NATIONAL DEPARTMENT OF TRANSPORT

Road Infrastructure Strategic Framework for South Africa

A Discussion Document

4. INFORMATION SYSTEMS AND DECISION SUPPORT

This chapter deals with the need to implement and maintain appropriate information and decision support systems (referred to as intra-authority systems) in all spheres of government to support optimal decision-making processes. Current shortcomings in this regard are highlighted by the evaluation of available visual condition data obtained from the provinces. The chapter also identifies the need to implement a national information system (referred to as an inter-authority system) to support decision-making processes at the National Department of Transport and the proposed roads co-ordinating body.

4.1. Introduction

Sound decisions should be made on the basis of validated and accurate information. In terms of Section 6 (1) of the National Land Transport Transition Act (NLTT), 2000 (Act No. 22 of 2000), this responsibility is assigned to the Minister of Transport. This section in the Act reads as follows: The Minister must develop, establish and maintain a national information system with regard to land transport, based on sound business process, and in collaboration with the provinces integrate that system with the information systems kept by provinces. Assigning responsibility for all aspects regarding setting up and maintaining information and decision support systems in all spheres of government is a prerequisite for the success thereof. In order to address this issue of information and decision support, this chapter firstly defines key concepts relating to the management of road networks. It then gives an overview of the current situation regarding management systems used at different spheres of government. It also describes a holistic approach towards managing road networks. This is followed by an overview of the condition of the South African road network and a discussion of optimum road network condition and service delivery. Given the importance of appropriate systems and the need for these systems to be in place to ensure good governance outcomes and optimal service delivery of the road network, conclusions are drawn and recommendations on the way forward are made.

4.2. Management Systems (MS)

4.2.1 Definition of key concepts

A number of important concepts are considered in the ensuing discussion. For the sake of clarity, their meanings for the purpose of this discussion are defined and explained below. The concepts and their linkages are also illustrated in Figure 12.

Figure 12: Generic Intra-Authority Road Management System.

Asset Monitoring:

Asset Monitoring (AM) is defined as that process whereby the condition and behaviour of road assets are assessed and recorded on a regular basis. This may be done on a formal and/or informal basis, where "informal basis" refers to feedback from road users, which typically occurs on an ad hoc basis. These road assets typically are the responsibility and under control of the road authority. See bottom sphere in Figure 12, which typically illustrates the generic structure of an intra-authority management system.

Road Asset Management System:

A Road Asset Management System (RAMS) is defined as a structured procedure, directed in a functionally integrated manner, to facilitate coordinated over-all management of a road authority aimed at ensuring the desired level of service delivery. A RAMS typically comprises the various (functional) Management Systems (MSs), such as the Construction Management System (CMS), Pavement Management System (PMS), Maintenance Management System (MMS), Bridge Management System (BMS), etc. See middle sphere in Figure 12.

Management Information System:

A Management Information System (MIS) is defined as any systemised and structured procedure whereby information pertaining to a well-structured management problem is gathered, retained, manipulated and/or supplied, either manually or by computer, in terms of pre-defined filters, aggregation rules, objective functions and/or optimisation algorithms. See middle sphere in Figure 12.

Decision Support System:

A Decision Support System (DSS) is defined as a system aimed at addressing semi-structured or unstructured strategic decision-making problems, and therefore facilitates the explorative search adaptation and use of multiple models, perspectives, or problem-processing methods. See also top management sphere in Figure 12.

Note: Typically, DSSs make use of certain key performance indicators (KPIs) to aid decision-making at strategic level, delivery strategy and to track changes of the road networks over time. For road performance, typical KPIs include the International Roughness Index (IRI) and the Visual Condition Index (VCI). KPIs also include funding indices such as costs/km, backlog costs, future costs, indices on poverty alleviation in order to aid in mobility and accessibility, etc. They also include scenario planning to trace and project functional and structural services to the public. There is a need to develop more South Africa-specific KPIs to assist in its current developmental state of road maintenance and provision both in the intra- and inter-authority management systems. See Section 4.2.2 for more details.

