Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015

Evaluating Proposed Transportation Infrastructure Projects in Metro Using the Transport Co-Benefit Analysis

Alexis M. FILLONEa aCivil Engineering Department, De La Salle University-Manila a E-mail: [email protected]

Abstract: Several large scale transportation infrastructure projects have been proposed, with several already under construction, in by the current Administration such as the elevated expressway above the PNR line also known as the NLEx-SLEx connector, the NAIA expressway, the CALA and Lakeshore expressway projects as well as the proposed LRT line 1 South Extension, BRT lines, subway, among others. Some of these transportation infrastructure projects are for possible Public-Private Partnership (PPP) funding. Aside from the National Government’s proposals, several private groups have also proposed transportation infrastructure projects in Metro Manila. This research analyzed and evaluated the impact of these proposed transport infrastructure projects on urban travel using the Transport Co-Benefit Analysis as developed by the Institute for Global Environmental Strategies (IGES). This research provides a macroscopic analysis regarding which proposed infrastructure projects would be beneficial to the people of Metro Manila.

Keywords: Co-benefit analysis, urban transport planning, Metro Manila

1. INTRODUCTION

The Transport Co-Benefit approach in analyzing proposed transport infrastructure projects was developed by the Institute for Global Environmental Strategies (IGES). This approach looks at the relationship between the current transport-related problems like , air pollution, traffic accidents, among others and their future global consequences (i.e. climate change) through the integration of multiple objectives into the project proposal and planning stage. These multiple objectives include addressing mobility, accessibility, road safety, air pollution and greenhouse gases emissions in an integrated manner. As a result, the co-benefit approach would maximize benefits as well as minimize costs. This study modeled all the proposed major transport infrastructure projects (both expressways and mass transit systems) being proposed in Metro Manila and neighboring provinces using the transport co-benefit analysis. The study looked at the best combination of transport projects depending on the results of the co-benefit analysis that maximized benefits but minimized costs.

1.1 Statement of the Problem and Objectives

Several large scale transport infrastructure projects have been proposed to solve the recurring traffic congestion problem as well as the inefficient public transport system in Metro Manila. The most recent of which is the proposed elevated expressway along Epifanio de los Santos Avenue (EDSA). However, most of these proposals are unsolicited that were developed by private developers and business investors to pursue their own private agenda. These proposals are usually being floated in the news media to generate support of the general public but

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Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 without the full backing of a detailed study. Going back to the MMUTIS 1998 study, the study proposed several transport infrastructure projects, most especially expressways and road interchanges that were identified to address the increasing growth in private vehicular traffic. Due to limited funds to finance these infrastructure projects, an insignificant number of these proposed infrastructure projects were realized up to the present time. Although MMUTIS also proposed several MRT/LRT lines to serve the commuting public, these were also expensive to construct and make take some time to build. There are another alternative to the MRT/LRT systems that is becoming popular and this is the Bus Rapid Transit (BRT) system. This study would like to quantify the impact of BRT systems by introducing them along the proposed MRT/LRT systems. Furthermore, additional objectives would be the following:

1. To develop a detailed methodology of how to quantify and measure the environmental, economic, and social impacts of the proposed transport projects in Metro Manila using the Transport Co-Benefit Approach.

2. To measure the impact on the travel patterns of urban travelers in Metro Manila of the proposed transport infrastructure project.

3. To quantify the environmental, economic and social impact of a proposed transport infrastructure project in Metro Manila using the Transport Co-Benefit Approach.

1.2 Significance of the Study

The study provides a comprehensive and integrative approach of evaluating proposed infrastructure projects in Metro Manila using the Co-Benefit approach thereby providing government agencies like DOTC and DPWH another view of measuring transport infrastructure benefits and/or costs as well as providing concerned policy makers and interested individuals data and information about the impact of proposed infrastructure projects in Metro Manila. The study could also add up to the wealth of knowledge of the developed methodology in conducting urbanwide impact analysis of proposed infrastructure projects.

2. LITERATURE REVIEW

The current practice of whether a proposed infrastructure project would get approval for its construction is through an economic feasibility study of the proposed project. Hyari and Kandil (2009) provide a comprehensive review of literature about feasibility studies for infrastructure projects and summarize the structure of a feasibility study as shown in Figure 1. The following are the major stages: (1) Identifying alternatives for the project under consideration, (2) Collecting all possible data about practical alternatives, (3) Making the necessary forecasts and projections of the base year data for the project useful life, (4) Determining evaluating method/s for appraising project alternatives, and the widely used evaluation method is the benefit-cost analysis, (5) Evaluating alternatives based on the selected evaluation method/s, and (6) Recommending action based on the findings of the study. Under the benefit-cost analysis approach, the evaluation method includes the Net Present Value (NPV), Internal Rate of Return (NRR), External Rate of Return (ERR), Benefit-Cost Ratio, and Payback Period. The benefit-cost approach usually considers the

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Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 saving in travel time and cost due to a new road as well as the cost of delay in the analysis but this could be further enhanced by considering other benefits and costs not currently being considered such as the cost of air pollution as well as traffic accidents. Under the IGES Co-Benefit Analysis method, the co-benefit framework was adopted from Japan’s Ministry of Energy Co-benefit platform where the economic, environmental and social benefits are considered in evaluating proposed transportation project.

