Philippine Science High School
Main Campus
Decision and Impact Analysis of a Proposed
Stormwater Management and Road
Tunnel in Metro Manila
Patricia Carmela V. Caronan
Ma. Angeline W. Jumangit
Arvin John S. Malapo
May 2020
Decision and Impact Analysis of a Proposed
Stormwater Management and Road
Tunnel in Metro Manila
by
Patricia Carmela V. Caronan
Ma. Angeline W. Jumangit
Arvin John S. Malapo
Submitted to the Faculty of the
Philippine Science High School - Main Campus
In partial fulfillment of the requirement for
Research 3
May 2020
ABSTRACT
Typhoons are the most common disasters that the Philippines experiences, resulting in numerous
instances of urban flooding. Heavy traffic also remains to be one of the main problems here,
specifically in the Metro Manila area. These problems are continuously being attempted to be
solved through various policies and revised systems being implemented in the country, though the
search for optimal solutions still persists. The study was conducted to assess the feasibility and
impact of a Stormwater Management and Road Tunnel System in Metro Manila. The assessment
of the proposed floodwater and tunnel system was done by gathering data from various
government and research institutions in order to assess the optimal location for the proposed
infrastructure. The data gathered was analyzed through different tests to determine the significant
values and figures that will be used in the simulation. The simulation of the proposed system was
then done using Vensim in order to assess and determine the most effective mechanism for the
system. The ideal route of the floodway was identified to start from the Marikina River in the area
near SM Marikina and end at Manila Bay while the road tunnel was determined to start from the
R6-C4 intersection and end at the R8-C2 intersection. The simulated model showed that the system
is effective in mitigating the flood levels in the Marikina River at different precipitation rates.
Simulation of the infrastructure systems through a physical model incorporated flood and traffic
data is recommended for further testing.
Keywords: tunnel, traffic, flood mitigation, Metro Manila, Marikina River
APPROVAL SHEET
This research work entitled, “Decision and Impact Analysis of a Proposed Stormwater
Management and Road Tunnel in Metro Manila” by Patricia Carmela V. Caronan, Ma.
Angeline W. Jumangit, and Arvin John S. Malapo, presented to the Faculty of the Philippine
Science High School - Main Campus in partial fulfillment of the requirements in Research 3, is
hereby accepted.
_____________________________
_____________________________
Jejomar O. Bongat
Mc Jervis S. Villaruel
Research Teacher
Research Teacher
i
ACKNOWLEDGMENTS
The researchers would like to thank Mr. Jejomar Bongat and Mr. McJervis Villaruel for
their constant support and help in providing valuable insights that led to the completion of the
project.
The researchers would also like to express their gratitude to Ms. Jordanne Zamora, a
student from the Department of Psychology at the University of the Philippines - Diliman, for
helping in the procurement of important references for this study.
ii
TABLE OF CONTENTS
Page
Approval Sheet…………………………………………………………………………………….i
Acknowledgments………………………………………………………………………………...ii
Table of Contents…………………………………………………………………………………iii
List of Figures…………………………………………………………………………………….iv
Introduction………………………………………………………………………………………..1
Background of the Study………………………………………………………………….1
Objectives of the Study……………………………………………………………………3
Significance of the Study………………………………………………………………….3
Scope and Limitations……………………………………………………………………..3
Literature Review………………………………………………………………………………….4
Philippine Geography……………………………………………………………………..4
Floods……………………………………………………………………………………...4
Factors affecting floods……………………………………………………………5
Flooding in the Philippines………………………………………………………..6
Traffic……………………………………………………………………………………..7
Factors affecting traffic……………………………………………………………8
Effects of traffic………………………………………………………………….10
Traffic in the Philippines………………………………………………………...11
Hazard Mitigation………………………………………………………………………..13
Flood Mitigation Systems………………………………………………………..14
Traffic Congestion Solutions…………………………………………………….15
SMART Tunnel………………………………………………………………………….16
Construction of SMART Tunnel………………………………………………...18
Mechanism of the system……………………………………………………..….20
Effects of tunnel utilization……………………………………………………....21
Methodology……………………………………………………………………………………..22
Process Flowchart…………………………………………………………………….….22
Collection of Flood and Traffic Data Sets……………………………………………….22
iii
Simulation of Proposed Tunnel System………………………………………………….22
Data Analysis…………………………………………………………………………….23
Results and Discussion……………………………………………………….………………….24
Analysis of Flood and Traffic Data Sets…………………………………...…………….24
Simulation of Proposed Tunnel System………………………………………………….26
Summary and Conclusion……………………………………………………………….……….28
Recommendations…………………………………………………………………………….….29
Bibliography……………………………………………………………………………………..30
iv
List of Figures
Figure
Title
Page
1
Metro Manila radial and circumferential road layout (“Metro
Manila Road Network”, 2015)
13
2 and 3
Junction of the Klang and Gombak River at the center of the city
(River of Life (Phase 1), 2018; Lai, 2016)
17
4
SMART Tunnel structure (Dethan, Kavitha, & Nair, 2018)
18
5
Tunnel Boring Machine operation at the SMART Tunnel site
(Kannapiran, 2005)
19
6
Double deck interior of the SMART Tunnel (Isah & Ali, 2015c)
20
7
SMART Tunnel modes of operation (Santhiman & Weei, 2006)
21
v
INTRODUCTION
Background of the Study
The rapid occurrence and increasing expansion of natural disasters remains to be a major
threat to the welfare and safety of individuals in present day society (Bronfman, Cisternas, Repetto,
& Castañeda, 2019). Through the numerous efforts to analyze the nature of these calamities, it was
found that Asia is the most-disaster prone region in the world due to it having the highest incidence
rates of flooding, earthquakes, and typhoons (Chan, Man, Lam, 2019). Numerous factors, such as
geography, climate, and the abundance of industrial advancements, have been found to affect the
prevalence of these conditions (Garcia & Hernandez, 2017). The Philippines is no stranger to these
occurrences and has been coined as one of the most disaster-prone areas in the world with typhoons
as one of the most frequently occurring catastrophes (World Bank, 2005).
Typhoons are natural disasters that adversely impact the people and environment of the
affected region. An average rate of 19.4 typhoons are experienced annually by the Philippines from
1951 to 2013 (Cinco et al., 2016). This is a phenomenon that is observable at different magnitudes
throughout the various regions of the country (De Viana, 2016), one of which includes Metro
Manila. The topographical characteristics of the region allow it to be directly affected by incoming
typhoons, as well as resulting floods and landslides (PAGASA, 2015). Other geographical
features, which include it being surrounded and traversed by rivers (Bankoff, 2003), contribute to
the occurrence of severe flooding that could be witnessed during long and short periods of rainfall
(Lagmay et al., 2017). Numerous projects have been established to counter the detrimental effects
of these situations, though they still remain inadequate in fully mitigating the incidents (Gilbuena
et al., 2013). Additionally, the severity of the flooding conditions in this area is increased by the
continuous pollution of the rivers found here (Helmer et al., 1997).
1
The frequent flooding in such urban areas then results in the disruption of transportation
systems (Pregnolato, Ford, Wilkinson, & Dawson, 2017). This involves the presence of intensified
traffic congestion, which has been identified as one of the major obstacles in the development of
a country (Teknomo et al., 2019). The increase of privately-owned vehicles has contributed to the
escalation of the congestion (Enslin, 2018). In line with this, innovations in road infrastructure are
constantly being developed in order to produce more sustainable and efficient systems to address
these problems. Numerous roadway structures have been designed to better suit the existing
infrastructure and lifestyle conditions in their area of placement. An example of which is the use
of underground tunnels as alternative passageways that minimizes the disruption of established
surface area life (Balasubramanian, 2014). Furthermore, multi-purpose structures that aim to
simultaneously address particular environmental issues, such as urban flooding, have also been
implemented.