Management System:

A Management System (MS) is defined as any structured process or procedure whereby information may be gathered, retained, manipulated and/or supplied, manually or by computer, in order to enable effective and efficient management. Figure 12 illustrates the generic structure of an intra-authority management system.

Intra-authority and inter-authority systems:

It is important to distinguish between intra-authority and inter-authority systems. An intra-authority system is defined as the system needed to enable a particular road authority to manage the road network effectively within its area of jurisdiction. The RAMS (as defined above) therefore is an intra-authority system. On the other hand, an inter-authority system is defined as a system needed for the coordination of activities between any selected group of road authorities, normally at a strategy level, in pursuing common goals and objectives. It consists of key information sourced from selected intra-authority systems and combined into a single inter-authority system. The data types contained therein would therefore be information needed for managing the road network for a specified region at a strategy level.

4.2.2 Effectiveness criteria and road network performance indicators

A country’s road network should be efficient in order to maximise economic and social benefits. Performance indicators are generally used to measure the efficiency or otherwise of this network. These performance indicators generally form part of a comprehensive performance management framework. Intrinsic to this aim is the acceptance of a role statement for this network. A suggested role statement for South Africa’s road network is as follows:

The road system comprises the road network and its users (vehicles, drivers and pedestrians) as well as vehicle loadings of passengers and freight. It is an integral part of the transport system and plays a significant role in achieving effective land-use and regional development and contributing to the overall performance and social functioning of the community.

In contributing to the community’s broad economic, social, and environmental goals, the principal role of the road system is:

To facilitate interaction between people and the exchange of goods and services by providing effective equitable land-based accessibility to a wide range of places and by enabling safe reliable mobility of people and transport of goods with the efficiency required to compete in the global economy.

A variety of performance measures are used in the roads field but they are rarely used consistently by the various authorities and are often employed at operating level as opposed to road system or programme level. Road officials have traditionally used performance measures to assist in decision-making regarding capacity enhancements and, more recently, maintenance activities. Sufficiency ratings measure the overall condition, safety and service level of road network segments by comparing their physical and operating characteristics against a set of minimum design standards. Historically present serviceability ratings (PSR), obtained from panels of individuals travelling down a road segment and rating the ride experience on a 1 to 5 scale, were used to measure rideability.

A second measure, present service ability index (PSI), obtained by mechanical equipment towed by a vehicle, was also used and correlated with PSR. However, PSR and PSI have largely been replaced by the International Roughness Index (IRI), a more objective measure of pavement roughness obtained from vehicles equipped with sensors that measure the longitudinal surface profile of a road. The resulting data are converted into a scale that represents road roughness.

Traditionally roughness data, along with various other measures of pavement condition, as well as the condition of shoulders drainage structures and roadside features, are also commonly used as outcome measures of highway maintenance programmes. These programmes also measure labour productivity and unit costs and sometimes quality of the work performed, in addition to the outcome measures to track their overall performance. Levels of service (LOS) ratings have long been used in highway systems planning and traffic operations. LOS measures roadway performance on an A to F scale for different classes of highway, with LOS A generally representing "free flow" conditions and LOS F representing gridlock.

Performance measures have also been used routinely by public transit agencies for some time both to manage their own systems and to report on their performance. Commonly used measures pertain to operating costs, labour and vehicle productivity, safety, service quality and reliability, ridership, utilization ratios, cost-effectiveness and financial performance.

Although most of the traditional performance measures used in the field are operationally oriented, broader models have been suggested to facilitate tracking the performance of road systems in their larger societal context. Whereas LOS consideration figures prominently in the planning of highway construction and traffic engineering projects, they may be limited when interpreted as comprehensive performance measures. Claiming that LOS measures are simply proxies for vehicle operating speed, one observer called for a paradigm shift in which four other kinds of measures would be used to guide transportation decision making in an era of growth management, as follows:

• Mobility, measuring the ease with which individuals can move about on highways or other transportation modes;
• Accessibility, measuring the ease with which desired activities can be reached from any particular location;
• Liveability, representing general attractiveness and quality of life in the immediate areas served by transportation facilities, and
• Sustainability, representing the ability of transportation facilities and services to meet travel needs of the present without compromising future generations in terms of impacts on natural resources and the environment.

