Technical Assistance Consultant’s Report
Project Number: 51115-001 February 2021
Democratic Republic of Timor-Leste: Baucau to Viqueque Highway Project
Road Asset Management Plan
Prepared by SMEC International Pty Ltd
Sydney, Australia
For Ministry of Planning and Finance, Development Partnership Management Unit; and Directorate of Roads, Bridges and Flood Control
This consultant’s report does not necessarily reflect the views of ADB or the Government concerned, and ADB and the Government cannot be held liable for its contents. (For project preparatory technical assistance: All the views expressed herein may not be incorporated into the proposed project’s design.
Promoting Sustainable Road Network Infrastructure Preface
MINISTRY OF PUBLIC WORKS
TA-9502 TIM: BAUCAU TO VIQUEQUE HIGHWAY PROJECT 8 ROAD ASSET MANAGEMENT PLAN
Reference No. TIM 51115 - 001 Prepared for Asian Development Bank
i Road Asset Management Plan Important Notice Important Notice
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i Road Asset Management Plan Preface Preface
This document is prepared to guide the development of the road network in Timor Leste and achieve the key road sector objectives included in the Strategic Development Plan 2011-2030. It is the outcome of a technical assistance (TA) funded by the ADB to support the Government of Timor Leste in this effort. The TA was designed to develop the following:
• Output 1a: Integrated national road network strategy and plan. Support will be provided to DRBFC to draft a comprehensive national road network strategy and plan that will encapsulate the following: (i) 20-year capital investments and operation and maintenance (O&M) strategy, (ii) organizational reform plan to implement the strategy; (iii) defined levels of service (LOS) expected from the national road network that will include communication mechanism to the public for feedback and performance measurement; (iv) asset management plan linked to an appropriate asset management system to support network prioritization and intervention thresholds; (v) an operational manual and training for planning, budgeting, procurement, supervision, and monitoring and evaluation, and (vi) operational plan targeting development of national road contracting and consulting industry, human resources and capacity development, and financing instruments to support private sector participation. Suitable policy instruments would be considered to integrate the draft strategy and plan in the wider context of Transport Sector Master Plan currently under review and endorsement by the Government.
• Output 1b: Concept design for a sustainable road maintenance program. This output will build on road maintenance initiatives by donors including ADB, JICA, European Union and the Roads for Development program financed by the Government of Australia to deliver (i) a draft policy paper to introduce Road Maintenance Fund; and (ii) a design for donor-supported 10-year road maintenance and operation program. An initial concept proposal for the program has been shared with the government and development partners, and broad support has been received (see attachment 1). An extensive stakeholders’ consultations and high-level policy dialogue will need to take place to draft the policy paper and the program suitable for donors’ technical and financial support.
This document provides an executive summary of the output of this work. It presents the key recommendations for the sustainable development and maintenance of the road network and it will hopefully serve as a reference and guideline in formulating the Government of Timor Leste’s future road sector strategies and plans. The list of reports summarized here in order are:
OUTPUT DELIVERABLE 1 Road Subsector Assessment 2 Road Investment and Maintenance Strategy 3 Maintenance Program 2020-2030 4 Road Maintenance Fund Policy Paper 5 Organizational Reform Plan 6 DRBFC Training Program 7 DRBFC Manuals for Operations 8 Road Asset Management Plan 9 Levels of Service 10 Operational Plan for National Industry
The TA’s conclusions and recommendations are based on the professional judgement and views of the TA consultants derived through a series of seminars, workshops, and consultations with various government agencies and offices, under the overall guidance received from the Ministry of Public Works, the Ministry of Finance and the Asian Development Bank.
The team is grateful for the support and information provided to it by the various units within the DRBFC and other government ministries.
ii Road Asset Management Plan Contents Contents
INTRODUCTION ...... 1 DATA COLLECTION ...... 3 2.1 Inventory Data ...... 3 2.2 Condition Data ...... 12 2.3 Traffic and Accident Data ...... 14 2.4 Project and Contract Data ...... 16 2.5 Data Collection ...... 18 DATA MANAGEMENT ...... 23 3.1 Data Validation and Entry ...... 23 3.2 Road Asset Management System ...... 24 DATA ANALYSIS AND PLANNING ...... 28 4.1 Five-Year Plan ...... 28 4.2 Budget Request ...... 28 4.3 Annual Report ...... 29 RAMS UNIT ...... 32 5.1 Data Collection ...... 32 5.2 Data Management ...... 32 5.3 Data Analysis ...... 33 5.4 Resources ...... 33 5.5 Technical Support ...... 34 ROAD ASSET MANAGEMENT PLAN ...... 35 6.1 Data collection ...... 35 6.2 Data management ...... 35 6.3 Data analysis and planning ...... 36 6.4 RAMS Unit ...... 36 APPENDIX A DATA TYPES, SOURCES AND COLLECTION FREQUENCIES ...... 39 APPENDIX B ROAD NETWORK LENGTHS ...... 43 APPENDIX C TECHNICAL ROAD CLASSES AND STANDARDS ...... 51
i Road Asset Management Plan Contents Tables
Table 1 Inventory data to be collected ...... 4 Table 2 Road design standards in Timor Leste by technical class ...... 8 Table 3 Surface categories and pavement classes ...... 9 Table 4 Condition data to be collected ...... 12 Table 5 Traffic and accident data to be collected ...... 14 Table 6 Vehicle types and their characteristics ...... 15 Table 7 Traffic class based on the average daily traffic (ADT) ...... 16 Table 8 Planning and contracting data to be collected ...... 17 Table 9 Data collection using survey vehicle ...... 19 Table 10 Data collection for structures ...... 20 Table 11 Data collection from traffic counts ...... 21 Table 12 Data to be collected from secondary sources ...... 22 Table 13 RAMS mapping options ...... 26 Table 14 Road network statistics for 2019 ...... 30 Table 15 Key Performance Indicators for 2019 ...... 31 Table 16 Road Asset Management Plan ...... 38 Table 17 Length of national road links ...... 43 Table 18 Length of municipal road links ...... 44 Table 19 Length of core rural road links ...... 45
Figures
Figure 1 RAMS and the wider context of road asset management ...... 1 Figure 2 Example road link code for national road link A01-03 from Baucau to Lautem ...... 5 Figure 3 Example road link code for municipal road link C08-01 from Laga to Baguia ...... 5 Figure 4 Example road code for rural road from Baguia to Defa Uassi in Baucau municipality ...... 5 Figure 5 Topographical map and rainfall map for Timor Leste ...... 8 Figure 6 ROMDAS DataView software for post-processing video data ...... 10 Figure 7 Example bridge code for a bridge in the national road A01 from Baucau to Lautem ...... 10 Figure 8 Example bridge code for a bridge in the municipal road C08 from Laga to Baguia ...... 10 Figure 9 Example bridge code for a bridge in the rural road from Baguia to Defa Uassi ...... 10 Figure 10 Examples of beam, truss and bailey bridges in Timor Leste ...... 11 Figure 11 Example culvert code for a culvert in the national road A01 from Baucau to Lautem ...... 12 Figure 12 Example culvert code for a culvert in the municipal road C08 from Laga to Baguia ...... 12 Figure 13 Example culvert code for a culvert in the rural road from Baguia to Defa Uassi ...... 12 Figure 14 Formula for calculating traffic flow based on moving traffic count ...... 15 Figure 15 Example of a prioritization matrix from Myanmar ...... 25 Figure 16 Proposed structure of the RAMS Unit ...... 32
ii Road Asset Management Plan Introduction Introduction 1. Road asset management looks at optimizing the level and the allocation of road maintenance funding in relation to medium- and long-term results on road conditions and road user costs1. Where traditional maintenance implementation is often aimed at repairing as much existing damage as possible within an available yearly budget, road asset management aims to achieve a specified service level or road network condition at the lowest cost. In doing so, it takes a long-term perspective, considering the future impacts of current budget allocations and optimizing the resulting road network conditions in light of the available budget. Such a change in approach often sees a shift from repairing roads in poor condition to preserving roads in good or fair condition, avoiding them from deteriorating and requiring costly repairs in the future. 2. Road asset management is based on an analysis of road data related to inventory, condition, and traffic, linking this to road deterioration models to predict future conditions. The collected data is entered into a Road Asset Management System (RAMS), allowing it to be analysed. A RAMS is considered to include any system that is used to collect, store and process road and bridge inventory, condition, traffic and related data, for road planning and programming purposes2. A RAMS generally involves a computerized road asset management system, encompassing data collection, data management (database), and data analysis. However, road asset management is more than just the RAMS, and includes the integration of the RAMS into the wider context of structures and procedures within which it operates, complementing the economic optimization criteria of the RAMS with other policy objectives (e.g., connectivity, accessibility, road safety). Figure 1 RAMS and the wider context of road asset management
Source: Compendium of Best Practices in Road Asset Management, ADB 2018 3. This document provides a plan for introducing road asset management in Timor Leste. The RAMS itself is being developed by DRBFC with support from the Roads for Development (R4D) programme with financial support from the Australian Department of Foreign Affairs and Trade (DFAT). This document therefore does not focus on the RAMS, but instead looks at the operation of the RAMS in terms of the three main steps of data collection, data management, and data analysis. The document also looks at the creation of a RAMS Unit within DRBFC, which will become responsible for the operation of the RAMS and the three steps mentioned above. 4. Chapter 2 gives specific attention to the data collection, with particular focus on the types of data to be collected and the means of doing so. Data collection for a RAMS requires basic data to be collected for the entire road network. This is different from data collection for project preparation, where more comprehensive data is collected for a limited number of roads for which funding has been allocated. The aim of the data collection for the RAMS has been to limit the amount of data collection as much as possible, since data collection and keeping the data up-to-date is generally the biggest challenge facing any RAMS. Many road asset management systems have failed because of incomplete or delayed data collection, with data in the RAMS quickly becoming obsolete and unsuitable for planning purposes. The initial aim should therefore
1 Compendium of Best Practices in Road Asset Management, ADB 2018. 2 Compendium of Best Practices in Road Asset Management, ADB 2018.
1 Road Asset Management Plan Introduction be to make the data collection as easy and inexpensive as possible. The list of data to be collected has been discussed extensively with DRBFC and with the development partners and related projects (R4D, World Bank, JICA and ADB). The data collection may be further expanded in future years, as additional needs are identified and capacities to collect and manage data improve. 5. Chapter 3 deals with the data management. This is mainly related to the RAMS that is currently being developed with R4D support. This document does not provide details on the structure of the RAMS, instead focusing on the operation and the procedures for data validation and entry, providing recommendations regarding the different modules that the RAMS will need to have to respond to the needs of DRBFC. 6. Chapter 4 deals with the data analysis. This again forms an integral part of the RAMS that is under development. This document therefore focuses on how the RAMS analysis may be integrated into the planning process, providing the basis for the preparation of the Five-Year Plans (FYP) and for the annual budget requests. Apart from the FYPs that are prepared as part of the government planning system, multiannual rolling programmes may be prepared and updated each year. This chapter also looks at how the RAMS may be used to improve monitoring of performance, discussing the use of the RAMS to prepare an Annual Report presenting the road network statistics and key performance indicators proposed under a separate deliverable prepared by this TA. 7. Chapter 5 looks at the RAMS Unit to be created within DRBFC, which will become responsible for the RAMS, including data collection, data management, and data analysis. This unit will support the planning and programming of road works by DRBFC. This chapter looks at the staffing requirements of the RAMS Unit, but also the additional resources and the technical support required. 8. Finally, chapter 6 gives an overview of the actual Road Asset Management Plan, identifying the actions to be undertaken in the coming 5 years, defining the timeline for completing the different steps of the RAMS development and its integration into DRBFC procedures, and identifying the responsibilities of the different actors. This chapter also looks at the resources and financing required and how these will be provided, gradually shifting from initial support from development partners to full implementation by government with government financing.
2 Road Asset Management Plan Data Collection Data Collection 9. The Road Asset Management System (RAMS) being developed by DRBFC with support from R4D will only be able to function properly if the required data is collected and kept up-to-date. For a RAMS to function properly it requires inventory data to be collected that describes the road network to be managed and its related structures (e.g. bridges). Condition data for the road and its structures will also need to be collected regularly, to determine the type of maintenance and repairs required and in the longer term to improve the calibration of the deterioration modelling. This will need to be complemented with traffic data in order to be able to determine the importance of the different road links, to assess the need for upgrading to a higher standard, and to estimate the effect of traffic on future road deterioration. Because the RAMS being developed in Timor Leste will also include a contract management module, project and contract data will also need to be collected for entry into the system, with linkages to the specific road segments under contract. 10. Data collection can be a costly exercise, especially since there is a tendency to want to collect more data and more detailed data. It is important to keep the data collection to a minimum, especially during the initial introduction of a RAMS. This reduces the costs of data collection, but also facilitates data entry and validation and the subsequent data analysis. This document describes the minimum data requirements to be able to operate a RAMS. The data to be collected can always be expanded at a later stage once additional data needs have been identified and capacities to collect and manage data have improved. 11. In determining which data should be collected, one needs to look at how the data will be used and how it will be collected. Certain data is essential for road management and will necessarily need to be collected and kept up-to-date (e.g. the length of the road). Other data may be useful for some specific types of analysis but is not an immediate need for the management of the road (e.g. the location of milestones or signs). For the latter type of data, its collection may entail a significant extra cost, without providing any significant benefit at this stage. 12. The accuracy and detail of the data is another aspect to consider. Data may be collected at very high detail (e.g. for every metre of road) and at very high accuracy (e.g. including several profilometers measuring the roughness for different parts of the travel lane), but this level of detail and accuracy in the data collection does not necessarily result in different or better results in the subsequent analysis. It is important to find a balance between the amount and detail of the data to be collected and the use that will be made of the data. 13. How data is collected may also influence whether it will be included in the minimum dataset or not. Some data types can easily be collected together with other data without any significant cost increase (e.g. a vehicle collecting roughness data can easily collect video data at the same time). Other data will require specific surveys and possibly equipment, increasing the cost of their collection (e.g. data on culverts cannot be collected from a drive-over survey and will need a separate survey). 14. As mentioned above, the data needs for road maintenance planning encompass inventory data, condition data, traffic data and contract data. The specific data needs related to each of these data categories are explained in the following sections. A full list of data needs, data sources and collection frequencies is provided in Appendix A. 2.1 Inventory Data 15. Inventory data serves to identify the different road elements that make up the road network. This data forms the basis of the RAMS, and all other data is linked to this. Inventory data can be very detailed, including the location and exact dimensions of each retaining wall, location and type of each road sign, etc. However, given that the focus of DRBFC at the moment is on maintaining the carriageway as well as the bridges and culverts that provide cross drainage, the inventory data will initially focus on these road elements. The list of data to be collected for these road elements is provided in Table 1 below. 16. Other road elements will not be included in the road inventory data, but will be included in identification of damages as part of the road condition data. The reason for this is that road inventory data needs to be collected for the entire road network, for all roads in the RAMS. This makes the addition of any dataset to
3 Road Asset Management Plan Data Collection the inventory data very costly. Data on damages in the road condition survey will only need to collect data for those road elements that are damaged, resulting in a much lower cost of collection. Table 1 Inventory data to be collected ADMINISTRATIVE DATA ROAD DATA BRIDGE DATA CULVERT DATA • Administrative class • Road code • Bridge code • Culvert code • Management entity • Road name • Bridge name • GPS location • Municipality • Link code • River name • Chainage location • Administrative Post • Link name • GPS location • Culvert type • Suco • GPS track • Chainage location • Number of cells • Population data* • Link length • Bridge type • Culvert width • Link start name • Deck material • Culvert height • Link start chainage • Bridge length • Photographs • Link start coordinates • Bridge width • Link end name • Number of spans • Link end chainage • Protection up • Link end coordinates • Protection down • Terrain class • Construction year • Rainfall class • Photographs • Technical Class • Surface type • Pavement class • Carriageway width • Number of lanes • Video data * This involves census data on population by municipality, administrative post and suco that may be used to determine the direct and indirect beneficiary population served by a specific road or road link. 2.1.1 Administrative Data 17. The administrative data serves to provide data on administrative aspects of the road that are not collected through a survey of the roads, but that are obtained from secondary data. The administrative class of each road link3 (national, municipal, urban or rural) is already defined for all road links, as is the management entity. Although the management entity is linked to the administrative class4, this may not always be the case in the future as some roads may be managed by the municipalities or by the private sector (e.g. concession contracts). 18. Data on the municipality, administrative post and suco in which a road segment is located can influence which road entity is responsible for a specific road segment (especially for lower level roads) and how contracts may be organized for those roads. Data on the municipality and administrative post boundaries is available with the GIS unit of DRBFC and can be used for this purpose. This may need to be expanded to also include suco boundaries that are used to determine the length of road managed by the community maintenance groups (CMG) that are formed at suco level. 19. Population data and its distribution across the country (including population data for each municipality, administrative post and suco) is used to determine the population served by each road link, indicating its importance. Distinction can be made between the population directly served by the road link concerned, and the population indirectly served by the road link (through other roads connecting to the road link concerned). For the purposes of determining the importance of roads, it is recommended to use the total of the direct
3 A road link refers to a fixed section of road which generally has homogeneous characteristics (width, surface type, technical class, traffic, etc.). New road inks are defined whenever a road passes a large population centre or intersects with another road of the same or higher level. 4 Currently all roads are the responsibility of DRBFC, but the management of rural roads is de facto carried out by the municipal road departments. In the future, the responsibility for these roads and possibly the urban roads may be formally handed over to the municipalities as part of the decentralization process.
4 Road Asset Management Plan Data Collection and indirect beneficiary population served by a specific road link. The population data is available from census data, but also from other secondary sources such as www.worldpop.org. 2.1.2 Road Data 20. Roads have different characteristics along their length. To reflect this, roads tend to be divided into a number of fixed road links, which are further divided into road segments. The road is generally divided into different road links where the road passes an important place (municipal centre or administrative post) or intersects with another road of similar or higher level. The road links tend to remain the same over time. The road links can be further divided into road segments where certain characteristics of the road change (surface type, technical class, condition, etc.). The road segments may change over time as road surfaces and road conditions change. 21. The road referencing system in Timor Leste identifies road codes and link codes. National road codes have the format of the letter “A” indicating the administrative class of the road5, followed by a two-digit code indicating the number of the road (there are currently 19 national roads as well as the expressway from Suai to Zumalai). National road link codes consist of the road code, followed by a dash and a two-digit code indicating the number of the link in the road. Figure 2 Example road link code for national road link A01-03 from Baucau to Lautem A01-03 = A 01 03 Link code Road class Road number Link number 22. Although this system is applied for national roads, for municipal roads the road code sometimes only contains a one-digit number, and the links are not always properly defined or numbered (sometimes a small letter is used to distinguish links). It is recommended to apply the same coding system for municipal roads. Figure 3 Example road link code for municipal road link C08-01 from Laga to Baguia C08-01 = C 08 01 Link code Road class Road number Link number 23. In the case of rural roads, the road code consists of a two-letter code for the municipality, followed by a dash and a three-digit code for the road number. Numbers 001-099 are reserved for core rural roads (class D), numbers 100-500 are reserved for non-core rural roads connecting smaller sucos (class E1), and numbers 501-999 are reserved for rural roads within rural sucos (class E2). Figure 4 Example road code for rural road from Baguia to Defa Uassi in Baucau municipality BA-023 = BA 023 Road code Municipality Road number 24. The different roads and road links and their codes have largely been defined by DRBFC. These are listed in Appendix B for national, municipal and rural roads. For national roads, road links are introduced wherever a national road intersects with another national road or passes a municipal centre. This principle is applied to most roads, although there are several exceptions that are listed below. A complete review of the road classification and coding is recommended to fit the logic of the RAMS.
