Analysis of the Future Demand Forecast and Proposed Development Plan in Dili Metropolitan Area, Timor-Leste

Yoshiyuki Tajima1 and Hisanari Ushirooka2

1Railway Planning Department, Nippon Koei Co., Ltd. 1-14-6 Kudan-kita Chiyoda-ku, 102-8539, (email:[email protected]) 2Research and Development Center, Nippon Koei Co., Ltd. 2304, Inarihara, Tsukuba-shi, Ibaraki 300-1259, JAPAN (email:[email protected])

ABSTRACT: The paper discusses the results of the first Traffic Count Survey and the Person Trip Survey (PT Survey) in Dill Metropolitan Area (DMA), capital of Timor-Leste. The future traffic demand was forecast based on these surveys. Four-step method was applied to analyze the future demand and the future development plans were proposed based on the analysis. As a result of the traffic demand forecast in 2030, more than 1.5 times Volume per Capacity (V/C) in congestion areas was estimated compared to exiting case in 2014 if no countermeasures are conducted. Congestion will be seen in busy areas in Central Business District (CBD) due to the reduction of capacity by on- parking. Thus, increasing the supply of the off-street parking was proposed as a short term project. Strengthening of the is also important for the short term project to reduce the thru traffic in CBD. Bypass road and improvement of Mass Transit are proposed as a long term project.

1. INTRODUCTION

Timor-Leste has about 1 million population. Dili, the capital of Timor-Leste, is comprised of 6 sub-districts, 31 Suco (village) and 241 Aldeias (small village). Dili district has a population of 234,026 (2010 Census) with annual population growth rate at 4.77% which is double the national average growth rate (2.4%) during 2004-2010. The urban population is expected to be 30% of the national population in 2020. The economic situation in Timor-Leste has shown remarkable growth in recent years. The GDP in Timor-Leste was one billion dollars in 2011, and annual growth rate was 12.2% during the last five years. Annual growth rate of GDP in DMA, Hera (east of

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DMA) and Tibar (west of DMA) was 4.8% for 6 years since 2004. From the viewpoint of the transportation, private have increased 18.2% annually from 2004 to 2012, and motorcycles have increased 20% annually. The number of private cars and motorcycles is expected to continue increasing in parallel with population and economic growth. Road infrastructure will need to handle the increasing number of vehicles. Road network in DMA has not been sufficiently developed. There are two east-west with four ; other roads have less than four lanes. Roads are being constructed and the pavement is being improved in DMA to expand the infrastructure. The DMA road network has the complicated network structure due to several one-way especially in CBD. On-street parking in several places also blocks the smooth flow of traffic. Fundamentally, the road network capacity cannot be fully utilized, especially in CBD. Other problems outside the CBD are the following: maintenance of arterial roads between cities is insufficient, alternative routes are nonexistent, and some national roads have bumpy, steep slopes and hairpin turn in precipitous areas. Mini-bus, called “Microlet”, is the only public transportation in DMA at present; rail-based transportation doesn’t exist. The passenger capacity of Microlet is approximately 10 persons. Overcrowding, with passengers hanging out of the vehicle, can be often seen in the peak hours. The waiting time for people at the bus stop in such situation is excessive.

N Central Busines District (CBD) ・A lot of onw-way street ・A lot of On-street parking

・Main East-West corridor Hera ・Four lanes road

Inter-city road Tibar ・Insufficient maintenance ・Bumpy, steep sloop and hairpin turn

Figure 1 Road Conditions in DMA

The paper discusses the results of the Traffic Count Survey and the PT Survey in DMA. The future traffic demand is forecast and future projects to improve the situation are proposed based on the analysis.

2. OUTLINE AND BASIC RESULT OF THE TRAFFIC SURVEYS

Two types of traffic surveys were conducted. Firstly, PT Survey was conducted to acquire information of residents’ travel activities such as Origin and Destination (OD) and trip purpose. The result is utilized for the analysis of current travel behaviors to develop traffic-demand models and to forecast the future traffic demand.

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Traffic Count Survey was also carried out in order to calibrate the PT Survey data obtained. 43 cross-section and sites were covered to obtain the current road traffic conditions.