The Stormwater Management and Road Tunnel (SMART) System is one of such structural
innovations designed to address the problems and consequences of flooding and traffic in urban
regions. Located in Kuala Lumpur, Malaysia, the SMART Tunnel is a multipurpose tunnel that is
utilized both as a roadway and a flood management system. It was found to be effective in
alleviating the frequent floods that occur in the area caused by inadequate drainage systems
aggravated by Malaysia’s typhoon-prone geographic location. The Philippines, currently having
problems with inefficient traffic and floodwater management systems, is in need of similar
innovations in order to improve the overall state of the country in the event of such natural
disasters.
2
Objectives of the Study
This study aimed to assess the effectivity of a proposed multipurpose tunnel system for
flood and traffic mitigation to be constructed in Metro Manila. To achieve this, the study was able
to determine the most optimal location for tunnel placement within the Metro Manila region
through data analysis. It was also able to produce a simulation model for the working mechanism
of the proposed tunnel system through the use of the Vensim simulation software.
Significance of the Study
Findings from this study will serve as a framework for future construction projects,
policies, and proposals that aim to implement a SMART roadway or other similar initiatives in a
Metro Manila setting. Additionally, the study offers an insight to the possibilities of multipurpose
structures that address the problems of both flooding and traffic in highly urbanized areas.
Scope and Limitations
The study was able to identify the most ideal location for the tunnel system within Metro
Manila, as well as determine its mechanism through the analysis of existing data points. Because
of this, the study is limited only to the theoretical aspect of the tunnel simulation. Furthermore,
other factors, such as the cost of building such a tunnel and consequent environmental impacts to
the area of construction, were not considered due to the lack of reliable sources for calculations
and due to time constraints.
3
LITERATURE REVIEW
Philippine Geography
The Philippine archipelago is composed of over 7,100 islands, encompassing 2,200,000
square kilometers of water area and approximately 300,000 square kilometers of land area. These
islands range in size from tiny islets to the large expanse of Luzon. The topography is highly
diverse with mountain ranges traversing the major islands from north to south, narrow coastal
plains bound islands and a complex network of inland waters that offers rich resources of coral
reefs, marine life and mangrove forests (Licuanan, Cabreira, & Aliño, 2019). It is also surrounded
by an array of seas and straits that stretch from Southeast Asia to Australia. The Celebes Sea to
the south, the Sulu Sea to the southwest, the Philippine Sea and Pacific Ocean to the east and the
West Philippine Sea to the west and north.
Located near the equator, the Philippines is found in the torrid zone and experiences
tropical climate. This climate is affected by the monsoons, or the major wind systems that affect
the temperature trend over land and oceans. They respond to the adjacent seas, more specifically,
the Pacific Ocean as weather systems travel from east to west (Case, 1927). The warm temperature
near the equator affects the wind such that moisture and pressure increase which causes winds to
move quickly, later forming a typhoon. The Philippines is prone to tropical cyclones as a result of
its geographical location and experiences approximately 20 tropical cyclones every year.
Floods
Floods are one of the most frequent and destructive types of natural disasters that occur as
any area where rain falls is vulnerable to them. Natural floods occur when water falls more quickly
than water can be absorbed or exceeds the river channel’s capacity, and consequently spills over
the embankments (Alfieri et al., 2018). The earliest civilization settlements were built near coasts
4
and the people enjoyed the excess water to support their basic necessities and livelihoods, this soon
became a serious problem when floods began to threaten their health, properties, economic activity
and safety (Dobrovicova, Dobrovic, Dobrovic, 2015). In the recent decade, floods have caused
more than $104 billion in damage worldwide every year and are reported to cause the most damage
compared to other natural disasters (Blöschl et al., 2017) These damages amount from structural,
economic and agricultural loss, and also significant impacts on the health and safety of the people.
Through the years, floods have become more common as a result of increased frequency of heavy
rainfall.
Factors affecting floods
While most floods take time to develop, multiple factors can accelerate flooding, thus
affecting the degree of damage it can cause its affected areas. Natural geographic features such as
landforms and bodies of water surrounding an area heavily affects susceptibility to flooding.
Coastal flooding, sourced from nearby bodies of water is common in coastal areas wherein water
can reach miles from shores, onto rivers and across flat land as the sea level rises (Nicholls et al.,
2011). Local factors such as the natural elevation, vegetation present, and subsidence also increase
the flood risk in these areas.
Similarly, the presence of different landforms influences the risk of flood in an area.
Mountains affect wind patterns as the pressure difference as the height increases attracting air to
rise, forming depressions and storms. Most mountainous areas tend to have wetter climates than
low lying areas (Spreafico, 2006). Additionally, other environmental factors such as the forests
and plant life impact the frequency and magnitude of floods. Deforestation and plant cover are
commonly associated with reduction in soil infiltration capacity and erosion. While the previously
mentioned processes both occur naturally, the removal of plant cover accelerates it and increases
5
sediment runoff that occurs during rainfall (Gholami, 2013). Combined with other factors such as
the terrain gradient, soil type and geological factors, some areas naturally considered to be more
flood prone than others.
Though flooding is considered a natural hazard, human activities have made more areas
susceptible to flood. The natural progression and modernization of the society have changed the
use of land with the intent of creating a more advanced and efficient way of living. However, rapid
growth of the population and inadequate urban planning are the main reasons behind clogged
waterways and blocked sewage systems that leave urban cities vulnerable to flooding.
With the human population expected to triple by 2050, the densification of buildings and
residential areas will continue to affect the natural environment such as the use of urban concrete
which affects the rate and volume of infiltration (Hammond et al., 2015). An increase in population
without effective urban planning in drainage networks increases runoff to streams causing them to
limit their capacity of floodwaters (Lagmay et al., 2017). Most flood-prone areas are found in
urban areas where streets and creeks intersect. The continuous removal of vegetation in and around
urban areas also increases the runoff volume which increases the pressure of the canals, resulting
in a faster rate of water level rising during heavy rainfall (Mei, et al., 2017).
Flooding in the Philippines
In the Philippines, floods and landslides are closely related to tropical cyclones. As a result
of its geographical features, the Philippines is named to be highly prone to natural disasters such
as earthquakes, volcanic eruptions but most especially, tropical cyclones and floods. The country
is most exposed to tropical storms in the world, averaging about 20 typhoons to enter Philippine
egion (Mohammed, 2018). The rainfall season, heavily influenced by the “Habagat” or southwest
monsoon, is normally pronounced from May until September. This is characterized by frequent
heavy rainfall and humid weather.
6
Despite being a recurring weather pattern in the country, records from the last two decades
have shown an increase in loss and damage from floods. Among the 20 tropical cyclones that enter
Philippine region, four to five are said to be potentially destructive (Mercado, Kawamura, &
Amaguchi, 2019). Severe destruction of houses, livelihoods, injuries and deaths of people in the
country are some of the worst effects from typhoons and floods. In recent records, the socioeconomic costs of floods and landslides have totaled to $1 billion from Typhoon Bopha in 2012
and $4.3 billion form Typhoon Ketsana (Ondoy) and Parma (Pepeng) in 2009 (Acosta et al., 2016).
Natural disasters, not just flooding, impact travel conditions and road networks. As a result
of these events, some areas would be less accessible or be greatly inconvenient for transport. This
event, combined with the dense population and the failing urban planning and drainage designs of
Metro Manila, all contribute to an increased volume in vehicles and road traffic during floods.
According to Tomtom’s 2019 Traffic Index, Manila is named as the second most traffic-congested
city in the world. An average Filipino in Manila spends an extra 71% on transportation alone and
road traffic conditions still worsen when heavy rain builds up to floods (Abad & Fillone, 2017).
Traffic
Continuous modernization in numerous countries and cities have resulted in the increased
occurrence of road traffic. This results to an increase in traffic congestion within these areas,
making it one of the most significant problems found here (Rithesh, Vignesh, & Anala. 2018).
Traffic congestion is a worldwide societal issue that happens when the road capacity is insufficient
in accommodating the requirements of roadway users (Isa, Mohamed, & Yusoff, 2015). The aspect
of time and space are heavily considered in determining the state of traffic, resulting in the two
types of such phenomenon: regular and occasional traffic congestion (Dong, Ma, Guo, & Liu,
2012). Regular traffic congestion is the result of the continuous inadequacy of road facilities in
7
line with the transportation networks of the area while occasional traffic congestion occurs during
the presence of accidents or other unforeseen events such as natural calamities.