Alternative Infrastructures/Design

Data Collection

Data Forecasting and Evaluation Methods Projections 1. Net Present Value (NPV) Determination of 2. Internal Rate of Evaluation Methods Return (IRR) 3. External Rate of Return (ERR) Evaluation of 4. Benefit/Cost Ratio Alternatives 5. Payback Period

Recommendation of Best Alternative

Figure 1. Basic structure of feasibility studies Source: Hyari and Kandil (2009)

3. THEORETICAL FRAMEWORK AND METHODOLOGY

Existing OD trip matrix data of both passengers (MMUTIS, 1999) and freight (HSH, 2009) were used and were projected using the estimated growth forecast from these studies. These projected trips were loaded into the baseline and design years networks. Traffic assignment under multi-class assignment (with several modes) is then run. Note that the truck traffic is constrained only on roads allowed for them to use under the truck ban period while public trips are constrained to use the public transport system, and the private vehicle trips could use whichever path they would like to take as they travel from their origins to their destinations. Furthermore, the scenario modelling conducted is shown in Figure 2, with 2014 as the base year, and the design years 2020 and 2030. The details of the scenarios especially what transport infrastructures are available especially during the design years (2020 and 2030) are provided in Table 1. The quantification of co-benefits borrow some of the conventional cost-benefit analysis (CBA) methods to estimate and quantify time savings, vehicle operating costs savings, traffic accident reduction, and environmental benefits from both local air pollutants and greenhouse gases. In each of the scenario modelling outputs, the co-benefit analysis is then applied and the details of this approach is explained in the IGES Co-benefit Guideline (2011).

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Trip Generation and Trip Distribution - MMUTIS, 1998 - HSH, 2009

Modal Split Analysis Freight Public Private Trucks Trips Trips

Traffic Assignment

Design Years (Scenarios) Base Year 2014 2020 2030 -w/o new -w/ new -w/ new expressways expressways expressways -w/ new -w/ new expressways, new expressways, new mass transit mass transit systems systems

Compare Results and Apply IGES Co-Benefit Methodology

 Travel Time Savings  Vehicle Operation Cost Reduction  Accident Reduction  Reduced Emission  Figure 2. The Modeling Framework

3.1 Co-Benefits

The direct benefits that road users could gain include changes in monetary, time and psychological factors related to driving. Specifically, this include savings on travel time, savings on vehicle operating cost like fuel costs, improvement of punctuality or reliability of road service due to reduction in traffic congestion, and the enhancement of driving comfort. The road user’s benefit is usually measured as the increment in user’s surplus before the project is constructed and with the project already in place by using the result of the estimated future traffic volumes. The future daily traffic is usually estimated as the annual average, neither specific to weekdays or to holidays. To obtain the annual average, the modelling process usually starts by obtaining the estimated peak hour traffic which is then multiplied by a factor to estimate the daily traffic. The daily traffic is then multiplied by the number of days of the year to obtain the annual average traffic. Travel Time Savings. Travel time is converted to monetary cost, simply by multiplying travel time with the value of time. The value of time can be estimated in two ways - resource value approach and behavioral value approach. The resource value means the marginal productivity of time with the assumption that a unit of time is used for production instead of driving. The behavioral value means the user’s willingness to pay for a unit of time

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Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 with the assumption that he/she can spend the time in other activities instead of driving. The value of time estimated from the HSH (2009) study will be used in this study projected to the current year (2014) and the design years (2020 and 2030).

Table 1 Proposed Transport Infrastructures in Each Scenario Modelling

Year Scenarios Transport Infrastructures Included 2014 1 (Baseline) Existing Road Infrastructure and Mass Transit Systems 2 NAIA and NLEx-SLEx Connector Expressways 2020 3 NAIA, NLEx-SLEx Connector, CALA, and Lakeshore Expressways 4 NAIA and NLEx-SLEx Connector Expressways 2030 5 NAIA, NLEx-SLEx Connector, CALA, and Lakeshore Expressways 6 NAIA, NLEx-SLEx Connector, CALA, and Lakeshore Expressways, BRT systems, LRT extensions, Private trips 0% shift to Public Transport 7 NAIA, NLEx-SLEx Connector, CALA, and Lakeshore 2020 Expressways, BRT systems, LRT extensions, Private trips 5% shift to Public Transport 8 NAIA, NLEx-SLEx Connector, CALA, and Lakeshore Expressways, BRT systems, LRT extensions, Private trips 10% shift to Public Transport 9 NAIA, NLEx-SLEx Connector, CALA, and Lakeshore Expressways, BRT systems, LRT extensions, Monorail, Subway, APM, Private trips 0% shift to Public Transport 10 NAIA, NLEx-SLEx Connector, CALA, and Lakeshore 2030 Expressways, BRT systems, LRT extensions, Monorail, Subway, APM, Private trips 5% shift to Public Transport 11 NAIA, NLEx-SLEx Connector, CALA, and Lakeshore Expressways, BRT systems, LRT extensions, Monorail, Subway, APM, Private trips 10% shift to Public Transport

The formula for the travel time savings computation is as follows:

Benefit of travel time saving BT  BT o  BT w

Total Travel time cost BTi   Qijl Tijl  j  365 Eq.(1) j l Here, BT : Benefit of travel time saving

BTi : Total travel time cost with/without project,

Qijl : traffic volume for j vehicle type on linkl , with/without project (vehicle/day)

Tijl : average travel time for vehicle type on link , with/without project (minute）

 j : value of time for vehicle type (Php/minute*vehicle) i : i  w with project, i  O without project, : vehicle type

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l : link 365: the number of days of the year

Vehicle Operating Cost Savings. Vehicle operating cost includes expenses for fuel and vehicle maintenance cost, including oil, tire and tube. These cost elements may vary with road and driving conditions. The formula for the vehicle operating cost reduction is of the same structure as that of the travel time savings: Since the OD demand is assumed to be fixed in the traffic demand estimation, the savings on total time and the savings on total vehicle operating cost are link-based summation over the whole network which gives the estimate of the user’s benefit. Unit Value for Monetary Evaluation. To measure the user’s benefit, monetary value is used for each vehicle type. The monetary values used in the last HSH (2009) study would be used in this study where there are four vehicle types as shown in Table 2. The time values were derived from Metro Manila Urban Expressways Network (MMUEN) study data. Supposing time value will be increased in accordance with inflation rate of 5% per year. The value of time depends on trip purpose, vehicle occupancy ratio, and other factors. However, since future traffic demand is generally not segmented by purpose but by vehicle type, the value of time is then given for each vehicle type.