Use of road links in national roads • A02 has an additional link from Maubisse to the Aituto intersection with A05 • A04 is missing separate links where it intersects with the A10 and A11 • A05 has an additional link in Betulala, but lacks a separate link where it intersects with the A13 • A06 has an additional link starting in Venilale • A08 has additional links at Uatucarbau and Iliomar (these are appropriate based on varying traffic levels) • A08 link numbering starts from Viqueque instead of Lautem (A01) which would make more sense
5 “A” for national roads, “C” for municipal roads, “D” for core rural roads and “E” for other rural roads. There is no separate letter for urban roads.
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• A09 has three additional links in Cribas, Laclubar and Mane Hat • A10 may need an additional link at Letefoho according to the planned link road to Maubisse starting there • A11 may need an additional link at Marobo bridge due to the difference in terrain and traffic volume • A13 has an additional link at Hate Udo • A14 has an additional link at Alas • A16 has an additional link at Fatululic (a link at Maukatar may be more appropriate, given the link to Suai)
25. For municipal roads, road links would follow a similar logic, introducing new links where they intersect with national roads or other municipal roads or pass an administrative post. Road codes have been assigned by DRBFC (see Appendix B), with a total of 32 municipal roads identified6. Municipal road links have been identified in the 2010 Road Geometric Design Standards, but link codes were not provided and are not always use. The identified links do not always comply with the principles mentioned above, and a full review of the links will need to be carried out. A more serious issue is that the list of municipal roads is not well-defined, with alignments changing and additional roads being included as municipal roads in different documents. For the municipal roads to be included in the RAMS, a full review of the list of municipal roads will need to be carried out with MPW to ensure that the resulting list of municipal roads and links is correct. 26. Rural roads have been surveyed as part of the preparation of the Rural Road Master Plan in 2014-2015. Each road has been assigned a road code. This may require review as there have been some changes since the Rural Road Master Plan was prepared. It is understood that the R4D programme is planning to update the Rural Road Master Plan in 2020. 27. For urban roads, no road codes have been assigned. Street names are used in many instances, but may not be available for all roads. If urban roads are to be included in the RAMS, a full survey and codification of these roads will be required. 28. Road names are not always applied consistently and will need to be properly defined. It is recommended to use a simple format of start and endpoint of the road, avoiding very long road names. The same would apply to the link name based on the start and end point of the link. The names are important as road codes and link codes are not always used. Contracts and contract management documents often do not include the road code or link code, using only the names of places that are connected by the road segments concerned. It is recommended that contracts in future include both the link code and the link name. 29. GPS tracks have been collected in the past and are available from DRBFC’s GIS unit. These are generally found to be in order, although some will need to be updated where road construction has resulted in changes to the road alignment. A clear example is the road from Dili to Tibar, where a realignment is under construction that will significantly change the GPS track and the resulting road length. 30. Once the road links have been properly defined, it will be important to determine their lengths and the related chainages. Data on road lengths is available from DRBFC, but there are several data sets in use, all of which have slight differences in road length. For the national roads, different length data is available from the 2010 Road Geometric Design Standards, from the 2015 Transport Sector Master Plan, from the GIS data, from the GPS track and from the Five-Year Plan, to name a few (see Appendix B). Each of these data sets includes slight differences in length, and no single data set is consistently used. Since the RAMS will need to make use of a single data set for the lengths of the road links, it is recommended to carry out a new survey of the road lengths. Use can be made of the odometer that is installed on the ROMDAS vehicle owned by MPW (currently with the PMU). This would need to result in a definitive dataset on road link lengths and chainages that would form the basis for the RAMS. It will then be important to ensure that these lengths are consistently used in planning and other documents of DRBFC and MPW. To ensure this, it is recommended that MPW issue a formal ministerial decree (diploma ministerial) listing the roads and road links and their measured lengths.
6 C12 is often omitted from the list of municipal roads. This connects Laulara to the A02, but is only 400 metres in length.
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31. For each road link, the start location and end location need to be defined. This should be done on the basis of the name of the start and end location (name of municipal centre, administrative post or nearest suco), as well as on the basis of GPS coordinates and the chainage. 32. Road chainage is important for use in road management and often forms the basis for planning and contracting (rather than GPS coordinates). Although road chainage is currently used in contracts, it is not always applied correctly. The starting point of the chainage indicated in the contract often does not correspond with the starting point of the road under contract. Introducing chainages for the start and end point of each road link will facilitate the proper usage of chainage. Where a road consists of multiple links, the chainage would need to continue from the start of the first link to the end of the last link. In Timor Leste the chainage generally runs from Dili eastwards or westwards, and from north to south. The only exception is the A08 where the chainage runs from Viqueque to Lospalos and on to Lautem – here it is recommended to change the direction of the chainage to conform with the rest of the road network. 33. Since chainage is generally used to mark the location of road segments, structures and other road elements, while GPS coordinates are invariably used to mark locations in RAMS systems and map these using GIS systems, there will be a need to link the chainage and GPS data in the RAMS, allowing a location to be presented by either means. Although GPS coordinates will not change over time (unless the location of a specific road element is changed), chainage data may change as the result of realignments further up the road, whereby the length of the road is changed and the chainages further down the road need to be adjusted. This is likely to happen in the roads as they are being upgraded, but will not be very frequent after the upgrading has been completed.
Use of chainage An example of the incorrect use of chainage are the contracts along the south coast road. This includes seven contracts from the border Moto’Masin border past Suai to Haemano bridge. The chainage used in the contracts starts in Moto’Masin and continues to Haemano bridge, whereas the first section of road involves the A15 that starts in Suai and ends in Moto’Masin, and the next section of road involves the last link of the A02 that starts in Zumalai and ends in Suai. The chainage in the contract therefore does not correspond to the chainage of the road. In addition to these contracts there are an additional three contracts from Haemano bridge through past Zumalai, for which the chainage starts at zero again at Haemano bridge. This makes it very complicated to check the contracts against the corresponding road segments. This also occurs in development partner funded projects, where the Manatuto-Natabora road, for instance, uses a chainage that starts in Dili, despite the road concerned (A09) starting in Manatuto.
34. Terrain is an important characteristic for determining the potential risk of landslides and slips, as well as other damages due to rock falls or fast flowing water. This data is generally defined as a terrain class (Flat, Rolling, Mountainous). This data can be considered constant and will only need to be collected once. The data may be collected through post-processing of video data, identifying the road segments with different terrain types, or by comparing the GPS track against topographic maps. 35. Rainfall also has an important effect on road deterioration due to erosion by runoff water and waterlogging affecting the strength of a road. This data is generally defined as a rainfall class (Low: <1,000mm, Medium: 1,000-2,000mm, High: >2,000mm). This data can be considered to be relatively constant and will only need to be collected once. The MPW is formally responsible for managing the Automatic Weather Stations (AWS) in Timor Leste. Currently there are 12 AWS locations but as this number increases, expanded rainfall profile data will be available both to the RAMS unit and to design firms.
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Figure 5 Topographical map and rainfall map for Timor Leste
Source: commons.wikimedia.org 36. Technical road classes, of which there are seven in Timor Leste, have been defined in the 2010 Road Geometric Design Standards (see Appendix C). These technical classes are defined in part by the administrative class of the road, but mostly depend on the traffic volume of the road. The technical class determines the minimum geometric and surface standards to be applied to the road. Of these seven technical classes, only four are being applied currently, as well as one additional one for the expressway from Suai to Zumalai. The main characteristics of these technical classes are indicated in Table 2 below. The old roads do not comply with these minimum technical standards and are considered underclass or substandard. As the roads are improved, however, more and more of the road network will comply with the technical standards of a specific technical class. Data will therefore need to be collected on the technical class of each road segment, identifying the specific technical class it complies to (EW, R1, R2, R3, R4, R5, RR1 or RR2)7 or whether it is substandard (SS). This data can be collected from contract documents or from the video survey using the post-processing approach described in paragraph 41. Table 2 Road design standards in Timor Leste by technical class STANDARD EW R1 R3 R5 RR1 Projected ADT >10,000 >10,000 2,000-10,000 400-2,000 20-400 Design period 20 years 20 years 20 years 10 years 5 years Design speed 80-100 km/h 60-100 km/h 50-70 km/h 30-60 km/h 20-40 km/h Surface Paved Paved Paved Paved (Un)Paved Lane width 3.6 m 3.5 m 3.0 m 2.5 m 3.5 m # of lanes 4 4 2 2 1 ROW width 40 m 40 m 30 m 25 m 15 m Source: Road Geometric Design Standards 2010, DRBFC design standards, Expressway designs 37. Surface type refers to the type of surface of the road carriageway. In national, municipal and urban roads a lot of use is made of asphalt concrete (AC), but other surface types include cement concrete (CC), gravel (GR), stone macadam (SM)8 or earthen (ER). In rural roads use is also made of penetration macadam (PM) and bituminous surface treatments (ST). Hand packed stone is also applied in rural roads, but this has similar characteristics to stone macadams and may be grouped in the same category. Data on the surface type may be collected through post-processing of the video data as described in paragraph 41. It is recommended to compare this with contract data where possible, especially in the case of bituminous pavements which can be hard to distinguish based on video footage alone. Based on the surface type, the pavement class may also be defined as either sealed or unsealed.
7 R2, R4 and RR2 are not applied in practice, and a decision may be taken in future to simplify the road classes to omit these, while at the same time adding the EW class. 8 This mainly involves the road bases of old roads where the overlying bituminous pavement has been destroyed.
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Table 3 Surface categories and pavement classes SURFACE TYPE ABBREVIATION PAVEMENT CLASS Asphalt Concrete AC Sealed Penetration Macadam PM Sealed Surface Treatment ST Sealed Cement Concrete CC Sealed Stone Macadam / Hand-Packed Stone SM Unsealed Gravel GR Unsealed Earthen ER Unsealed 38. Carriageway width will generally correspond to the technical class of the road. Currently national roads are constructed to a 6.0 metre R3 class standard (with the exception of some 4-lane segments), municipal roads to a 5.0 metre R5 class standard, and rural roads to a 3.5 metre RR1 class standard. This will need to be confirmed through the video post-processing. This can make use of measuring tools to confirm that the width does not vary strongly along the length of the road. Generally, the carriageway width will be constant for each contracted segment, and often for an entire road link. There are some road segments that have been improved in recent years and where a lower width has been applied (e.g. the road link from Ainaro to Cassa). Similarly, the old roads often have carriageway widths that differ from the standard widths of the different technical classes. 39. Number of lanes will also be recorded in the road inventory data. In principle the national and municipal roads will have two lanes, except the road link from Dili to Tibar and the expressway from Suai to Zumalai that have four lanes. Some urban roads may also have four lanes. Rural roads are generally constructed to a single lane 3.5 metre standard. The number of lanes can easily be determined from the video footage using post-processing software. 40. Video survey data can be recorded for the different roads. This may be collected in combination with other survey data. Based on the video survey data, post-processing can be carried out in the office. This is considered preferable as it is more easily verified and avoids issues with surveyor fatigue. Use can be made of a dashcam with integrated GPS as currently used by the Maintenance Department with JICA support, or a smartphone-based video recording app with GPS functionality (e.g. Road Recorder). However, this requires the data and GPS locations to be transferred manually to the database, requiring more resources and increasing the risk of errors. 41. A preferred option is to add a video camera and video logging software to the existing ROMDAS survey vehicle owned by MPW, allowing the collected video data to be incorporated with the other survey data. For the post-processing, use may be made of the ROMDAS DataView software that allows the identification of point data or track data which is then stored into the ROMDAS database format together with the GPS coordinates, from which it can be imported directly into the RAMS. This reduces the time involved and avoids errors related to manually copying data. Such equipment may also be provided by other companies, but since MPW already have a survey vehicle with ROMDAS equipment, it is recommended to use video cameras and post-processing equipment from the same company. 42. The video post-processing may be used for different types of data (inventory and condition data). It is recommended to use the video data and post-processing to identify the terrain class, surface type, pavement class, carriageway width, and number of lanes. The video data may further be used to identify the location of intersections, bridges and other structures or important features. Using the linkage between the video data and the GPS and chainage data that is collected by the same ROMDAS survey vehicle, these locations may be expressed in GPS coordinates and chainage.
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Figure 6 ROMDAS DataView software for post-processing video data
Source: romdas.com 2.1.3 Bridge data 43. Bridges are very important in Timor Leste, and a proper inventory of the bridges is necessary to ensure they can be properly monitored and maintained. However, given the large numbers of bridges it is recommended to keep the data collection to a minimum at this stage. This may be expanded in the future as more data becomes necessary. 44. A bridge code should be provided for each bridge. JICA has in the past initiated the development of a bridge database, including giving each bridge a code. This code included a two-letter code for the municipality, followed by a dash and a three-digit code for the bridge number. Although this code identified the municipality in which the bridge is located, it does not indicate the road in which it is located. It is therefore recommended to change the format for the bridge code to include the road code, making it easy to identify the road the bridge is located in, as well as the administrative class of that road. The proposed bridge code would start with the road code followed by a dash and the letter “B” to indicate it is a bridge code, and subsequently a two-digit code for the bridge. The numbering should as much as possible be in sequence of the chainage of the road Figure 7 Example bridge code for a bridge in the national road A01 from Baucau to Lautem A01-B03 = A01 B 03 Bridge code Road code Bridge Bridge number Figure 8 Example bridge code for a bridge in the municipal road C08 from Laga to Baguia C08-B02 = C08 B 02 Bridge code Road code Bridge Bridge number Figure 9 Example bridge code for a bridge in the rural road from Baguia to Defa Uassi BA-023-B01 = BA-023 B 01 Bridge code Road code Bridge Bridge number 45. The name of the bridge should also be identified for each bridge, together with the name of the river or watercourse that is crossed by the bridge. This data is often known locally or available from records. Bridge names are often important reference points and used in contract documents (e.g. Haemano bridge, Marobo bridge) and it is important that this data is included in the RAMS as much as possible.
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46. The GPS location and chainage of the bridge also needs to be recorded. This should be done for the start of the bridge (in the direction of increasing chainage). This data may be determined using the video data and post-processing. 47. The bridge type refers to a general categorization of bridges into one of five types, including beam, truss, bailey, arch and suspension bridges. Beam bridges have a simple steel girder or concrete beam that the bridge deck rests on - they are increasingly common in Timor Leste. Truss bridges have a steel framework above the bridge that supports the deck – they are very common in Timor Leste. Bailey bridges are prefabricated simple truss bridges with timber or steel plate decks – they are found in a number of roads in Timor Leste. Arch bridges have arches below the bridge to convey the weight to the abutments and pillars – they are not common in Timor Leste. Suspension bridges (including cable-stay bridges) have cables attached to the deck to carry the weight – they are not common in Timor Leste. Figure 10 Examples of beam, truss and bailey bridges in Timor Leste
48. Deck material is also recorded apart from the type of bridge. This may vary for different bridges of the same type. The deck material is generally concrete (possibly with a bituminous layer on top), steel or timber. This data can be collected from the post-processing of the video data or from a separate bridge survey. 49. The bridge length and width (of the carriageway) can be easily determined from the video post- processing and chainage data. This may be verified using a measuring tape in case a separate bridge survey is carried out. 50. The number of spans is also an important feature of any bridge. In some cases, this can be easily determined from the video footage, but in others it will require stopping at the bridge location and verifying this from the side of the bridge. It is recommended to include this in a separate bridge survey rather than combining it with the overall road survey (which does not include stopping). 51. Information on river protection measures upstream and downstream from the bridge, while not essential for the RAMS at this stage, is very important for DRBFC as its responsibility extends to flood control measures. Since many bridges in Timor Leste are affected by riverbank erosion, this data is important for overall works planning. The type of river protection is indicated as being none, stone riprap, gabion, stone masonry or concrete. This requires a separate bridge survey stopping at each bridge. 52. The construction year of the bridge may be obtained from the original construction contracts or from other secondary sources. In some cases, it may be mentioned on the bridge. 53. For each bridge it is important to include photographs of the bridge. At the very least this will include a photograph of the bridge along its length, which may be taken from the video data. It is preferable to include additional photographs from the side of the bridge (showing the spans and the status of the abutments, piers and bearings) and up- and downstream from the bridge (showing the protection measures). The additional photographs will require a separate bridge survey to be carried out and stopping at each bridge. 2.1.4 Culvert data 54. Culverts are important for the cross drainage in a road, avoiding that water flows over the road and causes damage. An inventory of the culverts is needed for the regular monitoring of their condition and to know the number of culverts to be maintained and their location. Inventory data on culverts is included here, but it must be noted that a separate culvert inventory will be required to collect this data since the culverts are not easily spotted from the road and most data will require stopping to assess each culvert (this may be
11 Road Asset Management Plan Data Collection combined with the bridge survey). It is not considered the highest priority to collect this data, and it is recommended to delay the collection of this data to a later date. 55. Each culvert should receive a culvert code to uniquely identify it. Similarly to the bridges, this should indicate the code for the road it belongs to, followed by a dash, the letter “C” and a three-digit culvert number. The numbering should as much as possible be in sequence of the chainage of the road, but additional culverts may be added later using the next unused number. Figure 11 Example culvert code for a culvert in the national road A01 from Baucau to Lautem A01-C003 = A01 C 003 Culvert code Road code Culvert Culvert number Figure 12 Example culvert code for a culvert in the municipal road C08 from Laga to Baguia C08-C002 = C08 C 002 Culvert code Road code Culvert Culvert number Figure 13 Example culvert code for a culvert in the rural road from Baguia to Defa Uassi BA-023-C001 = BA-023 C 001 Culvert code Road code Culvert Culvert number 56. For each recorded culvert, the location should be recorded using GPS coordinates and chainage. This will help to map the culverts and to will make it easier to locate them to monitor their condition. 57. The type of culvert should be recorded (steel pipe, concrete pipe, box, slab) as well as the number of cells of the culvert. The main dimensions of height and width of a cell also need to be recorded (for a pipe culvert this will be equal to the diameter of the pipe). 58. For each culvert a photograph will also be included in the records. This should include at least one photograph from the side of the road showing the type of culvert. Preferably this would include photographs from both sides of the road as well as a photograph along the length of the road. 2.2 Condition Data 59. Apart from the inventory data that describes the location and characteristics of the road, bridges and culverts, it is important to also collect data on the condition of the road network and its structures. This will focus on the condition of the road surface (the part of the road that is used by vehicles and has the most impact on road user costs) and of the bridges and culverts, as well as specific information on any damages encountered (slips, washouts, etc.). The data to be collected is described below. Here again it is important to avoid collecting too much data or at too high a level of detail and accuracy. Table 4 Condition data to be collected SURFACE CONDITION STRUCTURE CONDITION LOCALIZED DAMAGES • Roughness (IRI) • Bridge condition • Damage GPS location • Surface distress (SDI) • Culvert condition • Damaged element • Video data • River protection condition • Damage description • Condition description • Photographs • Photographs 2.2.1 Surface condition 60. For the road surface condition, it is important to collect data for the entire road network. This data will form the basis for determining the type of treatments to be carried out, and for prioritizing the allocation of the available budget to the different road links and segments. To facilitate the collection of surface condition data, use is made of survey equipment. MPW already has a survey vehicle, which will be used for this purpose. The MPW ROMDAS vehicle is currently with the PMU, where it is used to assess the condition of completed road construction works. In the future it will also be used for general condition surveys of the road network. The surface condition survey may be combined with the initial road inventory survey since use will be made of the same survey vehicle. It is recommended to repeat the surface condition survey at least once every 2 years, surveying different parts of the network each year.
