Data challenges in preparing the second road sector investment plan

Last Updated 4 weeks ago by Kenya Engineer

Road infrastructure is one of the most important public assets in Kenya. In 2010, Kenya’s public road network length was 161,451km with an estimated asset value of over Kshs. 2.5trillion (GoK, 2010). In addition, over 90% of freight and passenger traffic is transported by road. Roads are therefore big business and require continuous development, management and maintenance in a prudent and effective manner.

Due the high costs of road development and preservation of the assets through maintenance, the Government of Kenya desires to have an objective and scientific approach of determining the optimal allocation of road investments. The investments should target priority sections of the road network that would give the maximum economic and social benefits to the society at large. It is for that reason that the Kenya Roads Act 2007 now requires that a Roads Sector Investment Plan (RSIP) should be prepared once every five years.

The RSIP should outline the road development and maintenance priorities, the types of interventions, and funding requirements for the entire road network. Kenya Roads Board (KRB) coordinated the development of the first Phase of RSIP, which covered the period 2010 – 2014, and is currently coordinating the development of second Phase of RSIP. The de facto Road Investment appraisal tool is the Highway Development and Management Model (HDM-4) that was developed by the World Bank. This is the tool that was used in developing RSIP 1 and is being used for RSIP 2.

The objectives of this paper are to: (1)highlight the data challenges being faced in preparing the RSIP2; and, (2) make recommendations of addressing the management of the country’s road assets.

Preparation of RSIP2 has been delayed with the main challenge being unavailability of complete, accurate and reliable data sets to run HDM-4. To address the challenges, the paper makes the following recommendations that should strengthen Road Asset Management practice in Kenya:

1.    Formulating and adopting an Asset Management Policy outlining Government’s road asset management objectives, targets and plans.
2.    Establishing a sector wide integrated Road Asset Management System with centralized storage, retrieval and exchange of road network data. This will assist Road Managers to make evidence-based decisions in preparation and implementation of policies and programmes.
3.    Establishing a road management data collection regime for effective monitoring performance of the road network and to measure progress towards achieving overall transport sector objectives.

In addition, KRB and the road agencies should set up Long-Term Pavement Performance sites for representative sections of the road network. Such sites should be clearly identified and permanent warning signs of conventional nature should be placed at both ends of the section to inform the public that these are research sites that will be subjected to frequent inspections and there may be delays in carrying out maintenance works on these sections.

Key words:Road Sector Investment plan (RSIP), Highway Development and Management Model (HDM-4), RoadAsset Management System (RAMS)

 1.    Introduction
Roads are essential for economic growth and should therefore provide for the safe and efficient movement of people and freight to and from areas of economic and social activities. According to World Bank (2017), a one (1%) percentage increase in road infrastructure investment results in GDP growth of 1%, which indicates the direct relationship between GDP and investment in roads. Roads are enablers for trade and bring benefits such as increased trade, agricultural productivity, and access to social amenities such as schools and hospitals, improved living standards, poverty reduction, social inclusion, and support “just-in-time” inventory systems.

Road infrastructure is one of the most important public assets in Kenya. In 2010, Kenya’s public road network length was 161,451km with an estimated asset value of over KSh. 2.5trillion (GoK, 2010). In addition, over 90% of freight and passenger traffic is transported by road. Roads are therefore big business and require continuous development, management and maintenance in a prudent and effective manner.

For these benefits to be sustained, road improvements must be followed by a well-planned and prioritized programme of maintenance. Without regular planned maintenance, roads can rapidly fall into disrepair, preventing the realization of the longer term impacts of road improvements on development.It is estimated that repair costs of roads rise to six times the normal maintenance costs after three years of neglect, and to 18 times after five years of neglect (SANRAL, 2004).

Due the high costs of road development and preservation of the assets through maintenance, the Government of Kenya always desires to have an objective and scientific approach of determining the optimal allocation of road investments. The investments should target priority sections of the road network that would give the maximum economic and social benefits to the society at large. It is for this reason that the Kenya Roads Act 2007 now requires that a Roads Sector Investment Plan (RSIP) should be prepared once every five years.

The RSIP should outline the road development and maintenance priorities, the types of interventions, and funding requirements for the entire road network. Kenya Roads Board (KRB) coordinated the development of the first Phase of RSIP, which covered the period 2010 – 2014, and is currently coordinating the development of second 5 year Phase of RSIP.RSIP1 gave priority to routine maintenance of the entire road network and planned periodic maintenance of the paved network. It was expected to reduce maintenance backlog in the classified paved network, in addition to capacity enhancements of major urban roads and town bypasses. The second Phase of RSIP is envisaged to focus on upgrading the remaining unpaved national network, improvement of the urban paved road network, and capacity improvement by upgrading economically viable unpaved roads.