On an international level, a working group consisting of highway and other transportation administrators convened by the Europe-based Organization for Economic Cooperation and Development (OECD) has developed a "Family of Measures" designed to represent the performance of transportation systems from three different perspectives, including government, road administrators and road users. Much work, however, needs to be done before these measures can be used in South Africa.

Operating statistics and performance measures are often collected by U.S.A. federal agencies from state DOTs and other transportation agencies and these data can be distilled to provide a composite view of the nation’s transportation system. The most frequently used of these reporting systems include the Highway Performance Monitoring System (HPMS) and the National Bridge Inventory System maintained by the Federal Highway Administration, the Fatal Accident Reporting System (FARS) maintained by the National Highway Traffic Safety Administration, and the National Transit Data Base (NTDB) maintained by the Federal Transit Administration.

There is thus much work to be done in developing a comprehensive set of performance indicators for the South African road network, linked to the proposed functional classification of roads (see Chapter 3). This task should be addressed with urgency.

As an example, some of the performance indicators used for the Australian and New Zealand road networks¹⁴ are presented in Tables 9 to 11.

Table 9: Principal stakeholder outcomes

Table 10: Description and purpose of performance indicators (1)

Table 11: Description and purpose of performance indicators (2)

4.2.3 Overview of current situation regarding management systems

The overview presented in this section is based on extensive discussions and interactions held with road authorities over a period of time, as well as on discussions with consultants and other experts in the field. A comprehensive, scientific audit of Road Asset Management Systems (RAMSs) currently used in South Africa would still need to be conducted.

Overview of intra-authority systems

Table 12 explains the current situation regarding intra-authority systems. For each sphere of government, the following are indicated:

• Whether there are systems in place, how good they are, problems encountered, examples of good systems (where applicable);
• How systems for "peer" authorities (authorities in the same sphere of government) compare, and
• How systems between different spheres of government compare (i.e. national, provincial and three municipal spheres).

Table 12:Current situation regarding intra-authority systems in different spheres of government.

Table 12 reveals the wide spread regarding progress made in implementing management systems in the different spheres of government. At national roads level, for example, key systems are already in place, whereas almost half of the local municipalities have no systems at all. The general pattern that emerges is that the situation deteriorates as one moves from the top to the bottom of Table 12. Table 12 therefore highlights areas to which attention and resources for setting up information systems should be focused.

Overview of inter-authority system

At present, there is no custom-made inter-authority system specifically set up for managing the road network at a strategy level. The annual "Transport Statistics" document (currently in paper-based format only), published by the National Department of Transport, does go some distance in providing such information. However, it was never intended as an intra-authority management system. Also, it does not provide any information and/or key performance indicators for the municipal sphere of government. It is suspected that this is due to the fact that systems are not always in place for this sphere of government and, consequently, such information is not available. This highlights the need for inter-authority systems to be put in place. Once this is done, these systems can source a "purpose-made" intra-authority system that, in its turn, will facilitate the preparation of the "summary" documents such as the annual Transport Statistics publication.

4.2.4 Towards ideal management systems for South African road networks

Intra-Authority Road Management System

As a result of the identified lack of intra-authority systems summarised in Table 12, it is suggested that urgent attention be given to setting up such systems. As discussed previously, a generic intra-road management system is illustrated in Figure 12. The design of this figure is self-explanatory, but in essence, the road network (asset) data captured from the jurisdiction of the road authority feed directly into the MIS with its various road asset managements systems (RAMS, such as the PMS, BMS, GMS, AMS (including crash data base) etc. After validation, analyses and value addition the data enter the DSS at a strategic level. Inputs from political, social and other regional (spatial) information are analysed in the DSS sphere and are evaluated with the local information. The links to other road authorities (i.e. inter) are also shown in the figure. This intra-authority management system should be seen as an integral part of the IDPs, e.g. in the municipal sphere, which makes use of the local information typically produced in the MIS/DSS sphere. These road management systems ideally should link with a central database of selected information and key performance indicators under supervision of the proposed roads co-ordinating body (Figure 6, Chapter 2).

