The areas close to the ticket barriers are where more
congested periods occur. For that reason, alternative
solutions should be studied, in order to improve the overall
performance of the station.
A feasible solution could be to simply increase the
number of barriers; however, as it currently happens, the
number would be highly oversized for the off-peak periods.
For that reason, different solutions with lowers levels of
investment were decided to be tested first.
In a first scenario (scenario A1), a solution was tested
where only the direction of the ticket barriers was changed –
all the barriers from the same group should be only entry or
exit barriers. In a second scenario, the ticketing system was
changed to an “open system”, which means that the ticket
barriers could be totally removed, with the passenger
registration done with video surveillance and frequent
inspection operations or with new upcoming technologies
(scenario A2).
Scenario A1 – Changes in the direction of the ticket
barriers
This solution demands, in some cases, greater routes to
some passengers but largely eliminates some of the major
constraints caused by the bidirectional flows in the ticket
barriers areas.
The analysis carried out for both peak periods showed
that the average level of service was never worse than “B”,
although some levels of service of “D”/”E”/”F” were
registered during some moments. The average delay time
per passenger on their exit from the station decreases about
10% in the morning peak hour and 14% in the afternoon
peak hour with this new solution.
Scenario A2 – Elimination of the ticket barriers
(“open ticketing system”)
Although is not common to have “open systems” in
heavy transport modes (like subway or rail), the existing
technologies nowadays make this possible even for large
passenger volumes.
So, in the tested scenario, it was decided to maintain the
existing delimitation of the central lobby (where the
information about the subway service is placed), removing
just the ticket barriers. However, in an “open system”, the
elements that exist in the central lobby (ticket booths or
information about the service) could be rearranged.
The results corroborate the advantages of this type of
system: the average delay time of the passengers is
reduced by more than 40% in both peak periods.
Creation of new kiosks in the corridors
Given the inexistence of major problems in the corridors
of the station, there is the possibility of creating new kiosks
in order to generate additional revenues to the infrastructure
manager (in this case, Metropolitano de Lisboa).
Therefore, scenario B consists of the placement of two
kiosks of different sizes in two different corridors where the
level of service is less favorable in the “base scenario”
(corridors near the Colombo shopping center).
Both in the morning peak hour and in the afternoon peak
hour, only slights impacts were seen in these areas. In no
case the minimum level of service is worse than “C”, so the
placement of this new commercial equipment would be
possible. It should be noted, however, that the possible
reductions of space due to the queues in these places were
not considered.
9
Figure 10 – Corridor connecting the subway station to the bus station in scenario C.
Creation of routing systems that ensure
unidirectional flows
Although the corridors did not present greater constraints
in any of the peak periods of the “base scenario”, the
unidirectional volumes encourage the use of physical
elements to separate the different flows: in the morning peak
hour, some corridors present a distribution of 83%/17%,
while in the afternoon peak hour the distribution is 56%/44%
(but the flow is three times higher).
So, in scenario C, the impact of physical separators that
force the passengers to follow on a one-way corridor (Figure
10) was evaluated. The comparison between the average
delay times of the passengers revealed that no substantial
gains were obtained from the placement of those elements
(mostly due to the additional detours that some passengers
have to realize).
Demand increase
As a complement to the previous scenarios, the impact
of an increase in the demand was also studied, since it is
most likely to happen in the next years: the blue line is set to
be expanded in 2020, and this station will work as a
bifurcation, so its importance is going to increase. On the
other hand, two new office towers above the Colombo
shopping center are planned for construction, which should
also increase the demand of this station.
Therefore, the “base scenario” and the four alternative
scenarios were tested to an overall increase of 20% in the
demand of the afternoon peak hour (since it is clearly the
most congested one) and the results are summarized below:
In the “base scenario”, several areas present an
average level of service of “E”, and this indicator
reaches “D”, “E” or “F” in almost 50% of the 30-
second periods;
In scenario A1, some areas present a higher
number of levels of service of “D” comparing to the
current demand, and within longer periods;
In scenario A2, the station “responds” well to the
demand increase, with only slight increases on the
average delay of each passenger;
In scenario B, the levels of service remain almost
unchanged, proving that the new commercial kiosks
could be put even with higher demands;
Finally, in scenario C, the average delay increases
12% when compared to the current demand and
the new physical elements remain unjustified.
5. Conclusions
The analyses carried out for the Colégio Militar/Luz
subway station allow for a quick perception of the potential
of microsimulation tools applied to the performance analysis
of public transport interchanges.
The different analyses showed the existence of moments
where the level of service gets worse than “C”; however, in
average terms it would not be possible to detect those
moments or to figure out their extension. For that reason, the
consideration of the 30-second periods as suggested in the
methodology allows a better knowledge of the real
performance of an analyzed infrastructure.
The tested changes in the different scenarios provide
significant reductions in the average delay times of the
passengers and an improvement on their satisfaction
regarding the walking conditions through the station. The
tested demand increase also proves that, even if the present
situation is not perfect, it could get worse if no intervention is
done in this interchange.
The observed constraints are similar to many others that
occur in other subway stations in Lisbon and in the rest of
the world. Therefore, the suggested methodology aims to
become a contribution to the discussion on the use of
microsimulation models on interchanges analysis and the
benefits of these models.
However, deeper research should be done in order to
develop a more consistent and systematic approach
regarding to the consideration of the 30-second periods in
the performance analysis of the level of service. The criteria
used in this case was obtained for the current demand and
the specific number of arrivals observed in Colégio
Militar/Luz, so it is important to build a richer database in
order to apply this methodology to other interchanges.
Also, it is worth keeping in mind that Lam and Cheung
(2000) recommend caution in the application of international
10
examples, since different interventions or reference values
should be adjusted to each reality, which means that results
can't be directly transferred. For instance, the analyzed case
study does not seem to recommend the use of routing
systems to ensure unidirectional flows (maybe due to its
demand volumes); however, this technique is applied all
over the world and recognized as a contributor to
performance improvements (Helbing et al., 2005; Seriani
and Fernández, 2015).
There are some limitations that these tools will not be
able to avoid (the erratic behavior of tourists or unfamiliar
passengers, for instance), but those particular cases
generally represent a very small number of situations and
they are not evaluated with current methodologies. The
phenomena of walking in groups is also one type of behavior
that is not considered in current microsimulation tools, but
may be represented in upcoming tools, increasing the quality
of this kind of analysis.
Finally, a full cooperation between operators and
transport authorities is essential to an effective improvement
of the interchanges performance.
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