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61. One important condition indicator is the roughness of the road surface, expressed as the International Roughness Index (IRI) in m/km. The IRI can be determined using the laser profilometer installed on the MPW ROMDAS vehicle. For roads in poor condition and unpaved roads, where the laser profilometer is easily damaged, use may be made of the bump integrator installed on the same vehicle. Alternatively, use may be made of a smartphone application (e.g. RoadLab) for such roads, although this is less accurate. Since the ROMDAS vehicle is available, it is preferable to use that since the operating costs are very similar. The roughness survey will require the survey vehicle to be driven over the full length of the road (link). The data from the laser profilometer is averaged for each 100-metre segment of road and provided in tabular format linked to GPS and chainage data (both for the laser profilometer and the RoadLab smartphone application). Based on the IRI measurements, the condition class can be defined as being good (IRI≤4), fair (4
9 The SDI is also applied in Nepal to define the road surface distress.
13 Road Asset Management Plan Data Collection record data for the locations of those damages rather than collecting and recording damage data for the entire road network. 67. GPS coordinates and chainage will be recorded to locate the damage. The damage report should also indicate the type of road element that is damaged (road surface, shoulder, bridge abutment, bridge pier, retaining wall, culvert, etc.). A description of the damage should also be included. It is recommended to insert this as open text to allow different types of damage to be recorded accurately. Lastly, photographs of the damage should be added, showing the type and scope of the damage. 68. To facilitate the collection of this data a smartphone application may be used. This may be an existing application like RoadLab, or a specific application to be developed by the RAMS consultant under R4D. The application should allow the damaged road element to be selected from a drop-down menu, and a description of the damage to be entered manually. The application should also allow photographs to be added and the GPS coordinates and timestamp to be recorded automatically. The application should allow the data from different damages to be exported to the RAMS and visualized in tables and maps. 2.3 Traffic and Accident Data 69. Traffic data is required in order to determine the importance of different roads and to prioritize roads for maintenance based on the number of road users benefiting from that road. The traffic data is also required to predict the deterioration of the road over time. Accident data is also included under this heading, although the purpose of this data is very different, aimed primarily at identifying road segments that have a high risk of accidents. Table 5 Traffic and accident data to be collected TRAFFIC DATA ACCIDENT DATA • (Annual) Average Daily Traffic (AADT/ADT) • GPS location • GPS location • Accident date • Link code • Number of fatalities • Survey date • Number of serious injuries. • Survey type • Traffic class 2.3.1 Traffic data 70. Traffic volumes tend to remain the same along the length of a road, except where the road passes important population centres or intersects with other roads of a similar or higher class. Traffic data will therefore be collected for a specific road link. Where the traffic volume changes significantly along the length of the road link, traffic data may need to be collected at more than one location. Where traffic data is not expected to change much from one road link to the next, the traffic data from one link may be used also for the next link. 71. In the traffic data, different vehicle types will be distinguished. At the very least, this will distinguish between motorcycles, passenger cars (including pickups), buses and trucks. Where possible, a further distinction according to the following vehicle categories should be included. This is especially important for higher volume roads.
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Table 6 Vehicle types and their characteristics TARE OPERATING CATEGORY VEHICLE TYPE FUEL TYPE AXLES WHEELS WEIGHT WEIGHT Motorcycle Petrol 2 2 0.1 tonnes 0.2 tonnes Personal Passenger car Petrol 2 4 1.0 tonnes 1.2 tonnes Pickup/4WD Petrol 2 4 1.5 tonnes 1.8 tonnes Minibus (microlet) Petrol 2 4 1.1 tonnes 1.5 tonnes Passengers Two-axle bus Diesel 2 6 4.5 tonnes 6.0 tonnes Multi-axle bus Diesel 3 10 8.0 tonnes 10.0 tonnes Van/light truck Petrol 2 4 1.8 tonnes 2.0 tonnes Two-axle truck Diesel 2 6 4.5 tonnes 7.5 tonnes Goods Multi-axle truck Diesel 2-3 6-10 9.0 tonnes 13.0 tonnes Articulated truck Diesel 5 18 11.0 tonnes 28.0 tonnes 72. Traffic data will be collected by means of traffic counts to determine the average daily traffic. Traffic counts are ideally carried out at different times of the year, for at least 7 consecutive days, and for at least 16 hours a day (generally 06:00 am to 22:00 pm) in order to take account of variation in traffic levels in different seasons, on different days of the week and at different times of the day. This allows the annual average daily traffic (AADT) to be determined. 73. However, since traffic counts are costly to carry out, the frequency and duration of the traffic count may be reduced by counting at only one time in the year (preferably in the dry season when traffic volumes tend to be higher) and counting for only three consecutive days (this should include the day with the highest expected traffic levels - e.g. market day). The count duration may even be reduced to only one day, although this will further reduce the accuracy (in this case the count should be carried out on the day the highest traffic volume is expected - e.g. market day). This will allow the average daily traffic (ADT) to be determined. 74. These traffic counts may make use of electronic traffic counters that record the number of vehicles or axles using pneumatic tubes laid across the road or piezoelectric sensors and induction loops. Axle-based or length-based classification allows the data to be distinguished by various vehicle types. Apart from these devices, infrared, radar and video image detection devices can also be used to carry out traffic counts. Vehicle speeds can generally also be recorded using this equipment. Some of this equipment can be used in different locations, while other equipment is permanently installed at a fixed location. Using this equipment, traffic counts can be conducted over longer periods. 75. Alternatively, manual traffic counts may be carried out where the numbers of each vehicle type are recorded. This requires a survey team to manually record the number of vehicles on a traffic counting form or using a smartphone application. This is less accurate since surveyors may make errors in recording the data. It is also quite costly since the surveyors require salaries and per diems, as well as transport to and from the survey location. 76. As a final alternative, video data may be used to carry out so-called moving traffic counts. Here the video is used to count the number of different vehicles travelling in the opposite direction. Assuming that the survey vehicle is travelling at the same speed as the oncoming traffic and that the flow of traffic is the same in both directions, the traffic flow rate can be calculated using the following formula. This can be converted to the average daily traffic (ADT) by multiplying it by a correction factor that takes account of the time of day at which the moving traffic count was conducted (the video data was collected). This correction factor may be determined on the basis of more comprehensive traffic counts carried out in similar roads (showing the spread of traffic over the course of the day). Figure 14 Formula for calculating traffic flow based on moving traffic count FADT = Calibration factor based on time of day C = Number of counted vehicles travelling in opposite direction 퐶 t = Duration of the moving traffic count in hours 퐴퐷푇 = 퐹퐴퐷푇 푡
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77. Together with the traffic count data (differentiated by vehicle type where possible), the date and location of the traffic survey should be recorded together with the code of the road link the traffic count applies to. The traffic survey type should also be recorded (More than 7 days, 7-day, 3-day, 1-day, moving traffic count). 78. Lastly, based on the total number of vehicles determined in the traffic count, the traffic class should be determined. It is recommended to distinguish the following traffic classes. These are to a certain extent linked to the technical classes recommended for the road, although the technical class is defined by projected traffic volumes for the design life of the road (ranging from 5 years for low class roads to 20 years for high class roads) and not by current traffic volumes. Although moving traffic counts may not result in a very accurate estimation of the ADT, they can provide a reliable estimation of the traffic class. Table 7 Traffic class based on the average daily traffic (ADT) TRAFFIC CLASS AVERAGE DAILY TRAFFIC TECHNICAL CLASS T1 ADT≤20 RR2 T2 20
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Table 8 Planning and contracting data to be collected PROJECT DATA CONTRACT DATA • Strategic Plan • Project code • Project code • Contract number • Road code • Road code • Road name • Road name • Segment start name • Segment start name • Segment start chainage • Segment start chainage • Segment start coordinates • Segment start coordinates • Segment end name • Segment end name • Segment end chainage • Segment end chainage • Segment end coordinates • Segment end coordinates • Bridge code • Bridge code • Bridge name • Bridge name • Bridge coordinates • Bridge coordinates • Treatment type • Treatment type • Project description • Project description • Planned start date • Contract start date • Planned end date • Contract end date • Estimated cost • Contract price • Planned funding source • Contract funding source • Attached documents • Attached documents • Contractor number • Contractor name 2.4.1 Project data 83. The strategic plan (e.g. Strategic Development Plan, Transport Sector Master Plan, Rural Road Master Plan, Five-Year Plan) reflecting the origin of the planned project is included in project data. Each project will then receive a project code as a unique identifier. The project code should reflect the year that the project is planned to start as well as the entity responsible for its implementation. This project code also needs to be reflected in the contract data, showing what plan a specific contract was based on. The project code should also be reflected in the Five-Year Plan as submitted by DRBFC and MPW, allowing easy access to the project data and contract data. 84. The road code as well as the road name should be identified for the planned project for easy reference. Since most projects will not include the full road or road link, the road segment should be identified by the start and end of the segment (name, chainage and coordinates). In the case of a bridge project, the bridge code, name and coordinates should also be entered. 85. The treatment type should identify the type of project. These should be standard categorized treatment types, preferably with clear definitions. For instance, this would include road upgrading, road rehabilitation, periodic maintenance, routine maintenance, bridge maintenance, etc. This is used to get a general understanding of the type of works to be carried out and allows for a simple statistical analysis of the types of projects. 86. A detailed project description entered into several different categorized data fields would become very complicated for the many different types of projects. A text field in the database where a short description of the project can be entered, is therefore preferable. This will not allow for any statistical analysis, but can provide a clearer understanding of an individual project. The project data will further include the planned start and end date, as well as the estimated cost and the planned funding source, which is useful for budget planning. 87. Further information on the project can be provided by uploading project documents. These may include feasibility studies, design documents, cost estimates, etc. The database will allow several documents to be
17 Road Asset Management Plan Data Collection uploaded, which can subsequently be opened from the RAMS. This will allow users to access further details as necessary. 88. Including the planned projects in the RAMS allows the project road segments and bridges to be indicated in maps and RAMS data on these road segments and bridges to be included in the FYP. Future FYPs will likely be prepared using the RAMS. Indicating the inclusion of a specific road segment or bridge may then be carried out as part of the planning process. 2.4.2 Contract data 89. A contract code for the project will be assigned and filled in once the project is ready for contracting. The contract code should reflect the source of funding using the standardized approach already used for the different funding sources. The contract funding source should also be entered in the database. Much of the other contract data can be copied directly from the project data. By entering the project code, the project data can be copied automatically, with adjustments made where necessary. Additional documents will also be attached, including the bidding documents and ultimately the contract documents. 90. Some data may only be included once the contract has been awarded, including the contract price and the contract start and end dates. The contractor name should also be entered linked to a list of all the registered contractors, each with a specific contractor code. This will allow the contractor to be selected from a list and will avoid contractors being represented under different spellings of their name (this is a problem with the current contract management spreadsheet). 91. For monitoring purposes and for the future calibration of the road deterioration models used in the RAMS, it is important to keep track of the treatments carried out in each road segment. The year of the last treatment needs to be included in the road and bridge data, and information on previous treatments needs to be kept as part of the historical data of the RAMS. The contract data therefore needs to be linked to the road and structure data, allowing users to link from the road data directly to the contract data where further information on the contracts can be found, and vice versa. The contract number should be used as the main identifier for linking the two modules. 2.5 Data Collection 92. The required data will be collected using different methods and with different frequencies. This section looks at the main methods of data collection, identifying the collection frequencies and who will be responsible for the data collection. It distinguishes between the data collection by means of a drive-over survey using the road survey vehicle (and subsequent post-processing), the data collection for structures using smartphone applications or standardized survey forms, traffic data collection using automated or manual traffic counts, and the collection of data from secondary sources. 2.5.1 Road survey vehicle 93. MPW has a road survey vehicle provided under World Bank project funding in 2018. This has road survey equipment installed from ROMDAS, including a high accuracy odometer for tracking distance, a GPS device for tracking location, a laser profilometer for tracking pavement roughness, and a bump integrator for tracking general road roughness. The functionality of the vehicle may be expanded by including a forward- facing camera to collect video data. The different data is tracked together using the ROMDAS Data Acquisition software, ensuring that all data is linked together by means of the GPS coordinates. This can be expanded to include the ROMDAS DataView software that allows for post-processing of the collected data (mainly video and GPS data)10. 94. The survey vehicle is currently only used by the PMU to collect data on the roughness of completed road works, as a measure of assessing the performance of the contractor and the compliance with the contract. This survey vehicle should be used for wider network surveys, forming the basis for the inventory and condition data collection by DRBFC, and even allowing traffic estimations to be made. The World Bank is
10 The recommendation to use a ROMDAS camera and ROMDAS DataView software is based on the fact that the existing equipment is from ROMDAS and does not imply a specific preference for ROMDAS. Using equipment and software from the same manufacturer ensures that there are no compatibility issues and that support can be provided under the existing licenses.
18 Road Asset Management Plan Data Collection planning to use the vehicle for data collection in 2020 for the national and municipal road networks under its planned Branch Roads Project. The following data can be collected using the road survey vehicle, either directly or through subsequent post-processing of the data collected by the vehicle. Table 9 Data collection using survey vehicle DATA FROM SURVEY DATA FROM POST-PROCESSING Road inventory data Road inventory data • GPS track • Terrain class • Link length • Technical Class • Link start chainage • Surface type • Link start coordinates • Pavement class • Link end chainage • Carriageway width • Link end coordinates • Number of lanes • Video data Bridge inventory data Bridge inventory data • GPS location • Bridge type • Chainage location • Deck material • Bridge length • Bridge width • Number of spans Road condition data Road condition data • Roughness (IRI) • Roughness (IRI) • Video data • Surface distress (SDI) • Damage GPS location • Damaged element • Damage description 95. The collection of data using the road survey vehicle will require a simple drive-over survey, as well as the subsequent post-processing of the collected data in the office. This allows a relatively large length of road to be surveyed in one day, and a large portion of the network to be surveyed each year. A full survey of all roads should be carried out at least once every 5 years as a basis for updating the inventory data. Most of the road network should be surveyed at least once every two years as a basis for updating the condition data. In the case of the national roads, all roads should be surveyed at least once every two years, while for the municipal and urban road networks at least 80% of the network should be surveyed every two years (focusing on rehabilitated roads). For the core rural road network, the survey should focus only on the rehabilitated roads (currently around 30% of the network), which would need to be surveyed at least once every two years. This would require approximately 1,500 km to be surveyed each year, growing to 2,000 km per year as the rural roads are rehabilitated. Assuming that with proper planning at least 100 km of roads can be surveyed each day, this would require up to 20 days of surveys each year. Estimating up to double that number of days to travel to the roads and back, this would require up to 60 days of field surveys each year to collect the required data. 96. The post-processing would make use of the collected video, chainage and GPS data, with the ability to compare this with the roughness data if required using the DataView software. The video can be viewed from the office and data regarding the inventory and the condition of the road and to some extent of the bridges can be entered using the keyboard. This is then automatically linked to the GPS and chainage data and included in the internal ROMDAS database. From here it can be exported in MS Access format and imported into the RAMS database. This procedure is currently also being applied, but is making use of separate software and equipment for the collection of video and GPS data and its subsequent post-processing. This requires data to be transferred manually, increasing the time and effort involved and increasing the risk of errors. It also means that the data is not fully linked to the other data regarding chainage and roughness. 97. The drive-over survey will require a driver and a second person to operate the ROMDAS equipment. Currently only one member of the PMU is trained to operate the ROMDAS equipment. At least two trained operators are needed for the ROMDAS equipment in the RAMS Unit, with 2 or 3 additional people trained in
19 Road Asset Management Plan Data Collection the use of the equipment who may act as replacements if required. The post-processing will be done from the office and at least two people in the RAMS Unit should be trained to carry out the post-processing. These may be the same people that operate the ROMDAS equipment during the surveys, but it is recommended to have other staff responsible for the post-processing, allowing this to take place in parallel to the data collection. It is expected that the post-processing will require up to double the actual survey time, averaging around 40 days per year. 2.5.2 Data collection for structures 98. Although some of the data for the structures may be obtained from the drive-over survey, the full data collection for structures will require a separate survey, stopping at each bridge and/or culvert to collect the required data. For this survey a smartphone application with the required data fields can be used allowing data to be entered in the field and linked to time stamps and GPS coordinates, as well as linking it to photographs of the structures and their surroundings. The collection of the structure data can require significant effort, especially in the case of culverts. Initially, collecting the data for the bridges should be the focus, with culvert data perhaps collected at a later stage. The data collection for the structures will combine the inventory data and the condition data. The data collection should be repeated every 5 years, updating the inventory data if required, but focusing mainly on the condition data. Table 10 Data collection for structures BRIDGE DATA CULVERT DATA STRUCTURE CONDITION • Bridge code • Culvert code • Bridge condition • Bridge name • GPS location • Culvert condition • River name • Chainage location • Condition description • GPS location • Culvert type • Photographs • Chainage location • Number of cells • Bridge type • Culvert width • Deck material • Culvert height • Bridge length • Photographs • Bridge width • Number of spans • Protection up • Protection down • Construction year • Photographs 99. The data collection every 5 years may be spread over different years, collecting data for 20% of the bridges each year. This would allow the data to be collected by a small in-house team with surveys carried out during a short period every year. Alternatively, the data may be collected for all bridges once every 5 years, with a larger team responsible for the data collection. In this case, it is recommended to involve consultants to carry out the data collection. This second approach is easier to manage and ensures that the data is available for all bridges and is of a similar age. The suggested smartphone application would be provided to the consultants to use in the data collection. Data from previous surveys may also be provided for comparison. This would require a specific budget allocation every 5 years for the collection of this data. 2.5.3 Traffic data collection 100. Traffic data collection will require traffic counts to be carried out. For road links with high traffic volumes, fixed traffic counting stations that carry out the traffic counts on a more or less permanent basis should be installed. This will allow accurate calculations of the average annual daily traffic, appropriate for road links around Dili that have relatively high traffic volumes11. The data would be collected each year.
11 There are 16 road links with estimated traffic volumes exceeding 1,000 ADT, with only 5 road links having estimated traffic volumes exceeding 2,000 ADT (estimations based on 2009 traffic counts).
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Table 11 Data collection from traffic counts TRAFFIC DATA • (Annual) Average Daily Traffic (AADT/ADT) – by vehicle type • GPS location • Link code • Survey date • Survey type • Traffic class 101. Fixed traffic counting stations in road links with medium traffic volumes will require a large investment, and it is instead recommended to use portable traffic counting stations that can be moved from one road to another. This will still allow traffic counts of 7 days or more to be carried out, giving reasonably accurate estimations of the average annual daily traffic. The data collection may be repeated every 3-5 years. 102. For roads with low traffic volumes, manual traffic counts, often only for one day, are appropriate. This may be repeated once every 5 years. This may be complemented by moving traffic counts using the video data collected from the drive over survey. 103. The fixed and portable traffic counting stations should be managed directly by DRBFC staff. This may be done by staff from the RAMS Unit in the DRBFC central office, or alternatively from the DRBFC Regional Offices (once these have been reintroduced). At least one person in the RAMS Unit should be responsible for traffic data collection and operating the traffic counting stations. If the regional offices are reintroduced, one person in each regional office should be trained to operate the traffic counting equipment. In the case of manual traffic counts, this should be contracted out to a consultancy company to carry out, with clear guidelines on the method to be applied. 2.5.4 Secondary data collection 104. Some data will not be collected directly from the road network, but will be collected from secondary sources. This includes the administrative data regarding the centres and boundaries of the municipalities, administrative posts and sucos together with their population data. This data is largely already available with the GIS unit of DRBFC, but will need to be updated if new census data is made available or if changes are made to the administrative structure in the country. Accident data will need to be collected from the Traffic Police, at least once a year. The project and contract data will mainly be obtained from other units in DRBFC, including from the contract documents themselves. 105. The RAMS Unit will be responsible for obtaining the required data, coordinating with the other DRBFC units and other entities that are responsible for the data. Administrative data will not change much, and accident data only needs to be updated once a year. The project and contract data should be updated whenever a new project or contract is created. The project data may be updated as part of the planning process. In the case of the contract data, it is important to ensure that any contract going to tender or being awarded is properly reflected in the RAMS.