2-1 Outline of PT Survey Direct Household interview method was adapted for data collection. To provide for sufficient statistical sample, calculation formula which is instructed by Ministry of Land Infrastructure Transport and Tourism (MLIT) Japan is applied. According to the National Census in 2010, total number of households was 33,163 in the study area. For the number of zones, 25 zones were adopted to coordinate with National Census in 2010. Trip purpose was categorized into four types for demand forecasting: Home, Work, School and Others. Travel mode was categorized into four modes for demand forecasting: Walking (as a non-motor vehicle mode), Motorcycle, Private (such as passenger ), and Public (such as Microlet). Using the above parameters in the formula means that Household sampling size of 5.3% (1,758) is minimum required. In fact, 9.2% (3,035) interviews were conducted in the survey. Total interviewed household members totaled 8,048 persons. The large items in the questionnaire were classified as follows: (1) household information consisting of number of household members, household income and ownership of vehicles, (2) household member information consisting of age, sex, occupation, address of work or school, driver license, etc., and (3) trip information consisting of zone number of OD, travel mode and trip purpose.

2-2 Result of PT Survey Average number of trips per day per person was 2.57. This includes the behavior of persons having lunch at home which produces some duplicate trips at lunch time. Vehicle ownership by household is illustrated in Figure 2 (left). Motorcycle ownership rate is 48% and Car ownership rate is 15%. It is clear that Motorcycle is the most popular transport mode for citizens at present. Car transport mode is not so popular with citizens because of high initial investment and maintenance cost. Middle or more household income level is required to own cars; however, many middle income households have a motorcycle. Modal share of transport mode is described in Figure 2 (right). Motorcycle is the highest modal share 40% and is the second highest modal share. As mentioned earlier, modal share of Private transport is low. Others showed relatively high share. The passenger truck, called Anguna, which carries the passenger in the cargo area is classified as Other transport.

2667 3000 Walking (16.4%) 2500 12% 16% 2000 1500 803 Motorcycle (40.0%) 1000 261 295 Vehicle 93 64 55 20 500 29 15 26% Private (6.0%) 14 14 4 1 0 1 0 2 The Number of Owned of TheNumber

3 of Units No. 40% Public (25.5) 6% Others (12.1%)

Figure 2 Vehicle Ownership (Left) and Modal Share of Vehicles (Right)

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2-3 Outline of Traffic Count Survey Three types of the traffic count survey were conducted in DMA: Cordon line survey, Screen line survey and Traffic count survey. Cordon line survey site was located at the boundary of the study area along the major arterial roads. Screen line survey site was located at traffic barriers such as river. Traffic count survey site was located at city center intersection to capture the traffic movement and to grasp the traffic volume along the arterial roads including outside city center. Surveys were conducted during three days a week from Tuesday to Thursday. For interviews in Cordon line survey, the vehicles passing through the boundary of the study area were interviewed about OD to supplement the collection of traffic flow data. The same census zones as PT Survey were applied for OD zones.

2-4 Result of Traffic Count Survey The heaviest traffic, approximately 31,000 pcu /12 hour, was observed at Comoro which crosses over the North-South river located at western side of DMA as summarized in Figure 3. As described in the figure, morning peak from 7:30 to 8:30 and evening peak time from 17:00 to 18:00 were observed. In addition to this, small peak time from 12:00 to 13:30 for lunch time was observed, but no serious traffic jam has occurred under present conditions.

1800 Private(East -> West) Public(East -> West) NMT(East -> West) 1600 Private(West -> East) Public(West -> East) NMT(West -> East) 1400 1200 1000 800 600 400 200

Passenger, Vehicle Passenger, 0

Figure 3 Result of Traffic Count Survey at Comoro River

3. DEVELOPMENT OF THE DEMAND FORECAST MODEL

Four step method, which is the most basic analysis method, was applied to forecasting future demand. Travel mode is categorized as mentioned before: Walking, Motorcycle, Private and Public. Motorcycle is considered as an independent transport mode because of the high modal share in DMA. Trip purpose is also classified into four categories mentioned before: Home, Work, School and Others.

3-1 Calibration Step Traffic assignment flow is summarized in Figure 4. Population which is divided by sex, age and zone was calculated by PT Survey and National Census in 2010. Person trip OD by mode is calculated by the expansion factor which is calculated by the above population divisions. Vehicle trip OD is calculated by the occupancy rate from Cordon line survey. Traffic assignment is calculated by vehicle trip OD.

