1 Simulation of Passenger Flow and Queuing at MRT North

Simulation of Passenger Flow and Queuing at MRT North Station
Raquel Raiza T. FRANCISCO a, Mariecor Elaine R. TAGLE a, Edgardo G. MACATULAD a and
Leorey O. MARQUEZ b
a
Department of Geodetic Engineering, University of the Philippines
b
CSIRO Digital Productivity, Clayton, Victoria, Australia
([email protected], [email protected], [email protected],
[email protected])
Extended Abstract
Epifanio delos Santos Avenue or EDSA, a 24-km highway, is one of the busiest and most congested
transport corridors in Metro Manila. This is caused by the rapid increase of vehicles and commuters in
the city during workdays and even weekends. Various management strategies have been applied by the
government to decrease the traffic load on EDSA, one of which is the construction of Metro Rail Transit
3 (MRT3). MRT3 is a 16.9km light rail transit system that runs along EDSA from North Avenue in Quezon
City to Taft Avenue station in Pasay City. However, as MRT3’s ridership increased, overflowing passenger
volumes and extremely long service times in the ticket and turnstile queues became major sources of
delay for commuters. This paper presents the background and results of a case study aimed at
developing a simulation model to gain insights and develop greater understanding of the conditions
surrounding passenger flow and service at the North Avenue station. The simulation model is
implemented in Anylogic 7.1.2 which provides optimization features for exploring strategies for
increasing the efficiency of queue management and station design.
Problem Description
Long queues in mass transit nodes are a source of major inconvenience for commuters. More
importantly, like losses from road traffic congestions, delays in MRT3 queues translate to reduced
productivity for the country’s work force with the lost man-hours directly resulting in
sizeable
economic losses. The question that often arises when dealing with transportation queues is whether the
system is optimized or capable of properly handling the volume of commuters. Is it possible to do
adjustments in operational procedures or station design in order to obtain more efficient and responsive
queue management to cater to the increasing amount of passengers? How much will it cost the
government to implement these changes?
Motivation
The desire for mobility has always been the driving force for planning efficient transport systems. With
the continuous growth of population in cities like Metro Manila, traffic congestion in major roads is
becoming a primary problem for the government. The inability of road network infrastructure to cope
with the demand forces commuters to seek for alternative modes of transportation. As the only viable
alternative to road transport, the light rail system (LRT) provides three lines along three of the most
congested corridors in Metro Manila. The Metro Rail Transit 3(MRT3) along EDSA is the third line in the
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LRT system. MRT North Station at the northern end of the MRT3 line experiences crisis queueing
conditions starting at 5am, with some queue lines reaching lengths of over 100 people during the day.
As the ridership in the MRT3 has increased to overflowing levels, rail cars are packed to full capacity and
queues at stations entry points have reached crisis conditions. Unfortunately, the government gives
little prospect of obtaining major investment capital to finance required upgrades to the MRT3
infrastructure. Thus, the remaining avenues for obtaining improvements in station services lies with
station operations, management and maintenance. Queuing and station design are some of the aspects
that should be considered when optimizing efficiency for the facility. Properly managed queuing systems
lead to reduced waiting time and efficient use of the workforce's energy that could be spent on more
productive tasks. This, in turn, can reflect positively to the country's economy.
Related Work
In the current Metro Manila transportation scenario, the MRT3 System has become one of the most
important public modes of transportation. During its first two years of operation, the expected ridership
levels have not been met despite several strategies implemented to attract more rail transit users at
that time. In the study done by Martinez in 2002, he reported that the MRT3 light rail was intended to
cater to roughly 400,000 to 500,000 passengers daily (Martinez, 2002). From the final report of the
Metro Manila Urban Transportation Integration Study (MMUTIS), he found out that the MRT served
about 1.95% of the estimated travel demand along EDSA during that time. In the present condition
however, this figure is possibly greatly bigger which is evident in the usual jam-packed trains nearly all
the time. In a recent study by Ebia and Remirez (2014), they reported that the current number of trains
operating during peak hours is 16 trains with an average headway of 5 minutes (Ebia & Ramirez, 2014).
The objective of their research was improving the MRT3 operation by determining optimum headway
and train capacity values that can meet the demand of passengers during peak hours. This is a part of
the bigger complex operation of the MRT3. In our research, we will be looking at another aspect of the
MRT3 which is the queuing of passengers entering the station. The core of such transportation
operations are the spatial and temporal characteristics of the flow distribution of the passengers.
However, the modeling of such systems are said to be very difficult due to the scale and complexity,
thus computer simulation is adopted as solution to analyse and evaluate such complex systems (Yao et.
al., 2013). To optimize the MRT operation, specifically in the queuing of passengers, Anylogic Simulation
will be utilized to have a better understanding of the MRT3’s operation and how to adapt to the factors
affecting it.
Methodology
The research follows the general methodology shown in Figure 1 below.
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•Sample Floor Plan of
North Avenue
Station
•Volume of
Passengers per hour
Development of
Prototype Model
•Data Results
•Simulation Models
•Anylogic 6.7.1
•Data Palettes
•Passenger Flow
•Creating 4 Scenarios
Running of
Simulation
Data Gathering
Analysis of
Results
•Comparison of
Results
•Discussion of
Analysis
Figure 1. General Research Methodology
1. Data Gathering
The datasets to be used in this simulation are: (1) the North Avenue Station floor plan; (2) mean
passenger arrival rate per hour; (3) average volume or count of passenger per hour; (4) number
of ticketing booth; (5) number of turnstiles and schedule; and, if available, (6) CCTV footage of
the North Avenue Station for observation. Coordination with the MRT3 operations manager has
already been conducted to request for the above data.
