MEASURING EMISSION REDUCTION IMPACTS OF MASS RAPID TRANSIT IN BANGKOK: THE EFFECT OF A FULL NETWORK

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1 MEASURING EMISSION REDUCTION IMPACTS OF MASS RAPID TRANSIT IN BANGKOK: THE EFFECT OF A FULL NETWORK HIDENORI IKESHITA, NIHON UNIVERSITY CSHI11002@G.NIHON-U.AC.JP ATSUSHI FUKUDA, NIHON UNIVERSITY FUKUDA.ATSUSHI@NIHON-U.AC.JP PARAMET LUATHEP, PRINCE OF SONGKLA UNIVERSITY ALEXIS MORALES FILLONE, DE LA SALLE UNIVERSITY SITTHA JAENSIRISAK, UBON RATCHATHANI UNIVERSITY VARAMETH VICHIENSAN, KASETSART UNIVERSITY YASUKI SHIRAKAWA, CLIMATE CONSULTING, LLC PHYO THET THET HTUN, ALMEC CORPORATION This is an abrige version of the paper presente at the conference. The full version is being submitte elsewhere. Details on the full paper can be obtaine from the author.

2 MEASURING EMISSION REDUCTION IMPACTS OF MASS RAPID TRANSIT IN BANGKOK: THE EFFECT OF A FULL NETWORK Hienori Ikeshita, Nihon University cshi11002@g.nihon-u.ac.jp Atsushi Fukua, Nihon University fukua.atsushi@nihon-u.ac.jp Paramet Luathep, Prince of Songkla University Alexis Morales Fillone, De La Salle University Sittha Jaensirisak, Ubon Ratchathani University Varameth Vichiensan, Kasetsart University Yasuki Shirakawa, Climate Consulting, LLC Phyo Thet Thet Htun, ALMEC Corporation ABSTRACT Mass rapi transit (MRT) is a highly energy efficient transport compare with other roa transport. The introuction of MRT is an essential policy measure not only for alleviating traffic congestion but also in reucing energy consumption an environmental buren of the city. MRT evelopment has a goo influence in the reuction of carbon ioxie (CO2), especially in eveloping cities where transportation eman has increase consierably. However, existing approaches to reuce CO2 emission are ifficult to apply to urban transport projects ue to ata constraints. In aition, some authorities in Asian eveloping countries cannot aequately measure the impact of CO2 emissions from transport projects an they face the ifficulties of estimating emissions. In this stuy, the transportation eman forecasting moel is employe in response to this ifficulty using moels that has been evelope over many years base on logical frameworks such as the user equilibrium theory. Moreover, this moel is wiely use when estimating the impact of traffic eman ue to MRT network evelopment in many evelope countries. Therefore, the estimation of CO2 emissions base on this metho coul be appropriate. This stuy shows the metho of estimating CO2 emissions reuction on a full MRT network in Bangkok an its impacts, e.g. co-benefits, measure by employing an existing transportation eman forecasting moel. Keywors: CO2 emission, Co-benefit approach, Mass Rapi Transit, Bangkok 1

3 1. INTRODUCTION Carbon ioxie (CO2) has been recognize as one of the global-warming culprits. In general, the average CO2 emission generate by the transport sector is aroun a quarter of the total CO2 emissions. Those transport-relate CO2 emissions are mainly from roa transport. As it is known toay in both evelope an eveloping countries, private cars generally prouce CO2 emissions per passenger-kilometer much higher than other public transport moes. One major ifference between evelope an eveloping countries is the quantity of urban public transport evelopment. Santos et al. (2010) note that public transport can play a role in reucing problems relate to several transport externalities: in general, accients an traffic congestion ecrease with higher use of public transport. In fact, rail-base mass rapi transit has become a worl interest as a low carbon transport moe. One reason is that roa transport is the biggest proucer of greenhouse gases in the transport sector, although the motor car is not solely responsible for all emissions (Chapman, 2007). Therefore, transport-relate CO2 emissions shoul strongly take into account not only along the rail-base mass rapi transit line(s) but also along the roas in the vicinity of the target area. A single urban transportation system project might realize a small amount of CO2 reuction comparing with what is expecte. However, the Clean Development Mechanism (CDM) methoology is quite ifficult to apply in orer to estimate the amount of emission reuction for the whole network. Existing CDM methoology firstly estimate the amount of emission reuction for each route an then summate it for the whole network but consequently it has some limitations. In aition, few methoologies have been approve. Since traffic congestion reuction effect cannot be counte on existing CDM methoology, measurement of traffic congestion reuction effect is not aresse in accorance with CDM. Thus, it is necessary to explain the feasibility of measurement. For this reason, an estimation of CO2 reuction from the whole network is strongly necessitate from the viewpoint of measuring against global warming. Thus, a metho to estimate CO2 emission shoul be evelope in full-evelopment of the urban transportation system network. However, some authorities in Asian eveloping countries cannot aequately measure CO2 emissions impact of transport projects an they face the ifficulties of estimating emissions. Schipper et al. (2009) mentione that Measuring carbon in transport cannot be carrie out well in the majority of Asian countries because of the profoun lack of ata on vehicles, transportation activity, an fuel use by vehicle type. An they suggeste CO2 measuring methoology that the activity-structure-intensity-fuel (ASIF) type moel as bottom-up approach against the top-own approach of the IPCC. However, this approach nees statistical ata of the target area. But in the present stuy, the approach to estimate future traffic eman base on eman forecasting moel was employe. Data which is require to estimate traffic eman has been properly evelope in many cities. Thus, in the present stuy, ieal conitions were presente with the iea of applying in other eveloping city in which ata have never been 2