Example

In Section 4.3 an example is used from the Pavement Management System (PMS) in the RAMS, which is housed in the MIS sphere, together with all the other management systems. (i.e. BMS, GMS, TMS, etc.). See shaded area in the MIS sphere in Figure 12. For example, from the PMS data, the Visual Condition Index (VCI) is extracted and used as a performance indicator for change in road condition over time. These data sets are also housed in the MIS sphere in the road management system as shown in Figure 12 (shaded area).

Inter-Authority Road Management System

To facilitate inter-authority linkages on a nation wide scale, both vertically and laterally, selected information from each road authority’s MIS and DSS should ideally be captured centrally in a national "nationwide" information system, as is required by the NLTTA, (Act no. 22 of 2002). The proposed roads co-ordinating body discussed in Chapter 2 and shown in Figure 6 can effectively manage this. The linkages of such a national information system with all road authorities are generically illustrated in Figure 13.

Figure 13: Proposed relationship between regional and national road management, and a central nationwide database of selected information under supervision of the proposed roads co-ordinating body

4.3 Road Network Condition, Management and Service Delivery

4.3.1 Problem Statement

The general maxims related to good management also hold true for road networks. Hence, the need for current validated information with which to support efficient management decision support systems is indispensable for the effective management of a road network, at both the strategic as well as the operations level. It facilitates good governance outcomes and optimal service delivery. In this section, it will become clear that the basic network information with which an efficient decision support system can be maintained is lacking in many road authorities. The need to rectify this unhealthy situation motivates the extended conclusions and recommendations given at the end of this section.

4.3.2 Present condition of the road network in South Africa

Background

The general state (or condition) of a gravel or paved¹⁵ road network system is usually described in terms of a Visual Condition Index (VCI) and is normally reflected in the PMS. The VCI of a road network is ideally quantified annually (or bi-annually) and, if given over time, it shows the trend in road conditions. The VCI uses a five-point scale, i.e. Very Good, Good, Fair, Poor, and Very Poor, as defined in document TMH 9¹⁶, which is well established road pavement engineering methodology in South Africa. The higher the VCI, the better the service delivered to the public in terms of accessibility and safety, as well as of vehicle operation costs (VOC) and overall economy. Referring to Figure 12, the VCI is obtained from the information in the PMS. In principle, the VCI key performance indicator can be measured for paved and un-paved (gravel or earth) roads.

Figure 14 indicates the normally accepted reduction in VCI over time for any road pavement. It is clear that normal road deterioration will occur because of normal traffic use and environmental factors. However, the rate of deterioration could be drastically increased by factors such as overloading and/or heavy rainy periods. The figure also indicates that the condition of the road can be improved with normal maintenance activities, such as crack sealing, diluted emulsion treatment and resealing, especially if done timeously (preventative maintenance). Once a terminal level of VCI is reached, rehabilitation of the facility, implying one or other form of reconstruction, is needed.

Figure 14: Change in Road Network condition over time.¹⁷

Example of Gautrans

Figure 15 gives an example of the Network Condition Number (NCN) monitoring, computed from a weighted VCI over time, for Gauteng provincial roads – one of the more prosperous provinces. The figure shows that the general condition of the road network of Gautrans decreased from a healthy NCN of 78 per cent (ideal state is generally accepted to be 75 per cent of roads in good to very good condition) during 1985, to a disconcerting 56 per cent in 1996/97. Currently, the NCN has stabilised at around 57 per cent. While by far the majority of the other road authorities show similar and often worse trends when their visual condition information is analysed, this specific type of graph is not easily obtainable, since few of the provinces monitor their network condition as diligently as Gautrans. It should be noted that this detailed type of assessment is currently still only being done for the surfaced component of the provincial road network. However, some of the roads authorities have lately started doing it to a lesser degree of detail on their gravel road component.

Figure 15: Road network condition over time for Gautrans (since 1985).