2. Development of Prototype Model
Upon acquisition, the data will be accessed by the simulation model implemented in Anylogic
6.7.1. Two simulation approaches are employed: the discrete event and agent-based. The
discrete event is used for simulating the ticket booths and turnstiles operation while the agentbased is used for modeling the passenger flow.
Figure 2. Mean Passenger Arrival Rates per hour
To create the prototype simulation model, the first step is the digitization of the North Ave.
Station floor plan to place the locations of station entrance, ticket booths and turnstiles. To
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model the passenger flow, the built-in Pedestrian Library in Anylogic is used. The mean
passenger arrival rate per hour is used to simulate the arrival of passengers at the station. Figure
2 above shows the mean passenger arrival rates per hour in the 17-hour working schedule of
MRT3 from 5am to 10pm. In the simulation model, every 5 second step is simulated as
equivalent to 1 hour.
Figure 3 shows the process flow implemented in Anylogic to model the passenger flow. The
arrival of passengers is manually injected every hour (every 5 simulation steps) based on the
mean passenger arrival rate. Based on a percentage-chance, the model creates two types of
passengers: those who will proceed to the ticket booth and those who already have storedvalue cards (SV) that proceed directly to the turnstiles. Service windows and Service gates are
used to model the ticket booths and turnstiles respectively. A Waiting Area is also created to
hold the passengers when the queuing line reached its full capacity.
Figure 3. Passenger Flow implemented in Anylogic
In order to look at possible ways to optimize the queuing management in the case study station,
the current queuing system is modeled as well as a proposed single-channel queuing. In the
current queuing system, all passengers without SV that arrive in the station will line up to any
available ticket booth or to the shortest ticket booth queue before proceeding to the turnstiles.
In contrast, in the single-channel queuing, those passengers will line up in a single line where
they wait for any available ticket booth. Passengers who already have SV proceed to the
turnstiles. Currently, there are ten available turnstiles but only five of these are open, possibly
due to on-going maintenance and operation scheduling. Four major scenarios are observed in
this study as listed in Table 1 below. A snapshot of each scenario is also shown in Figure 4.
SCENARIO
1
2
3
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Table 1. Four Major Scenarios in the Simulation Model
DESCRIPTION
Current Queuing System with 10 available turnstiles
Current Queuing System with 5 available turnstiles
Single-Channel Queuing with 10 available turnstiles
Single-Channel Queuing with 5 available turnstile
To incorporate the presence of passengers having SV, a percentage-chance is implemented
upon arrival of the passenger in the station. Thus, every scenario is further categorized into 3
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subtypes where percentage-chance of passengers with SV is used as parameter with the values:
15%, 50% and 85%. There will be a total of 12 different simulation scenarios to be analyzed.
Figure 4. Four types of Scenarios simulated in the Model
3. Running of Simulation
The four scenarios are run with the 3 subtypes each. For all runs, the count of passengers in
queue, the count of passengers exited, and the count of passengers with SV are monitored for
each hour and the values written to corresponding datasets which are exported as spreadsheet
files. Figure 5 and Figure 6 show sample images of scenarios from the working MRT North
Avenue Station simulation models.
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Figure 5. Image of the Sample North Avenue Station Scenario 1 Simulation Model in 2D and 3D
Figure 6. Image of the Sample North Avenue Station Scenario 3 Simulation Model in 2D and 3D
4. Analysis of Results
Graphs and statistics will be generated from the results of all the simulation runs. These will be
compared with each other to analyse the effect of changing the different parameters (queuing
system, available turnstiles, percentage-chance of passengers with SV) to the number of
passengers that will exit the station to enter the train platform. The percentage of passengers
who exited can also be computed from the count of passengers exited and the count of
passengers that has already arrived. A comparison of the graphs of passenger count exiting the
turnstiles from the initial test simulations results is shown in Figure 7 below.
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Figure 7. Comparison of Passenger Count exiting at the turnstiles at SV percentage chance:
85%
Conclusion
The situation at MRT North Station represents the crisis conditions engulfing the entire Metro system as
increasing volumes of passengers overwhelm station infrastructure and resources. Ticket queues at the
station can extend beyond 200 meters on the street below considering that the queue has already
started from the station four storeys above and has snaked down a flight of stairs to the street. These
extreme queues waste an enormous amount of time and productivity, and causes high levels of stress
on both passengers and station personnel. With no funding available for urgently needed capital
improvements and extensions, the station management relies on creativity and flexibility to implement
operational techniques and resource allocation measures on an hour-by-hour basis, to extract greater
efficiency from staff, reduce queue lengths and waiting times, and improve service rates. The results
from the case study, which are being compiled for publication, will show that simulation modelling can
be a valuable tool in formulating and fine-tuning these management techniques, evaluating their impact
on the queueing system and selling the concept to station management. The next version of this paper
will present the discussion and analysis of these results and propose new features that will extend the
capabilities of the model.
References
Ebia , E. O. and Ramirez, PS. V., (2014). Improving Metro Manila Metro Rail Transit Operations by
Optimizing Train Capacity and Headway. Institute of Civil Engineering, University of the Philippines
Diliman. Undergraduate Research Project
Martinez, J. G. (2002). Policies for Promoting Rail Transit Usage in Metro Manila: Transport Pricing and
Rail Service Quality Improvements. School of Urban and Regional Planning, University of the Philippines
Diliman. Master’s Thesis.
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Yao, X,M., Zhao, P., & Qiao, K. (2013). Simulation and evaluation of urban rail transit network based on
multi-agent approach. Journal of Industrial Engineering and Management, 6(1), 367-379.
http://dx.doi.org/10.3926/jiem.686
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