Table 2 Time Evaluation Value by Vehicle Type (Unit: Php/hour) Y2009 Y2020 Y2030 Car 331.4 566.8 923.3 465.9 796.9 1,298.1 Bus 1,524.2 2,606.9 4,246.4 Truck 873.2 1,493.5 2,432.7 Source: HSH (2009)

Vehicle Operating Cost (VOC). In the case of the Japan, the unit vehicle operating cost were be defined as a function of road type and driving conditions, travel speed and other factors. The elements of vehicle operating cost are fuel, oil, tire and tube, maintenance and depreciation of the vehicle. For Metro Manila, the VOC were basically divided into two road-based transport modes, public and private, depending on the speed of the vehicles as used in the MMUTIS Study shown in Table 3. The VOC values decrease from 0 to 20 kph then increases again after 20 kph. The VOC of the railway transport system, including the LRT, MRT and PNR, was estimated to be Php 0.287/passenger-km.

Table 3 Vehicle operating cost(VOC) of public and private transport modes (MMUTIS, 1996) Speed (km/hr) Public Private Php/km Php/hr Php/km Php/hr 0 4.757 25.35 3.268 16.98 10 4.197 40.25 2.849 23.68 20 3.197 47.70 2.640 27.08 30 3.730 50.84 2.284 26.96 40 3.632 52.13 2.379 25.93 50 3.670 52.78 2.342 24.70 60 3.842 53.16 2.352 23.90 70 4.103 53.61 2.422 22.39 80 4.558 54.50 2.562 21.43 90 5.339 56.33 2.805 21.66

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Measurement of Traffic Safety Benefit. With regards to traffic accidents reduction, the guideline deals with tangible losses such as human damage (i.e. casualty and injury), material damage, public loss arising from emergency rescues or accident management, and loss due to traffic congestion caused by an accident. However, due to unavailability of reliable data on traffic accidents in Metro Manila especially the point source of the accidents in order to develop traffic accident models, this was neglected in the study.

Environmental Benefits of Transport Projects. The environmental benefits of transport projects may come from the reduction of air pollution, noise, vibration, land use changes, ecology and landscape. Transport project can have an impact in climate change mitigation through the reduction of greenhouse gases such as carbon dioxide (CO2) as well as air pollutants such as particulate matter (PM) and nitrogen oxide (NOx). In this section, the method of computing the environmental benefits for the improvement of local air pollution as well as the climate change mitigation impact in general from transport projects is presented. From the view point of climate change mitigation, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) are important greenhouse gases from motor vehicles. Among these, CO2 is selected since it is the most dominant greenhouse gas from motor vehicles, and other gases are significantly smaller. From the view point of improvement of local air pollution, the major air pollutants, nitrogen oxide (NOx), particulate matter (PM) and carbon monoxide (CO) are selected.

Calculating Emissions. In this study, the bottom-up approach is used where the emissions were estimated by summing up link-based emissions. The approach could apply, for example, to public transportation projects (railway, BRT, etc) or traffic flow improvement projects. In the calculation of environmental emissions improvement with and without the transport project, the MMUTIS 1996 study approach was used where the air pollution parameters were estimated for CO, NOx, and SPM and the air pollutants can be estimated by road link during the peak hour as follows:

Air pollutants = travel distance (veh-km) x emission factor at average running speed (g/veh-km) + total stop time (min) x emission factor at stop time (g/min) Eq. (3)

However, for this study the stop time was neglected and instead of the average running speed the average travel speed was used. It should be noted that the costs and benefits are measured by road segment. In each scenario modelling result, the final outputs are all road segments of the whole study area.

4. MAJOR TRANSPORTATION INFRASTRUCTURE CONSIDERED

4.1 Major Mass Transit Infrastructures under Consideration

The study would not only focus on the BRT projects but all major land-based transport infrastructure projects being proposed by the national government and private entities and with some already being constructed as of today. From previous studies, either commissioned by the National Government, international funding institutions (ADB, World Bank, USAID, JICA, among others), private companies or independent academic researches, there are already several transport infrastructure plans (MRT, LRT, BRT, subway, as well as expressways) being proposed in Metro Manila. The list and the individual description of

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Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 these mass transport projects (under construction and being proposed) are as follows:

1. The proposed LRT/MRT routes under the Metro Manila Urban Transportation Integration Study (MMUTIS) in 1998 and so far as of 2011, the blue lines are the existing MRT/LRT routes. This study will only consider those LRT /MRT routes that are being bid out under the PPP arrangement, most specifically the MRT 7 alignment and the LRT1 extension to Cavite province as further discussed below. 2. There are more detailed route plans that came out of the DOTC pronouncements in the present administration like the MRT7 line. The MRT7 which an estimated cost of $1.235 billion, will have 14 stations that include North Avenue, Quezon Memorial, University Avenue, Tandang Sora, Don Antonio, Batasan, Manggahan, Doña Carmen, Regalado Avenue, Avenue, Quirino, and Sacred Heart in ; Tala in City; and Araneta in San Jose del Monte. 3. Another more detailed proposal is the alignment of the LRT 1 extension going to the province of Cavite This alignment as well as the station locations will also be considered in the study. 4. The National Center for Transportation Study (NCTS) and USAID pre-feasibility study about potential BRT corridors in Metro Manila. Some of the LRT/MRT routes have also been identified as BRT routes. There are also additional BRT routes being considered going further towards the neighboring provinces like the Line 2 (going to ), Line 5B (going to Cavite), Line 1C (going to Bulacan). Line 3A (C5-Commonwealth Avenue) is just one of the proposed routes where the Co-Benefit analysis was applied. In the current situation, only three BRT alignments will be considered in the study, namely the Ortigas-R5 (Line 2- Ortigas- Taytay), C5 (Commonwealth – FTI), and Quezon City – Manila (Tandang Sora – Manila City Hall) highly flexible to service CBDs , Fort Bonifacio, Ortigas. 5. Very recently there is a proposed subway system under a PPP arrangement that will run from Fort Bonifacio to Makati to SM Mall of Asia with an estimated cost of Php 374 billion. The subway will be a build-transfer-operate (BTO) project, rather than the usual build-operate-transfer (BOT) which means that the concessionaire will transfer ownership to the government before the 31-year concession agreement is over.