Factors affecting traffic
Traffic congestion is affected by multiple factors, but these are simplified into four
categories, namely: environmental, mechanical, human, and infrastructural. The first three
categories are collectively known to be non-recurring types, meaning they happen occasionally
and are transitory. Infrastructural factors, on the other hand, are considered to be of the recurring
type as they occur regularly and happen when vehicles overcrowd due to the physical limitations
of existing roadways (Geotab, 2018; Federal Highway Authority, 2020). The state of development
in an urbanized region is often influenced by numerous aspects, some of which directly affect the
magnitude of traffic congestion apparent in these areas (Koźlak & Wach, 2018). The weight of
each of these factors as contributors to the problem vary depending on the period of observation
and location of the area. Poor conditions in the fields of transportation, urban planning, and
population or individual behavior are some of the major contributors to this.
The modes of public transit present in respective cities are some of the most crucial
elements in the function of a society (Stjernborg & Mattisson, 2016). Despite the problem of high
number of vehicles being addressed, the mere presence of public transportation options remains
insufficient unless the consequent demand from travelling individuals for these services are met
(Beaudoin & Lawell, 2017). Furthermore, attempts at reduction of traffic congestion through
public transportation is highly affected by the proper implementation of such initiatives (Koźlak
& Wach, 2018). An example of this is the negative effects of bus transportation to the overall state
of traffic congestion due to the placement of bus stops, varying bus operation methods, and road
behavior, such as the number of lanes taken up by these vehicles (Nguyen-Phuoc, Young, Currie,
8
& De Gruyter, 2020). Poor public transport conditions due to the lack of convenience and comfort
offered to its users result in an increased reliance of individuals on private modes of transportation
instead (İmre & Çelebi, 2017). The increase of private vehicles increases the road density on
highways significantly, thus worsening the state of traffic (Masood, Khan, & Naqvi, 2011).
Furthermore, the urban planning of a land area, specifically its road networks and public
transit locations, play a vital role in traffic congestion. An initial foundation of a city layout is
crucial since a lack of such results in any infrastructure-related modernization and development to
be increasingly difficult (Maji, 2017). According to He & Zhao (2013), travel time on roads is
significantly affected by mass of vehicles at intersections, as well as the placement of bus stops.
The layout and placement of roads and their utilities, such as speed bumps and stop lights,
contribute to the occurrence of bottlenecks on highways and disturbs the flow of traffic (Mahona,
Mhilu, Kihedu, & Bwire, 2019). Additionally, the presence of residential areas was found to
contribute to the rate of congestion due to the direct correlation between the density of residing
citizens and the need for transportation options (Zhang, Sun, Yao, & Rong, 2017).
The rapid urbanization of countries and cities around the world would also mean that there
is a rapid increase in the human population (Buhaug & Urdal, 2013). As the number of citizens
increase, so will the necessity for the utilization of various roadway facilities (Vencataya,
Pudaruth, Dirpal, & Narain, 2018). This contributes heavily to the congestion problem since it was
found that at peak hours, bumper to bumper traffic is mainly caused by the number of vehicles on
the road at the given time period. It was also found that cities with high population rates are at
greater risk of an increase in congestion, travel time, and accidents (Goetz, 2019). Other than the
number of the population, its behavior also serves as a factor (Arai & Sentinuwo, 2012). A
common example of this is the changing of lanes of vehicles while on the highway, which was
9
found to disrupt the smooth flow of traffic due to its consequent impact on surrounding vehicles
(Laval & Daganzo, 2006). Another is the occurrence of road blocking due to the improper use of
designated parking spaces or the illegal use of non-designated ones (Rumidi, 2014), as well as the
disruption caused by the entrance and exit of vehicles into such parking lots.
Effects of traffic
The vital role of transportation as an element of the proper function of society is apparent
through the effects of traffic beyond the transportation sector. Its negative impact could be evident
in aspects such as the economy, environment, and individual health, lifestyles, and safety
(Falcocchio & Levinson, 2015).
Traffic congestion has been observed to be an indicator of economic activity since it could
be caused by road use for profession-related tasks of a company or an individual (Harriet, Poku,
& Emmanuel, 2013). However, it is also associated with a slower rate of productivity in the output
and production of goods (Sweet, 2013). Much time used for work is often invested in the prolonged
travel time caused by congestion, limiting the efficiency of execution of assigned activities for job
completion (Anciaes, Metcalfe., & Heywood, 2016). Numerous problems in various occupational
sectors, such as logistics or transportation, can also be observed due to such phenomenon (Kellner,
2015). According to Levy, Buonocore, & von Stackelberg (2010), a huge economic loss annually
results from these conditions alone.
Traffic has been constantly recorded as a source of declined quality in the lifestyles of
various individuals (Anciaes, Metcalfe, & Heywood, 2016). A study by (Ghazali, 2019) has found
that most citizens formulate their daily routines and practices based on the state of traffic in their
area of residence. Furthermore, the simple act of being on the road has been found to result in
varied behavioral reactions, ranging from aggression to distraction, among drivers (Hennessy &
10
Wiesenthal, 1999). This deterrent effect on the mental state of individuals could then be reflected
in physical manifestations like chest pains and nausea. This could lead to road accidents occurring
in congested areas, which though are lower in fatality compared to accidents free-flowing vehicle
situations, could still occur more frequently due to the higher traffic volume (Wang, Quddus, &
Isan, 2009).
Other than this, a decline in the health of citizens could occur due to environmental impacts
of traffic. Carbon emissions from road vehicles are a major source of air pollution, thus reducing
the air quality within the area (Zhang & Batterman, 2013). The extremity of this contamination
results in around 60% of atmospheric pollution to come from this congestion, under which around
90% comes from private vehicles (Chin & Rahman, 2011). Furthermore, varying levels of noise
pollution is observable due to the different vehicles and road structures found within a city (Marve,
Bhorkar, & Bautule, 2016). Overall, episodes of traffic congestion contribute to the depletion of
natural resources and deterioration of the ecosystem (Condurat, Nicuţă, & Andrei, 2017).
Traffic in the Philippines
Among many developing countries, the Philippines experiences rapid urbanization with
effects apparent through the increase in economic progression and population density in the city
center, Metro Manila (Yujuico, 2015). However, this immense productivity has resulted in
problems as the resources of the region, specifically in the transportation sector, are unable to
accommodate all of the users residing in the region. This then results in the heavy traffic conditions
that are apparent throughout various parts of the city.
The Metro Manila transport scene, just like the rest of the country, relies heavily on onroad transit (Muromachi et al., 2015). Despite the presence of rail transit, specifically the Metro
Manila Rail Transit System (MRT), that could alleviate some of the vehicle mass found on the
11
roads, most modes of public transportation (Mijares, Suzuki, & Yai, 2016) still occur on highways.
Furthermore, regardless of the presence of paratransit, such as jeepneys, tricycles, or FX services
that could help alleviate traffic on main highways (Mabazza, 2018), they are still considered as
individual operators that add to the road density apparent during travel hours. Poor public
transportation methods then result in increased reliance on private vehicles that worsen the traffic
conditions of the city (DOTR, GIZ, 2016).
Based on a study by Lidasan, Espada, & De Leon (2010), Metro Manila traffic is mainly
caused by the poor urban planning and management systems present. As seen in Figure 1, the
layout of the city road network is comprised of radial and circumferential roads ten and five in
number respectively (Roth, 2001). Within this area, the Epifanio de los Santos Avenue (EDSA),
or C4 road, is one of the busiest routes due to the high number of vehicles passing through here
(DOTr, GIZ, 2018). Despite the establishment of C5 road to alleviate the motor density on EDSA,
the state of traffic is yet to improve for both these areas (Boquet, 2013).