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Divided population by sex and PT Survey Cordon Line Survey age at each zone (PT Survey)

Expansion factor PT Survey OD by Mode

Occupancy Rate form Cordon Divided population by sex and Vehicle Trip OD form PT Survey Line Survey age at each zone (Estimated population in 2014) Vehicle Trip OD to Traffic Assignment Vehicle Trip OD form Cordon Line Survey Divided population by sex and age at each zone (2010 Census) Result of Traffic Assignment PCU form Cordon Line Survey Figure 4 Traffic Assignment Flow

3-2 Trip Production Total trip production per day is the control total number of trip generated and attracted forecast. Trip rate per person per day is calculated by person trips. Trip rate per person is calculated among Employees, Students and Unemployed. As mentioned before, total trip per person per day is 2.57 and trip per person per day of all combinations is between 2.54 to 2.59. This indicates that there is no outstanding difference in trip rate among Employees, Students and Unemployed. Future framework was developed to accommodate social economic indices. Middle Growth scenario of GDP at a national level was applied for forecasting GRDP per capita in Dili. Forecasting of population including the daytime population and night time population and the number of households also adopted the medium growth scenario of population projection. Future total trip production was estimated by expanding the trip production rate and future framework. Total trips in 2030 is forecast to increase to more than one million trips, approximately twice the number of trips in 2014.

3-3 Trip Generation and Attraction Forecasting Trip generation which departs from each zone and trip attraction which arrives to each zone is forecast according to the following workflow. The model parameters were established to forecast the trip generation and attraction at each zone. Linear regression model was applied for forecasting. Gi=ai*X1i + bi*X2i + …, Aj= aj*X1j + bj*X2j + … Where, Gi:Trip Generation in Zone i Aj:Trip Attraction in Zone j X1i,X2j: Attributes in Zone i, j ai, aj, bi, bj: Coefficient

Explanatory variables were established by models and summarized in Table 1. R-squared shows more than 0.8 and it would be implied as the high value. The volume of generation and attraction forecast by each zone and trip purpose was adjusted with the overall trip production forecast result.

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Table 1 Trip Generation and Attraction Model Parameters Population 5 Student at Employees at Student at Employees Model Type Purpose Constant R-squared and above Residence Residence Enrollment at Work Home - - - 1.994 0.770Place 3,038 0.9845 Trip Work - - 1.668 - - 2,024 0.9427 Generation School - 0.655 - - - 1,851 0.8222 Others 0.079 - - - - 546.2 0.8685 Home 0.787 - - - - 4,279 0.9119 Trip Work 0.296 - - - 1.273 376.5 0.9525 Attraction School - - - 0.738 - 1,583 0.9476 Others - - - 0.220 - 571.4 0.8732 3-4 Trip Distribution Forecasting Generation and attraction volume among each zone are linked by the distribution forecasting. The volume of travels among zones as trips depart the zone and arrive to another zone was forecast. The gravity model for inter-zonal trips and trip rate model for intra-zonal trips were applied for trip distribution forecasting, as shown in following equations. This intra-zonal trip length created a model with 0.5 km in each zone. Inter zonal trip Xij = K * Oi^α * Dj^ β / Lij^ γ Intra zonal trip Xij = Ri * Oi , Ri = Xii / Oi Where, Xij: Inter zonal trip distribution zone i to j Oi: Trip generation in zone i Xii: Intra zonal trip distribution in zone i Dj: Trip attraction in zone j Lij: Travel length from zone i to j (km) Ri: Intra trip rate K, α, β, γ: Model parameters

To adjust the total trip generation and attraction volume by each zone, the distribution forecast by a gravity model was calculated. After forecasting by this gravity model, a frater balancing method is applied for convergence calculation. The total trip generation and attraction volume for each zone is converged according to the trip generation and attraction volume of the zone.

Table 2 Intra Zonal Trip Distribution Model Parameters Trip Purpose α β γ Log(K) R-squared Home 0.6453 0.7812 -0.4712 -0.0007 0.7374 Work 0.7232 0.6486 -0.395 -0.0016 0.716 School 0.579 0.5841 -0.408 -0.0012 0.6723 Others 0.1765 0.2459 0.0103 -1.4847 0.3719 Home, Work and School trip indicates the relatively high and minimum R-squared value was obtained. It is considered that the reason why Others shows low R-squared is because of low collected sample size of Others (6%) and short trip length.