4.2 Major Road Infrastructure Projects Under Construction/Feasibility Study

The following major road transport infrastructure projects in were considered in the scenario modelling, the NAIA Expressway project, the NLEx-SLEx Connector roads, the Expressway project, the CALA Expressway project, and the Laguna Lakeshore Expressway Dike project. These were also considered in the study. There are other transport infrastructure projects being planned by the government as well as unsolicited proposals from private groups. This include the North-South Commuter Rail, the Guadalupe to NAIA monorail, the mass transit loop system in (BGC), the LRT2 East Extension, the MRT7, and the Automated People Mover (APM) in City.

5. URBAN TRANSPORT MODELING IN EMME

The most thorough urban-wide transport study in Metro Manila was conducted in 1996-1998 also known as the Metro Manila Urban Transportation Integration Study (1998). It was conducted to provide a comprehensive and integrated approach of addressing the worsening

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Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 traffic congestion in Metro Manila not only through the provision of transport infrastructures but also the improvement of the public transportation system as well as traffic management measures. It was only during this study that a properly sampled Home Interview Survey (HIS) was conducted where the Origin-Destination (OD) Person Trip Matrix was developed. Hence, the OD trip matrix developed during this study would be used as basis in the projection of the current OD trip matrix. Given the trends of socio-economic data in the cities and municipalities of Metro Manila as well as neighboring provinces from the National Statistics Office, the 1996 OD person trip matrix was then projected to the 2014, 2020 and 2030 levels while calibrating it to the current traffic characteristics. The High Standard Highway (HSH) Network Development in the done by JICA in 2009 was also used as basis especially the volume of truck . The HSH projection about truck traffic for 2020 and 2030 was also used in the modeling. The zoning system used was that of MMUTIS (1999) with 181 zones plus additional zones of 15 considering the location of PEZAs external zones and ports (Subic, Batangas, and Manila) where truck traffic are generated for a total of 196 zones. The roads where truck traffic are allowed as well as the truck ban period were considered in the modeling process.

5.1 Data Needs and Modeling Requirements

The following secondary data were used in the transport modelling process:

a. Trip Generation Growth Rates. With regards to the background/other vehicular traffic due to people trip movements, the MMUTIS (1999) and HSH (2009) projections were used as shown in Table 4. These growth rates were used to estimate the total daily trips generated for the design years 2020 and 2030.

Table 4 Trip Generation Percent Growth Estimates of MMUTIS (1999) and HSH (2009) Design Periods Year 1996 2010 2015 MMUTIS (1999) 1.00 1.62 1.84 Year 2009-2020 2021-2030 HSH (2009) 2.5 1.5

b. O-D Trips in Metro Manila. Passenger demand projection in terms of person-trips using both the public and private transport was based from MMUTIS (1999) as well as the HSH Report (2009) for the truck traffic projection. The passenger demand projections for both studies as previously shown in Table 4 are not that different and hence the MMUTIS data was used. The estimated OD trips for the base year of 2014, and design years of 2020 and 2030 are provided in Table 5 below.

Table 5 Estimated Peak-Hour OD Trips for 2014, 2020 and 2030 Public Private Daily Trucks (Person-Trips) (Person-Trips) (Veh-trips) Truck Estimate (2009) 642,714 Baseline (2014) 2,075,416 1,490,139 Design Year (2020) 2,406,735 1,728,039 872,329 Design Year (2030) 2,793,312 2,005,412 1,069,841

c. Hourly Truck and Other Vehicular Flow Trends. Based from the study by Castro, et.al. (2003), the shape of the hourly truck traffic does not coincide with the peak hour

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volume during the day. Using this finding, the impact of the truck traffic volume during the peak hour even though the former is less compared to other vehicular traffic as well as during the off peak hour (after the truck ban) could also be modelled. Truck traffic is only around 3 to 4 % during the morning or afternoon peak hour period of its 16 hour traffic volume. The truck traffic reaches its peak at around 9% of its 16hr traffic volume during the 10-11AM period and does not go lower than 8% during the 10AM to 3PM time period. It is further assumed that during the 8PM to 6AM period, the truck traffic averages around 6% of the 16 hour traffic volume. The percentage of truck traffic, especially for the morning peak hour period, was used in the modelling processes while also considering the private and public trips during the said period.

d. Cargo Demand Generation. The HSH (2009) shows that the total generation of cargo demand in the study area is expected to increase from 2,565,622 tons per day in 2009 to 3,478,682 tons per day in 2020 and 4,292,515 tons per day in 2030. Approximately, two thirds of the total cargo movements would be continuously generated and/or attracted within Metro Manila.

e. Time Evaluation Value. In the quantification of costs and benefits the value of time of commuters is needed. In order to combine both travel time and cost (such as public transport fare and cost of toll) to come up with a generalized cost of travel, this value is needed. The time values were obtained from the Metro Manila Urban Expressway Network (MMUEN) study (see Table 6). In the year 2020 and 2030 projections, it is supposed that the value of time will increase in accordance with inflation rate of 5% per year.