(It should be noted that the trend of deteriorating NCN in Gauteng roads was arrested only when a greater investment of funds into pavement rehabilitation and maintenance activities was made – R 88 million in 1998 by comparison with R 30 million in 1994.)

Provincial Road Condition Statistics

The condition and trends of the South African surfaced road network (about 56 464 km) based on past and current best information from a desk study by the CSIR, and also defined by the VCI for all the provinces for 1999/2000/2001/2002¹⁸, is illustrated in Figures 16 and 17, and Table 13. In Figure 16, the VCI of the road network for each province is given on a comparative basis, while Figure 17 compares the "good" roads component with the "poor" roads component.

In a recent survey, only five provinces (Free State, Gauteng, KZN, Northern Cape and Western Cape) were able to present new VCI information. The summary of the weighted VCI given in Figure 18 is based on this new information. Although there seems to be a reduction¹⁹ of the percentage of roads in "poor" condition, by comparison with those reported in CR-2001/9 (2001), the percentage of "very poor" roads increased to approximately 12 per cent. This represents an increase of approximately 3 per cent in roads in the "very poor" category since 2000.

Figure 16: VCI of all surfaced roads in all provinces showing trends over time²⁰

Figure17: VCI of provincial roads comparing the proportions of "good" and "poor" roads.

Table 13: VCI data of provinces, reflecting the percentage of surfaced roads in a poor to very poor condition (1997 to 2002).

( - ): No data available from authority.

4.3.3 Discussion of the condition of the surfaced provincial roads

The reader is referred to Figures 16 and 17 and Table 13. The figures for 1997 to 1999 are from report CR-2001/9 (2001). To afford a basis for evaluation it should be kept in mind that it is generally acceptable to have 5 to 10 per cent of a road or road network in a "failed" or "very poor" condition for a limited period before remedial action is executed.

Eastern Cape (EC): The only surveyed data available are those for 1997, when 41 per cent of the road network was in "good" condition and 21 per cent of the network was in "poor" condition. Telephonic discussions with provincial officials indicate that conditions have deteriorated even further.

Free State (FS): The general condition of the road network in this province is very poor indeed. It started in 1997 with about 44 per cent being in "poor" and "very poor" condition and deteriorated by a further 23 per cent to a most alarming figure of 67 per cent in 2001. The roads in "good" condition decreased by 13 per cent.

Gauteng (GP): While starting with an unacceptable 28 per cent of the network in "poor" and "very poor" condition in 1997, it seemed as if this province might turn the tide. Unfortunately, by 2002, it could barely maintain the status quo. The roads in "good" condition decreased by 8 per cent, while the roads in "poor" condition remained basically the same.

KwaZulu-Natal (KZN): Indications are that the condition of the roads in this province is the poorest of all the provinces. Even so, a further increase (6 per cent) in the percentage of roads in a "poor" condition occurred from 1997 to 2001. A marginal improvement in the condition is observed - the roads in "good" condition increased by 2 per cent. This improvement could possibly be due to increased funding and improved service delivery.

Limpopo (LP): This province started off in 1997 with a very acceptable 5 per cent of its road network being in a "poor" condition. Although surveys were not carried out every year since then, it may be noted that this figure had increased by 13 per cent, to an unacceptable 18 per cent in 2000. Telephonic discussions with provincial officials indicate that this percentage has increased even further.

Mpumalanga (MP): The only information available is that for 1999. In the 1997 survey 25 per cent of the road network was in "good" condition and that 33 per cent of the network was in "poor" condition. Telephonic discussions with provincial officials indicate that no other surveys have been done and that the road network condition has deteriorated alarmingly over the past few years.

Northern Cape (NC): The only data available are for the years 1999 and 2001, during which period a notable improvement in the general road network condition occurred. The roads in "good" condition increased by 11 per cent and the roads in "poor" condition decreased by 9 per cent. Telephonic discussions with the provincial officials indicate that they are making a concerted effort to maintain this positive trend.