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3-5 Modal Split Forecasting The trip modal split forecasting model is based on the forecast and analysis of transportation modes choice at the time of a particular trip of an individual or a group. Generally, the volume of trips and share for each traffic mode will be forecast. The most commonly applied method to study modal split is the logit model. The modal split models consists of three models, “Walking Split Model”, “Motorcycle-Other Split Model” and “Private-Public Split Model” as shown in Figure 5. It uses the binary choice method for splitting into two transportation modes for each step. The split of these models is established as trip purpose using the PT survey data. The “Walking Split Model” splits a walk and the other traffic. “Motorcycle-Other Model” splits other than a walk into a motorcycle and other traffic. The “Private-Public Split Model” splits other than walking and motorcycle into a private mode (a privately-owned car and a taxi) and a public mode (a Microlet and a bus).

All Modes OD Walking Split Model Walking OD Other Modes OD Motorcycle-Other Split Motorcycle OD Other OD Private-Public Split Private OD Public OD Figure 5 Basic Flow of Modal Split Model

3-5-1 Walking Split Model Diversion curve models are used in Inter Zonal Walking Split Model. The independent variable used in this model is the trip distance of the shortest pass on the road network. Although a walk is mostly based on trip distance, it is also different per trip purpose or car ownership conditions. The car ownership conditions which are established with the future framework are also taken into consideration. Thus, the walk share curve is a model for each trip purpose in consideration of a car ownership rate. The model equation taken by the PT survey is shown in Figure 6. Although wide varieties of trip length and walk share are seen in Others, other trip purpose shows the acceptable value in this condition. Intra Zonal Walking share is approximately 30% regardless of purpose, car owner and motorcycle owner. Therefore, total walk share is applied for intra zonal walk split.

HOME WORK 100% 100%

80% 80% y = -0.0117x + 0.3554 y = -0.0105x + 0.3326 60% 60%

40% 40% Walk Share Walk Walk Share Walk 20% 20%

0% 0% 0.0 5.0 10.0 15.0 20.0 0.0 5.0 10.0 15.0 20.0 Trip Length (km) Trip Length (km)

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SCHOOL OTHERS 100% 100%

80% 80% y = -0.0195x + 0.3993 y = 0.5497e-0.122x 60% 60%

40% 40% Walk Share Walk 20% Share Walk 20%

0% 0% 0.0 5.0 10.0 15.0 20.0 0.0 5.0 10.0 15.0 20.0 Trip Length (km) Trip Length (km)

Figure 6 Inter Zonal Walk Split Model

3-5-2 Motorcycle-Other Split Model and Private-Public Split Model The Logit model is generally applied for the modal split model, and model equations and model parameters are summarized below. Initial cost and operating cost of Motorcycle, Private and Public are estimated for input data of the modal split model. Regarding the public transport, the base fare for initial cost and uniform mileage rate were applied for analysis. P=1/ (1+ exp(ΔV)), V=αCb+βCo+γ

Where, Cb: Motorcycle (Private) Cost, Co: Other (Public) Cost, α、β、γ:Model Parameter

Table 3 Model Parameters of “Motorcycle-Other” and “Private-Public” Trip Motorcycle-Other Private-Public Purpose α(Motorcycle) β(Other) γ(Consist) α(Private) β(Public) γ(Consist) Home 0.1608 0.1828 0.4915 -0.6685 -16.905 -2.3758 Work 4.5943 2.5943 1.4541 -0.1966 -13.577 -2.1281 School -6.5391 -3.39 -0.9724 -1.3872 -41.964 -5.1973 Others -55.295 -85.017 -54.605 -1.07E-14 -1.66E-13 -

3-6 Traffic Assignment Forecasting Road network was developed based on the existing road conditions as summarized in Table 4.