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Table 12 Data to be collected from secondary sources ADMINISTRATIVE DATA PROJECT DATA CONTRACT DATA • Administrative class • Strategic Plan • Project code • Management entity • Project code • Contract number • Municipality • Road code • Road code • Administrative Post • Road name • Road name • Suco • Segment start name • Segment start name • Population • Segment start chainage • Segment start chainage • Segment start coordinates • Segment start coordinates ACCIDENT DATA • Segment end name • Segment end name • GPS location • Segment end chainage • Segment end chainage • Accident date • Segment end coordinates • Segment end coordinates • Number of fatalities • Bridge code • Bridge code • Number of serious injuries. • Bridge name • Bridge name • Bridge coordinates • Bridge coordinates • Treatment type • Treatment type • Project description • Project description • Planned start date • Contract start date • Planned end date • Contract end date • Estimated cost • Contract price • Planned funding source • Contract funding source • Attached documents • Attached documents • Contractor number • Contractor name
22 Road Asset Management Plan Data Management Data Management 106. The collected data will need to be managed in order to make it easily accessible and to allow it to be further analysed. For this purpose, DRBFC is currently developing a road asset management system (RAMS) with support from R4D. This RAMS will include a database to enter the data as well as additional modules to manage and analyse the data. This chapter looks at the validation and entry of the collected data into the RAMS and at the different modules of the RAMS itself. 3.1 Data Validation and Entry 107. The collected data will need to be entered into the RAMS. For small amounts of data such as the results of a traffic count in a particular location, this may be done by hand. For larger amounts of data such as the collection of roughness data for a part of the road network, this may be done in a more automated manner by importing a file containing the collected data. In both cases, the data will need to be validated before it is entered into the RAMS. 108. Data validation refers to the checking of the data to verify that it is in the correct format and that there are no striking errors in the data. It does not involve checking each data item to see if it is correct, but simply reviewing the data to remove or correct any glaring inconsistencies or errors. An example is a road length expressed as 12,700 km instead of the intended 12.7 km or 12,700 metres. It may also be that the data has been entered in the wrong field for the data entry form, with the road length for instance indicating “mountainous”. Where standard data entry forms are used for importing the data into the RAMS, the format of the form needs to be checked, ensuring that the columns are in the right order so the data is uploaded to the right fields in the database. These are all examples of simple errors, but if the data is not checked and validated, they can result in serious errors in the database that can cause problems in the data analysis and can even cause the database to crash. 109. The data validation takes place before the data is entered into the RAMS database, checking the individual data or else the data entry forms. The data entry forms are generally generated by exporting the data from the data collection software. For instance, in the case of the ROMDAS drive-over survey equipment, the collected data can be exported in a MS Access database with a fixed format, allowing the data to subsequently be imported into the RAMS. Data from the smartphone applications used for the structure inventory and condition survey can be exported in a comma-separated-value format, allowing it to be opened in MS Excel for instance, and imported directly to the RAMS. These files need to be reviewed before they are imported into the RAMS, checking the data and the format of the file to see if these are in order. After importing the data into the RAMS, this should again be checked to see if it has been imported correctly. To a certain extent the data validation may be automated, with the RAMS checking the format and values of data to be imported, avoiding incorrect data from being imported. This will not be 100% fool proof, however, and manual checking will continue to be needed. 110. Errors that are not identified and corrected during the data validation process, can be difficult to identify and correct in the RAMS. As much as possible, errors in the data should be corrected before entering the data in the RAMS. However, this should only be done where it is clear what the correct value or format should be. It is recommended to check the original data, data collection device or surveyor to see what the correct data should be. Even after the data has been validated and entered, it is recommended to keep the original data collection files for a period of at least 10 years, so that these may be reviewed if errors are encountered at a later stage. It is important that these data collection files are stored in a safe place, with clear procedures for naming them so that they can be found easily (this may make use of the automatic naming system of the data collection equipment). 111. The data validation and entry into the RAMS should be done by trained staff of the RAMS Unit. Initial processing of collected data (e.g. combining the results from different traffic counts in a single file) may be contracted out to the consultants collecting the data, but a final validation of the data should be carried out by the RAMS Unit before entering or importing the data into the database. At least two staff members should be trained and responsible for data validation and entry, with additional staff also trained as possible replacements. Although the length of the road network and the types of data to be collected will be limited, the data will include several different surveys and the validation and entry will therefore take quite some
23 Road Asset Management Plan Data Management time. This is especially the case at the start when all the inventory data needs to be reviewed and entered. It is unlikely that the data validation and entry will take up all the time of the staff members involved, and they will likely also be able to be involved in other aspects of the RAMS (e.g. data collection, mapping or data analysis). 3.2 Road Asset Management System 112. The RAMS being developed by DRBFC with support from R4D is likely to have different modules, each of which may have different submodules. The main module will be the road network module, with submodules for the road data, bridge data, culvert data, traffic data, etc. A second project module will be linked to the road network module and will include data regarding the planned projects and the related contracts to be carried out. Linking the two, will be a planning module, that serves as a tool in preparing the (multi)annual plans based on the available road inventory and condition data. This will include various algorithms and selection or prioritization criteria that will be applied to the data from the road network module. A separate mapping module will allow the inventory and condition data as well as planning and project data to be visualized in map form. 3.2.1 Road network module 113. The road network module will form the main database of the RAMS, including all the data collected in the road network. All data will be geo-referenced, using GPS coordinates to identify the location and link it to other data for that same location. Some data will have a GPS point as reference (e.g. a culvert or bridge), while other data will have a GPS track as reference (e.g. a road link or segment). More information on this can be found in Appendix A for the different data types. 114. The data linked to GPS tracks may involve fixed road links that do not change much over time (e.g. the name or length of the link), while other data may involve segments of the road with variable lengths that change over time (e.g. road surface or road condition). These road segments may be different for different data (e.g. condition and surface data may not be consistent for the same segments), and the RAMS will need to be able to incorporate such varying segments. 115. Different options exist to address this issue. The first is to have fixed segments of 100 metres, for instance, and to give each segment a specific value for all the different data related to the road (this assumes that the data values are constant for the 100-metre segment for each data type). The problem with this approach is that it results in a very large number of data sets, many of which have the same values for the different data, which makes the database cumbersome and slow (e.g. 20 km of road with the same surface, width, terrain, rainfall class, etc. will result in 200 datasets with essentially the same data). The second option is to apply different segments to different datasets, with new segments only created if the specific data that it refers to, changes. This results in fewer data sets as the segments can be considerably longer, especially for some data sets (e.g. the technical class of the road will not change much over the length of the road). However, this still requires parallel segments to be created for different data sets, even though the data may remain quite constant for the different data sets (e.g. the administrative class of the road and the technical class of the road may remain constant for the entire road link or even the entire road). A third option is to allow for segments of variable length, with all data sets linked to the single segment, and the segment length defined by a change in any of the data. For instance, a new segment is started where the condition changes or where the road surface changes. The data within any segment is consistent, but this may apply for much longer segments of several kilometres compared to the first option. As a result, the amount of data is smaller, and the database may be quicker. Independent of how the data is stored inside the database, the RAMS should be able to export the available data (inventory, condition and traffic) per 100 metre segment, per 1 kilometre segment and per homogenous road segment, allowing the data to be further analysed using other software. 116. In the road network module, it is furthermore important that historic data is kept when new data is entered, rather than replacing the data. This allows the data to be used to determine changes over time. Especially for condition data this can be a very useful tool in calibrating the deterioration models used for predicting future road conditions. Although algorithms for road deterioration exist, these will need to be
24 Road Asset Management Plan Data Management adjusted to each country and road network. Having actual data on road conditions over several years allows the algorithms to be calibrated so they more accurately reflect actual deterioration patterns. 3.2.2 Planning module 117. The planning module will apply a set of selection and prioritization criteria or algorithms to the road network data, allowing the user to determine the maintenance needs for a particular year or set of years, as well as determining the future road conditions based on the amount and type of maintenance to be carried out. The former allows the actual budget needs and required maintenance treatments to be determined, while the latter allows the impact of different budget levels and treatment types on the future network condition to be predicted. 118. In its simplest form, the planning module is a simple matrix that defines the required maintenance treatment based on the last received treatment (e.g. periodic maintenance every 7 years). The budget needs are calculated by multiplying the length of required treatments by a unit rate for the treatment concerned. A certain degree of differentiation in the periodic maintenance cycle and the unit rates is possible depending on the type of road. Prioritization of interventions may be determined by a second matrix looking at the main road characteristics (e.g. traffic, surface type, condition, technical class). These matrices may be prepared based on experiences from other countries or developed through a more thorough analysis of the data for Timor Leste. For instance, a strategy analysis using the Highway Design and Management (HDM-4) software developed by the World Bank and used in many countries, can identify optimal treatments and prioritize roads based on a specific budget scenario. An example of such a prioritization matrix is given below, identifying the types of treatments to be applied to different roads depending on their surface type, condition and traffic levels (this is the result of a detailed HDM4 analysis of data collected in Myanmar and is applicable for a specific budget amount – for a different budget amount the optimal combination of treatments would be different). Figure 15 Example of a prioritization matrix from Myanmar Unsealed Good Fair Poor Bad Very Bad Asphalt concrete Good Fair Poor Bad Very Bad AADT<50 Routine AADT<50 Routine 50
119. More complex planning modules make use of algorithms to predict future road conditions based on the planned treatments or lack thereof. Predicted road conditions will determine future road user costs, while the planned treatments will determine the road agency costs (maintenance costs). Based on the expected budget, a set of treatments to be applied in the different roads can be determined that will result in the lowest total transport costs (sum of road user costs and road agency costs). This is considered to be the option with the highest economic benefits for the country. This approach will identify individual roads or road segments to receive priority, defining the treatment they should receive. 120. The second type of planning module can be more accurate, resulting in a more efficient and effective use of available funding. However, it requires a higher skill level and more data to calibrate the algorithms and set the different variables. If the necessary skills and data are lacking, the results may not necessarily be better than when the simple matrix is used. In such a case, it may be preferable to carry out a one-time strategy analysis using HDM-4 to develop the corresponding treatment and prioritization matrix, which can then be applied using the data from the road network module. Such a prioritization matrix is much easier to understand and apply. The World Bank has indicated that it will support an initial strategy analysis for national and municipal roads using HDM-4. 121. It is not clear at this stage what type of planning module will be developed for the RAMS. In any case, the planning module will allow the maintenance needs to be estimated (type of treatment required and related cost), and it will allow the road (segments) to be prioritized and ranked. This in turn allows the
25 Road Asset Management Plan Data Management available budget to be allocated to those roads with the highest priority. The planning module will therefore result in a list of priority roads or road segments and the treatments they need to receive over the next planning period. This will form the basis for the annual and five-year plans. The planning module will also allow future road conditions to be predicted based on the treatments to be financed. This will form an important tool in justifying the budget requests by showing the impact of the requested budget on future road conditions (as well as showing the negative impact of reduced budget allocations on road network conditions). 3.2.3 Project module 122. The results of the planning module may be used to prepare the actual five-year plans and annual plans. These may then be defined as a set of projects and ultimately as contracts in the project module. In the project module, the prioritized road segments will be identified (start and end of segment), indicating the proposed treatment and estimated cost as determined in the planning module. In the project module the expected implementation dates (year of implementation) will also be determined as well as the source of funding. As the projects are developed, project documents may be uploaded to the database (e.g. feasibility reports, design documents, detailed cost estimations, etc.). 123. Once the projects have been approved for inclusion in the annual budget allocation to road maintenance, each project will be converted into one or more contracts. Apart from defining the planned projects and contracts, the project module may also serve as the basis for contract management, allowing the physical and financial progress to be mapped and all project and contract documents to be stored in a single place with easy access. 124. The project module is again a real database, with actual data stored together with linked documents. The project data and the contract data are closely related, and much of the data will be the same. Only where changes have been made to the original project, or a project has resulted in various contracts, will the data be significantly different. 3.2.4 Mapping module 125. The mapping module serves to link the road and project databases with the GIS mapping functionality of the RAMS. This may be used to present certain data or certain plans but can also serve for further analysis of the data. Mapping is extremely useful to show the spatial relationship of certain data, for instance showing the distribution of sealed roads over the country and relating this to the population density. 126. The RAMS will need to include a number of standard mapping options that allow easy generation of maps by users. The RAMS will therefore include a mapping page where different items can be selected for inclusion in the map. Certain map items will have a simple checkbox allowing them to be made visible or not (e.g. the municipal centres), while for other map items certain sub-items can be selected, for which different categories can be made visible or not. In the latter case, the sub-items will have predefined categories (e.g. road condition of good, fair, poor and bad) and one or more of these categories can be made visible (e.g. showing only the roads in bad condition). Distinction between different categories will be made using colours. These standard mapping options will allow users of the RAMS to easily prepare maps indicating those aspects they are interested in. This includes selecting specific road links, bridges or culverts and highlighting these on the map. The mapping module should also allow different background maps to be made visible. This can include street maps, topographic maps or satellite images (e.g. Google Maps or Open Street Map). Table 13 RAMS mapping options MAP ITEM SUBITEM VISIBILITY OPTIONS • Municipal centres (+ names) Administrative centres • Administrative posts (+ names) Visible or not • Suco centres (+ names) • Municipal boundaries (+ names) Administrative boundaries • Administrative post boundaries (+ names) Visible or not • Suco boundaries (+ names) Population • Density Visible or not Road network • By administrative class Visible or not
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MAP ITEM SUBITEM VISIBILITY OPTIONS • Road code Road code Visible or not • Link code • By administrative class • By technical class • By surface type Select for each characteristic Road network • By pavement class which categories to show • By roughness category • By surface distress category • By link code • By road network • By type Select for each characteristic Bridges • By condition which categories to show • By bridge code • By road network • By type Select for each characteristic Culverts • By condition which categories to show • By culvert code Damages • By damaged element Select which categories to show 127. Despite such an elaborate set of standard mapping options, there will always be a need to prepare personalized maps with (a combination of) options that are not available in the standard maps. This will require such maps to be prepared specifically using a GIS program. The GIS unit under the DRBFC Projects Department is currently responsible for preparing such maps, and it will continue to need to do so in the future. Apart from the specialized maps, they will also need to enter all the GIS data into the RAMS. With the transformation of the GIS unit into a RAMS unit, two GIS specialists in the RAMS unit will be needed to continue the GIS support to DRBFC and the RAMS. These staff would be responsible for managing the GPS and GIS data and for entering this into the RAMS, for ensuring the proper operation of the mapping module of the RAMS, and for preparing any specialized maps required by DRBFC or others. 3.2.5 Web interface 128. To allow the RAMS to be used by different users, a web interface allowing remote access to the database will need to be created. The RAMS itself will be located in the cloud, with a cached version available on a server in DRBFC. Connection will be through the internet. Connection to the cached version on the server will be by means of a Local Area Network (LAN) intranet connection in the DRBFC office in Dili through to allow usage even when the internet connection is not fully functional. This may be expanded to include access by MPW, municipal roads departments and possible future DRBFC regional offices through a Wide Area Network (WAN). 129. The access will be restricted for most users, with different user categories defined and given different levels of access depending on their location or on usernames and passwords. The RAMS unit staff will have full access allowing them to enter and change all data. Other DRBFC units as well as MPW staff may have access to all data, but will only be able to look at the data without being able to make any changes (alternatively they may be allowed to make changes, but such changes will need to first be approved by the RAMS Unit before they are uploaded to the database. Municipalities may only have access to the rural road and urban road data for their specific municipality. The wider public may only be provided with access to general statistics (accessible without username or password). The number of user categories and their levels of access will need to be defined, as well as the means by which the RAMS can be accessed (LAN, WAN or internet). This may also be amended over time. 130. The web interface will allow access to the different modules (depending on the user category) and will allow users to look up specific data or roads. Selected data may be exported in PDF or XLS format, allowing it to be used for preparing reports, plans, etc. The remote access to the RAMS will ensure that all users have access to the same set of data. This is currently not the case, with different DRBFC staff using different datasets, causing inconsistencies between different plans and reports.
27 Road Asset Management Plan Data Analysis and Planning Data Analysis and Planning 131. The main purpose of the RAMS is as a support tool in the planning of road network interventions, allowing DRBFC to determine the required funding and the optimal allocation of available funding to different roads and treatments. This chapter looks at how the RAMS will fit into the planning process for (multi)annual planning of road maintenance. This links up with current government planning procedures that involve Five- Year Plans that form the basis for annual work plans as part of the annual budget requests. 4.1 Five-Year Plan 132. An important function of the RAMS is to facilitate the analysis of the collected data using the RAMS planning module or by exporting certain data to other planning software. The RAMS makes the data easily accessible and allows different datasets to be compared. This can help determine intervention needs, for instance in identifying which roads require periodic maintenance in the form of overlays or seals. It can also help prioritize future improvements, for instance in selecting which road to rehabilitate based on the number of people or vehicles served by different candidate roads. 133. For maintenance, the planning module of the RAMS forms an important tool in determining the maintenance needs and in identifying the roads to receive priority. Using the planning module, a list of priority roads and treatments can be identified that should receive funding over the next five-year period. This then forms the basis for the Five-Year Plan for road maintenance. The RAMS will also allow different budget scenarios to be modelled, identifying the optimal combination of roads and treatments for each budget. The RAMS may also allow the impact of these different budget scenarios on future road conditions to be predicted, identifying what the road network conditions will likely be depending on the amount of budget to be received. This can help identify the minimum budget required to avoid deterioration of the road network over time. This can be an important tool in negotiating the budget request with government and Parliament. 134. Although the RAMS will allow the identification of the required treatments and priority road segments, the results should not be copied one-to-one to the FYP. The results of the RAMS data analysis will need to be reviewed and checked to see if they result in comprehensive improvements to the road network. This includes ensuring that the different prioritized road segments result in entire corridors being kept in good or fair condition (not leaving out certain segments and thus undermining the benefits of the improved segments). But also avoiding that orphan road segments are selected that are connected by other roads that are not yet in good or fair condition. The review of the prioritized road segments will also look how these may be combined into packages of treatments that may form an appropriate contract which will gain sufficient interest from contractors. For instance, a short segment of 1 km of road requiring a seal in a remote location of road will be difficult to contract out – it may be preferable to postpone the required seal and combine it with other road segments, or to bring other planned works forward. 135. In making the adjustments to the proposed treatments and prioritized roads, care should be taken not to deviate too much from the RAMS proposal, as this will likely have a negative impact on the future road network condition and reduce the benefits to road users and the economy in general. 4.2 Budget Request 136. The RAMS analysis will form the basis for the budget request for maintenance each year12. This will include both planned routine and periodic maintenance as well as unforeseen emergency maintenance needs. The budget process in Timor Leste runs roughly from March to December each year. The budget preparation by DRBFC is concentrated in May-June each year, after receiving the budget call circular from the Budget Office in May. The budget process is summarized below.
12 The RAMS may also be used to determine rehabilitation and reconstruction needs, but with almost all roads either recently improved or severely deteriorated and requiring rehabilitation or reconstruction, this is not a very important function of the RAMS at this stage.
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Budget process in Timor Leste The budget process in Timor Leste runs roughly from March to December each year. Ministries start preparing their Annual Action Plans defining key objectives and activities around March. In April, the Government decides its priorities and the total amount it wants to spend, known as the ‘fiscal envelope’. The Budget Office subsequently prepares a budget call circular in May, summarizing the Government’s strategy for the coming budget and outlining the budget process and timetable. The Ministries submit their budget submissions around the end of June. The Budget Review Committee meets in July and August to review each budget submission and invites ministries to defend their submissions. After all ministries have presented their submission, the Budget Review Committee forms a proposed budget for the full Council of Ministers to consider. By law, the Government must submit the proposed budget to the National Parliament by 15 October. After a brief initial consideration, the National Parliament refers the proposed budget to Committees (Commissions) for about one month. The Commissions review the budget, interview Ministers, and accept comments from the community and civil society, preparing a report to the Parliament on their findings. Then the Parliament debates the budget, including proposing amendments to the budget. After the budget is passed, the President of the Parliament sends it to the President of the Republic to consider and sign. The budget becomes law when it is finally promulgated in the Journal of the Republic on the order of the President of the Republic. Starting from January 1st in the following year, ministries can start spending money approved in the budget to implement the Government’s program for the year.