Table 6 Time Evaluation Value by Vehicle Type Unit: Peso/hour Y2009 Y2020 Y2030 Car 331.4 566.8 923.3 Jeepney 465.9 796.9 1,298.1 Bus 1,524.2 2,606.9 4,246.4 Truck 873.2 1,493.5 2,432.7

5.2 Transport Infrastructures and Modes in Metro Manila

Current Road Transport Infrastructures. Currently, there are five expressways that serve Metro Manila and its neighboring provinces, namely, the , the , the Metro Manila , the Cavite Expressway and its Extension, and the CALABARZON Expressway. Table 7 provides a summary description of these expressways.

Public Transport Modes and their Service Characteristics in Metro Manila. The major public transport system of Metro Manila is composed of the four railway systems (LRT1, LRT2, MRT3, PNR), city buses (both airconditioned and non-airconditioned), jeepneys, AUV or FX Taxi. Apart from these major modes, there are also tricycles and pedicabs that serve as feeder modes to the major modes of transport. Table 8 provides the estimated vehicle trips in Metro Manila and surrounding areas in 2009 as well as the projection for design years 2020 and 2030.

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Table 7 Expressways in Metro Manila and Neighboring Provinces Expressway Location Length No. of lanes (km) North Luzon EDSA, Quezon City to 83.7 2 to 4 lanes, in Expressway McArthur Highway, Mabalacat, both directions Pampanga Metro Manila , Makati to 31.2 1 to 3 lanes, in Skyway Bunye Road, Alabang, both directions South Luzon , Manila to 60.0 2 to 4 lanes, in Expressway Southern Tagalog Arterial both directions Road, Santo Tomas, Batangas Roxas Blvd-Naia Road, 14.0 2 to 3 lanes, in Cavite Expressway Paranaque to Tirona Highway, both directions Kawit, Cavite CALABARZON Sto. Tomas to Lipa to Batangas 42.0 1 to 2 lanes, in Expressway both directions

Table 8 Estimated Vehicle Trips for 2009, 2020, and 2030

Source: HSH (2009)

Onboard surveys conducted on major public transportation in 2013 and 2014 shows their service operating characteristics like average travel speed, as shown in Table 9. These service operating characteristics were obtained during the morning peak hour period.

Table 9 Primary Modes Average Travel Speeds and Level of Confidence of Speed Primary Modes Average Travel 95% Level of Confidence Speed (kph) Lower Limit Upper Limit ABus 13.25 12.09 14.42 NBus 16.91 15.25 18.57 Jeepney 12.93 10.45 15.41 Fx/SVan 15.80 14.08 17.38 LRT1 24.78 23.36 26.20 LRT2 26.83 24.32 29.33 MRT3 31.65 29.38 33.92 PNR 20.15 17.92 22.41

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5.3 Estimated Impact on Urban Travel of Proposed Infrastructure Projects

The estimated number of person-trips by public and private modes as well as for the trucks in year 2014 and design years 2020 and 2030 are summarized in Table 10.

Table 10 Estimated Peak Hour Trips by Mode in Metro Manila for the Design Periods

Trip Type Baseline, 2014 Design Year Design Year 2020 2030 Public 2,075,416 2,406,735 2,793,312 Private 1,490,139 1,728,039 2,005,412 Truck 61,794 76,343 93,619

The baseline scenario is modelled and calibrated in such a way that it could reflect the year 2014 situation during the morning peak hour period. The grand average of the average speeds of the primary public transport and private transport modes is obtained to be around 19.90 km/hr. After several runs and calibration, the model network average speed for the baseline scenario is obtained at 20.76 km/hr for an absolute difference of 0.86 km/hr or around 4.3% of the estimated actual speed which is a good enough estimate. Hence, the baseline scenario could now be used for future design projections. Focusing on specific roads like the EDSA, C-5 and the expressways in Tables 11 and 12, we could see the same trend as discussed. Urban travel characteristics will be worst along C-5 than along the EDSA while travel along the expressways would be better than the other two. The average travel speed along C-5 is also estimated to be lower than that of the EDSA because of the presence of bottlenecks along the corridor such as the elevated U-turn slots in the intersection that narrowed the C-5 section and also because most of the truck traffic going in the South to North direction uses the road more than the EDSA.

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Table 11 Estimated Urban Travel Characteristics during the Morning Peak Period in Metro Manila along Specific Roads

Scenarios Travel Specific Roads Scenario 1 Scenario 2 Scenario 3 (with Scenario 4 Scenario 5 (with Characteristics (Baseline) (NAIA and NAIA, (NAIA and NAIA, Year 2014 NLEx-SLEx NLEx-SLEx, NLEx-SLEx NLEx-SLEx, Expressways) CALA and Expressways) CALA and Year 2020 LAKESHORE Year 2030 LAKESHORE Expressways) Expressways) Year 2020 Year 2030 Average Travel EDSA 11.85 8.84 8.69 7.55 7.41 Speed (kph) C-5 8.37 5.52 5.13 4.43 4.12 Expressways 29.58 29.33 30.00 27.09 27.65 EDSA 185,765.5 240,786.7 243,357.5 278,007.7 281,154.9 VDT (Veh-km) C-5 118,948.1 160,424.7 167,044.1 185,629.6 193,543.1 Expressways 1,027,889.0 1,317,822.0 1,393,568.0 1,491,172.0 1,586,182.0 EDSA 37,672.0 74,930.5 78,018.9 111,771.8 116,673.5 VHT (Veh-hr) C-5 22,865.2 51,817.5 53,530.1 78,270.6 81,333.7 Expressways 147,867.4 371,970.8 372,445.8 569,038.3 571,885.4