The de facto Road Investment appraisal tool is The Highway Development and Management Model (HDM-4) that was developed by the World Bank. This is the tool that was used in developing RSIP 1 and is being used for RSIP2. The HDM-4 structure is shown in Figure 2 of the Appendix.

2.    Objectives of the Paper
The objectives of this paper are to: (1) highlight the data challenges being faced in preparing the RSIP2; and, (2) make recommendations of addressing the management of the country’s road assets.

3.    Road Sector Investment Plan
Scope
In January 2016, the Principal Secretary of Infrastructure appointed an interagency Task Force to develop the 2nd RSIP. The development of the RSIP 2 is aimed at improving decision-making on expenditures in the road sector by enabling effective and sustainable utilization of the latest HDM-4 knowledge and investment appraisal techniques that consider multi-criteria analysis.
Also, a Consultant was engaged to:
1.    To improve the configuration and calibration parameters of HDM-4 Kenyan Workspace. The update of configuration and calibration parameters will be done for the latest version of HDM-4 (Version 2.08).
2.    In close consultation with the RSIP Task Force, develop a detailed short term 5 year Road Sector Investment Programme (2015 – 2019) anchored on long term sector plans (including the 15 year RSIP) and national priorities.
The RSIP2 is expected to be ready by end of June 2017.

Data Requirements
The analytical framework of HDM-4 is based on the concept of pavement life cycle analysis, which is typically more than 15 years, depending on the pavement type. The HDM 4 tool is applied to predict road deterioration (RD), road works effects (WE), road user effects (RUE), and socio-economic and environmental effects (SEE) (Odoki and Kerali, 2005). The underlying operation of HDM-4 is common for the project, programme or strategy applications. In each case, HDM-4 predicts the life cycle pavement performance and the resulting user costs under specified maintenance and/or road improvement scenarios. The agency and user costs (i.e. RAC and RUC, respectively) are determined by first predicting physical quantities of resource consumption and then multiplying these by the corresponding unit costs.

The main data sets required as inputs for HDM-4 analyses are categorized as follows:
1.    Road network datacomprising: inventory, geometry, pavement type, pavement strength, and road condition defined by different distress modes;
2.    Vehicle fleet data including vehicle physical and loading characteristics, utilisation and service life, performance characteristics such as driving power and braking power, and unit costs of vehicle resources;
3.    Traffic data including details of composition, volumes and growth rates, speed-flow types and hourly traffic flow pattern on each road section;
4.    Road works data comprising historical records of works performed on different road sections, a range of road maintenance activities practised in the country and their associated unit costs.
5.    Economic analysis parameters including time values, discount rate and base year.
6.    Details of Ongoing and Committed projects, budget levels
The Kenya road agencies are expected to maintain these road databases for their various road networks.

4.    Data Challenges

The data stored by the Road Authorities were to be provided and processed for the HDM-4 analysis. The main challenges encountered in the exercise to configure and calibrate HDM-4 to conditions in Kenya related to data. Some of the major challenges were as follows:
1.    It was particularly difficult not only to retrieve this data from the databases but also to obtain complete historical data sets that can be used to calibrate HDM-4 RD and WE models. This was addressed by extracting data from design reports.
2.    Data on the current status of the road network is required in order to configure and calibrate HDM-4 models. This data needed to be collected from the field however it was costly and time consuming.
3.    A lot of field data was collected and the processing of that data into suitable formats required as inputs for carrying out HDM-4 configuration and calibration posed a major challenge to the road agencies. The use of sampling methods has helped in reducing the amount of effort required for this task.
4.    Dealing with the issue of missing incomplete or lack of data was also a major challenge. This was addressed by applying theInformation Quality Level(IQL) concept and using look-up tables ofrepresentative road sections.
The challenges relating to data emanate from weak Road Asset Management practices in the country (Mott Macdonald, 2013).

5.    Impact of Inaccurate Data

Figure 1 illustrates the impact of the accuracy of input data on road deterioration predictions and the timing of future maintenance interventions (Bennett and Paterson, 2000).