137. Most of the data collection for the RAMS will be carried out in the dry season from May/June to November/December. This allows the post-processing and data entry to take place in the first few months of the rainy season (December to February), with the data analysis taking place in March to determine the Annual Action Plan and define the key activities. This will be worked out further in April and May, adjusting the annual plan to the fiscal envelope for the year concerned. This is also the end of the rainy season, allowing a final assessment to be made of the damage caused during the rainy season which will need to be repaired and for which budget will need to be made available. 138. Because budgets only become available at the beginning of the rainy season, after which the procurement still needs to be carried out, there will be a considerable delay between the original data collection in the year before the budget preparation, and the actual start of contracts in the year after the budget preparation (roughly 2 years between data collection and the start of contracts). To account for this, it will be necessary to amend the maintenance treatments after the budget approval by Parliament, taking account of updated data as much as possible. Performance-based multiannual contracts should be used wherever possible, avoiding that procurement delays have a significant impact on maintenance coverage (for instance, having maintenance contracts end in the dry season, giving sufficient time after budget approval by Parliament for new contracts to be procured to start in the same dry season). 139. The Government of Timor Leste is currently preparing to introduce a Road Maintenance Fund that will be financed from government budget allocations and road user charges. This will provide more predictable funding, allowing the budget requests to be aligned more closely to the available funding. It will also allow for multiannual planning and contracting, spreading the required maintenance interventions over several years. 4.3 Annual Report 140. Apart from serving as a tool for the preparation of five-year plans and annual plans, the RAMS also serves an important function in monitoring the performance of the road network. The data contained in the RAMS can be used to prepare annual statistics for the road network. By doing this on an annual basis, the status and condition of the road network can be compared year by year, showing how the network develops over time. Publishing such road statistics is considered an important aspect of transparency for a road agency such as DRBFC. The RAMS allows such statistics to be prepared automatically. These may subsequently be published on the DRBFC website as well as in the DRBFC annual report. An example of possible statistics to be reported on is provided on the following page.
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Table 14 Road network statistics for 2019 SURFACE TYPE NATIONAL ROADS MUNICIPAL ROADS RURAL ROADS (CORE) Network length 1,448 km 34% 795 km 19% 1,975 km 47%
TECHNICAL CLASS NATIONAL ROADS MUNICIPAL ROADS RURAL ROADS (CORE) EW 30 km 2% - - - - R1 11 km 1% - - - - R2 ------R3 424 km 29% - - - - R5 4 km 0% 34 km 4% - - RR1 - - - - 552 km 28% Under construction 406 km 28% 123 km 15% 55 km 3% Underclass 573 km 40% 638 km 80% 1,368 km 69% Total 1,448 km 100% 795 km 100% 1,975 km 100%
SURFACE TYPE NATIONAL ROADS MUNICIPAL ROADS RURAL ROADS (CORE) Sealed (Bituminous / Concrete) 489 km 34% 28 km 3% 276 km 14% Unsealed (Stone Macadam / Gravel) 511 km 35% 453 km 57% 276 km 14% Unsealed (Earth) 43 km 3% 191 km 24% 1,368 km 69% Under construction 406 km 28% 123 km 15% 55 km 3% Total 1,448 km 100% 795 km 100% 1,975 km 100%
ROAD STATUS NATIONAL ROADS MUNICIPAL ROADS RURAL ROADS (CORE) Handed over 121 km 8% 14 km 2% 552 km 28% PBM 92 km 6% - - - - DNP 276 km 19% 21 km 3% - - Ongoing 406 km 28% 123 km 15% 55 km 3% Pending 553 km 38% 638 km 80% 1,368 km 69% Total 1,448 km 100% 795 km 100% 1,975 km 100%
ROAD ROUGHNESS NATIONAL ROADS MUNICIPAL ROADS RURAL ROADS (CORE) Good (IRI≤4) 160 km 15% 0 km 0% - Fair (4
SURFACE DISTRESS NATIONAL ROADS MUNICIPAL ROADS RURAL ROADS (CORE) Good (SDI≤2) 457 km 44% 28 km 4% 216 km 13% Fair (2
MAINTENANCE NEEDS NATIONAL ROADS MUNICIPAL ROADS RURAL ROADS (CORE) Routine $4,149,656 $3,759,986 $2,208,000 Periodic $829,931 $751,997 $7,710,750 Emergency $414,966 $375,999 $991,875 Total $5,394,553 $4,887,982 $10,910,625
MAINTENANCE BUDGET NATIONAL ROADS MUNICIPAL ROADS RURAL ROADS (CORE) Routine $1,764,350 43% $696,900 19% $2,355,700 107% Periodic - 0% - 0% $6,957,150 90% Emergency - 0% - 0% 0% Total $1,764,350 33% $696,900 14% $9,312,850 85%
MAINTENANCE COVERAGE NATIONAL ROADS MUNICIPAL ROADS RURAL ROADS (CORE) Routine 208.9 km 98% - 0% 306.1 km 55% Periodic - 0% - 0% 238.6 km 43% Emergency - 0% - 0% - 0% Total 208.9 km 98% - 0% 544.7 km 99%
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141. Apart from the general statistics, the RAMS also allows a number of so-called key performance indicators (KPIs) to be calculated. These KPIs are indications of the performance of the road network and its management by DRBFC. This includes indicators regarding the road status (technical class, road surface, rehabilitation status), regarding the collection of data (roughness, surface distress, travel speed, traffic), regarding the road condition (roughness, surface distress, travel speed, user satisfaction) and regarding the maintenance coverage of rehabilitated roads (all maintenance, periodic maintenance, maintenance budget). An example of the KPIs is provided below. As long as the required data is collected and entered into the RAMS, the KPIs can be prepared automatically. Table 15 Key Performance Indicators for 2019 NATIONAL MUNICIPAL RURAL ROADS ROAD NETWORK STATUS 2030 2030 2030 ROADS ROADS (CORE) KPI 1: Road length complying 469 km 32% 100% 34 km 4% 80% 552 km 28% 75% with technical standards KPI 2: Road length that is 489 km 34% 100% 28 km 3% 80% 276 km 14% 40% sealed KPI 3: Road length pending 553 km 38% <5% 638 km 80% <5% 1,368 km 69% 25% rehabilitation
NATIONAL MUNICIPAL RURAL ROADS ROAD NETWORK DATA 2030 2030 2030 ROADS ROADS (CORE) KPI 4: Road length with up- 1,074 km 74% 100% 88 km 11% 80% - 0% 50% to-date roughness data KPI 5: Road length with up- - 0% 100% - 0% 80% - 0% 50% to-date surface distress data KPI 6: Road length with up- 891 km 62% 100% 67 km 8% 80% - 0% 50% to-date travel speed data KPI 7: Road length with up- - 0% 100% - 0% 80% - 0% 50% to-date traffic data
NATIONAL MUNICIPAL RURAL ROADS ROAD NETWORK CONDITION 2030 2030 2030 ROADS ROADS (CORE) KPI 8a: Road length in 307 km 21% 80% 1 km 0% 60% - 0% 50% good/fair condition (IRI ≤ 6) KPI 8b: Road length in 566 km 32% 80% 51 km 3% 60% 661 km 33% 50% good/fair condition (SDI ≤ 3) KPI 9a: Road length in bad 546 km 38% <5% 67 km 8% <10% - 0% <25% condition (IRI > 10) KPI 9b: Road length in bad 86 km 33% <5% - 76% <10% 1,014 km 51% <25% condition (SDI > 4) KPI 10: Average speed (km/h) 55 45 30 38 km/h 34 km/h N/A of network km/h km/h km/h KPI 11: User satisfaction N/A 4.5 N/A 4.0 N/A 3.5 score (1-7)
NATIONAL MUNICIPAL RURAL ROADS MAINTENANCE COVERAGE 2030 2030 2030 ROADS ROADS (CORE) KPI 12: Rehabilitated road 209 km 98% 100% - 0% 90% 545 km 99% 90% length under maintenance KPI 13: Rehabilitated road - 0% 15% - 0% 10% 239 km 43% 15% under periodic maintenance KPI 14: Maintenance budget $1.8 $0.7 $9.3 33% 75% 14% 75% 85% 75% compared to needs million million million 142. The DRBFC Annual Report should be prepared each year, within 6 months after the end of the year. The report should contain at least the road network statistics and key performance indicators prepared using the RAMS, but may also include more information regarding DRBFC and the road network (e.g. on staffing, contracting, etc.). The type and number of statistics and KPIs may be expanded on over time.
31 Road Asset Management Plan RAMS Unit RAMS Unit 143. DRBFC will require a specific RAMS Unit to coordinate the data collection, to manage the data validation and entry as well as the operation of the RAMS and related mapping, and to carry out the data analysis as a basis for (multi)annual planning. Such a RAMS Unit may be created by expanding the current GIS unit13 under the Projects Department of DRBFC, which is already involved in similar activities. 144. The RAMS unit is expected to need a minimum of 4 full-time staff (preferably these 4 full-time staff would be supported by one or two lower-level technical staff). Each staff member will be responsible for one or more of the activities listed below. For instance, one staff member may be responsible full-time for data validation and management, while another may be mainly responsible for data collection and coordination, supporting the data validation and management when required (especially during the rainy season). As such, each of the 4 main activities of the RAMS unit will be headed by one of the 4 staff members, but each staff member will also provide support to one or more other activities as required. The RAMS unit will be headed by a unit head, who will be one of the 4 full-time staff and will be responsible for one of the activities listed below (in principle the data analysis and planning, being the main output of the RAMS). • Data collection and coordination • Data validation and management • GIS data management and mapping • Data analysis and planning (unit head) Figure 16 Proposed structure of the RAMS Unit RAMS Unit
Data Collection & Data Validation & GIS & Data Analysis & Coordination Management Mapping Planning
5.1 Data Collection 145. The RAMS unit will be responsible for coordinating the collection of data. Part of the data collection may be done directly by the unit, while the collection of other data may be outsourced to the private sector or obtained from other units and entities. 146. For the data collection, the RAMS Unit will need at least one person dedicated primarily to data collection, as well as a driver (the driver may be shared with other DRBFC units). This person would preferably be supported by the person responsible for data management. These two staff members would be responsible for carrying out the drive-over surveys and subsequent post-processing, as well as carrying out the traffic counts using the fixed and portable traffic counting stations. They would also be responsible for coordinating the collection of the structure data to be contracted out to the private sector. Lastly, they would be responsible for coordinating with other units within DRBFC and with other entities regarding the collection of any secondary data. In the future they may be assisted by the staff in the DRBFC regional offices. 147. During the dry season they would work on data collection and post-processing on a more or less full- time basis, but in the rainy season they may spend more time on other activities (e.g. data validation and entry). It is expected that one staff member will work more or less full-time on data collection, while the other would have a more supportive role, dedicating a larger portion of time to other activities. 5.2 Data Management 148. The RAMS unit will be ultimately responsible for all data validation and data entry into the RAMS, as well as managing the RAMS itself. Part of the preparatory work for the data processing may be contracted
13 The GIS unit currently has two full-time staff and one full-time TA consultant.
32 Road Asset Management Plan RAMS Unit out, but the final data validation and entry should be carried out by the staff of the RAMS unit. This includes both the alphanumerical data collected during surveys and from secondary sources, but also the GPS and GIS data for the mapping. Ensuring proper linkage between the collected data and the different road segments will be an important function. 149. The RAMS unit will need at least one staff member dedicated primarily to data validation and entry, supported by at least one other staff member as required (in periods in which large amounts of data need to be processed). It is recommended that this be the person responsible for data collection, allowing these two staff members to work together in both data collection and data management. 150. The RAMS unit will also need at least one staff member to manage the GIS data and to carry out the related mapping. This staff member should be dedicated full-time to the GIS mapping, but may be called on to assist other members of the RAMS Unit as required. 151. The data validation and data entry will be especially important at the end of the dry season and the beginning of the rainy season, ensuring that all collected data is available in the RAMS at the beginning of the planning process in March. Throughout the planning process, the GIS specialists will also be involved in preparing the necessary maps in support of the planning process and the budget request. 5.3 Data Analysis 152. The RAMS unit will also be responsible for the data analysis and the preparation of the related inputs for the planning. This will be carried out directly by the RAMS unit staff and will result in a list of prioritized roads and related treatments along with the necessary maps, which will form the basis for the five-year plan or annual plan and related budget request. The actual finalization of the plan will involve other units within DRBFC (e.g. the Maintenance Department and Projects Department). 153. The RAMS unit will need at least one staff member dedicated to the data analysis and the preparation of the necessary maps. It is further recommended that the head of the RAMS unit also be responsible for the data analysis, as this is the main output of the RAMS unit. It is further recommended that this person be able to use the GIS software as part of the process of data analysis, supporting the processing of GPS and GIS data as required. 154. The data analysis will be especially important in the second quarter of the year, as the plans and budget requests need to be prepared and justification needs to be provided to the Council of Ministers and to the Parliament. 5.4 Resources 155. The RAMS unit will be directly responsible for managing the ROMDAS survey vehicle and the traffic counting stations. The RAMS unit will furthermore need access to a second general vehicle to travel around the country for the traffic counting stations and other data collection (this vehicle may be shared with other units under DRBFC). The RAMS unit should be housed in a suitable office with sufficient space. The current office of the GIS unit will be sufficient initially. The different staff members of the RAMS unit will need high- end computers with large computer screens for post-processing the collected data, for data validation, for managing the RAMS and for analysing the data and preparing the necessary GIS maps. The RAMS unit will also need colour printers and large colour plotters to print the reports and maps. The RAMS itself will be run in the Cloud, but should be cached on a server located either in the RAMS unit office or elsewhere in the DRBFC office. In order to function properly, the internet and intranet LAN connections within DRBFC will need to be significantly improved. 156. The RAMS unit will need an annual budget allocation to allow it to operate. This will primarily cover the costs of data collection and the related surveys. This will be a relatively steady annual allocation covering surveys of a portion of the road network each year (mainly per diems and fuel costs). The cost of the annual condition surveys using drive-over surveys is estimated to be in the range of $20,000 to cover surveys of up to 2,000 km of roads over a period of up to 60 days in the field (excluding staff salaries). 157. Every 5 years an additional budget allocation will be required to cover the costs of the inventory data collection together with the wider survey of the road structures carried out by consultants. This will cover
33 Road Asset Management Plan RAMS Unit the entire road network and will include the different structures. The cost is estimated to be in the range of $100,000-$200,000 for the entire network. The exact cost will depend on the type of data collected and how much of the existing data is up-to-date and only needs to be checked, and how much new data will needs to be collected and processed (either due to missing data or as a result of road improvements). 158. Each year some equipment maintenance and replacement may also be needed (tyres, general vehicle maintenance, replacement of survey equipment). This will need to form part of the annual budget request. The cost is estimated to be in the range of $5,000 per year, with an additional $25,000-$50,000 every 5 years or so to replace survey equipment (especially the ROMDAS equipment and related vehicle). 159. Apart from the costs of the data collection, the RAMS Unit will need funds to cover the operation of the RAMS and the related data management and data analysis. This will mainly involve costs of ink and paper for the printers and plotters, but may also include the replacement of equipment and other office supplies. It is estimated that this cost will average approximately $5,000 per year. 160. On average, between $50,000 and $75,000 will be required each year for data collection. This amount is not evenly spread, however, with peaks every 5 years or so to cover inventory updates and equipment replacement. This does not include the salary costs of the RAMS unit staff involved in data collection, processing and analysis. 5.5 Technical Support 161. The RAMS unit and DRBFC will require technical support for the first 5 years at least. This will include general support for the management of the database and for introducing adjustments to the structure as required. In part this may be provided by the IT specialist of DRBFC, with support from the IT unit under MPW (training is recommended for the IT staff of DRBFC and MPW to be able to provide the necessary support). However, the consultant responsible for developing the RAMS should continue to be involved in ongoing support for a number of years, preferably having one person working directly with the RAMS Unit on a part- time basis. The consultant is based in Timor Leste and would be able to provide such continued support. This may be for a minimum number of persondays per year or covering certain activities under a lumpsum. Additional support requirements may be paid on a needs basis. 162. Apart from the general support to the RAMS, DRBFC and the RAMS unit will also need support in completing the data collection for the entire road network. The World Bank is planning to provide support in collecting the data for the national and municipal roads under the Branch Roads Project. The R4D programme is planning to update the Rural Road Master Plan in 2020, which will involve the surveying of the core rural road network. This initial data collection will cover a large portion of the data collection needs. It is expected that the consultants will also support the RAMS unit in the data validation and entry, forming an on-the-job training. Further support to the RAMS unit will be required as they become responsible for the annual data collection. 163. Based on the data collection and entry into the RAMS, the RAMS may be used to prepare the maintenance programs for subsequent years. It is recommended to ensure that there will be consultant support to work together with the RAMS unit during at least 3 consecutive years to carry out the data analysis and prepare the annual road maintenance plans. This should culminate with the preparation of the next Five- Year Plan for road maintenance for the period 2024-2028. This may also provide support to the preparation of the FYP for road rehabilitation and upgrading works.
34 Road Asset Management Plan Road Asset Management Plan Road Asset Management Plan 164. This chapter gives an overview of the actions to be undertaken in the coming 5 years, defining the timeline for completing the different steps of the RAMS development and its integration into DRBFC procedures, and identifying the responsibilities of the different actors. This chapter also looks at the resources and financing required and how these will be provided, gradually shifting from initial support from development partners to full implementation by government with government financing. An overview of the Road Asset Management Plan including the different steps, responsibilities and resources is provided in Table 16. 6.1 Data collection 165. The limited road network data currently available is severely outdated. In order to make the RAMS a useful planning tool, the collection of data will be a priority. World Bank have indicated that they will support the initial data collection regarding road inventory and condition data (excluding specific structure data) as well as traffic data for the national and municipal roads under the upcoming Branch Roads Project (approximately 2,250 km – data collection tentatively planned for 2020). R4D is furthermore planning to update the rural road data in 2020 as part of an update to the Rural Road Master Plan (approximately 1,975 km). This will allow a large portion of the required data to be collected and entered into the RAMS in 2020. 166. It is further recommended that the upcoming ADB multiannual programme for the road sector will support the initial data collection for the bridges in the national and municipal road network, as well as support DRBFC in collecting the project and contract data for the different road networks, thus allowing the RAMS data to be completed. The ADB programme will also provide further training and technical support to the RAMS Unit over a number of years as it becomes responsible for the annual condition data collection. This will also include support in the collection of inventory data for recently improved roads and the updating of traffic data for important roads in 2023 in preparation for the 2024-2028 Five-Year Plan. 167. MPW already has a ROMDAS vehicle that may be used for the data collection. An additional video camera and the post-processing software will likely be procured under the current ADB technical assistance. R4D is planning to develop the necessary smartphone applications to complement the data collection as part of the ongoing RAMS development. The World Bank is planning to provide portable traffic counting equipment to support the collection of traffic data. Further equipment support may be required from development partners in the form of smartphones for use in data collection (for DRBFC as well as for the municipal Road Departments). 168. DRBFC through its RAMS Unit will become increasingly responsible for annual updates to the data, with surveyed road lengths to be increased each year. This will require annual budget allocations to cover the survey costs, as well as the costs of maintaining and replacing the survey equipment. Initially this may be partially supported by development partners, but by 2024 DRBFC should be in a position to fully carry out the annual surveys with only minimal technical support from development partners and with financing to be provided from the General State Budget (OGE) or from the proposed Road Maintenance Fund (RMF). 6.2 Data management 169. The R4D programme is currently supporting the development of the RAMS that will be used to manage the data. This process is expected to be completed in the course of 2020. The data to be collected by World Bank and the R4D programme will need to be validated and entered into the RAMS. This will be done by staff from the RAMS Unit, with technical support from World Bank and R4D consultants. By the end of 2020 there should be a functional RAMS with most of the required data collected and entered. 170. To ensure that the RAMS continues to operate as planned, the ADB programme will subsequently provide technical support to the RAMS Unit in processing the data collected in subsequent years. This will include continued training of RAMS Unit staff and local consultants, as well as on-the-job training. This technical support will also include any additional changes and amendments to be made to the RAMS itself. It is recommended that one of the original developers of the RAMS be engaged for an extended period (on a part-time basis) under the ADB programme to support the RAMS unit in the daily operation of the RAMS and in the processing and management of the collected data.