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Table 12 Estimated Urban Travel Characteristics during the Morning Peak Period in Metro Manila along Specific Roads with Mass Transport System in Place under 0%, 5%, and 10% shift from Private to Public Mode

Scenarios Travel Specific Roads Scenario 6 Scenario 7 Scenario 8 Scenario 9 Scenario 10 Scenario 11 Characteristics Year 2020 Year 2020 Year 2020 Year 2030 Year 2030 Year 2030 No Shift 5% shift to 10% shift to No Shift 5% shift to 10% shift to public public public public Average Travel EDSA 8.69 9.15 9.70 7.45 7.81 8.23 Speed (kph) C-5 5.16 5.55 6.03 4.11 4.42 4.78 Expressways 30.00 30.96 31.98 27.67 28.40 29.38 EDSA 243,617.6 231,967.1 219,776.0 281,054.4 267,752.8 253,717.1 VDT (Veh-km) C-5 167,037.6 158,882.3 150,593.6 193,416.6 184,174.6 174,251.2 Expressways 1,393,787.0 1,333,283.0 1,273,007.0 1,585,742.0 1,519,340.0 1,445,790.0 EDSA 78,242.6 68,341.6 58,887.8 116,593.7 101,662.9 87,404.7 VHT (Veh-hr) C-5 53,644.6 46,614.6 40,068.1 81,232.2 70,608.7 60,411.8 Expressways 372,222.4 321,496.1 275,582.3 569,863.1 494,786.8 420,170.3

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6. CO-BENEFIT ANALYSIS OF PROPOSED TRANSPORT INFRASTRUCTURES

The co-benefit estimates of the proposed infrastructure projects (both roads and mass transit systems) could be obtained by computing the co-benefit per each scenario results. As previously introduced in Table 1, regarding what transport infrastructures are available per scenario as well as the assumed percentage shift from private to public mode, results per scenario are shown in Tables 13 to 15. Due to the increasing growth in urban travel trips, especially for the private trips, it is expected that the value of time, vehicle operating cost, and environmental cost would increase as shown in Table 13. However, when there is some shift from private to public travel even just for a maximum of 10%, a considerable amount could already be saved as shown in Table 14 for year 2020 and Table 15 for year 2030. Comparing first the scenarios with only road infrastructures available, like for example Scenario 1 (2014) with Scenario 2 (2020) with only the NAIA and NLEx-SLEx connector expressways present in the latter scenario, with the rest remain the same, the environmental cost would more than double by year 2020 at 120.8% while the grand total cost including VOC, VOT and environmental cost would increase by 116.5%. Comparing further Scenario 2 (2020) with Scenario 4(2030) with only the NAIA and NLEx-SLEx connector expressways present in both cases, the environmental cost would further increase by 51.3% while the grand total cost including VOC, VOT and environmental cost would increase by 50.2%. The addition of more expressways would not decrease the environmental cost or even the total cost (sum of VOC, VOT and environmental cost). Comparing Scenario 2 (2020) with Scenario 3 (2020) where in the latter there is the addition of the CALA and Lakeshore expressways, the environmental cost increased by 2.2% and the grand total cost by around 2.3%. The same is true for Scenario 4 (2030) and Scenario 5 (2030) with the same addition of expressways, the environmental cost as well as the grand total cost increased by around 2.0%. The only positive effect maybe because the roads where there are public transport could have been freed up when private vehicles shift to the use of the expressway thereby improving a bit the service performance of the public transport system resulting to a decrease in their total cost (VOC, VOT, and environmental cost of public transport only). Assuming now the all the proposed mass transit systems currently in the feasibility studies or being bid out like the LRT1 extension, the BRT systems along , C-5 as well Extension to Rizal province, if we compare Scenario 2 (2020) with Scenario 6 (2020) where in the latter have the mentioned mass transit systems available, we can see some slight increase of around 5.1% in the environmental cost and a 5.0% increase in the grand total cost. However, with the availability of better mass transit systems and the further decrease in travel time due to the increasing number of private car users, let us then assume that around 5 percent of the private car users will shift to the use of the mass transit system. Comparing Scenario 6 (2020) with Scenario 7 (2020) where in the latter there is a 5% shift from private to public modes, we can see a 15.3% decrease in the environmental cost or a 15.0% in the grand total cost. Comparing further Scenario 6 (2020) with Scenario 8 (2020) where in the latter there is a 10% shift from private to public modes, we can see a 27.2% decrease in the environmental cost or a 26.7% decrease in the grand total cost. The same is true would happen in the year 2030 wherein if we compare Scenario 9 (2030), 0% shift from private to public mode with Scenario 10 (2030), 5% shift from private to public mode, a 13.0% decrease in environmental cost would be experienced or a 12.9% decrease in the grand total cost. If we further increase the percent shift to 10% in Scenario 11 (2030) and comparing ith with Scenario (9) at 0% shift from private to public mode, we can experience a 25.6% decrease in the environmental cost or a 25.2% decrease in the grand total cost.