12
Figure 1. Metro Manila radial and circumferential road layout (“Metro Manila Road Network”, 2015)
Hazard Mitigation
Hazard mitigation is concerned with diminishing the harmful impacts of disasters, as
opposed to preventing their occurrence altogether. The concept recommends policies to be
proactive, rather than reactive, to the consequences of forthcoming disasters. There is also a
tendency for policies to be reparative when it comes to addressing the aftermath of disasters. These
solutions, although effective, prove to be costly in the long run due to the repetitive cycle of
damage and restoration. Mitigative policies avoid this cycle by reducing the amount of damage
that would normally happen which, in turn, will incur less economic consequences and loss of life,
ultimately cutting the cost for repairs (Godschalk, 2003; Los Angeles Emergency Management
Department, n.d.).
Flood Mitigation Systems
Communities employ the concept of flood mitigation through various strategies that
manage and control the movement of floodwaters and people. These strategies are categorized
either as structural or non-structural, depending on the method used. Structural flood mitigation is
identified by the Northern Territory Government of Australia (2018) as methods “where physical
structures are constructed or modified to reduce the impact of flooding on individual properties or
whole catchments.” Some methods classified under this category include the building of dams,
levees (embankment), culverts, canals, and bridges; the installation of flood-proofing devices, such
as door flood barriers and flood vents; and the maintenance and improvement of such structures
(Northern Territory Government of Australia, 2018). On the other hand, non-structural flood
mitigation centers on improving the disaster-preparedness of people within the community (Reed,
2015). Reducing flood impact is achieved through this type of flood mitigation by establishing and
following relocation plans for people and properties, property surveys, building and development
13
codes, flood modeling, and early warning systems, among other methods (Northern Territory
Government of Australia, 2018).
The two types of flood mitigation strategies are equally effective in minimizing the harmful
effects of floods; however, according to the National Conference of State Legislatures (2019),
structural flood mitigation strategies have become less favored due to the gradual weakening of
existing structures. To address this, Reed (2015) suggests utilizing both types in future solutions
for added reliability and safety.
The Philippines, most especially metropolitan areas such as Metro Manila and Metro Cebu,
actively utilizes various flood control structures combined with flood forecasts and warning
systems to alleviate and prepare for devastating effects of inundations. Deficiencies in these
existing solutions, however, were identified in a study by Gilbuena et al. (2013). Faulty structural
designs and unreliable information and warning systems are among the enumerated problems that
significantly contributed to the flooding in Metro Manila after Typhoon Ketsana (Ondoy)
(Gilbuena et al., 2013).
Traffic Congestion Solutions
Akin to floods, traffic congestion may also be alleviated through structural and nonstructural methods. Still, these methods are further subdivided into two, temporary and virtuous,
wherein the type of reduction method is determined by whether the population adjusts to the
situation (temporary) or changes transportation preference (virtuous) accordingly (Smarter
Cambridge Transport, 2016).
Temporary methods are, as the name implies, not meant to be long-term solutions to traffic
congestion; they must only be used to alleviate daily traffic conditions in the meantime while more
large-scale and radical solutions are in progress. Included in this category are solutions such as
14
increasing road capacity, improving road junctions, designing and implementing traffic plans,
rationing of road space, and strategizing traffic policies (Kumarage, 2004). In these methods,
people adjust to the traffic situation in their respective communities.
In contrast, virtuous methods provide solutions to improve the quality, convenience, and
dependability of alternative transportation, particularly of public transportation systems (PTS).
Solutions under this type allow for positive feedback loops to occur: people start preferring public
vehicles, such as trains and buses, over private ones, which will increase the demand and operation
times for the former. In turn, PTS becomes more reliable and appealing for the people; they will
gradually shift away from driving private vehicles, ultimately relieving road congestion (Smarter
Cambridge Transport, 2016). Improving the compatibility of the PTS with the benefitting
population and accommodating their needs and wants is an example of a virtuous-type solution.
In the Philippines, there are numerous road widening projects along major roads throughout
the country. Additionally, daily road space rationing through the Unified Vehicular Volume
Reduction Program (UVVRP), popularly known as number coding, is also observed in Metro
Manila and other populous municipalities (Aguilar, 2018). These solutions are considered
temporary methods in alleviating traffic. Although these can effectively reduce congestion
problems, the fast rate of urbanization in metropolitan areas still renders these solutions inefficient
in the long-run as the demand overwhelms the resources. Furthermore, Sergio R. Ortiz-Luis, Jr.,
president of the Philippine Exporters Confederation notes that the number coding system will urge
people to purchase more private vehicles in an attempt to bypass it, ultimately defeating its purpose
and potentially worsening traffic (Tubadeza, 2015). As such, solutions that combat the need for
private vehicle usage are recommended (Miguel, 2019).
15
SMART Tunnel
The Stormwater Management and Road Tunnel, or the SMART Tunnel, is an
infrastructural innovation located in Kuala Lumpur, Malaysia intended for the alleviation of the
immense flooding that occurs within the area while also functioning as an alternate route for road
vehicles during strong weather disturbances (Varadharajan & Bailey, 2013). Its strategic location
was established due to the topography of Kuala Lumpur, which makes it one of the most heavily
affected areas during typhoons. The city is surrounded by numerous rivers with the center of the
city situated near the convergence of two major rivers, the Klang and Gomback river, as seen in
Figures 2 and 3. Rapid modernization of the areas from these bodies allow them to overflow during
periods of rainfall, thus intensifying the magnitude of the impact of these calamities (Lai, 2016).
This resulted in the construction of the SMART Tunnel in order to alleviate the damages caused
by these flash floods by rerouting some of the water from these rivers.
Figures 2 and 3. Junction of the Klang and Gombak River at the center
of the city (River of Life (Phase 1), 2018; Lai, 2016)
The tunnel finished construction in 2007 and began operation in the same year. As seen in
Figure 4, the resulting infrastructure was composed of two parts: a 9.7-kilometer-long waterway
for flood diversion and a 3 kilometers double-decked roadway segment (Soon, Isah, Ali, & Ahmad,
2017). It also has a diameter of 13 meters and a water capacity of 3,000,000 cubic meters for flood
16
diversion. Other components of the system include a holding pond, storage reservoir, diversion
tunnel, road gates, and flood gates (Dethan, Kavitha, & Nair, 2018). The holding pod is located at
the beginning of the tunnel for the collection of the water from the river and into the tunnel, while
the storage reservoir is located at the end for flood discharge. These two features are connected by
the diversion tunnel. On the other hand, the road and flood gates are structures used to manage the
contained flood water during varied weather conditions.
Figure 4. SMART Tunnel structure (Dethan, Kavitha, & Nair, 2018)
Construction of SMART Tunnel
Underground infrastructure projects, such as the SMART tunnel, require the execution of
preliminary geotechnical investigations prior to their construction (Balasubramanian, 2016). This
is essential since the geological features of the site for construction serves as a basis for the suitable
methods and materials to be utilized, as well as the environmental impact of the project (Isah &
Ali, 2015a). For the SMART Tunnel, it was found that the underlying features of the soil of Kuala
Lumpur included much karstic limestone, thus resulting in the presence of numerous underground
holes or caves (Parise, Gabrovsek, Kaufmann, & Ravbar, 2018). This, along with the urbanized
17
surface conditions of the target location, was heavily considered in finalizing the structure design
in order to limit the risk of any disasters or disturbances to already existing landmarks.
Despite the presence of numerous options for machinery, the Tunnel Boring Machine
(TBM), specifically the Slurry Shield TBM, was chosen for the SMART tunnel (Isah & Ali,
2015b), which can be seen in Figure 5. This machine was deemed optimal for the project due to
its efficiency given the geologic and groundwater conditions of the area. The Slurry Shield TBM
allows continuous underground drilling and tunnel lining production while balancing the pressure
all throughout the affected points of construction (Ochmański, 2015). Other than the hydraulics
system in the machine that allows this consistency, the Slurry Shield TBM also fills in underground
holes with a plaster-like substance for evenly distributed pressure and security of placement
throughout the structure. Other than the advantages in terms of effectivity, utilization of a Slurry
Shield TBM also ensures safer conditions on a surface level since it minimizes the risk of arising
sinkholes (Kannapiran, 2005).