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171. It is expected that by 2024 the RAMS Unit and other staff in DRBFC will have acquired sufficient experience in operating the RAMS, that they may do so without further technical assistance. Any problems encountered or changes required after 2024 will need to be contracted specifically by DRBFC, using the annual budget allocations to the RAMS Unit. 6.3 Data analysis and planning 172. The main purpose of the RAMS is to support the preparation of the Five-Year Plans and the annual plans and related budget requests. For this purpose, the RAMS will include a planning module that will allow the maintenance needs to be identified, as well as allowing roads and related treatments to be prioritized. Data from the RAMS may also be exported for use in other planning software such as HDM-4. 173. Based on the data collection for national and municipal roads by World Bank, an initial analysis will be carried out using HDM-4. This will involve a strategy analysis that will allow optimum intervention strategies to be identified for different budget scenarios. This may be compared with the results of the RAMS planning module, allowing the latter to be calibrated if necessary. This initial strategy analysis may also form the basis for negotiations with the Ministry of Finance and Parliament regarding actual maintenance needs and the impacts of different funding levels on future road conditions. The initial strategy analysis will be supported by World Bank and is planned to be carried out in 2020 after the data collection has been completed. 174. Apart from a strategy analysis that looks at general budget requirements and allocations to the network, there will be a need to develop detailed maintenance programmes that identify the priority road segments and required treatments that can be carried out for a specific budget. This will be prepared using the RAMS planning module or by exporting the RAMS data to other planning software such as HDM-4. This process will likely commence in 2021 after all the collected data has been processed and entered into the RAMS. ADB will support the RAMS Unit in carrying out this analysis, providing training and technical assistance. This support will be continued for a number of years, allowing the RAMS Unit to go through the process several times. 175. The maintenance programme prepared using the RAMS will form the basis for the annual road maintenance plans and related budget requests for 2022 and subsequent years. In 2023 a more extensive data collection will be carried out to bring the inventory, condition and traffic data up-to-date, allowing a more thorough analysis to take place as the basis for the preparation of the next Five-Year Plan 2024-2028. The ADB programme will continue to support the RAMS unit in the data analysis and planning up to 2024, assisting in the preparation of the annual plans for 2022, 2023 and 2024, as well as the preparation of the FYP 2024-2028. After this the technical assistance will be scaled down. 176. The preparation of the annual and five-year plans will take account of general government planning procedures and timing. Where data collection will largely take place in the dry season, data processing and analysis will be carried out at the start of the rainy season, with preliminary results available early in the calendar year. These may then form the basis for the preparation of the annual plans that generally starts in May each year. 177. The RAMS will also be used to prepare annual reports on road network statistics and key performance indicators (KPIs). The RAMS will include programming that allows the statistics and indicators to be calculated automatically and included in an Annual Report to be issued by DRBFC. A first version of the DRBFC Annual Report will be prepared for the year 2020 using the data collected in that year. This Annual Report will be published in the first six months of 2021. 6.4 RAMS Unit 178. The proper functioning of the RAMS, including the coordination and implementation of data collection, data management and data analysis, will require a dedicated RAMS Unit to be formed. This will need to be staffed by a minimum of 4 persons and a driver (the driver may be shared with other DRBFC units). Each of the 4 staff members will head one of the main areas of the RAMS (data collection, data management, GIS mapping, data analysis), but each area will also be supported by the other members of the RAMS unit as the focus of activities shifts from data collection to data processing to data analysis in the course of the year. 179. With RAMS development currently ongoing and data collection planned to start next year, it is important that this RAMS Unit be created and staffed as soon as possible. The RAMS Unit may be created by
36 Road Asset Management Plan Road Asset Management Plan transforming the current GIS Unit under the Projects Department and increasing its staff numbers. The RAMS unit should be formed and staffed in 2020, allowing staff to benefit from on-the-job training regarding the RAMS and the planned data collection by World Bank and R4D consultants. Although the formal creation of the RAMS Unit through a Ministerial Decree may take place at a later date, the staff members that will work in the RAMS Unit should be identified as soon as possible. These should be technical staff that will ultimately be involved in the operation of the RAMS on a full-time basis. As such they are different from the Working Group members that are currently coordinating the development of the RAMS on a part-time basis. 180. The RAMS Unit will require an annual budget allocation to cover the costs of operating the RAMS. This will mainly be used to cover the costs of data collection. Initial collection of road inventory, condition and traffic data will be carried out in 2020 with World Bank and R4D support, complemented by bridge data collection in 2021 with ADB support. The RAMS Unit will become responsible for the collection of road condition data only in 2021. This will start with a portion of the national road network (approximately 500 km) and gradually grow to cover approximately half the improved roads each year (approximately 1,500 km by 2024, further increasing over time as more roads are improved). The costs of the data collection are estimated to be $10,000 per year in 2021, increasing to $20,000 per year by 2024. In 2023 it is recommended to update some of the inventory and traffic data as a basis for the preparation of the new FYP 2024-2028. This will entail additional costs in the order of $25,000, focusing on the newly improved roads that were not covered in the 2020 inventory survey. 181. The RAMS Unit will also require budget allocation to cover the operational costs of the unit, especially the ink and paper costs for the different printers and plotters required to prepare plans and maps for other DRBFC units. The RAMS unit will also be responsible for the maintenance and replacement of the survey equipment, especially the ROMDAS vehicle (tyres, repairs, etc.). These different costs for supplies and equipment are estimated to add up to $10,000 per year, but may increase significantly if any equipment replacement is required. The total costs of operating the RAMS unit (including data collection) will likely be in the order of $20,000 in 2021, increasing to $30,000 in 2024. The costs will add up to $55,000 in 2023 as additional data collection is planned for this year. These costs do not include any replacement of survey equipment. 182. The RAMS Unit will further require training in the operation of the RAMS and the different activities of data collection, data management and data analysis and planning. Initial support will be provided by World Bank and R4D in 2020, focusing on data collection and processing. Further support will be provided by the ADB programme from 2021 to 2024. This will involve a small group of consultants supporting the RAMS Unit on a more or less full-time basis, with specific attention given to the preparation of the data collection plan, the processing of the collected data, the analysis of the data in the RAMS, and the preparation of the annual and five-year plans. Regarding the plans, the support will be expanded to include the wider Projects Department and Maintenance Department that are responsible for preparing these plans. This support will be continued for at least three years, covering the preparation of three annual plans and one five-year plan. From 2024 onwards the technical support will be scaled down and DRBFC will become fully responsible for operating the RAMS and preparing the plans.
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Table 16 Road Asset Management Plan 2020 2021 2022 2023 2024 • Road data (inventory, • Bridge data collected • Road condition data • Road + bridge inventory • Road condition data Data condition and traffic) (inventory and condition) collected for national and data collected for improved collected for national, collection collected for all national and for all national and municipal roads road segments municipal and rural roads municipal roads municipal roads (1,000km/$15,000/DRBFC) (500km/$20,000/DRBFC) (1,500km/$20,000/DRBFC) (2,250km/$400,000/WB) ($50,000/ADB) • Traffic data collected for • Road data (inventory, • Road condition data important road links condition and traffic) collected for national roads (500km/$5,000/DRBFC) collected for all core rural (500km/$10,000/DRBFC) • Road condition data roads collected for national, (1,975km/R4D) municipal and rural roads (1,500km/$20,000/DRBFC) • Data processed and entered • Data processed and entered • Data processed and entered • Data processed and entered • Data processed and entered Data into RAMS (DRBFC with into RAMS (DRBFC with ADB into RAMS (DRBFC with ADB into RAMS into RAMS (DRBFC) management WB/R4D support) support) support) (DRBFC with ADB support) • Initial data analysis for • RAMS used as basis for 2022 • RAMS used as basis for 2023 • RAMS used in preparation • RAMS used as basis for 2025 Data analysis national and municipal budget request budget request of FYP 2024-2028 budget request (DRBFC) and planning roads (DRBFC with ADB support) (DRBFC with ADB support) (DRBFC with ADB support) • Publish Annual Report 2023 (WB using HDM-4) • Publish Annual Report 2020 • Publish Annual Report 2021 • RAMS used as basis for 2024 (DRBFC) • Data analysis for updating (DRBFC) (DRBFC) budget request Rural Road Master Plan (DRBFC with ADB support) (R4D) • Publish Annual Report 2022 (DRBFC) • RAMS Unit created and • Funding allocated to RAMS • Funding allocated to RAMS • Funding allocated to RAMS • Funding allocated to RAMS RAMS Unit staffed (DRBFC) Unit ($20,000 OGE or RMF) Unit ($25,000 OGE or RMF) Unit ($55,000 OGE or RMF) Unit ($30,000 OGE or RMF) • RAMS unit trained in data • On-the-job training RAMS • On-the-job training of RAMS • On-the-job training of RAMS collection and processing unit in data collection and unit and PD+MD in RAMS unit and PD+MD in RAMS (WB/R4D) processing (ADB) operation (ADB) operation (ADB) • RAMS unit trained in data • Training of RAMS unit and analysis (ADB) PD+MD in FYP preparation • PD+MD trained in planning (ADB) using RAMS (ADB) PD=Projects Department, MD=Maintenance Department, FYP=Five-Year Plan, WB=World Bank, R4D=Roads for Development, ADB=Asian Development Bank, OGE=General State Budget, RMF=Road Maintenance Fund
38 Road Asset Management Plan Appendices Appendix A Data Types, Sources and Collection Frequencies
ROADS TYPE UNIT SOURCE REMARKS FREQUENCY Administrative class Link Category DRBFC data, legal documents A, C, D, E 5 years Management entity Link Category DRBFC data, legal documents MPW/DRBFC, Municipality, Private 5 years Municipality Segment Category GIS administrative boundary data Municipality list 5 years Administrative Post Segment Category GIS administrative boundary data Administrative Post list 5 years Suco Segment Category GIS administrative boundary data Suco list 5 years Road code Link X## DRBFC data, legal documents Existing codes 5 years Road name Link Text DRBFC data, legal documents 5 years Link code Link X##-## DRBFC data, legal documents Road code-two digit link number 5 years Link name Link Text DRBFC data, legal documents 5 years Start name Link Text DRBFC data, legal documents 5 years Start chainage Link #+### m ROMDAS odometer survey 5 years / After project Start GPS coordinate Link X,Y,Z ROMDAS GPS survey 5 years / After project End name Link Text DRBFC data, legal documents 5 years End chainage Link #+### m ROMDAS odometer survey 5 years / After project End GPS coordinate Link X,Y,Z ROMDAS GPS survey 5 years / After project GPS track Link X,Y,Z ROMDAS GPS survey 5 years / After project Link length Link km (m) ROMDAS odometer survey 5 years / After project Terrain class Segment Category ROMDAS video data post-processing Flat, Rolling, Mountainous 5 years Rainfall class Segment Category Rainfall maps <1000mm, 1000-2000mm, >2000mm 5 years Technical class Segment Category DRBFC data R1,R2,R3,R4,R5,RR1,RR2, underclass 1-2 years / After project Surface type Segment Category ROMDAS video data post-processing, contract data AC,PM,ST,CC,SM,GR,ER 1-2 years / After project Pavement Class Segment Category ROMDAS video data post-processing, contract data Sealed, Unsealed 1-2 years / After project Carriageway width Segment m ROMDAS video data post-processing, contract data 1-2 years / After project Number of lanes Segment # ROMDAS video data post-processing, contract data 1-2 years / After project Video data Link Video/GPS ROMDAS video survey .xls/.mp4 1-2 years Roughness 100m IRI ROMDAS profilometer / ROMDAS bump integrator For network analysis 1-2 years Roughness survey date 100m Date ROMDAS profilometer / ROMDAS bump integrator 1-2 years Surface distress class Segment SDI ROMDAS video data post-processing For network analysis 1-2 years Surface survey date Segment Date ROMDAS video data post-processing 1-2 years Last treatment Segment Year DRBFC data 1-2 years / After project Last treatment Segment Contract DRBFC data Link to contract database 1-2 years / After project Five Year Plan Segment Year Five Year Plan Year of planned works 5 years BRIDGES TYPE UNIT SOURCE REMARKS FREQUENCY Bridge code Point X##-B## Appoint Based on road code+B+two-digit code 5 years / After project Bridge name Point Text DRBFC data 5 years / After project River name Point Text DRBFC data 5 years / After project GPS location Point X,Y,Z Bridge survey / ROMDAS video post-processing Start of the bridge 5 years / After project Chainage Point #+### m Bridge survey / ROMDAS video post-processing Start of the bridge 5 years / After project
39 Road Asset Management Plan Appendices