4 evelope properly. Through the present stuy, it woul be easy to ientify the necessary conitions present to apply the metho to other cities. This stuy aims to show the metho of estimation of CO2 emissions reuction in the full MRT network in Bangkok an its impacts, e.g. co-benefits, measure by employing existing transportation eman forecast moel. The present stuy uses the results from the moel to estimate further on emission reuctions. To illustrate the merit of this paper, MRT network evelopment projects in Bangkok, incluing the existing lines an future extensions, is use to etermine the reuction of CO2 emissions an show some co-benefits from the MRT evelopment projects. The remainer of the paper is outline as follows. Section 2 escribes the methoology use in this paper. Some important results are highlighte in Section 3. Finally, Section 4 conclues the paper an iscusses future research issues. 2. METHODOLOGY In this stuy, to estimate future traffic eman an CO2 emissions from roa transport, we employe the eman forecasting moel. We expect to reuce CO2 emission from roa transport by introucing MRT in the existing network base on the following conitions. Moal-Shift Effect: CO2 emissions can be reuce because a passenger will shift from conventional transport moes (e.g., bus, passenger car), in which energy consumption an CO2 emissions of these moes are relatively high compare to a more efficient moe (i.e., MRT); Traffic Congestion Mitigation Effect: The traffic congestion aroun the MRT routes can be mitigate by MRT network evelopment (i.e., future line extensions). Consequently, average travel spee of passenger cars can be improve an CO2 emissions on the whole roa network can be lessene. In other wors, the more MRT network extensions, the more increases in average travel spee of passenger cars. However, consequences of an increase in total vehicle kilometers of travel nee to be carefully consiere. Therefore, it is necessary to figure out whether these consequences cause more CO2 emission reuction. A framework of the stuy is shown in Figure 1. 3

5 Figure 1 Flowchart of the Stuy As shown in Figure 1, in orer to estimate travel eman, the four-step transportation eman forecasting moel is aopte in this paper an explaine in Section 2.1. Then, Section 2.2 explains the methoology for CO2 emission estimation. Section 2.3 presents a metho to evaluate co-benefits from CO2 emission reuction. Finally, Section 2.4 escribes scenario setting for the evaluation of CO2 emission. 2.1 Development of Transportation Deman Forecasting Moel In orer to evaluate the changes in traffic volume ue to the shift from private cars to MRT route, a traitional four-stage moel is evelope on VISUM platform for highway traffic assignment moelling whereas a micro-traffic simulation moel is then execute on VISSIM platform. The roa an MRT network in the Bangkok Metropolitan Area (BMA) an the originestination (OD) matrix were evelope base on M-MAP project (OTP, 2010). Moel Description The network moel in the base year 2010 consists of 244 zones, 59,536 OD pairs an 4,704 roa links. For public transportation, 276 public transport services serving within the BMA area were inclue in the moel. Among these 276 services, two of which are BTS (sky train) lines, one for MRT (subway) line an the others are bus lines. This 2010 network is evelope base on M-MAP moel to represent the evelopment of roa network an public transportation services in the BMA area an the network traffic conition setting is shown in Table 1. Moreover, future networks (2020, 2030) were also create base on M-MAP project. Figure 2 represents the 2010 network evelope on VISUM platform an the etails of the 4