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Table 13 Estimated Annual Cost of Projects for Scenarios with Road Transport Infrastructures in Place

Baseline (2014) Scenario 2 (2020) Scenario 3 (2020) Scenario 4 (2030) Scenario 5 (2030)

VOC CAR Php 17,231,711.21 Php 23,510,195.37 Php 24,535,153.83 Php 27,674,071.16 Php28,784,866.45 VOC PUBLIC Php 837,003.61 Php 855,362.73 Php 855,240.90 Php 866,903.94 Php859,785.90 VOT CAR 56,172,608.14min Php 310,260,039 127,694,828.20min Php 705,301,101 130,726,903.40min Php 722,048,262.9 195,382,534.5min Php 1,079,162,865 199546212.40min Php 1,102,160,247 VOT PUBLIC 1,911,500.26min Php 31,700,638.9 3,198,070.05min Php 53,037,326.7 3,193,509.77min Php 52,961,698.3 4,207,874.76min Php 69,784,096.3 4179157.06min Php 69,307,837.2 CO2 CAR 143,210,140.70g Php128,545.42 196,823,196.00g Php176,668.50 205,149,424.10g Php184,142.12 232,026,451.50g Php208,266.94 241363639.00g Php216,648.00 CO2 PUBLIC 1,419,927.96g Php1,274.53 1,449,689.87g Php1,301.24 1,449,121.02g Php1,300.73 1,468,670.53g Php1,318.28 1456530.03g Php1,307.38 NOX CAR 15,412,790.73g Php1,019,785.76 20,466,074.20g Php1,354,135.75 21,389,727.04g Php1,415,249.15 23,970,624.53g Php1,586,013.97 24947876.51g Php1,650,673.75 NOX PUBLIC 1,794,722.01g Php118,747.60 1,823,513.89g Php120,652.61 1,823,619.51g Php120,659.60 1,845,674.089g Php122,118.84 1830975.45g Php121,146.31 SOX CAR 68,894.98g 93,980.16g 98,009.69g 110,621.27g 114938.12g

SOX PUBLIC 31,163.89g 32,653.23g 32,586.82g 33,242.46g 32950.71g SPM CAR 353,379.90g Php1,159,091.38 493,805.23g Php1,619,688.58 514,005.91g Php1,685,947.09 583,752.75g Php1,914,717.78 605617.84g Php1,986,435.61 SPM PUBLIC 317,877.90g Php1,042,644.29 340,677.44g Php1,117,427.10 339,465.84g Php1,113,453.04 348,260.12g Php1,142,298.43 344989.34g Php1,131,570.20 Total Cost Php 345,430,766.9 Php 762,728,301.5 Php 779,530,712.9 Php 1,153,921,696 Php 1,176,575,865 (Environment) Total Cost Php 329,799,172.77 Php 731,961,789.20 Php 749,868,755.09 Php 1,110,545,934.85 Php 1,134,798,870.81 (Car) Total Cost Php 33,700,308.93 Php 55,132,070.38 Php 55,052,352.57 Php 71,916,735.79 Php 71,421,646.99 (Public) Grand Total Php 1,206,220,517.80 Php 363,499,481.70 Php 787,093,859.58 Php 804,921,107.66 Php 1,182,462,670.64 Cost

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Table 14 Estimated Annual Cost of Projects for Scenarios with Road Transport Infrastructures and Mass Transit Systems in Place, Yr 2020 Scenario 6 (2020) Scenario 7 (2020) Scenario 8 (2020) 5% shift 10% shift VOC CAR Php 24,540,661.9 Php 23,199,328.5 Php 21,796,489.6 VOC PUBLIC Php 855,301.6 Php 852,307.5 Php 848,844.8 VOT CAR 130,729,666.2 min Php 722,063,522.9 113298228.3 min Php 625,783,881.1 96,984,023.6 min Php 535,675,090 VOT PUBLIC 3,194,774.2 min Php 52,982,667.6 2918600.4 min Php 48,402,555.3 2,646,743.7 min Php 43,894,038.3 CO2 CAR 205,173,527.2 g Php 184,163.8 193,756,334.3 g Php 173,915.7 181,665,606.3 g Php 163,063.0 CO2 PUBLIC 1,449,195.1 g Php 1,300.8 1,444,542.7 g Php 1,296.6 143,9019.9 g Php 1,291.7 NOX CAR 21,395,750.2 g Php 21,395,750.2 20,309,378.5 g Php 1,343,768 19,192,215.7 g Php 1,269,851.0 NOX PUBLIC 1,823,694.7 g Php 1,823,694.7 1,819,147.0 g Php 120,363.7 1,813,672.5 g Php 120,001.5 SOX CAR 980,366.7 g 92,696.1 g 87,141.9 g

SOX PUBLIC 32,590.5 g 32,362.1 g 32,090.9 g

SPM CAR 514,147.3 g Php 1,686,411 484476.0 g Php 1,589,088.0 453,366.8 g Php 1,487,050 SPM PUBLIC 339,520.8 g Php 1,113,633.3 335,042.6 g Php 1,102,224.8 331,924.1 g Php 1,088,716.1 Total Cost Php 801,251,144.30 Php 678,517,093.20 Php 583,699,101.60 (Environment) Total Cost (Car) Php 769,870,509.80 Php 652,089,981.30 Php 560,391,543.60 Total Cost (Public) Php 56,776,598.00 Php 50,478,747.90 Php 45,952,892.40 Grand Total Cost Php 826,647,107.80 Php 702,568,729.20 Php 606,344,436.00

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Table 15 Estimated Annual Cost of Projects for Scenarios with Road Transport Infrastructures and Mass Transit Systems in Place, Yr 2030 Scenario 9 (2030) Scenario 10 (2030) Scenario 11 (2030) No shift 5% Shift 10% Shift VOC CAR Php 28789761.6 Php 27,295,894.8 Php 25698256.6 VOC PUBLIC Php 859732.5 Php 858,164.2 Php 856585 VOT CAR 199,531,693.4 min Php 1,102,079,556 172,875,523.6 min Php 954,849,141.9 147,295,867.8 min Php 813,564,176.4 VOT PUBLIC 4,176,180 min Php 69,258,465 3,823,103.1 min Php 63,237,137.5 3,445,835.3 min Php 57,146,306.9 CO2 CAR 241,420,005.6 g Php 216,698.6 228,655,743.9 g Php 205,241.4 215,097,269.9 g Php 193,071.3 CO2 PUBLIC 1,456,560.1 g Php 1,307.4 1,453,988.3 g Php 1,305.1 1,451,576.9 g Php 1,302.9 NOX CAR 24,948,703.7 g Php 1,650,728.5 23,694,590.2 g Php 1,567,750.2 22,353,191.7 g Php 1,478,996.7 NOX PUBLIC 1,830,989.4 g Php 121,147.2 1,828,392.4 g Php 120,975.4 1,825,936.2 g Php 120,812.9 SOX CAR 114,945.6 g 109,014.5 g 102,616.3 g