Bridge type Point Category Bridge survey / ROMDAS video post-processing Beam,Arch,Truss,Suspension,Cable,Other 5 years / After project Deck material Point Category Bridge survey / ROMDAS video post-processing Concrete, timber, steel 5 years / After project Bridge length Point m Bridge survey / ROMDAS video post-processing 5 years / After project Bridge width Point m Bridge survey / ROMDAS video post-processing 5 years / After project Bridge spans Point # Bridge survey / ROMDAS video post-processing 5 years / After project Upstream protection Point Category Bridge survey None, Concrete, Stone masonry, Gabion 5 years / After project Downstream protection Point Category Bridge survey None, Concrete, Stone masonry, Gabion 5 years / After project Construction year Point Year DRBFC data 5 years / After project Photographs Point Photos Bridge survey 5 years / After project Bridge structural condition Point Category Bridge survey Good,Fair,Poor,Very Poor 5 years / After project Condition description Point Text Bridge survey 5 years / After project CULVERTS TYPE UNIT SOURCE REMARKS FREQUENCY Culvert code Point X##-C### Appoint Based on road code+C+three-digit code 5 years / After project GPS location Point X,Y,Z Culvert survey 5 years / After project Chainage Point #+### m Culvert survey 5 years / After project Culvert type Point Category Culvert survey Slab, Box, Pipe 5 years / After project Number of cells Point # Culvert survey 5 years / After project Culvert width Point m Culvert survey Diameter for pipe culverts 5 years / After project Culvert height Point m Culvert survey Diameter for pipe culverts 5 years / After project Photographs Point Photos Culvert survey 5 years / After project Culvert structural condition Point Category Culvert survey Good,Fair,Poor,Bad 5 years / After project Condition description Point Text Culvert survey 5 years / After project DAMAGES TYPE UNIT SOURCE REMARKS FREQUENCY Damage GPS location Point X,Y,Z Condition survey / ROMDAS video post-processing As encountered,1-2 years Damaged element Point Category Condition survey / ROMDAS video post-processing Pavement, shoulder, bridge, culvert, etc. As encountered,1-2 years Damage description Point text Condition survey / ROMDAS video post-processing As encountered,1-2 years Photographs Point Photos Condition survey / ROMDAS video post-processing As encountered,1-2 years TRAFFIC TYPE UNIT SOURCE REMARKS FREQUENCY GPS location Point X,Y,Z Traffic counts / Moving traffic counts 2-5 years Link code Link X##-## Traffic counts / Moving traffic counts Link for which traffic is measured 2-5 years Survey date Point Date Traffic counts / Moving traffic counts 2-5 years Survey type Link Category Traffic counts / Moving traffic counts 7-day, 3-day, 1-day, moving traffic count 2-5 years Traffic class Link Category Traffic counts / Moving traffic counts <20,21-200,201-500,501-1000,1000-5000,>5000 2-5 years Annual Daily Traffic (ADT) Link Category Traffic counts / Moving traffic counts 2-5 years Motorcycle Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years Passenger car Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years Pickup/4WD (4 wheels) Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years Mini-bus/ (4 wheels) Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years Two-axle bus (6 wheels) Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years Multi-axle bus (>6 wheels) Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years Van/light truck (4 wheels) Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years
40 Road Asset Management Plan Appendices
Two-axle truck (6 wheels) Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years Multi-axle truck (>6 wheels) Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years Articulated truck Link ADT/AADT Traffic counts / Moving traffic counts 2-5 years ACCIDENTS TYPE UNIT SOURCE REMARKS FREQUENCY GPS location Point X,Y,Z Accident data Responsibility of MTC + Police Every year Accident date Point dd/mm/yyyy Accident data Responsibility of MTC + Police Every year Number of fatalities Point Number Accident data Responsibility of MTC + Police Every year Number of serious injuries Point Number Accident data Responsibility of MTC + Police Every year PROJECTS TYPE UNIT SOURCE REMARKS FREQUENCY Strategic Plan Segment Category Strategic plans List of strategic plans Once for each project Project code Segment Code Automated number according to standard Standardized code Once for each project Road code Segment X## Project survey data Once for each project Road name Segment Text Automated entry according to road code Once for each project Segment start name Segment Text Project survey data Once for each project Segment start chainage Segment #+### m Project survey data Once for each project Segment start coordinates Segment X,Y,Z Project survey data Once for each project Segment end name Segment Text Project survey data Once for each project Segment end chainage Segment #+### m Project survey data Once for each project Segment end coordinates Segment X,Y,Z Project survey data Once for each project Bridge code Point X##-B## Project survey data Once for each project Bridge name Point Text Automated entry according to bridge code Once for each project Bridge coordinates Point X,Y,Z Automated entry according to bridge code Once for each project Treatment type Segment Category Project survey data List of standard treatments Once for each project Project description Segment Text Project survey data Once for each project Planned start date Segment dd/mm/yyyy Project survey data Once for each project Planned end date Segment dd/mm/yyyy Project survey data Once for each project Estimated cost Segment $ Project survey data Once for each project Planned funding source Segment Category Project survey data List of funding sources Once for each project Attached documents Segment Attachment Project survey data Different document types Once for each project CONTRACTS TYPE UNIT SOURCE REMARKS FREQUENCY Project code Segment Code Automated number according to standard Once for each contract Contract number Segment Code Automated number according to standard Standardized code Once for each contract Road code Segment X## Copied from project data - adjustable Once for each contract Road name Segment Text Automated entry according to road code Once for each contract Segment start name Segment Text Copied from project data - adjustable Once for each contract Segment start chainage Segment #+### m Copied from project data - adjustable Once for each contract Segment start coordinates Segment X,Y,Z Copied from project data - adjustable Once for each contract Segment end name Segment Text Copied from project data - adjustable Once for each contract Segment end chainage Segment #+### m Copied from project data - adjustable Once for each contract Segment end coordinates Segment X,Y,Z Copied from project data - adjustable Once for each contract Bridge code Point X##-B## Copied from project data - adjustable Once for each contract
41 Road Asset Management Plan Appendices
Bridge name Point Text Automated entry according to bridge code Once for each contract Bridge coordinates Point X,Y,Z Automated entry according to bridge code Once for each contract Treatment type Segment Category Copied from project data - adjustable List of standard treatments Once for each contract Project description Segment Text Copied from project data - adjustable Once for each contract Contract start date Segment dd/mm/yyyy Contract data Once for each contract Contract end date Segment dd/mm/yyyy Contract data Once for each contract Contract price Segment $ Contract data Once for each contract Contract funding source Segment Category Contract data List of funding sources Once for each contract Attached documents Segment Attachment Contract data Different document types Once for each contract Contractor number Segment ### Contract data List of registered contractors Once for each contract Contractor name Segment Text Contract data Once for each contract
42 Road Asset Management Plan Appendices Appendix B Road Network Lengths Table 17 Length of national road links ROAD LINK RGDS TSMP GIS GPS FYP FROM - TO CODE CODE 2010 2015 DATA TRACKS 2019-2023 A01-1 Dili-Manatuto 58.7 63.7 63.0 62.41 A01-2 Manatuto-Baucau 57.3 59.3 60.0 59.44 A01 202.90 A01-3 Baucau-Lautem 59.8 59.9 60.1 59.58 A01-4 Lautem-Com 20.3 19.0 21.1 20.89 A02-1 Dili-Aileu 43.7 47.4 45.3 45.77 A02-2 Aileu-Maubisse 25.0 26.0 26.1 25.84 A02-3 Maubisse-Aituto 13.1 12.3 13.3 13.14 A02 A02-4 Aituto-Ainaro 26.3 27.3 27.9 27.61 178.30 A02-5 Ainaro-Cassa 21.1 22.3 19.8 19.63 A02-6 Cassa-Zumalai 17.0 17.5 17.2 17.01 A02-7 Zumalai-Suai 29.8 32.0 30.1 29.80 A03-1 Dili-Tibar 7.2 12.9 13.8 12.89 A03-2 Tibar-Liquica 20.0 22.2 19.4 19.21 118.30 A03 A03-3 Liquica-Batugade 75.9 76.3 79.1 62.13 A03-4 Batugade-Mota'ain 3.3 3.0 3.0 2.99 A03-5 Batugade-Maliana 41.3 45.1 41.9 41.44 35.80 A04-1 Tibar-Gleno 33.6 33.0 33.6 33.29 A04 45.00 A04-2 Gleno-Ermera 11.9 12.4 11.9 11.79 A05-1 Aituto-Betulala 10.8 13.0 29.7 29.45 A05 A05-2 Betulala-Same 18.6 17.0 53.60 A05-3 Same-Betano 25.2 27.2 24.6 22.51 A06-1 Baucau-Venilale 23.9 30.8 27.4 27.19 A06 63.10 A06-2 Venilale-Viqueque 33.4 33.4 35.9 35.63 A07 A07-1 Viqueque-Natabora 43.0 47.0 45.9 45.52 48.80 A08-1 Viqueque-Uatucarbau 65.0 57.1 63.0 62.53 A08-2 Uatucarbau-Iliomar 16.3 25.1 21.1 16.25 A08 153.90 A08-3 Iliomar-Lospalos 45.0 45.4 46.7 46.35 A08-4 Lospalos-Lautem 27.9 27.4 28.4 28.25 A09-1 Manatuto-Cribas 22.3 20.0 22.4 22.22 A09-2 Cribas-Laclubar 13.1 17.9 13.2 13.07 A09 85.20 A09-3 Laclubar-Mane Hat 29.7 35.9 29.8 29.52 A09-3 Mane Hat-Natabora 15.7 10.8 15.7 15.58 A10 A10-1 Gleno-Lourba 76.0 63.9 68.8 68.15 68.50 A11 A11-1 Ermera-Maliana 64.0 55.8 63.1 61.29 62.50 A12-1 Malania-Oeleu 15.5 16.4 15.5 15.35 A12 A12-2 Oeleu-Lourba 9.8 18.4 9.9 9.83 50.90 A12-3 Lourba-Zumalai 26.1 26.9 25.9 25.69 A13-1 Aiassa-Hate Udo 17.0 17.0 17.1 16.94 A13 24.60 A13-2 Hato Udo-Cassa 6.6 8.0 7.5 7.43 A14-1 Natabora-Alas 37.7 37.7 19.0 18.80 A14 48.50 A14-2 Alas-Betano 8.6 9.1 27.7 29.33 A15-1 Suai-Tilomar 12.5 12.2 12.6 13.04 A15 28.00 A15-2 Tilomar-Uemassa 14.8 15.0 15.0 14.25 A16-1 Oeleu-Fatululic 28.7 33.0 A16 77.4 75.32 68.70 A16-2 Fatululic-Tilomar (A15) 48.0 46.7 A17 A17-1 Pante Macassar-Oesilo 28.0 27.6 29.1 28.77 28.30 A18 A18-1 Pante Macassar-Citrana 45.0 40.5 44.5 43.53 47.50 A19 A19-1 Pante Macassar-Sacato 15.0 12.4 14.5 14.32 14.32
43 Road Asset Management Plan Appendices
Table 18 Length of municipal road links GIS GPS FYP ROAD LINK FROM - TO RGDS 2010 RRMP 2015 DATA TRACKS 2019-2023 C01 C01-1 Lospalos-Lore-Iliomar 57.70 57.70 53.60 54.0 57.70 C02 C02-1 Trisulu-Tutuala 38.30 38.30 30.28 30.5 38.30 C03 C03-1 Com-Trisulu 15.20 15.20 15.31 15.4 15.20 C04-1 Buihomao-Luro 14.20 14.20 13.8 C04 29.40 24.20 C04-2 Luro-Junction A08 10.00 10.00 15.8 C05 C05-1 Luca-Dilor (Lacluta) 12.40 12.40 12.59 12.7 12.40 C06-1 Mulia-Quelicai 17.80 17.80 17.9 C06 C06-2 Quelicai-Junction C07 59.30 10.4 52.70 34.90 34.90 C06-3 Junction C07-Ossu 31.5 C07 C07-1 Uatulari-Laisorulai 34.00 34.00 23.21 23.4 34.00 C08-1 Laga-Baguia 37.60 37.60 37.8 C08 68.07 59.70 C08-2 Baguia-Uatucarbau 22.10 22.10 30.9 C09 C09-1 Atauro Vila-Biqueli 20.00 20.00 11.32 11.4 20.00 C10 C10-1 Aileu-Ermera 17.50 17.50 18.26 18.4 17.50 C11-1 Be'eluru-Liquidoe 16.90 16.90 15.2 C11 27.66 27.80 C11-2 Liquidoe-Aileu Vila 10.90 10.90 12.7 C13 C13-1 Ermera-Fatubesi 11.40 11.40 11.49 11.6 11.40 C14-1 Aileu-Remixio 4.20 4.20 4.4 C14 C14-2 Remixio-Laclo 45.00 45.00 55.98 38.9 65.80 C14-3 Laclo-Manatuto 16.60 16.60 13.2 C15 C15-1 Lei-Laclubar 9.60 9.60 10.83 10.9 9.60 C16 C16-1 Tokoluli-Bazartete 27.60 27.60 19.76 19.9 27.60 C17 C17-1 Bazartete-Aipelo 14.40 14.40 13.70 13.8 14.40 C18 C18-1 Lacluta-Korlule 27.00 27.00 28.72 29.0 27.00 C19 C19-1 Bauqia-Passabe 27.60 27.60 24.55 24.8 27.60 C20 C20-1 Oesilo-Tumio 9.00 9.00 7.72 18.6 9.00 C21 C21-1 Suai-Fatululic 26.10 26.10 25.69 25.9 26.10 C22 C22-1 Letefoho-Hatubuilico 18.40 18.40 20.45 20.6 18.40 C23 C23-1 Hato Udo-Ainaro 26.70 26.70 25.02 25.2 26.70 C24 C24-1 Hatubuilico-Nunumogue 9.15 9.2 18.30 C25 C25-1 Maubisse-Hatubuilico 18.30 18.30 12.90 13.0 20.40 C26 C26-1 Maubisse-Turiscai 20.40 20.40 21.53 21.7 38.00 C27 C27-1 Turiscai-Alas 38.00 38.00 41.90 42.3 38.00 C28 C28-1 Dotic-Alas 38.00 38.00 16.25 16.4 12.00 C29 C29-1 Mane Hat-Soibada 9.50 9.50 6.63 8.1 9.50 C30 C30-1 Laclubar-Soibada 23.60 23.60 20.91 19.7 9.50 C31 C31-1 Weberek-Alas 18.00 18.00 17.60 17.7 22.80 C32 C32-1 Beco-Lolotoe 22.80 22.80 26.70 27.0 17.50
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Table 19 Length of core rural road links ROAD ADMINISTRATIVE LENGTH SURFACE SURFACE POPULATION MUNICIPALITY SUCO CODE POST IN KM TYPE CONDITION SERVED AL001 Aileu Remexio Fahisoi 5.0 Gravel Good 2,915 AL002 Aileu Liquidoe Acubilitoho 6.7 Gravel Poor 2,800 AL003 Aileu Aileu vila Suco Liurai 11.4 Gravel Good 2,300 AL004 Aileu Aileu vila Suco Liurai 7.5 Bitumen Poor 2,250 AL005 Aileu Laulara Madabeno 2.0 Bitumen Fair 2,000 AL006 Aileu Remexio Hautoho 2.9 Gravel Fair 1,925 AL007 Aileu Laulara Fatisi 12.3 Gravel Fair 1,900 AL008 Aileu Aileu vila Seloi Malere 2.0 Bitumen Fair 1,267 AL009 Aileu Aileu vila Seloi Craic 3.2 Bitumen Poor 1,167 AL010 Aileu Aileu vila Fatubosa 8.4 Gravel Fair 1,150 AL011 Aileu Aileu vila Seloi Craic 4.0 Bitumen Poor 1,067 AL012 Aileu Aileu vila Suco Liurai 4.0 Bitumen Fair 1,050 AL013 Aileu Remexio Acumau 10.1 Gravel Poor 1,050 AL014 Aileu Remexio Fadabloco 4.6 Gravel Good 1,000 AL015 Aileu Aileu vila Seloi Craic 4.0 Gravel Fair 950 AL016 Aileu Remexio Fadabloco 5.0 Gravel Bad 950 AL017 Aileu Liquidoe Fahisoi 2.6 Gravel Fair 900 AL018 Aileu Liquidoe Acubilitoho 11.5 Gravel Poor 850 AL019 Aileu Aileu vila Hoholau 2.8 Earth Poor 650 AL020 Aileu Liquidoe Faturilau 7.0 Gravel Poor 650 AL021 Aileu Laulara Talitu 6.7 Gravel Poor 600 AL022 Aileu Aileu vila Suco Liurai 4.0 Gravel Poor 550 AL023 Aileu Remexio Fadabloco 2.5 Gravel Poor 550 AL024 Aileu Aileu vila Seloi Craic 3.3 Gravel Poor 517 AN001 Ainaro Maubisse Maulau 10.9 Gravel Poor 1,950 AN002 Ainaro Maubisse Maulau 6.6 Gravel Poor 1,700 AN003 Ainaro Maubisse Maulau 2.7 Gravel Poor 1,050 AN004 Ainaro Maubisse Maubisse 12.0 Gravel Good 1,050 AN005 Ainaro Ainaro Ainaro 10.8 Gravel Fair 1,000 AN006 Ainaro Hatu-Builico Mulo 4.9 Gravel Fair 750 AN007 Ainaro Maubisse Suco Liurai 3.0 Gravel Fair 750 AN008 Ainaro Hatu-Udo Foho-ai-Lico 7.1 Gravel Poor 750 AN009 Ainaro Maubisse Maulau 4.5 Gravel Poor 700 AN010 Ainaro Ainaro Ainaro 3.8 Bitumen Fair 650 AN011 Ainaro Hatu-Builico Nuno-Mogue 3.7 Gravel Fair 510 AN012 Ainaro Hatu-Builico Nuno-Mogue 2.0 Gravel Fair 500 AN013 Ainaro Maubisse Maulau 2.9 Gravel Poor 500 AN014 Ainaro Maubisse Maubisse 2.1 Gravel Poor 500 BA001 Baucau Baucau Triloca 0.9 Bitumen Poor 4,765 BA002 Baucau Baucau Triloca 3.9 Bitumen Poor 4,265 BA003 Baucau Quelicai Afaca 12.0 Earth Poor 3,800 BA004 Baucau Vemase Ostico 1.1 Gravel Fair 3,465 BA005 Baucau Quelicai Guruca 3.3 Gravel Bad 3,100 BA006 Baucau Baucau Caibada 1.2 Bitumen Poor 2,900 BA007 Baucau Vemase Ostico 4.0 Gravel Fair 2,650 BA008 Baucau Venilale Uma Ana Ulo 4.8 Bitumen Poor 2,600 BA009 Baucau Baucau Gariuai 1.6 Bitumen Good 2,300 BA010 Baucau Baucau Gariuai 3.0 Bitumen Fair 2,300 BA011 Baucau Quelicai Afaca 2.3 Gravel Fair 2,300 BA012 Baucau Venilale Uataco 3.0 Bitumen Poor 2,300 BA013 Baucau Baucau Buibau 1.5 Gravel Poor 2,150 BA014 Baucau Venilale Uataco 4.0 Bitumen Fair 2,125 BA015 Baucau Baucau Buibau 7.7 Bitumen Poor 2,100 BA016 Baucau Baucau Buruma 4.8 Bitumen Fair 2,000
45 Road Asset Management Plan Appendices
ROAD ADMINISTRATIVE LENGTH SURFACE SURFACE POPULATION MUNICIPALITY SUCO CODE POST IN KM TYPE CONDITION SERVED BA017 Baucau Venilale Baha Mori 4.4 Bitumen Bad 2,000 BA018 Baucau Baucau Caibada 6.0 Gravel Fair 1,900 BA019 Baucau Laga Tequino Mata 4.0 Bitumen Fair 1,900 BA020 Baucau Quelicai Afaca 9.6 Gravel Poor 1,800 BA021 Baucau Venilale Fatulia 6.9 Earth Poor 1,800 BA022 Baucau Baucau Buibau 1.2 Bitumen Poor 1,650 BA023 Baucau Baguia Defa Uassi 12.7 Gravel Good 1,650 BA024 Baucau Laga Samalari 3.5 Bitumen Fair 1,550 BA025 Baucau Quelicai Bualale* 8.0 Earth Poor 1,550 BA026 Baucau Quelicai Baguia 1.6 Bitumen Poor 1,525 BA027 Baucau Quelicai Macalaco 7.0 Earth Bad 1,450 BA028 Baucau Baucau Trilolo 5.0 Gravel Good 1,400 BA029 Baucau Baucau Gariuai 4.3 Gravel Fair 1,300 BA030 Baucau Baguia Defa Uassi 5.9 Earth Poor 1,100 BA031 Baucau Venilale Bado Ho'o 6.4 Gravel Good 1,100 BA032 Baucau Baucau Caibada 5.0 Gravel Good 1,000 BA033 Baucau Laga Tequino Mata 8.2 Gravel Fair 900 BA034 Baucau Baguia Lavateri 4.6 Bitumen Fair 900 BA035 Baucau Venilale Uailaha 8.0 Bitumen Bad 850 BA036 Baucau Venilale Bado Ho'o 9.8 Bitumen Poor 800 BA037 Baucau Baucau Buruma 2.7 Bitumen Bad 750 BA038 Baucau Vemase Uatu-Lari 4.4 Gravel Fair 750 BA039 Baucau Baucau Wailili 4.0 Bitumen Poor 700 BA040 Baucau Venilale Uailaha 7.0 Gravel Fair 625 BA041 Baucau Baucau Samalari 10.8 Gravel Poor 600 BA042 Baucau Baucau Wailili 2.3 Earth Poor 600 BA043 Baucau Laga Sagadati 4.2 Gravel Poor 600 BA044 Baucau Baucau Samalari 4.9 Earth Poor 600 BA045 Baucau Venilale Baha Mori 3.8 Bitumen Bad 600 BA046 Baucau Quelicai Laisorolai de Baixo 3.0 Gravel Poor 500 BO001 Bobonaro Bobonaro Bobonaro 3.2 Gravel Poor 5,550 BO002 Bobonaro Bobonaro Bobonaro 12.3 Gravel Poor 4,150 BO003 Bobonaro Maliana Tapo/Memo 5.8 Gravel Good 3,400 BO004 Bobonaro Bobonaro Soilesu* 9.8 Gravel Poor 2,600 BO005 Bobonaro Balibo Leohito 6.7 Gravel Fair 2,500 BO006 Bobonaro Maliana Ritabou 1.2 Gravel Poor 2,400 BO007 Bobonaro Maliana Tapo/Memo 4.0 Gravel Good 2,400 BO008 Bobonaro Bobonaro Molop 17.0 Gravel Poor 2,350 BO009 Bobonaro Maliana Ritabou 2.0 Gravel Poor 2,050 BO010 Bobonaro Cailaco Atudara 4.0 Bitumen Poor 1,970 BO011 Bobonaro Balibo Balibo Vila 10.0 Gravel Good 1,850 BO012 Bobonaro Maliana Tapo/Memo 9.8 Gravel Poor 1,700 BO013 Bobonaro Lolotoe Lebos 2.0 Gravel Good 1,500 BO014 Bobonaro Cailaco Dau Udu 7.0 Earth Poor 1,370 BO015 Bobonaro Balibo Leolima 8.1 Gravel Poor 1,240 BO016 Bobonaro Maliana Ritabou 11.6 Earth Poor 1,200 BO017 Bobonaro Bobonaro Carabau 3.1 Gravel Good 1,200 BO018 Bobonaro Bobonaro Leber 3.5 Earth Poor 1,200 BO019 Bobonaro Cailaco Meligo 3.4 Earth Poor 1,100 BO020 Bobonaro Maliana Ritabou 7.1 Gravel Poor 1,100 BO021 Bobonaro Atabae Aidabaleten 6.0 Gravel Poor 800 BO022 Bobonaro Bobonaro Soilesu 2.1 Gravel Poor 800 BO023 Bobonaro Bobonaro Bobonaro 6.2 Gravel Bad 800 BO024 Bobonaro Maliana Ritabou 5.6 Gravel Poor 750 BO025 Bobonaro Bobonaro Bobonaro 4.9 Earth Fair 750