6 network statistics are presente in Table 2. Following M-MAP project, Figure 3, Figure 4 an Figure 5 show the evelopment of public transport services in 2010, 2020, an 2030, respectively. Following the stuy of Sittha et al. (2009), a transportation eman forecasting moel of BMA area can be evelope. The etails are explaine in the next section. Table 1 Network Traffic Conition Setting Item Content Remarks Object Link conitions Roa Public transport route (bus, railway, subway) Route istance Roa capacity Free flow spee Link performance function Toll on the toll roa Direction regulation Operation frequency The network ata of the moel use within the M-MAP an public transport line ata are use. The link conitions set up within the M-MAP is use. Figure 2 Bangkok Metropolitan Area Network in

7 Table 2 Network Statistics Base an Design Years OD pairs 59,536 Number of zones 244 Moes of transport Automobile traffic (private car), Public transport (bus, MRT, BTS) Number of public transport routes Number of railway lines Number of railway stations Total length of railway (km) Traffic Generation, Attraction, an Distribution OD matrices for 2020 an 2030 scenarios were estimate base on the OD matrix in By assuming that the trips will increase continuously following the growth rate of the population, the OD matrices can be estimate by multiplying the current 2010 OD ata with the population growth rates, i.e., 19.6% for 2020 an 49.6% for 2030 (Unite Nations, 2007). The effect of moal shift by improving MRT is estimate base on the following utility function erive from generalize cost between OD an the amount of travel eman for future scenario in 2020 an 2030 are also estimate. Utility Function of Private Transport: PrT PrT U ASC travel u FC (1) Here, ASC : alternative specific constant for OD pair, travel : value of travel time (1.27 T baht/min), u Pr : private transport travel time between OD pair (min), FC : fuel cost for OD pair (3 baht/km). Utility Function of Public Transport: PuT PuT U travel u F wait w (2) PuT Here, travel : value of travel time (1.27 baht/min), u : public transport travel time between OD pair (min), wait : value of waiting time (1.46 baht/min), W : total waiting time spen in taking public transport to travel between OD pair. Base on the above utility functions, the respective shares of the eman of private transport an public transport for OD pair are efine by using a logit moel as follows. Here, Q PuT PrT travel Q Q PuT travel Q Q exp exp PrT exp U PrT PuT U exp U PuT exp U PrT PuT exp U Q : total eman for OD pair, Q PrT : private transport eman for OD pair, travel : public transport eman for OD pair. U (3) (4) 6

8 Figure 3 Base year2010of railway network(office of Transport an Traffic Policy an Planning, 2010) Figure 4 Map of Network uner First 10-year Plan (within 2020) (Office of Transport an Traffic Policy an Planning, 2010) 7

9 Figure 5 Map of Network uner First 10-year Plan (within 2030) (Office of Transport an Traffic Policy an Planning, 2010) Traffic Assignment Base on the OD tables obtaine from the previous section, a logit moel is then applie for the moe choice istribution of the total eman matrix, following Sittha et al. (2009). The link performance function was estimate by using the following formula. Automobile traffic: 3 auto auto V 0 a a tcur t a (5) C a travel Public transport (bus): 3 auto bus 0 V a ta 1.1t a (6) C a Here, t 0 a : free flow travel time of link a (min), V a : hourly volume of link a (vehicles), C a : capacity of link a (veh./hr), a : toll fee (baht), travel : value of travel time (1.27 baht/min). The present stuy, in traffic assignment phase, is calculate by static user equilibrium assignment. The moal shift effecte by the MRT network extensions was estimate from the utility functions erive from the generalize cost between OD while the volume of travel emans for future scenarios in 2020 an 2030 were also estimate. 8

10 2.2 Methoology for Estimating CO2 Emission Reuction Outline of CO2 emission estimation To estimate the CO2 emission, we first calculate the average travel spee an average aily traffic volume of each link. Afterwars, the CO2 emissions of each link for both with- an without-mrt extension cases can be calculate by the average aily traffic volume on each link multiplie by the emission factor which epens on the average travel spee on that link. Emission estimation flow is shown in Figure 6. Figure 6 Emission estimation flow Finally, the ifference in total CO2 emission reuction is compare between with- an without cases. Note that the urban evelopment along MRT extension lines is not taken into consieration in this paper. The moal shift effect was taken into consieration in the process of moal split classifie by moes of transportation, an the traffic congestion mitigation effect was taken into consieration in the stage of the traffic assignment. However, from the viewpoint of consiering the route changes ue to congestion mitigation aroun the MRT route, it was necessary to use user equilibrium state while calculating for the traffic assignment. Estimating CO2 Emissions Reuction The amount of CO 2 emission reuction is estimate by using the results of the transportation eman forecasting moel an its applicability is emonstrate in this stuy. The outline of the analysis using also the estimation results of the transportation eman forecasting moel an its methoology is presente. When using the results of the transportation eman forecasting moel, the emission reuction calculation shoul take into consieration an appropriate reboun effect an local traffic conitions, i.e. egress transportation. Furthermore, the effect of roa congestion mitigation shoul be measure an refine by the simulation metho as well as verifie by the traffic surveys. But in this stuy, we analyse the simulation metho for emission reuction calculation. 9