Table 4 Road Capacity Rank Divide Location Surface Capacity Divided Urban Paved 4 40,000 National Suburban Paved 2 25,000 road Undivided Suburban Unpaved 2 20,000 Divided Urban Paved 4 30,000 Urban Paved 2 20,000 Urban road Undivided Urban Unpaved 2 15,000 Urban Paved 1 10,000 - Paved 3 35,000 Other - Paved 2 20,000

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Road capacity was developed according to the following factors: road rank, location, surface condition and number of lanes. The actual road capacity is applied in order to adjust the capacity deduction. On-street parking is often seen in the urban area and Microlets temporarily stopping for boarding and alighting is also seen in DMA. These conditions are reflected in the development of the road network. Table 5 shows the average occupancy rate and PCU. Current OD is made in the traffic assignment flow. Occupancy rate was calculated from Cordon line survey. Vehicle OD outside of study area was coordinated by Cordon line survey. PCU was also established. Car/Tax, Light Truck and Heavy Truck are included in Private. Microlet and Bus are included in Public.

Table 5 Average Occupancy Rate and PCU Mode Motorcycle Private Public Zone Intra Inter Intra Inter Intra Inter Total Passenger (person) 164 186 1,143 3,830 501 5,531 Total Vehicle(vehicle) 110 128 281 829 60 359 Average occupancy rate(peon/vehicle) 1.5 1.5 4.1 4.6 8.4 15.4 PCU 0.5 1.6 2.1

The traffic volume which passes through each link which constitutes a transportation network was forecast. The traffic assignment forecasting model calculates whether the traffic volume among zones will be assigned on some routes in the zone. The forecast traffic volume of each link is considered as the index for studying the solution of the traffic problems that are forecast in the future. Vehicle trip is assigned to the individual road link in process of a trip assignment forecasting. This step takes as input of the OD matrix that indicates the volume of vehicle trips between OD pairs. User equilibrium assignment was used for the estimation method. User equilibrium assignment was formulated by considering all the trip persons have the information of the road characteristics so it chooses the road link, and choose the minimum route for travel time or cost. The input of the link performance function is necessary for the user equilibrium assignment. This function describes the travel time to pass through the link under conditions under various congestions by the ratio of traffic and capacity, etc. 40,000

30,000

20,000 y = 0.8533x

Estimation R² = 0.9086 10,000

0 0 10,000 20,000 30,000 40,000 Survey result Figure 7 Comparison with Estimated and Observed at Cordon and Screen Line Survey

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By using the above described Current OD in 2014, the volume of vehicle trips was forecast applying the equilibrium assignment model to the present road network. Comparison with present estimate and screen line survey line and cordon line survey observed is shown in the Figure 7. R-squared by PCU is 0.9086 in this correlation. Reproducibility is high.

4. FUTURE TRAFFIC DEMAND FORECAST

4-1 Forecast Cases Six cases were analyzed as shown below. All cases were forecast for the target year of 2030. Analysis contents of On-going projects were collected by interviews with the Ministry of Public Works, Transportation and Communications. These maintenance and construction works will be completed by 2030. Alignment of new bypass and Bass Rapid Transit (BRT) are tentatively proposed for analysis.

Table 6 Summary of Forecasted Cases No. Forecasting Case Analysis contents Case-1 Do-nothing ・Existing road network is applied Case-2 On-going project ・Comoro No.3 is opened ・Maintenance of National Road #1, road between Comoro and Tibar and road along Comoro River Case-3 Do-minimum ・Comoro No.3 is opened. ・The problems of on-street parking and temporary stop for Microlet boarding and alighting are removed. ・Road widening is conducted around the fringe urban area. Case-4 Bypass project ・Projects of Do-minimum case are completed. ・New bypass road, 10 km and two lanes, is constructed to connect the Tibar to Ring Road of the city center. Case-5 Mass Transit ・Projects of Do-minimum case are completed. project ・BRT is introduced through main road. Case-6 Do-maximum ・Bypass project and Mass Transit project are all completed.

4-2 Result of Demand Forecast Future traffic demand in 2030 was forecast to apply the above analysis method. Table 4 summarizes result of vehicle-km total, vehicle-hours total, average speed of peak and average and V/C in congested areas. V/C in congested areas for existing conditions shows 0.60 even in the busy areas. This implies that current road network capacity is not fully occupied and there are no big differences between average speed of peak time and average time. On the other hand, if any project hasn’t been carried out by 2030, average V/C and average speed of peek time will be worse. In addition, even though On-going projects had been carried out by 2030, average V/C and average speed of peak time may not have recovered effectively. Thus, it is required to provide new roads or public transportation system to enhance the capacity of road network and decrease the volume of private car in the

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Bypass project case and BRT project.