Figure 5. Tunnel Boring Machine operation at the SMART Tunnel site (Kannapiran, 2005 )
Aside from the actual tunnel, other safety features were established within the tunnel
structure. Among these include various systems for lighting, ventilation, radio and cellular
communication, CCTV, thermal systems, and fire protection (Isah & Ali, 2015c). As seen in Figure
6, another unique feature of the tunnel is its double-decked roadway structure that allows travel in
18
only one direction per level. An emergency lane could also be found on top of each of these, as
well as exit routes every 250 meters for sudden occurrences of disasters. Maintenance of the tunnel
is also ensured on a weekly to monthly basis.
Figure 6. Double deck interior of the SMART Tunnel (Isah & Ali, 2015c)
Mechanism of the system
The SMART Tunnel operates under three various conditions depending on the amount of
rainfall and flood water height, which are shown in Figure 7. The first mode is the normal condition
of performance, which occurs when there is low rainfall or no tropical cyclone. This allows the
roadways to be utilized by vehicles while the water from the rivers are contained in the lowest
floodway level of the tunnel. For the second mode, this involves closing off the lower roadway
level for its utilization as a flood containment unit during moderate storms. Finally, during extreme
typhoon conditions, all levels of the SMART tunnel would be utilized for holding of the flood
water (Santhiman & Weei, 2006). Upon returning to normal weather conditions, the tunnel is
slowly drained and cleared of debris to allow resumption of normal operations within a span of 48
hours (UNOSSC, 2012)
19
Figure 7. SMART Tunnel modes of operation (Santhiman & Weei, 2006)
The resulting mode of operation of the SMART Tunnel is determined through the flood
detection system of the structure (Kannapiran, 2005). Through the flood monitoring stations
situated along the rivers near the tunnel, information regarding the criticality of the current water
level is easily accessible and quickly analyzed. This data could then be easily disseminated to the
public by the assigned authorities to do so through telecommunication methods, specifically SMS.
Effects of tunnel utilization
Utilization of the SMART Tunnel as a flood management system has been frequent,
specifically 83 times during different periods of rainfall, and has resulted in effective flood
mitigation since the beginning of its operation in 2007 (Isah & Ali, 2015). It has significantly
minimized damage and risk from flood-related disasters throughout its surrounding areas.
Consistent maintenance checks of its features have also been done to ensure proper execution of
the tunnel objectives, specifically the conditions of the flood gates and proper cleaning of the
tunnel.
In the traffic mitigation aspect of the project, it has also been successful in improving the
conditions of traffic flow through lessened travel time and more manageable state of traffic in the
city (UNOSSC, 2012). Furthermore, travel time to the center of the city has significantly lessened
to 4 minutes from the original time of 15 minutes.
20
METHODOLOGY
Process Flowchart
Collection of Flood and Traffic Data Sets
Simulation of Proposed Tunnel System
Data Analysis
Collection of flood and traffic data sets
Various government and research institutions were contacted to request for information,
specifically data sets and statistics, regarding the state of flooding and traffic in Metro Manila.
Maps of the existing and proposed mass transportation systems within the region and the
environmental impact assessment of the Metro Manila Subway Project were obtained from the
Department of Transportation. Other than this, flood height data in specific coordinates throughout
Metro Manila were retrieved from Kaggle, an online data science and machine learning
community, which was accessed through public data sets (Kaggle, 2019). The annual average daily
traffic from the years 2012 to 2018 was also retrieved from the Metropolitan Manila Development
Authority. From this, the respective traffic density for the major road systems of the area were
computed (Al-Soby & Mousa, 2016). Additional traffic data and simulation models were then
retrieved from the University of the Philippines National Center for Transportation Studies (UPNCTS).
Simulation of Proposed Tunnel System
A model on Vensim Software for simulating the floodway mechanism of the tunnel was
designed for the determination of the effectiveness of the system during various rainfall conditions.
21
A variable representing Marikina River during these conditions was also included in the model as
the basis for the activation of the actual flood mitigation system throughout the tunnel. Quantified
values for precipitation, elevation, flood height, and river level were incorporated into the system.
On the other hand, the program for the traffic mitigation system was designed for the determination
of the optimal entry and exit points of the tunnel system. The data values for the traffic density in
various routes of the Manila Road System were taken into account for the simulation model.
Data Analysis
The flood data set was subjected to multiple linear regression in order to identify the flood
heights with respect to the elevation and precipitation in specific coordinates. Significant values,
such as the R2 and p-values, were observed and interpreted to determine the validity and
significance of the data. The coordinates with the highest flood elevation were then identified, and
examined with the existing floodways in Metro Manila, in order to determine the optimal tunnel
entry and exit points for effective flood mitigation. The traffic data was then analyzed to determine
the most congested areas in Metro Manila, thus serving as a basis for the route of the tunnel within
the region. The traffic simulation of the UP-NCTS was also viewed to aid in determining the ideal
locations of exit points along the length of the tunnel.
22
RESULTS AND DISCUSSION
Analysis of flood and traffic data sets
Through linear regression of the elevation and precipitation data set, an R2 = 0.005879 and
p-values of 0.000328 for elevation and 0.000702 for precipitation. The R2 level assesses the
accuracy of the predictability of the model. A low R2 implies that the model cannot accurately
predict the outcome if external factors that affect flood height are included. However, the p-values
of the elevation and precipitation variables are smaller than 0.05 which indicates that there is strong
evidence of a relationship between the variables and the flood height variable.
Figure 8. Scatter plots of projected flood heights (left)
and residuals (right) fitted to the map of Metro
Manila
The low value for the R2 variable of the linear regression of the elevation and precipitation
data set was caused by the lack of other variables considered during the analysis. Several other
factors such as soil type, presence of infrastructures, and environmental conditions during the
23
period of data collection were not addressed, explaining the low predictability (Pradita &
Ariyaningsih, 2018).
From the procured flood data set, it was found that the eastern part of Metro Manila was
the most susceptible to flooding. It was then decided that the area of Marikina River near SM
Marikina and Riverbanks was the most ideal location for the entry gate of the flood tunnel since it
is easily flooded and a visual measuring tool for the river level has already been installed there.
On the other hand, the Annual Average Daily Traffic (AADT) data set showed that the
densest roads in terms of traffic are R6, R8, and C2, which are all in the Manila area. The C4 road,
which is found in the eastern part of the region, was also found to attain the highest AADT. This
allowed the determination of the direction of the tunnel system to begin from the Marikina area
leading all the way to the Manila area in order to tackle both problems. The openings for the
floodway system of the tunnel were then determined to open at a point near Marikina River with
a route traced to the C4-R6 intersection, which would then be the opening for the road tunnel
system, leading all the way to an exit point for the tunnel mechanism in Manila Bay, with the end
of the road tunnel being at the C2-R8 intersection.
Figure 9. Entry and exit points of the tunnel
24
The traffic data showed that the roads with the highest number of vehicles did not
necessarily mean they had the highest density due to the varying lengths and widths of the roads
(Al-Soby & Mousa, 2016). This explains why despite C4 having the highest AADT for each year,
the dimensions of the road allowed it to be low in traffic congestion. Furthermore, the
establishment of the tunnel in the designated locations allows an increase in road capacity within
these heavily congested areas (Chowdhury et al., 2016).
Simulation of Proposed Tunnel System
The model was able to successfully show the conditions of the Marikina River with respect
to various elevation and precipitation rates. From testing the model at a constant elevation, it was
found that as the level of precipitation increased, so did the height of the resulting flood. As the
flood height increased, the river level of the Marikina River also increased with the rate of increase
depending on the tested precipitation.
The inflow mechanism of the tunnel was then identified to be effective in mitigating the
flooding in Marikina River at a rate of 0 to 0.875 meters of water to inflow through the tunnel
opening. The resulting rate was successful in lessening the flood level of the Marikina River.