11 Calculation Formulas of Greenhouse Gas Emission an Its Reuction Emission calculation formulas are escribe as follows. The amount of emissions reuction for the whole MRT network in Bangkok as well as for the whole country can be estimate by applying the following formulas for each project. In Bangkok, para-transit service is operating as a feeer system to connect the main trunk roa with Soi (local-branch) streets. Although it has a certain amount of CO 2 emissions, it is not taken into consieration in the transportation eman forecasting moel. Since paratransit service is registere with the Department of Lan Transport uner the Ministry of Transport, the amount of emission base on these registere vehicle numbers can be estimate. The following section shows the estimation of the emissions of each scenario. Reference Scenario Emissions (a) Emissions from Roa Users It computes the amount of emission of the target roa section which is etermine in avance. It is assume that the aily traffic volume is being use. However, if hourly traffic volume is available, it can also be use for calculation. 6 BEroa, y Di TVbi, j EF( v) KM, i, j (7) i j Where, BE roa,y : Baseline emissions from whole roa network in year y (tco 2 /year) D i : Length of route i (km) TVb i,j : Baseline traffic volume of vehicle j in route interval i (vehicle/ay) EF(v) KM,i,j : Emission factor for baseline travel spee Vb i,j of vehicle j in route interval i (gco 2 /km) 365: A constant, the number of ays in a year an as similarly use in the succeeing formulas Project Emissions (a) Emissions from MRT Operation The amount of emissions from MRT operation is calculate. PE EC EF (8) MRT, y MRT, y gri Where, PE MRT,y :Project emissions from MRT operation in year y (tco 2 /year) EC MRT,y :Power consumption by MRT operation in year y (MWh/year) EF gri :CO 2 emission factor for electric power system (tco 2 /MWh) (b) Emissions from Roa Users It computes the amount of emission of the target roa section which is etermine in avance. 6 PEroa, y Di TVpi, j EF( v) KM, i, j (9) i j 10

12 Where, PE roa,y :Project emissions from roa network in year y (tco 2 /year) Di :Length of route i (km) TVp i,j :Project aily traffic volume of vehicle j in route interval i (vehicle/ay) EF(v) KM,j :Emission factor for project travel spee Vp h,i,j of vehicle j in route i (gco 2 /km) Emissions Reuction Emissions reuction is calculate by the following formula. ER BE PE y y y BEMRT, y BEroa, y PEMRT, y PEroa, y Where, ERy :The amount of Greenhouse gas emissions reuction in year y (tco2/year) BEy :The amount of reference scenario emissions in year y (tco2/year) PEy :The amount of project emissions in year y (tco2/year) The rest of the variables are as previously efine. (10) 2.3 Reference Scenario an Stuy Area Bounary Setting Reference Scenario Accoring to "Law-Carbon Society Vision 2030 Thailan" (November, 2010), estimation is performe in the Business as Usual (BaU) scenario an Counter Mitigation measures (CM) scenario with year 2030 as the target year for Bangkok in the future. This project applies to this future estimation, promotion of the moal shift of the passenger transport in CM scenario. Therefore, in orer to be consistent with the existing future estimation in Thailan, the reference scenario is set up as follows. Reference scenario: BaU scenario (the MRT project oes not exist) Project scenario: CM scenario (state which unertook the MRT project in the BaU scenario) Setting the Bounary Various measures in the transport sector are planne in Thailan an Bangkok metropolitan area as mentione above. It is esirable to set up the bounary base on preliminary investigation an the influence on these measures among others. However, in this project, the bounary was set up focusing on the moal shift effect ue to MRT network evelopment. That is, because the MRT line to be targete by this project are sprea across one capital an five neighbouring provinces of Bangkok metropolitan area, the bounary was set an compose of the capital an five neighbouring provinces of Bangkok metropolitan area where traffic conitions are mostly affecte. 11