46 Road Asset Management Plan Appendices
ROAD ADMINISTRATIVE LENGTH SURFACE SURFACE POPULATION MUNICIPALITY SUCO CODE POST IN KM TYPE CONDITION SERVED BO026 Bobonaro Cailaco Guenu Lai 6.1 Gravel Poor 700 BO027 Bobonaro Balibo Balibo Vila 6.0 Gravel Fair 700 BO028 Bobonaro Balibo Balibo Vila 1.2 Bitumen Good 700 BO029 Bobonaro Bobonaro Ai-Assa 7.1 Gravel Bad 700 BO030 Bobonaro Balibo Cowa 9.3 Earth Poor 650 BO031 Bobonaro Balibo Batugade 4.6 Gravel Poor 650 BO032 Bobonaro Lolotoe Gildapil 2.3 Gravel Bad 600 BO033 Bobonaro Bobonaro Sibuni 3.7 Earth Bad 600 BO034 Bobonaro Maliana Tapo/Memo 4.3 Bitumen Poor 500 BO035 Bobonaro Maliana Tapo/Memo 2.2 Earth Poor 500 BO036 Bobonaro Cailaco Meligo 2.0 Earth Poor 500 BO037 Bobonaro Bobonaro Bobonaro 3.0 Earth Fair 500 CO001 Covalima Suai Camenaça 3.9 Bitumen Fair 3,300 CO002 Covalima Maucatar Matai 4.0 Bitumen Poor 2,700 CO003 Covalima Fatumean Fatumea 11.8 Bitumen Fair 2,600 CO004 Covalima Fatumean Fatumea 3.2 Bitumen Poor 2,600 CO005 Covalima Tilomar Foholulic 3.6 Gravel Fair 1,930 CO006 Covalima Forohem Lactos 6.0 Bitumen Good 1,687 CO007 Covalima Maucatar Belecasac 7.0 Earth Poor 1,600 CO008 Covalima Tilomar Maudemo 3.0 Bitumen Fair 1,500 CO009 Covalima Tilomar Maudemo 3.7 Bitumen Fair 1,500 CO010 Covalima Zumalai Raimea 4.1 Gravel Fair 1,459 CO011 Covalima Fatululic Taroman 7.4 Gravel Poor 1,300 CO012 Covalima Tilomar Maudemo 4.0 Bitumen Good 1,300 CO013 Covalima Suai Suai Loro 6.7 Gravel Fair 1,100 CO014 Covalima Zumalai Tashilin 4.4 Bitumen Fair 1,006 CO015 Covalima Forohem Dato Tolu 6.0 Gravel Poor 1,000 CO016 Covalima Zumalai Zulo 5.2 Gravel Poor 891 CO017 Covalima Tilomar Foholulic 5.7 Gravel Bad 840 CO018 Covalima Tilomar Casabauc 2.7 Earth Poor 800 CO019 Covalima Tilomar Lalawa 3.7 Bitumen Good 700 CO020 Covalima Tilomar Foholulic 5.1 Gravel Fair 640 CO021 Covalima Zumalai Zulo 2.0 Bitumen Good 516 CO022 Covalima Forohem Dato Tolu 6.9 Earth Poor 500 CO023 Covalima Suai Labarai 7.3 Gravel Poor 500 CO024 Covalima Suai Beco 3.2 Gravel Poor 500 CO025 Covalima Suai Labarai 2.0 Gravel Fair 500 CO026 Covalima Suai Labarai 4.2 Earth Poor 500 CO027 Covalima Zumalai Tashilin 5.0 Bitumen Fair 500 DI001 Dili Nain Feto Lahane Oriental 1.5 Gravel Poor 2,750 DI002 Dili Nain Feto Santa Cruz 2.3 Bitumen Fair 2,500 DI003 Dili Atauro Maquili 5.4 Gravel Poor 1,800 DI004 Dili Dom Aleixo Bairro Pite 1.1 Gravel Fair 1,200 DI005 Dili Vera Cruz Lahane Ocidental 0.5 Gravel Good 900 DI006 Dili Vera Cruz Lahane Ocidental 5.7 Gravel Fair 750 DI007 Dili Dom Aleixo Comoro 4.7 Gravel Poor 600 DI008 Dili Atauro Biceli 0.5 Gravel Fair 500 DI009 Dili Atauro Biceli 7.1 Gravel Fair 500 ER001 Ermera Railaco Taraço 6.0 Earth Good 4,230 ER002 Ermera Ermera Ponilala 2.0 Gravel Fair 3,626 ER003 Ermera Ermera Estado 5.9 Gravel Fair 3,500 ER004 Ermera Hatolia Urahou 12.9 Gravel Poor 3,368 ER005 Ermera Ermera Riheu 6.0 Earth Fair 3,300 ER006 Ermera Ermera Poetete 3.9 Earth Fair 3,050 ER007 Ermera Letefoho Haupu 6.9 Gravel Good 3,030
47 Road Asset Management Plan Appendices
ROAD ADMINISTRATIVE LENGTH SURFACE SURFACE POPULATION MUNICIPALITY SUCO CODE POST IN KM TYPE CONDITION SERVED ER008 Ermera Letefoho Catrai Leten 2.1 Earth Bad 2,463 ER009 Ermera Ermera Leguimea 6.7 Earth Poor 2,400 ER010 Ermera Hatolia Fatubolu 2.8 Gravel Poor 2,350 ER011 Ermera Hatolia Fatubessi 2.3 Gravel Poor 2,341 ER012 Ermera Atsabe Leimea Leten 6.8 Gravel Good 2,150 ER013 Ermera Hatolia Fatubessi 11.9 Gravel Poor 1,991 ER014 Ermera Railaco Samalete 12.3 Gravel Good 1,654 ER015 Ermera Hatolia Lissapat 4.9 Gravel Poor 1,629 ER016 Ermera Railaco Fatuquero 1.0 Bitumen Good 1,623 ER017 Ermera Ermera Ponilala 3.2 Earth Fair 1,612 ER018 Ermera Ermera Poetete 5.5 Earth Fair 1,600 ER019 Ermera Atsabe Lasaun 4.0 Gravel Poor 1,350 ER020 Ermera Ermera Ponilala 2.1 Earth Poor 1,324 ER021 Ermera Hatolia Asulau 4.6 Gravel Poor 1,308 ER022 Ermera Letefoho Catrai-Craic 8.7 Earth Poor 1,300 ER023 Ermera Ermera Poetete 4.1 Earth Fair 1,248 ER024 Ermera Atsabe Batumanu 1.6 Bitumen Poor 1,150 ER025 Ermera Hatolia Manusae 6.6 Gravel Poor 1,150 ER026 Ermera Ermera Estado 4.1 Earth Poor 1,100 ER027 Ermera Atsabe Lasaun 4.9 Gravel Poor 1,100 ER028 Ermera Letefoho Hatugau 3.9 Gravel Poor 1,059 ER029 Ermera Ermera Leguimea 2.2 Earth Fair 1,050 ER030 Ermera Ermera Poetete 4.4 Gravel Poor 1,000 ER031 Ermera Hatolia Asulau 2.5 Earth Poor 991 ER032 Ermera Atsabe Laclo 3.1 Earth Poor 950 ER033 Ermera Atsabe Batumanu 4.2 Gravel Poor 900 ER034 Ermera Atsabe Laubono 11.1 Gravel Poor 898 ER035 Ermera Ermera Riheu 1.2 Gravel Fair 850 ER036 Ermera Atsabe Atadame/Malabe 5.2 Gravel Poor 850 ER037 Ermera Ermera Leguimea 2.3 Earth Poor 800 ER038 Ermera Ermera Raimerhei 2.5 Gravel Fair 800 ER039 Ermera Atsabe Obulo 1.8 Gravel Poor 800 ER040 Ermera Hatolia Manusae 8.8 Gravel Poor 800 ER041 Ermera Letefoho Eraulo 1.0 Earth Fair 767 ER042 Ermera Hatolia Fatubolu 3.4 Gravel Poor 750 ER043 Ermera Hatolia Manusae 0.9 Gravel Poor 750 ER044 Ermera Hatolia Fatubessi 7.5 Gravel Poor 722 ER045 Ermera Hatolia Samara 10.1 Earth Poor 708 ER046 Ermera Letefoho Catrai-Craic 3.0 Earth Poor 700 ER047 Ermera Atsabe Leimea Leten 1.9 Earth Poor 650 ER048 Ermera Ermera Poetete 8.6 Earth Fair 600 ER049 Ermera Ermera Mirtutu 1.4 Gravel Poor 600 ER050 Ermera Letefoho Goulolo 4.1 Gravel Poor 550 ER051 Ermera Letefoho Ducurai 2.1 Earth Poor 550 ER052 Ermera Letefoho Ducurai 1.4 Earth Fair 550 ER053 Ermera Atsabe Laubono 7.5 Gravel Poor 550 ER054 Ermera Letefoho Eraulo 2.1 Earth Poor 507 ER055 Ermera Railaco Samalete 1.2 Earth Bad 500 ER056 Ermera Railaco Lihu 0.9 Bitumen Good 500 ER057 Ermera Ermera Leguimea 2.8 Earth Poor 500 ER058 Ermera Letefoho Ducurai 2.3 Earth Poor 500 ER059 Ermera Atsabe Leimea Leten 2.1 Gravel Poor 500 ER060 Ermera Hatolia Coliate-Leotelo 9.0 Earth Poor 500 LA001 Lautem Lautem Baduro 7.9 Bitumen Poor 2,100 LA002 Lautem Lospalos Fuiloro 3.0 Bitumen Fair 1,850
48 Road Asset Management Plan Appendices
ROAD ADMINISTRATIVE LENGTH SURFACE SURFACE POPULATION MUNICIPALITY SUCO CODE POST IN KM TYPE CONDITION SERVED LA003 Lautem Lautem Parlamento 2.1 Bitumen Fair 1,800 LA004 Lautem Lospalos Fuiloro 11.0 Bitumen Fair 1,750 LA005 Lautem Iliomar Cainliu 4.0 Gravel Good 1,640 LA006 Lautem Luro Cotamutu 5.6 Gravel Poor 1,620 LA007 Lautem Lospalos Fuiloro 3.0 Bitumen Fair 1,300 LA008 Lautem Lautem Parlamento 7.9 Bitumen Fair 1,200 LA009 Lautem Lautem Baduro 10.3 Gravel Poor 1,000 LA010 Lautem Lautem Parlamento 5.7 Bitumen Fair 750 LA011 Lautem Luro Cotamutu 7.1 Gravel Good 750 LA012 Lautem Lautem Ililai 10.4 Earth Fair 650 LA013 Lautem Luro Cotamutu 6.2 Earth Poor 650 LA014 Lautem Lospalos Souro 2.7 Bitumen Fair 600 LI001 Liquica Maubara Gugleur 4.3 Bitumen Poor 2,900 LI002 Liquica Liquiçá Dato 9.0 Gravel Fair 2,825 LI003 Liquica Maubara Vaviquinia 1.0 Bitumen Fair 2,375 LI004 Liquica Liquiçá Dato 2.9 Bitumen Poor 2,325 LI005 Liquica Liquiçá Dato 1.1 Bitumen Poor 2,175 LI006 Liquica Liquiçá Dato 7.1 Gravel Poor 2,175 LI007 Liquica Maubara Lissadila 7.4 Bitumen Poor 1,550 LI008 Liquica Liquiçá Leoteala 0.2 Bitumen Poor 1,500 LI009 Liquica Liquiçá Hatuquessi 1.9 Bitumen Fair 1,450 LI010 Liquica Maubara Vatuvou 6.4 Bitumen Fair 1,450 LI011 Liquica Liquiçá Dato 5.7 Bitumen Fair 1,225 LI012 Liquica Maubara Vaviquinia 2.0 Bitumen Fair 1,150 LI013 Liquica Maubara Vatuvou 4.7 Bitumen Poor 1,000 LI014 Liquica Bazartete Ulmera 2.0 Gravel Fair 750 LI015 Liquica Liquiçá Hatuquessi 5.2 Gravel Poor 750 LI016 Liquica Liquiçá Hatuquessi 4.0 Gravel Poor 750 LI017 Liquica Maubara Gugleur 1.3 Bitumen Poor 750 LI018 Liquica Bazartete Leorema 1.9 Bitumen Bad 650 LI019 Liquica Bazartete Fahilebo 11.1 Gravel Bad 650 LI020 Liquica Bazartete Fatumasi 2.6 Bitumen Poor 625 LI021 Liquica Liquiçá Açumano 1.8 Bitumen Fair 600 LI022 Liquica Liquiçá Dato 2.5 Earth Poor 600 LI023 Liquica Maubara Lissadila 2.4 Bitumen Fair 600 LI024 Liquica Bazartete Lauhata 4.0 Gravel Fair 550 LI025 Liquica Bazartete Fahilebo 6.4 Gravel Fair 550 LI026 Liquica Maubara Gugleur 7.6 Gravel Poor 550 LI027 Liquica Maubara Gugleur 2.6 Gravel Poor 500 MT001 Manatuto Laleia Lifau 5.8 Bitumen Fair 2,000 MT002 Manatuto Laclubar Funar 3.0 Gravel Fair 1,950 MT003 Manatuto Laclubar Funar 4.0 Bitumen Good 1,950 MT004 Manatuto Laclubar Funar 1.4 Earth Poor 1,950 MT005 Manatuto Laleia Cairui 5.0 Bitumen Fair 1,750 MT006 Manatuto Manatuto Iliheu 7.0 Gravel Poor 1,450 MT007 Manatuto Soibada Manufahi 10.7 Gravel Poor 850 MT008 Manatuto Barique/Natarbora Aubeon 5.9 Gravel Fair 650 MF001 Manufahi Same Letefoho 5.0 Bitumen Bad 3,424 MF002 Manufahi Same Holarua 6.0 Bitumen Poor 2,150 MF003 Manufahi Same Letefoho 5.9 Gravel Poor 1,974 MF004 Manufahi Fatuberliu Clacuc 7.9 Bitumen Fair 1,500 MF005 Manufahi Turiscai Manumera 13.0 Gravel Poor 1,378 MF006 Manufahi Alas Dotic 7.0 Bitumen Good 986 MF007 Manufahi Same Tutuluro 5.6 Earth Bad 950 MF008 Manufahi Fatuberliu Fatucahi 17.5 Gravel Poor 926
49 Road Asset Management Plan Appendices
ROAD ADMINISTRATIVE LENGTH SURFACE SURFACE POPULATION MUNICIPALITY SUCO CODE POST IN KM TYPE CONDITION SERVED MF009 Manufahi Turiscai Beremana 2.6 Gravel Poor 802 MF010 Manufahi Alas Taitudac 2.2 Gravel Bad 750 MF011 Manufahi Same Letefoho 2.0 Gravel Fair 700 MF012 Manufahi Alas Taitudac 6.4 Gravel Poor 650 MF013 Manufahi Fatuberliu Fatucahi 2.3 Earth Bad 626 MF014 Manufahi Same Holarua 6.0 Bitumen Poor 500 MF015 Manufahi Same Daisua 6.6 Gravel Good 500 MF016 Manufahi Same Betano 6.2 Gravel Poor 500 OE001 Oecusse Pante Macasar Bobocase 2.0 Bitumen Fair 3,100 OE002 Oecusse Pante Macasar Bobocase 2.0 Bitumen Poor 2,700 OE003 Oecusse Passabe Malelat 8.0 Gravel Poor 2,450 OE004 Oecusse Pante Macasar Taiboco 4.0 Earth Poor 2,200 OE005 Oecusse Pante Macasar Lifau 9.2 Bitumen Poor 2,100 OE006 Oecusse Pante Macasar Naimeco 8.0 Gravel Fair 2,100 OE007 Oecusse Pante Macasar Naimeco 4.0 Bitumen Good 1,650 OE008 Oecusse Nitibe Usi-Taco 4.3 Earth Poor 1,600 OE009 Oecusse Nitibe Banafi 6.0 Gravel Poor 1,600 OE010 Oecusse Nitibe Usi-Taco 9.2 Earth Poor 1,500 OE011 Oecusse Oesilo Usi-Tacae 2.5 Earth Poor 1,400 OE012 Oecusse Pante Macasar Taiboco 4.0 Gravel Poor 1,300 OE013 Oecusse Nitibe Usi-Taco 2.0 Earth Poor 1,300 OE014 Oecusse Pante Macasar Costa 5.5 Earth Bad 1,050 OE015 Oecusse Pante Macasar Costa 8.0 Gravel Fair 1,050 OE016 Oecusse Pante Macasar Taiboco 11.1 Earth Bad 950 OE017 Oecusse Nitibe Usi-Taco 4.1 Gravel Fair 900 OE018 Oecusse Nitibe Lela-Ufe 2.7 Gravel Poor 850 OE019 Oecusse Oesilo Bobometo 1.8 Gravel Poor 840 OE020 Oecusse Pante Macasar Costa 2.7 Gravel Good 800 OE021 Oecusse Pante Macasar Nipani 4.6 Earth Poor 650 OE022 Oecusse Nitibe Banafi 5.9 Earth Poor 600 OE023 Oecusse Nitibe Lela-Ufe 7.8 Gravel Good 600 VI001 Viqueque Viqueque Bahalarauain 4.0 Gravel Good 3,470 VI002 Viqueque Viqueque Bahalarauain 1.0 Earth Poor 2,690 VI003 Viqueque Viqueque Bahalarauain 8.1 Gravel Good 2,440 VI004 Viqueque Lacluta Laline 12.0 Gravel Poor 2,400 VI005 Viqueque Ossu Builale 8.5 Gravel Poor 1,900 VI006 Viqueque Ossu Ossu de Cima 7.6 Earth Poor 1,600 VI007 Viqueque Uatucarbau Afaloicai 8.8 Gravel Fair 1,500 VI008 Viqueque Watulari Afaloicai 5.0 Gravel Fair 1,150 VI009 Viqueque Ossu Liaruca 10.0 Bitumen Bad 1,130 VI010 Viqueque Lacluta Laline 9.0 Gravel Good 1,100 VI011 Viqueque Ossu Uagia 6.5 Gravel Fair 960 VI012 Viqueque Viqueque Bahalarauain 6.0 Bitumen Fair 850 VI013 Viqueque Lacluta Ahic 13.7 Gravel Fair 850 VI014 Viqueque Ossu Ossorua 5.8 Earth Poor 760 VI015 Viqueque Lacluta Laline 12.0 Gravel Good 750 VI016 Viqueque Watulari Afaloicai 15.2 Gravel Poor 700 VI017 Viqueque Ossu Liaruca 4.4 Gravel Poor 680
50 Road Asset Management Plan Appendices Appendix C Technical Road Classes and Standards
National Roads Rural Roads Road Classification Municipal Roads Design standard R1 R2 R3 R4 R5 R6 RR1 RR2 Projected ADT >10,000 5,000-10,000 2,000-5,000 1,000-2,000 400-1,000 50-400 20-50 <50 Access Control Full Partial Partial Partial Partial Non-control Non-control Non-control Surface Paved Paved Paved Paved Paved (Un)Paved (Un)Paved (Un)Paved
Criteria Criteria Design Forecast Year 20 20 10 10 10 5 5 5 Mount- Mount- Mount- Mount- Mount- Mount- Mount- Mount- Flat Rolling Flat Rolling Flat Rolling Flat Rolling Flat Rolling Flat Rolling Flat Rolling Flat Rolling
Terrain ainous ainous ainous ainous ainous ainous ainous ainous Design Control and and Control Design Design Speed 100 70 60 80 70 60 70 60 50 60 50 40 60 50 30 50 40 30 40 30 20 20 20 20 Travel Lane Width 3.50 m 3.50 m 3.00 m 2.75 m 2.50 m 2.25 m 3.50 m 2.50 m Number of Lanes multi-lane two-lane two-lane two-lane two-lane two-lane single-lane single-lane Carriageway Width ≥14.0 m 7.0 m 6.0 m 5.5 m 5.0 m 4.5 m 3.5 m 2.5 m Shoulder Width 3.0 m 1.0-3.0 m 1.0-3.0 m 1.0-3.0 m 0.5-2.0 m 0.5-2.0 m 0.5-1.5 (passing bay) 0.5-1.0 (passing bay) Travel Lane 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 4% 4% Cross Slope Shoulder 4% 4% 4% 4% 4% 4% passing bay 4% passing bay 4% Superelevation rate (max) 6% 8% 8% 8% 8% 8% 8% 8%
Cross Section Elements Section Cross Maximum Relative Gradient 0.47% 0.55% 0.60% 0.50% 0.55% 0.60% 0.55% 0.60% 0.65% 0.60% 0.65% 0.70% 0.60% 0.65% 0.70% 0.65% 0.70% 0.75% 0.70% 0.75% 0.80% 0.80% 0.80% 0.80% Widening 0.60-2.50 0.60-2.50 0.60-2.50 0.60-2.50 0.60-2.50 0.60-2.50 - -
Fore Slope - ratio 1:4 to 1:6 1:2 to 1:6 1:2 to 1:6 1:2 to 1:6 1:2 to 1:6 1:2 to 1:6 1:2 to 1:6 1:2 to 1:6 Fill Cut- Back Slope (ratio) 1:2 to 1:3 1:1.5 to 1:3 1:1.5 to 1:3 1:1.5 to 1:3 1:1.5 to 1:3 1:1.5 to 1:3 1:1.5 to 1:3 1:1.5 to 1:3 Stopping Sight Distance 160 105 85 130 105 85 105 85 65 85 65 50 85 65 35 65 50 35 100 70 40 40 40 40 Passing Sight Distance 615 485 410 540 485 410 485 410 345 410 345 270 410 345 200 345 270 200 ------Min Horizontal Curve Radius 336 184 123 229 168 113 168 113 73 113 73 41 113 73 20 73 41 20 41 20 15 15 15 15 Minimum Length of Spiral 60 55 50 60 55 50 45 40 35 40 35 30 35 30 25 30 25 25 40 40 35 35 35 35 Maximum Gradient 4 6 8 4 6 8 5 6 10 5 9 11 7 9 12 7 10 12 7 11 15 9 12 15 Minimum Gradient 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Crest Vertical Curve (K-value) 52 17 11 26 17 11 17 11 7 11 7 4 11 7 2 7 4 2 8 4 2 2 2 2
Sag Vertical Curve (K-value) 45 23 18 30 23 18 23 18 13 18 13 9 18 13 6 13 9 6 18 12 6 6 6 6 Geometric Design Elements Elements Design Geometric Clear Zone 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m 3.0 m ------
Vertical Clearance 5.3 m 5.3 m 5.3 m 5.3 m 5.3 m 5.3 m 5.3 m 5.3 m 5.3 m 5.1 m 5.1 m 5.1 m 5.1 m 5.1 m 5.1 m 5.1 m 5.1 m 5.1 m ------Safety Safety
Clearance Clearance Minimum ROW width 40 m 40 m 40 m 30 m 30 m 30 m 30 m 30 m 30 m 30 m 30 m 30 m 20 m 20 m 20 m 20 m 20 m 20 m 20 m 20 m 20 m 20 m 20 m 20 m
51
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