North-West (NW): The only data available are for the years 1998 and 2001 (obtained by telephonic discussion). In the 1998 survey, 54 per cent of the road network was in "good" condition and a disconcerting 15 per cent of the network was in a "poor" condition. The percentage of the network in a "poor" condition increased to about 20 per cent during 2001.

Western Cape (WC): It seems that this province has been more successful than the other provinces in maintaining the general quality of service of its road network. It had a very low percentage of roads in "poor" condition in 1997 and maintained this level noticeably well over the following years, fluctuating between 6 per cent and 10 per cent, with 8 per cent being in "poor" condition in 2001. The roads in "good" condition decreased on average by about 4 per cent during this period.

4.3.4 Summary: The general picture on road condition

In terms of the VCI scale, it is apparent from Figures 16, 17 and 18 that approximately 35 per cent of the surfaced provincial roads are in a poor to very poor state. This percentage is somewhat higher than the 33 per cent reported in an earlier study (in 2000) by the Automobile Association (AA) and approximately similar to those reported in the previous NDoT study of March 2001 (CR-2001/9). According to the data in the present report, a notable deterioration has occurred - approximately 3 per cent of the road network has moved from "poor" to "very poor". Between July 2000 and the year 2002, the total has grown from 9 to approximately 12 per cent. [Note: Given the fact that the most recent available data are for only five of the nine provinces, and for none of the local municipalities, the actual condition of both surfaced and gravel (un-surfaced/un-sealed) roads could be much worse than the 35 per cent (poor to very poor) reported here]. To put this into perspective, it should be borne in mind that in 1988, only about 5 per cent of the provincial rural road network was in "poor" to "very poor" condition. This trend certainly warrants concern. It is clear that at this rate the country will soon find itself - if it is not there already - in the untenable position in which more funding and construction resources than can be mustered in the country will be necessary to rectify the situation. The general consensus is also that the condition of gravel (or unsealed) roads is far worse than that of surfaced roads. Unfortunately, for most provinces there are no official working pavement management systems for unsealed roads. This system still needs to be implemented in accordance with the guidelines given in draft TMH 12.²²

It is again noted that, ideally, not more than 5 to 10 per cent of the roads networks should be in a poor to very poor condition (according to the VCI performance indicator) for a limited period before remedial action is executed.

4.3.5 Scenario if funding is not spent

It is clear from the above that, at current funding rates (see Chapter 5), the target of having less than 10 per cent of all the roads (gravel and paved) in South Africa in a poor to very poor state (i.e. 90 per cent of the roads in a fair to very good condition) is extremely high. If road maintenance is delayed, the cost for repairs, rehabilitation etc. increases exponentially. According to SANRAL²³, a delay in road maintenance of 3 to 5 years increases the required repair costs by between 6 and 18 times. Also, because of the subsequent decrease in riding quality, the vehicle f VCI information, the available VCI statistics indicate a worsening scenario for the surfaced roads in most of the provinces. Therefore, a scenario of not spending on road infrastructure can simply not be allowed.

Figure 18: Most recent weighted average VCI of all surfaced roads in five provinces. ²⁴

4.3.6 Towards optimum road network condition and service delivery

The information contained in Figure 18 illustrates the condition of the road network since the CR-2001/9 (2001) report was issued and Table 13 shows how this change has manifested itself on an annual basis since 1997. Table 13 also reveals that more than half the provincial road authorities curbed or stopped doing the surveys. Although the data afford ready assessment of the service delivery performance of the provinces in terms of road network (accessibility and mobility), another disturbing factor becomes very clear. Some provinces have very little quality information on which to base managerial performance evaluation and needs-identification processes. It seems that this could be one of the primary causes for the poor condition of the provincial road networks in general. Lack of funding is not necessarily the sole or even the main reason for the poor condition of the country’s road network, but lack of appropriate managerial decision support information could very well be an important cause of this.

Clearly, the basic key performance parameters (indicators) of a number of the provincial road networks are not monitored sufficiently to afford an overall picture of the network condition and needs, let alone to allow for timely planning and cost-efficient intervention. Although this type of information could not be obtained from the municipalities, discussions suggest that the same situation is relevant to these roads authorities. This situation in turn reflects negatively upon the availability and application of managerial experience and insight – good governance. It is a basic management principle that the less resources/funding one has, the more systematic and exact one’s management and control of one’s activities and expenditure need to be.