Figure 10. Simulation model for the flood mechanism of the
tunnel
25
The restriction of the tunnel inflow rate to 0 to 0.875 meters allows outflow of the Marikina
River volume that is either equal or slightly greater than the volume increase rate of the river
brought by any precipitation rate. The immediate-release through the tunnel system of any
increased volume of water (tunnel outflow - river volume inflow from rainfall) allows a constant
river level throughout the period of rainfall (O’Bannon & Ma, 2019). Other than ensuring that the
river level would not be greater than 15 meters, the controlled rate allows it to never reach drastic
conditions by ensuring a resulting river level that would not go lower than 10 meters.
26
SUMMARY AND CONCLUSION
The study found that the R6-C4 intersection and R8-C2 intersection in Metro Manila were
the optimal locations for the ends of the road tunnel system. The increase in road capacity within
these areas would allow the effective mitigation of the traffic congestion present here. However,
the floodway’s entry and exit points would be in SM Marikina and the Manila Bay. This system
is seen to be effective as it allows the instant release of the increased river volume during any
rainfall condition, constantly keeping the river in safe water level conditions.
27
RECOMMENDATIONS
Future implementation of the study highly recommends the creation of a simulation model
for the traffic component for the tunnel. Quantification of the financial and environmental
implications for its construction would also aid in the determination of the project feasibility.
28
BIBLIOGRAPHY
Abad, R.P.B. & Fillone, A.M. (2017). Vulnerability Assessment of Alternate Traffic Routes to
Flooding in Metro Manila, Philippines. Journal of the Eastern Asia Society for
Transportation Studies. 12, 167-181
Acosta, L.A., Eugenio, E.A., Macandog, P.B.M., Magcale-Macandog, D.B., Lin, E.K.-H.,
Abucay, E.R., Cura, A.L., Primavera, M.G. (2016). Loss and damage from typhooninduced floods and landslides in the Philippines: Community perceptions on climate
impacts and adaptation options. International Journal of Global Warming. 9, 33–65.
Aguilar, M. (2018, October 16). Things You Need to Know about Number Coding in the
Philippines. Retrieved August 03, 2020, from https://www.carmudi.com.ph/journal/thingsneed-know-number-coding-philippines/
Alfieri, L., Cohen, S., Galantowicz, J., Schumann, G.J., Trigg, M.A., Zsoter, E., Rudari, R. (2018).
A global network for operational flood risk reduction. Environmental Science Policy, 84,
149–158.
Al-Sobky, A.-S. A., & Mousa, R. M. (2016). Traffic density determination and its applications
using smartphone. Alexandria Engineering Journal, 55(1), 513–523. doi:
10.1016/j.aej.2015.12.010
Anciaes, P. R., Metcalfe, P. J., & Heywood, C. (2016). Social impacts of road traffic: perceptions
and priorities of local residents. Impact Assessment and Project Appraisal, 35(2), 172–183.
doi:10.1080/14615517.2016.1269464
Arai, K. & Sentinuwo, S. (2012). Spontaneous-braking and lane-changing effect on traffic
congestion using cellular automata model applied to the two-lane traffic. International
Journal of Advanced Computer Science and Applications, 3(8)
Bakar, N.A.A & Mat, S.H.C. (2014). The effects of infrastructure development on economic
growth. in the northern states of Malaysia. Journal of Research in Humanities and Social
Science, 9(9): 28-32
Balasubramanian, A. (2014). Tunnels-types and importance. Centre for Advanced Studies in Earth
Science.
Balasubramanian, A. (2016). GEOTECHNICAL INVESTIGATIONS FOR TUNNELLING.
10.13140/RG.2.2.22472.52480.
Bankoff, G. (2003). Constructing vulnerability: The historical, natural and social generation of
flooding in metropolitan manila. Disasters, 27(3): 95-109
Beaudoin, J., & Lawell, C.-Y. C. L. (2017). The Effects of Urban Public Transit Investment on
Traffic Congestion and Air Quality. Urban Transport Systems. doi:10.5772/66834
29
Blöschl, G., et al., (2017). Changing climate shifts timing of European floods. Science, 357, 588–
590. doi:10.1126/science.aan2506
Boquet, Y. (2013). BATTLING CONGESTION IN MANILA: THE EDSA PROBLEM.
Transport and Communications Bulletin for Asia and the Pacific, 82, 45-59
Bronfman, N. C., Cisternas, P. C., Repetto, P. B., & Castañeda, J. V. (2019). Natural disaster
preparedness in a multi-hazard environment: Characterizing the sociodemographic profile
of
those
better
(worse)
prepared.
PLOS
ONE,
14(4),
e0214249.
doi:10.1371/journal.pone.0214249
Brown N., Amadore, L., & Torrente, E. (1991). Philippine Country Study. Disaster Mitigation in
Asia and the Pacific. Asian Development Bank, Manila.
Buhaug, H., & Urdal, H. (2013). An urbanization bomb? Population growth and social disorder in
cities. Global Environmental Change, 23(1), 1–10. doi:10.1016/j.gloenvcha.2012.10.016
Chan, E., Man, A., & Lam, H. (2019). Scientific evidence on natural disasters and health
emergency and disaster risk management in Asian rural-based area. British medical
bulletin, 129(1), 91–105. https://doi.org/10.1093/bmb/ldz002
Chin, H. & Rahman, M.H. (2011). An Impact Evaluation of Traffic Congestion on Ecology.
Planning Studies & Practice, 3, 32-44.
Chowdhury, T. U., Raihan, S. M., Fahim, A., & Bhuiyan, M. A. A. (2016). A Case Study on
Reduction of Traffic Congestion of Dhaka City: Banani Intersection. International
Conference on Agricultural, Civil and Environmental Engineering (ACEE-16) April 1819,
2016,
Istanbul,
Turkey.
Retrieved
from:
http://uruae.org/siteadmin/upload/AE0416238.pdf
Cinco, T. A., de Guzman, R. G., Ortiz, A. M. D., Delfino, R. J. P., Lasco, R. D., Hilario, F. D., …
Ares, E. D. (2016). Observed trends and impacts of tropical cyclones in the Philippines.
International Journal of Climatology, 36(14), 4638–4650. doi:10.1002/joc.4659
Condurat, M., Nicuţă, A. M., & Andrei, R. (2017). Environmental Impact of Road Transport
Traffic. A Case Study for County of Iaşi Road Network. Procedia Engineering, 181, 123–
130. doi:10.1016/j.proeng.2017.02.379
Dethan, P.D.,m Kavitha, S., & Nair, N.G. (2018). Storm Water Management and Road Tunnel.
International Research Journal of Engineering and Technology (IRJET) 5(10).
De Viana, A.V. (2016). The Philippines’ Typhoon Alley: Te Historic Bagyos of the Philippines
and their Impact. International Journal on Social Innovation & Research, 7(1)
30
Dobrovičová S , Dobrovič R, Dobrovič J. (2015). The economic impact of floods and their
importance in different regions of the world with emphasis on Europe. Procedia Economics
and Finance, 34, 649–655.
Dong, H., Ma, S., Guo, M., & Liu, D. (2012). Research on Analysis Method of Traffic Congestion
Mechanism Based on Improved Cell Transmission Model. Discrete Dynamics in Nature
and Society, 1–11. doi:10.1155/2012/854654
DOTr, GIZ (2016). Transforming Public Transport in the Philippines -The Jeepney+ NAMA of
the
Philippine
Government,
Manila,
Philippines.
Retrieved
from
http://www.transferproject.org/
DOTr, GIZ. (2018). National Urban Mobility Program EDSA-Bus Case Study: Operations and
Business
Model.
Retrieved
from
https://www.changing-transport.org/wpcontent/uploads/2018-12-07-EDSA-Bus-Report.pdf
Enslin, J.R. (2018). Traffic Woes, Metro Manila, and Collaborative Problem Solving: A Case
Study of Computer Mediated, Peer Produced Information Infrastructure in the Field of
Transportation in the Philippines
Falcocchio, J.C. & Levinson, H.S. (2015) The Costs and Other Consequences of Traffic
Congestion. In: Road Traffic Congestion: A Concise Guide. Springer Tracts on
Transportation and Traffic, 7, 159-182 https://doi.org/10.1007/978-3-319-15165-6_13
Federal Highway Authority. (2020, April 10). Congestion: A National Issue. Retrieved August 03,
2020, from https://ops.fhwa.dot.gov/aboutus/opstory.htm
Garcia, M.E.J.N & Hernandez, A.A. (2017). Pattern Analysis of Natural Disasters in the
Philippines.