Figure 1: Impact of the accuracy of input data on road deterioration predictions and the timing of future maintenance interventions. (Source: Bennett and Paterson (2000))

HDM-4 uses incremental-recursive models and the existing condition (denoted by point C1 or C2) is the start point for the modelling. The pavement will deteriorate and reach that condition, defined by a given set of criteria for maintenance intervention, in a certain period of time depending on the existing condition. The difference in the start point will a great impact on when the treatments are triggered as will the calibrated deterioration factor.
The figure also illustrates a second point: that HDM-4 model predictions are based on the mean deterioration rate and therefore will have a certain time interval within which a particular treatment will be triggered by a given set of intervention criteria. Typical values that define the slower and faster rates of deterioration into a band vary across the different distresses modelled. The further into the future one predicts the deterioration, the greater the spread in the trigger interval. Consequently, this will impact on the analysis results as costs incurred in the future are discounted to the base year value.

5.    Conclusions

HDM-4 has been adopted for prioritization of road interventions in Kenya and to develop the RSIP 2. The HDM-4 work space has been configured and calibrated to Kenyan conditions to improve accuracy of the model. Further, multi-criteria survey was conducted in 10 counties and appropriate weightings of economic, social and environmental parameters determined.

RSIP2 is expected to deliver an objective and scientific criteria to determine optimal allocation of resources for road maintenance and development based on actual road network requirements. It is also expected to determine road investment priorities which will yield maximum returns to the economy of Kenya. Hence, RSIP2 will be an important tool to institutionalize road asset management in the country. If the customized HDM-4 Kenya workspace is used by all road agencies, the results will improve the accuracy of road investment planning and guide road investment decision making in the country.

However, the predictions of the calibrated HDM-4 model will heavily depend on the historical and current input data, which were generally found to be unreliable (inaccurate and inconsistent), unavailable, or incomplete. The recommendations made below, if implemented, should eliminate the data challenges so far experienced.

6.    Recommendations

Preparation of RSIP2 has been delayed with the main challenge being unavailability of complete, accurate and reliable data sets to run HDM-4. To address the challenges, the paper makes the following recommendations which shouldstrengthenRoad Asset Management practice in Kenya:

1.    Formulating and adopting an Asset Management Policy outlining Government’s road asset management objectives, targets and plans (Figure 2, Appendix).
2.    Establishing a sector wide integrated Road Asset Management System with centralized storage, retrieval and exchange of road network data. This will assist Road Managers to make evidence-based decisions in preparation and implementation of policies and programmes.
3.    Establishing a road management data collection regime for effective monitoring performance of the road network and to measure progress towards achieving overall transport sector objectives.
In addition, road agencies in collaboration with KRB and Materials Testing & Research Department should set up Long-Term Pavement Performance sites for representative sections of the network. Such sites should be clearly identified and permanent warning signs of conventional nature should be placed at both ends of the section to inform the public that these are research sites that will be subjected to frequent inspections and there may be delays in carrying out maintenance works on these sections.

7.    Appendix

Figure 2: HDM-4 Structure

Figure 3: Asset Management System Structure

8.    References
1.    Constitution of Kenya, 2010
2.    Heggie I.G, Management and Financing of Roads: An Agenda for Reform, World Bank Technical Paper No. 275 Africa Series, 1995
3.    Kenya Gazette, Road Register, January 2016
4.    Ministry of Devolution and Planning. (2016). Policy on Devolved system of government: Government of Kenya.
5.    Ministry of Medical Services and Ministry of Public Health and Sanitation (2012). Devolution and Health in Kenya, Consultative Meeting Report; Ministry of Medical Services Ministry of Roads. (May 2011). Road Sector Investment Programme & Strategy 2010 – 2024, (Republic of Kenya).
6.    Mott Macdonald, Strategic Asset Management, Ministry of Transport and Infrastructure, 2013
7.    Potter, J. Graham, Devolution and globalization: Implications for local decision – makers. Paris: Organization for Economic Cooperation and Development (OECD), 2001.
8.    SADC Protocol on Transport, Communications and Meteorology, 1999
9.    SANRAL (South African National Road Agency Ltd). 2004. Annual Report 2004: Sustainability Report. Pretoria, South Africa
10.    World bank website visited on 18th April 2017, http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:22629797~pagePK:64165401~piPK:64165026~theSitePK:469382,00.html
11.    Bennett, C.R. and Paterson, W.D.O. (2000). A Guide to Calibration and Adaptation – Volume 5. International Study of Highway Development and Management Series, World Road Association (PIARC), PARIS. ISBN: 2-84060-063-3