4.3.7 RNMS, COTO and the new NDoT Transport Infrastructure Division

Chapter 2 pointed out the need for institutional restructuring and development programme regarding management and service delivery of the road network, on an intra-, as well as an intergovernmental sphere (i.e. within government spheres and between different government spheres). In this respect, the previous "Road Network Management Systems (RNMS) Committee", a subcommittee of the Committee of Transport Officials (COTO), could play a very valuable role. The mission of the former RNMS Committee was initially defined as follows: To coordinate and guide local development and application of RNMSs, with special attention to PMSs, towards national compatibility (harmonization) and integration in accordance with the National Transport Policy. The former "RNMS Committee" has been working in this direction for a number of years, being instrumental in the drafting and publication of various guidelines (e.g. TRH22, TMH9 and TMH12) related to the topic of road network management.

However, at the time of its transformation from COLTO to COTO (some two years ago), COTO suspended the activities of all of its subcommittees in order to redefine and restructure itself. In July 2002, however, the chairman of the COTO Road Co-ordinating Committee (RCC) approved for the re-activation of the RNMS Committee. This action opened up the ideal opportunity for the RNMS Committee to apply its mandate towards the motivation and assistance of member authorities; to re-instigate regular network condition monitoring; to supply applicable management information, and to prepare managerial decision support information.

Strictly speaking, the successful completion of such an "RAMS restructuring programme" must address all the MSs in order to define, initiate and maintain the optimum management framework (RAMS), at intra- and inter-government spheres. This would necessitate the participation of all the COTO subcommittees that are responsible for any of the "planetary" MSs. Not only that, but the team should also include representatives from all the government spheres, in order to achieve optimum buy-in and government sphere development. The newly instituted Transport Infrastructure Division of the NDoT has a vital role to play here, within the context of the newly proposed delivery model as discussed in Chapter 2 (see Figure 6). As discussed in Chapter 2, this body is to incorporate representation of the 284 municipalities.

Footnotes:

14. Austroads. Australian and New Zealand Road System: National Performance Indicators - 1999. Austroads, Sydney, 2000.
15. Paved = Surfaced = Sealed
    Unpaved = Unsealed = Unsurfaced
16. CSRA, 1992. Committee of State Road Authorities (CSRA). 1992. Pavement Management Systems: Standard Visual Assessment Manual for Flexible Pavements, Draft TMH 9. Department of Transport, Pretoria, 1992.
17. Adapted from: Gautrans, 1999. Road Network Management System. Annual Report 1998/1999. Gautrans, Department of Public Transport, Roads and Works. Report No. RNMS/D/12/E, Edition 1.0, Gauteng Provincial Government, November 1999.
18. The reason why the VCI data are spread over a number years is because VCI surveys were not done (and/or reported) annually in all the provinces.
19. This decrease noted for the roads in the “poor” category could also be due to uncertainty about the actual km of surfaced roads in South Africa. The total surfaced road reported in CR-2001/9 was 56 464 km, whereas Transport Statistics (2001) reported approximately 63 000 km.
20. Assessment of Provincial Rural Road Infrastructure in South Africa. CR-2001/9. National Department of Transport, Pretoria, March 2001.
21. Data Source: NDoT/CSIR Contract Report CR-2001/9, March 2001, updated by telephonic and e-mail interaction between CSIR and Provincial Road Authorities, September 2002.
22. CSRA. 2000. Committee of State Road Authorities (CSRA). Draft Technical Methods for Highways – TMH 12.
    Pavement Management Systems: Standard Visual Assessment Manual for Unsealed Roads, Version 1.
23. Horizon Twenty Ten. The Strategic Vision of the South African National Roads Agency Ltd for the Year 2010. SANRAL, Pretoria, 2002.
24. This graph represents approximately 30 000 km of provincial surfaced roads. Data has been updated since the
    publication of NDoT document CR-2001/9.