Retrieved
from
https://www.researchgate.net/publication/324692045_Pattern_Analysis_of_Natural_Disa
sters_in_the_Philippines
Geotab. (2018, July 26). What Causes Traffic Congestion? Retrieved August 03, 2020, from
https://www.geotab.com/blog/traffic-congestion/
Ghazali, W. (2019). The Effect Of Traffic Congestion On Quality Of Community Life. 759-766.
10.15405/epms.2019.12.77.
Gholami, V. (2013). The influence of deforestation on runoff generation and soil erosion (Case
study: Kasilian Watershed). Journal of Forest Science, 59(7): 272–278.
Gilbuena, R., Kawamura, A., Medina, R., Amaguchi, H., & Nakagawa, N. (2013). Gap analysis
of the flood management system in Metro Manila, Philippines: A case study of the
aftermath of Typhoon Ondoy. Floods: From Risk to Opportunity, 357, 32-40.
31
Godschalk, D.R. (2003). Urban hazard mitigation: Creating resilient cities. Natural hazards
review, 4(3), 136-143.
Goetz, A.R. (2019). Transport challenges in rapidly growing cities: is there a magic bullet?,
Transport Reviews, 39(6), 701-705, DOI: 10.1080/01441647.2019.1654201
Hammond, M.J, Chen, A.S., Djordjevic, S., et al. (2015). Urban flood impact assessment: A stateof-the-art review. Urban Water Journal. 12,(1), 14-19
Harriet, T., Poku, K., & Emmanuel, A.K. (2013). An Assessment of Traffic Congestion and Its
Effect on Productivity in Urban Ghana. International Journal of Business and Social
Science, 4(3)
He, N., & Zhao, S. (2013). Discussion on Influencing Factors of Free-flow Travel Time in Road
Traffic Impedance Function. Procedia - Social and Behavioral Sciences, 96, 90–97.
doi:10.1016/j.sbspro.2013.08.013
Helmer, R., Hespanhol, I., & World Health Organization (1997). Water Pollution Control - A
Guide to the Use of Water Quality Management Principles. ISBN 0 419 22910 8.
Hennessy, D. & Wiesenthal, D. (1999). Traffic congestion, driver Stress, and driver aggression.
Aggressive Behavior. 25, 409-423. doi: 10.1002/(SICI)1098-2337(1999)25:6<409::AIDAB2>3.0.CO;2-0.
İmre, Ş., & Çelebi, D. (2017). Measuring Comfort in Public Transport: A case study for İstanbul.
Transportation Research Procedia, 25, 2441–2449. doi:10.1016/j.trpro.2017.05.261
Isa, N., Mohamed, A., Yusoff, M. (2015) Implementation of Dynamic Traffic Routing for Traffic
Congestion: A Review. In: Berry M., Mohamed A., Yap B. (eds) Soft Computing in Data
Science. SCDS 2015. Communications in Computer and Information Science, 545.
Springer, Singapore. https://doi.org/10.1007/978-981-287-936-3_17
Isah, N. & Ali, M.B. (2015). A Thematic Analysis On The Roles And Functions Of Smart Tunnel
In Flood Disaster And Traffic Flow Management In Kuala Lumpur Malaysia. International
Journal of Research and Review 2(5), 256-268
Isah, N. & Ali, M.B. (2015). A Thematic Analysis on the Causes of Flood Disaster and Roles of
Smart Tunnel in Flood Disaster Management in Kuala Lumpur Malaysia. International
Journal of Research and Review 2(10), 224-240.
Isah, N. & Ali, M.B. (2015). A Review of the Literature on the Roles and Features of SMART
Tunnel, Kuala Lumpur, Malaysia. International Journal of Research and Review 2(7), 465475.
Kaggle. (2017). Metro Manila Flood Landscape Data: Metro Manila Flood Reports, Elevation,
and Precipitation Compiled Data (Kriging) (Version 1) [Compiled dataset}
32
Retrieved from https://www.kaggle.com/giologicx/aegisdataset
Kannapiran, R.K.M. (2005). A Study and Evaluation on SMART Project, Malaysia. [Bachelor
dissertation, University of Southern Queensland]
Kellner, F. (2015). Insights into the effect of traffic congestion on distribution network
characteristics – a numerical analysis based on navigation service data. International
Journal
of
Logistics
Research
and
Applications,
19(5),
395–423.
doi:10.1080/13675567.2015.1094043
Koźlak, A., & Wach, D. (2018). Causes of traffic congestion in urban areas. Case of Poland. SHS
Web of Conferences, 57, 01019. doi:10.1051/shsconf/20185701019
Kumarage, A.S. (2004). Urban traffic congestion: The problem & solutions. Economic Review.
Lagmay A.M, Mendoza J., Cipriano, F., Delmendo, P.A., Lacsamana, M.N., Moises, M.A., et al
(2017). Street floods in metro Manila and possible solutions. Journal of Environmental
Science. 59:39–47
Lai, K.X.O. (2016). IMPACT OF THE SMART TUNNEL OUTFLOW ON THE HYDRAULICS
OF THE KERAYONG RIVER, MALAYSIA. [Masters Dissertation, Colorado State
University, Fort Collins, Colorado]
Laval, J. A., & Daganzo, C. F. (2006). Lane-changing in traffic streams. Transportation Research
Part B: Methodological, 40(3), 251–264. doi:10.1016/j.trb.2005.04.003
Levy, J.I., Buonocore, J.J. & von Stackelberg, K. (2010). Evaluation of the public health impacts
of traffic congestion: a health risk assessment. Environ Health, 9(65).
https://doi.org/10.1186/1476-069X-9-65
Lidasan, H.S., Espada, I.C., & De Leon, M.R.M. (2010). A Needs Assessment of Transport
Planning and Traffic Management of Local Cities: The Case of the Philippines
Los Angeles Emergency Management Department. (n.d.). What is Hazard Mitigation? Retrieved
August 03, 2020, from https://emergency.lacity.org/what-hazard-mitigation
Mabazza, D.L. (2018). Redefining the role of paratransit as feeder mode in the mass transit in
Metro Manila. International Conference in Urban and Regional Planning, March 14-15,
2018, Manila, Philippines
Mahona, J., Mhilu, C., Kihedu, J., & Bwire, H. (2019). Factors Contributing to Traffic Flow
Congestion in Heterogenous Traffic Conditions. International Journal for Traffic and
Transport Engineering, 9(2), 238-254
Maji, S. (2017). Traffic Congestion And Possible Solutions A CASE STUDY OF ASANSOL.
Journal of Research in Humanities and Social Science, 5(9), 42-46
33
Marve, S.R., Bhorkar, M., & Bautule, P. (2016). A Survey on Environmental Impacts Due to
Traffic Congestion in Peak Hours. International Journal of Science Technology &
Engineering, 2(8)
Masood, M.T. , Khan, A., & Naqvi, H.A.(2011). Transportation Problems in Developing Countries
Pakistan: A Case-in-Point. International Journal of Business and Management, 6(11).
doi:10.5539/ijbm.v6n11p256
Mei, X., Van Gelder, P.H.A.J.M., Dai, Z. et al. (2017). Impact of dams on flood occurrence of
selected rivers in the United States. Front. Earth Sci. 11, 268–282 doi.org/10.1007/s11707016-0592-1
Mercado, J. M. R., Kawamura, A., & Amaguchi, H. (2019). Analysis of Barriers on Integrated
Flood Risk Management in Metro Manila, Philippines, by Interpretive Structural Modeling
Approach. In World Environmental and Water Resources Congress 2019: Emerging and
Innovative Technologies and International Perspectives (pp. 152-161). Reston, VA:
American Society of Civil Engineers.