13 2.4 Analysis of Co-Benefits In this stuy, aitional co-benefit evaluation is performe base on the result of transportation eman forecasting moel as mentione above. Since the transportation eman forecasting moel estimate the traffic volumes for passenger car an bus for the base year (2010) an the future years (2020 an 2030), the pollutant emission reuction effect is also calculate base on these years. The etails are explaine as follows: Items to be evaluate Items to be evaluate are four types of air pollution emissions incluing Nitrogen oxie (NOx), Carbon monoxie (CO), Particulate matter (PM), an Tetrahyrocannabinol (THC). Baseline / Project Scenario A baseline scenario is the scenario in which the MRT network in Bangkok is not improve an emissions continue to increase by the conventional means of transportation (passenger car an bus). On the other han, a project scenario is the scenario in which moal shift an traffic congestion mitigation are brought by the MRT network evelopment an consequently emissions are ecrease. Baseline Evaluation Metho Emissions of air pollution in the baseline scenario are calculate by integrating the amount of emissions of the target roa section with the following equation. Emission factors use are the most recent value at the time of evaluation an the other parameters are estimate by using the results of the transportation eman forecasting moel. BE D TVb EF 6 k i i, j v (11) k,, i j Where, i j BE k D i :Emissions of air pollution in the baseline scenario (t/year) :Length of route i (km) TVb i, j :Baseline traffic volume of vehicle type j, interval i (vehicle/ay) EF vk, i, j Vp i :Emission factor for air pollution for running spee, j, y of vehicle type j, route i (g/km) Calculation Process an Result of Trial Calculation (Quantification) before Project Implementation Similarly, emissions of air pollutants in the project scenario are calculate as follows. PE D TVp EF 6 k i i, j v (12) k,, i j i j 12

14 Where, PE k : Emissions of air pollution in the project scenario (t/year) TVp, i j : Project traffic volume of vehicle type j, interval i (vehicle/ay) Base on the above calculation, the emission reuction effect of the air pollution is calculate as follows. ERk BEk PEk (13) Where, ER k :Amount of emission reuction of air pollution (t/year) 3. RESULTS 3.1 Valiation of Assignment Result of the Base Year Moel The assignment result of the base year moel was valiate by traffic volume count on 899 links (about 16% of all links) an its etermination coefficient R 2 is The result can be shown in Figure 7. Figure 7 Observe link flows vs VISUM link flow Transportation Deman Forecast Moelling Results Traffic assignment results for current conition 2010, 2020 without the MRT project (reference scenario) an 2030 without the MRT project (reference scenario) are illustrate in Figure 8, Figure 9 an Figure 10, respectively. Project scenarios for 2020 an 2030 are shown in Figure 11 an Figure 12, respectively. These figures give the results that the high 13

15 link volumes occur aroun the owntown area of Bangkok an low link volumes occur in the surrouning areas. For the links on the western area of the network, the link volumes are relatively high espite their locations at the suburban area. It is because all the eman in the western area of BMA is only serve by those few links On the other han, the links locate at the north-east area of BMA seems to be relatively low link volumes. This is mainly ue to the aggregate efinitions of zones an centroi connectors in those areas causing the eman not flowe to some of the links. In Figure11 an 12, it coul be seen that the majority of the link aroun the MRT lines have reuce traffic volume compare to reference scenario of each year (Figure 9 an 10). Figure 8 Traffic Assignment Results in 2010 Figure without MRT Project (Reference Scenario) Figure with MRT Project (Project Scenario) Figure without MRT Project (Reference Scenario) Figure with MRT Project (Project Scenario) 14