Table 7 Vehicle Assignment Results Average V/C Vehicle-km Vehicle-hours Case Year Speed(km/h) (Congest total(pcu-km) total (pcu-hour) Peak Average ed areas) Existing 2014 719,405 27,912 25.8 28.7 0.60 Do-Nothing 2030 1,352,428 78,284 17.3 25.4 1.06 On-going project 2030 1,339,484 69,846 19.2 26.4 0.98 Do-minimum 2030 1,336,013 67,550 19.8 26.7 0.90 Bypass project 2030 1,318,791 54,566 24.2 29.1 0.70 BRT project 2030 975,755 42,071 23.2 27.9 0.81 Do-maximum 2030 958,060 35,495 27.0 30.1 0.54

4-3 Proposed Development Plan Table 7 has shown that “Do-minimum case” must be implemented not to be worse than the present condition. In the analysis of Do-minimum case, realistic road network condition is applied to modify the road network capacity. This means that the problems of on-street parking and temporary stopping for Microlet boarding and alighting are removed and road widening project is conducted around the fringe urban area. Under the circumstances, road network capacity enhancement projects are proposed for the short term project. Recommended projects are: (1) Development of off-street parking and fringe parking, and (2) Strengthening of the ring road. (1) Development of off-street parking and fringe parking aim at increasing traffic capacity of the street in the urban area by removing vehicle parking on the streets and by increasing the actual number of lanes of the street. Enhancement of safety of pedestrians and motorcycles are also expected by decreasing on-street parking. It is desirable to improve around parking lots to improve access to road user. (2) Improvement of Ring-road aims at development of CBD and other Dili urban centers by reducing thru traffic in CBD and relieving , thus providing smooth traffic flow. This project includes improvement of intersections along Ring-road and road widening of current narrow sections as well as reconsidering one-way regulations in CBD. It includes improvement of sidewalks or introduction of motorcycle-lane together with road widening. The improvement of intersection components includes improving intersection shape such as introduction of right-turn lane or improving traffic control. In addition to this, the following long term projects must be implemented: (3) New bypass project, and (4) BRT project to be able to recover the average speed and V/C. (3) New bypass project aims at strengthening the development of CBD and Tibar by developing new routes. One possible route is to develop the short cut on an existing route. Another possibility is to develop a new bypass approximately 10 km long between Tibar junction and Ring Road. This bypass will be avoid mountainous areas and may include tunneling work. (4) BRT project aims at the promotion of public transportation and mitigating modal shift to private car. The development of Transit Oriented Development (TOD) is expected by the project. Although alignment and station places are all tentative at

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present, BRT is successful to decrease the traffic volume dramatically. This project includes development of BRT stations and improvement of exclusive lanes for BRT bus vehicle from priority lane and bus priority signals.

(1) Do-minimum (Parking prject) Hera

P

(4) BRT project P P

(3) New bypass project P

Tibar (2) Improvement of Ring-road Figure 8 Proposed Development Projects

5. CONCLUSION

This is the first time to conduct such a large scale transport survey in Timor Leste. One result of PT survey shows that Motorcycles are the most popular transport mode in Dili. However, it is expected that private cars will increase with economic growth. Not only the simple tabulation, but also future traffic demand was forecast with the useful data of the survey. Demand forecast analysis shows that the traffic situation will be worse than that of now if countermeasures haven’t been conducted by 2030. Four possible projects shown in Figure 8 are proposed in this paper. Two are based on the analysis: (1) Development of off-street parking and fringe parking and (2) Improvement of Ring-road to be implemented by 2030 as a short term project. The other two projects are based on analysis at macro level and are also effective to increase the capacity and decrease the volume of private car: (3) New bypass project, and (4) BRT project.

ACKNOWLEDGMENTS

Funding from the Japan International Cooperation Agency (JICA) foundation is grateful acknowledged. This study was supported by JICA through “The Project for Study on Dili Urban Master Plan in the Democratic Republic of Timor-Leste”.

REFERENCES Ministry of Finance, Timor Leste, “National Census in 2010” Ministry of Land Infrastructure Transportation and Tourism, Japan (2007), “Methodology of Total Urban Transportation Survey”, Guideline of Urban Transportation Survey, 30

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