Metro Manila Road Network [Digital image]. (2015). Retrieved from http://emcph.net/tag/metromanila-major-roads/
Mijares, A.C., Suzuki, M., & Yai, T. (2016). Passenger Satisfaction and Mental Adaptation under
Adverse Conditions: Case Study in Manila. Journal of Public Transportation, 19(4).
Miguel, C.G. (2019, November 09). A primer to traffic congestion in the Philippines: The causes,
effects, and possible solutions. Retrieved August 03, 2020, from https://philkotse.com/carbuying-and-selling/philkotse-pick-9-must-have-accessories-for-your-pickup-in-20204387
Mohammed, M. P. (2018). Disaster risk reduction and management of Tarlac City. Procedia
Engineering, 212, 77-84. https://doi.org/10.1016/j.proeng.2018.01.011
Muromachi, Y., Lim, I., Wicaksono, A., Vergel, K.N., Choocharukul, K., Tan, V.H., Terai, K.,
Fukuda, D., & Yai, Tetsuo. (2015). A Comparative Study on Road-based Urban Public
Transport Policies in Six Asian Countries from the Viewpoint of Governance, Urban
PLanning, and Financial Aspects. Journal of the Eastern Asia Society for Transportation
Studies, 11
National Conference of State Legislatures. (2019, September 10). Flood Mitigation. Retrieved
August 03, 2020, from https://www.ncsl.org/research/environment-andnatural-resources/flood-mitigation.aspx
Nguyen-Phuoc, D. Q., Young, W., Currie, G., & De Gruyter, C. (2020). Traffic congestion relief
associated with public transport: state-of-the-art. Public Transport. doi:10.1007/s12469020-00231-3
34
Hanson, S., Nicholls, R., Ranger, N., Hallegatte, S., Corfee-Morlot, J., Herweijer, C., Chateau, J.
(2011). A global ranking of port cities with high exposure to climate extremes. Climatic
Change, 104, 89-111
Northern Territory Government of Australia. (2018, June 06). Structural and non-structural flood
mitigation measures. Retrieved August 03, 2020, from https://dipl.nt.gov.au/lands-andplanning/flood-mitigation/flood-mitigation-measures
O’Bannon, D. J., & Ma, Y. (2019). Post Implementation Assessment of WSUD Approaches.
Approaches to Water Sensitive Urban Design, 523–539. doi:10.1016/b978-0-12-8128435.00025-3
Ochmański, M. (2015). Numerical model for slurry shield TBM tunnelling.
PAGASA. (2017). Improving resilience to severe weather and climate change in the Philippines.
Retrieved
from
https://www.preventionweb.net/files/54521_170015pagasacasestudyfinal.pdf
Parise, M., Gabrovsek, F., Kaufmann, G., & Ravbar, N. (2018). Recent advances in karst research:
From theory to fieldwork and applications. Geological Society, London, Special
Publications, 466(1), 1-24. doi:10.1144/sp466.26
Pradita, Luky & Ariyaningsih. (2018). Factors Affecting Vulnerability to Flood Risk (Case Study:
Flood Risk In Balikpapan City). Journal of Architecture and Planning, 4. 119-805.
Pregnolato, M., Ford, A., Wilkinson, S. M., & Dawson, R. J. (2017). The impact of flooding on
road transport: A depth-disruption function. Transportation Research Part D: Transport
and Environment, 55, 67–81. doi: 10.1016/j.trd.2017.06.020
Reed, M. (2015, May 06). Mitigation: Structural and Non-structural. Retrieved August 03, 2020,
from https://sites.psu.edu/workworthdoing/2015/05/06/mitigation-structuraland-non-structural/
Rithesh, R. N., Vignesh, R., & Anala, M.R. (2018). Autonomous Traffic Signal Control using
Decision Tree. International Journal of Electrical and Computer Engineering, 8(3), 15221529
River
of Life (Phase 1). (2018, April 23). Retrieved July
http://www.constructionplusasia.com/my/river-of-life-phase-1/
24,
2020,
from
Roth, L.C. (2001). An economic approach to urban development and transportation in Metro
Manila. [Masters Dissertation, Linkoping University]
35
Rumidi, N.A.B. (2014). A STUDY ON DISTURBANCE FACTORS OF TRAFFIC FLOW TO
THE COMMUNITIES IN JALAN MARAN-GAMBANG (PEKAN GAMBANG),
PAHANG DARUL MAKMUR. [Bachelor dissertation, Universiti Malaysia Pahang]
Santhiman, M.S. & Weei, L.H. (2006). ITS of SMART, PIARC International Seminar on
Intelligent Transport System (ITS) In Road Network Operations (The Legend Hotel, Kuala
Lumpur, Malaysia), August 14, 2006 to August 16, 2006
Smarter Cambridge Transport. (2016, December 12). Reducing Traffic Congestion and Pollution
in
Urban
Areas.
Retrieved
August
03,
2020,
from
https://www.smartertransport.uk/smarter-cambridge-transport-urban-congestion-enquiry/
Soon, N. K., Isah, N., Ali, M. B., & Ahmad, A. R. (2017). Effects of SMART Tunnel Maintenance
Works on Flood Control and Traffic Flow. Advanced Science Letters, 23(1), 322-325.
doi:10.1166/asl.2017.7172
Spreafico, M. (2006). Flash floods in mountain areas. Climate Variability and Change-Hydrological Impacts, 308, IAHS Publication. 232–238.
Stjernborg, V., & Mattisson, O. (2016). The Role of Public Transport in Society—A Case Study
of General Policy Documents in Sweden. Sustainability, 8(11), 1120.
doi:10.3390/su8111120
Sweet, M. (2013). Traffic Congestion’s Economic Impacts: Evidence from US Metropolitan
Regions. Urban Studies, 51(10), 2088–2110. doi:10.1177/0042098013505883
Teknomo, K., Gardon, R. W., & Saloma, C. (2019). Ideal flow traffic analysis: A case study on a
campus road network. Philippine Journal of Science, 148(1), 51-62
Tubadeza, K. P. (2015, August 28). Odd-even scheme won’t solve traffic -- experts. Retrieved
August 03, 2020, from https://www.bworldonline.com/content.php?section=Nation
United Nations Office for South-South Cooperation. (2012). KUALA LUMPUR, MALAYSIA
Case Study (Mixed Use Tunnel).
Varadharajan, R.B. & Bailey, C.J. (2013). Performance analysis of the stormwater management
and road tunnel - SMART in Kuala Lumpur. International Journal of Water Resources and
Environmental Engineering 5(7), 410-417. DOI 10.5897/IJWREE2013. 0390
Vencataya, L., Pudaruth, S., Dirpal, G. & Narain, V. (2018). Assessing the Causes & Impacts of
Traffic Congestion on the Society, Economy and Individual: A Case of Mauritius as an
Emerging Economy. Studies in Business and Economics, 13.(3), 230-242. doi:
10.2478/sbe-2018-0045.
Wang, C., Quddus, M. A., & Ison, S. G. (2009). Impact of traffic congestion on road accidents: A
spatial analysis of the M25 motorway in England. Accident Analysis & Prevention, 41(4),
798–808. doi:10.1016/j.aap.2009.04.002
36
World Bank. (2005). Natural Disaster Risk Management in the Philippines : Enhancing Poverty
Alleviation
Through
Disaster
Reduction.
Retrieved
from
https://openknowledge.worldbank.org/handle/10986/8748
Yujuico, E. (2015). Considerations in the diffusion of a public traffic app for Metro Manila.
Journal of Transport Geography, 42, 48–56. doi:10.1016/j.jtrangeo.2014.11.001
Zhang, K., & Batterman, S. (2013). Air pollution and health risks due to vehicle traffic. The
Science
of
the
total
environment,
450-451,
307–316.
https://doi.org/10.1016/j.scitotenv.2013.01.074
Zhang, T., Sun, L., Yao, L., & Rong, J. (2017). Impact Analysis of Land Use on Traffic Congestion
Using Real-Time Traffic and POI. Journal of Advanced Transportation, 2017, 1–8.
doi:10.1155/2017/7164790
37