16 3.2 CO2 Emission Estimation Result Baseline emission from roa users for the current conition (2010), reference scenario emissions (BE roa ) an project scenario emissions (PE roa ) for the year 2020 an 2030 are calculate by using transportation eman forecasting results mentione above. These emission amounts are shown in Table 3. Table 3 The amount of annual CO2 emissions by roa users (Unit: t-co2/year) Present conition Year 2020 Estimation Year 2030 Estimation BE roa BE roa PE roa BE roa PE roa 13,317,246 18,251,177 12,736,649 25,279,356 17,453,825 Aitionally, the amount of CO2 emissions in Bangkok an the whole Thailan was estimate base on Thailan Energy Statistics 2010 by Department of Alternative Energy Development an Efficiency (DEDE) an the statistics are as follows. The whole Thailan:58,843,664 t-co2/year BMA:13,212,094 t-co2/year 3.3 Results of Co-Benefit Analysis The calculation results of air pollution matter are shown in Table 4. Aitionally, the amount of emissions here represents only the contribution from passenger car an bus an it oes not inclue the contribution by freight cars such as trucks. Furthermore, it is necessary to keep in min that there is a possibility that the emission values are overestimate compare to actual emissions since transportation eman forecasting is carrie out only for peak hours. Figure 13 to Figure 20 show the annual mean concentration of NOx, CO, PM an THC in present conition year 2010 an target year 2030 where the reuction effects are emphasize. Those figures shows that NOx, CO an THC have relatively highly reuction effects not only along the MRT lines but also the entire network, especially pronounce in the ensely urbanize area in the eastern part the city. Since PM is concerne with buses, its reuction is expectely observe along the bus routes. Table 4 - Amount of Emissions an Reuction for Air Pollution from Passenger Car an Bus (Unit: t/year) Present conition Year 2020Estimation Year 2030Estimation BE k BE k PE k ER k BE k PE k ER k NOx 105, , ,766 32, , ,424 37,946 CO 298, , , , , , ,288 PM THC 87, ,581 82,118 56, , ,232 83,436 15

17 Legen Legen Figure13 Annual mean concentration of NOx in 2010 (Only from buses an passenger cars) Figure14 NOx reuction effects of the evelopment of MRT network in 2030 Legen Legen Figure15 Annual mean concentration of CO in 2010 (Only from buses an passenger cars) Figure16 CO reuction effects of the evelopment of MRT network in 2030 Legen Legen Figure17 Annual mean concentration of PM in 2010 (Only from buses an passenger cars) Figure18 PM reuction effects of the evelopment of MRT network in

18 Legen Legen Figure19 Annual mean concentration of THC in 2010 (Only from buses an passenger cars) Figure20 THC reuction effects of evelopment of MRT network in CONCLUSIONS This stuy emonstrate that the amount of CO2 emission in the BMA area can be reuce by MRT link extensions. To estimate the impact of CO2 emissions on the full MRT network evelopment in Bangkok, we evelope a new approach of CO2 emission estimation which aopts a concept of transportation eman forecast moelling. Note that we consier not only moal-shift effect but also traffic congestion mitigation effects of the whole network from the MRT evelopment. The metho of estimating the amount of CO2 emission reuction coul not be amitte in the existing approaches, such as CDM methoology, for traffic congestion mitigation affects the whole network. In the present stuy, we estimate this effect by using transportation eman forecasting an setting its bounary. To eepen the iscussion in omestic an international arenas, it is necessary to tackle such calculations. Thus, this approach woul be useful an esirable from the viewpoint of appropriate estimation of CO2 emission impacts of MRT evelopment. Through this, it is easy to ientify the conitions, what kins of ata shoul be prepare, an apply to other cities. However, it is necessary to verify the estimate values of CO2 emission reuction. ACKNOWLEDGEMENT PTV vision which provies an acaemic license to Nihon University helpe to a great extent in the realization of this stuy. Furthermore, in this stuy CO2 an co-benefit estimation were mainly carrie out by Japan Weather Association an Climate Consulting, LLC. This stuy is a part of a project fune by New Mechanism Feasibility Stuy FY 2011 uner the Ministry of the Environment. An this stuy is partially grante by the Japan Ministry of Environment "Global Environment Research Fun (S-6)." 17

19 REFERENCES Santos, G., Behrent, H. an Teytelboym, A. (2010). Part II: Policy instruments for sustainable roa transport, Research in Transportation Economics, 28, 1, Chapman, L. (2007). Transport an climate change: a review, Journal of Transport Geography, 15, 5, Schipper, L., Fabian, H. an Leather, J. (2009). Transport an Carbon Dioxie Emissions: Forecasts, Options Analysis, an Evaluation, Asian Development Bank, 9. Office of Transport an Traffic Policy an Planning -OTP (2010). I Mass Rapi Transit Master Plan in Bangkok Metropolitan Region: M-Map. Jaensirisak, S., Sumalee, A., Ongkittikul, S., Julio Hung Wai Ho an Luathep, P. (2009) Integrating Congestion Charging Schemes an Mass Rapi Transit Systems in Bangkok, ATRANS Research Grant 2009 Final Report Unite Nations (Population Division of the Department of Economic an Social Affairs of the Unite Nations Secretariat) (2007) Worl Population Prospects: The 2006 Revision an Worl Urbanization Prospects: The 2007 Revision, Thailan, Urban population (Accese on Jan./8/2012) 18