SOX PUBLIC 32,952.2 g 32,825.8 g 32,707.2 g

SPM CAR 605,670.7 g Php 1,986,609.0 573,739.1 g Php 1,881,873 53,9029 g Php 176,8023 SPM PUBLIC 345,013.96 Php 1,131,651.0 343,102.6 g Php 1,125,381.6 341,313.4 g Php 1,119,513.1 Total Cost (Environment) Php 1,176,446,162.70 Php 1,022,988,806.10 Php 875,392,203.20 Total Cost (Car) Php 1,134,723,353.70 Php 985,799,901.30 Php 842,702,524.00 Total Cost (Public) Php 71,372,303.10 Php 65,342,963.80 Php 59,244,520.80 Grand Total Cost Php 1,206,095,656.80 Php 1,051,142,865.10 Php 901,947,044.80

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Figure 3 summarize the trend for the grand total costs at different scenarios tested. We can therefore say that only when there is a percentage shift from private vehicle user to public transport users could we experience a significant decrease in the cost using the co-benefit analysis approach.

Figure 3 Grand Total Cost Comparison

7. SUMMARY OF FINDINGS AND CONCLUSION

In order to capture the costs and benefits of transport infrastructure projects in Metro Manila, especially those being planned in the future, the co-benefit approach as developed by IGES was used. Several scenarios were developed to compare the impact of both road infrastructures (i.e. expressways) as well as mass transit systems (like BRT, LRT, MRT, monorail, and subway) to urban travel in Metro Manila. Several interesting results could be deduced from the results of this research and these include:

1. Building more roads like expressways may not necessarily improve urban travel in terms of travel time and average travel speed in Metro Manila. There may be improvements in travel time and average travel speed on roads near the expressways but overall, this will increase travel time and decrease travel speed. Furthermore VDT as well as VHT over the network of Metro Manila would also increase. This would also translate to higher environmental cost as well as the overall transport cost, including the VOC and VOT. 2. However, when there is a shift from the use of private transport to public transport, there is a significant improvement in urban travel in terms of average travel speed, VDT and VHT. The higher the shift, the more significant the improvement in urban travel is achieved. This improvement in travel due to the shift from private to public transport would also decrease the environmental cost as well as the overall transport cost. 3. The co-benefit analysis also showed that environmental cost would only increase with the construction of road infrastructure and the only way to decrease this is to invest in more mass transit systems so that people using the private mode would shift to the use of the mass transit.

8. RECOMMENDATIONS

One item that was not computed in the co-benefit analysis is the cost of traffic accidents with

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and without the projects being proposed in the scenario modelling. Even though there are available Japanese models of how to estimate the number of accidents that would occur along a road segment or intersection given the vehicular volumes using the road, the models developed in Japan may not be appropriate given the driven conditions and behaviour there compared to Metro Manila. It would be then necessary to develop similar models of estimating accidents happening along a road segment or intersection considering Metro Manila’s traffic conditions and behaviour. In the EMME modelling process, the fare for public transport and the toll fees for private transport were not considered. Only the travel time variable was considered in the modelling process. It was assumed that the cost of public transport and toll fees charged along expressways are not important in the decisions of commuters to use what public transport mode or for private car users whether to use the expressway or not. There was no available cost per unit weight for the emission gas, SOx. Since the trend of its effect on the scenario modelling is similar to the other emission gases, its impact would not be significant in the scenario comparisons. Nevertheless, its cost can easily be included if its cost per unit weight is already available. The results of this study could also be further used in cost-benefit analysis with the addition of the cost of constructing the proposed road and mass transit systems as well as their annual operating and maintenance cost but this is already beyond the scope of the study.

ACKNOWLEDGMENT

The author would like to thank the financial support provided by the University Research Coordination Office (URCO) of De La Salle University-Manila.

REFERENCES

Castro, J.T., Kuse, H., and Hyodo, T. (2003), A Study on the Impact and Effectiveness of the Truck Ban Scheme in Metro Manila, Journal of the Eastern Asia Society for Transportation Studies, Vol. 5 Fillone, A. (2005) Discrete Choice Modeling of Work Trips in Metro Manila and Urban Transport Policy Applications, PhD Dissertation, University of the Philippines, Quezon City Hidenori, I., Fukuda, A., Luathep, P., Fillone, A., Jaensirisak, S., Vichiensan, V., Shirakawa, Y., and Thet Thet Htun, P. (2013), Measuring Emission Reduction Impacts of Mass Rapid Transit in Bangkok: The Effect of a Full Network, conference paper in the 13th World Conference in Transport Research, July 15-18, 2013, Rio de Janeiro, Brazil Hyari, K. and Kandil, A. (2009), Validity of Feasibility Studies for Infrastructure Construction Projects, Jordan Journal of Civil Engineering, Vol. 3, No. 1 “Master Plan Study on High Standard Highway Network Development in the Philippines by Japan International Cooperation Agency” (Jica) for DPWH, 2009. Metro Manila Urban Transportation Integration Study (MMUTIS), 1998 Pre-Feasibility Study on Bus Rapid Transit (BRT) System for the Greater Metro Manila Area (2007), National Center for Transportation Studies for USAID-ECAP and DOTC Mainstreaming Transport Co-benefits Approach: A Guide to Evaluating Transport Projects, Institute for Global Environmental Strategies (IGES), Japan, March 2011

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