Rural engineering infrastructures design and public facility locations

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List of Figures

Figure 2.1: The accessibility planning cycle (Dixon-Fyle, 1998). ... 15

Figure 2.2: Link options from unconnected settlement (Singh, 2010). ... 19

Figure 2.3: System approach to rural road development (Kumar & Kumar, 1999). ... 23

Figure 2.4: Transport network generation model (Shrestha & Routray, 2002). ... 28

Figure 2.5: Rural road network generation using accessibility criteria (Singh, 2010). ... 30

Figure 2.6: The graph of a typical area (Makarchi & Tilloston, 1991). ... 33

Figure 2.7: The MST for the typical area (Makarchi & Tilloston, 1991). ... 34

Figure 3.1: Longitudinal geological subdivision of Nepal Himalaya (Gansser, 1964). ... 47

Figure 3.2: Physiographic features of Nepal (RAIDP, 2009). ... 53

Figure 3.3: Settlements and cultivated land in hill slopes of Nepal (Google Earth, 2013). ... 53

Figure 3.4: A Model for young fold mountains (Fookes et al., 1985). ... 55

Figure 3.5: A typical cross-section in cut and fill (DRILP, 2006). ... 63

Figure 3.6: width of cut vs volume of cut (slope 1º). ... 64

Figure 3.7: Slope vs volume of cut (2m width). ... 64

Figure 3.8: A typical cross-section in full cut (DRILP, 2006). ... 65

Figure 3.9: A typical Cross-section in mild slopes (DRILP, 2006). ... 65

Figure 4.1: Location of the VDC centre and rural roads network in the study area. ... 79

Figure 4.2: Nodal villages obtained from the solution of the covering problem (case study). 81

Figure 4.3: MST of the rural road network (case study). ... 81

Figure 4.4: Settlements in case 1: Gorkha A. ... 83

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Figure 4.6: Settlements in case 3: Lamjung A. ... 85

Figure 4.7: Settlements in case 4: Lamjung B. ... 86

Figure 4.8: Effect of coverage in service distance. ... 88

Figure 4.9: Rural road network in the study area: Gorkha A. ... 93

Figure 4.10: Rural road network in the study area: Gorkha B. ... 93

Figure 4.11: Rural road network in the study area: Lamjung A. ... 94

Figure 4.12: Rural road network in the study area: Lamjung B. ... 94

Figure 4.13: Identified nodal points and MST of the rural road network (Gorkha A). ... 95

Figure 4.14: Identified nodal points and MST of the rural road network (Gorkha B). ... 95

Figure 4.15: Identified nodal points and MST of the rural road network (Lamjung A). ... 96

Figure 4.16: Identified nodal points and MST of the rural road network (Lamjung B). ... 96

Figure 4.17: Backbone and branch rural road network (Gorkha A). ... 98

Figure 4.18: Backbone and branch rural road network (Gorkha B). ... 100

Figure 4.19: Backbone and branch rural road network (Lamjung A). ... 101

Figure 4.20: Backbone and branch rural road network (Lamjung B). ... 103

Figure 4.21: Structure of backbone and branch road network. ... 104

Figure 5.1: Rural road network for model application. ... 126

Figure 5.2: Node numbering scheme for model RRNM-3 and model RRNM-4 with backbone and branch links. ... 127

Figure 5.3: An optimal network intervention for a budget of NRs 400 million (RRNM-1). . 135

Figure 5.5: An optimal network intervention for a budget of NRs 30 million (RRNM-2). ... 136

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Figure 6.1: Rural road network for application of model. ... 148

Figure 6.2: Pareto frontier for budget level NRs 400 millions. ... 149

Figure 6.3: Pareto frontier for budget level NRs 600 millions. ... 150

Figure 6.4: Decision options and surface level of links for budget level NRs 400 millions (contd. …). ... 151 Figure 6.4: Decision options and surface level of links for budget level NRs 400 millions. . 152

Figure 6.5: Decision options and surface level of links for budget level NRs 600 millions (contd. …). ... 152

Figure 6.5: Decision options and surface level of links for budget level NRs 600 millions. . 153

Figure 6.6: Map of the planning region. ... 156

Figure 7.1: Proposed rural road network planning process. ... 161

Figure 7.2: Proposed multi-objective rural road network planning process. ... 163

Figure C1: Settlements and rural road network in the study area: Gorkha A ... 189

Figure C2: Settlements and rural road network in the study area: Gorkha B ... 190

Figure C3: Settlements and rural road network in the study area: Lamjung A ... 191

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List of Tables

Table 2.1: Road density and distance to roads (United Nations, 1979) ... 13

Table 2.2 Examples of Accessibility Indicators (AI) (Edmonds, 1998) ... 15

Table 4.1: Coverage provided by the nodal points for a service distance of 4 km ... 80

Table 4.2: Coverage of settlements using various covering distances ... 87

Table 4.3: Coverage of the health centre by nodal points ... 89

Table 4.4: Coverage of the market centre by nodal points ... 89

Table 4.5: Coverage of schools by nodal points ... 90

Table 4.6: Coverage of public facilities by nodal points ... 90

Table 4.7: Location of nodal points and linkage to the nodal points-Gorkha A ... 97

Table 4.8: Location of nodal points and linkage to the nodal points-Gorkha B ... 99

Table 4.9: Location of nodal points and linkage to the nodal points-Lamjung A ... 101

Table 4.10: Location of nodal points and linkage to the nodal points- Lamjung B ... 102

Table 4.11: Rural road network linkage lengths ... 105

Table 5.1: Scoring system for prioritization of new linkages (DoLIDAR, 2010) ... 122

Table 5.2: Scoring system for prioritisation for upgrading and rehabilitation (DoLIDAR, 2010) ... 122

Table 5.3: Traffic Unit (DoLIDAR, 2010) ... 123

Table 5.4: Weight based on population, person-km, population per unit construction cost and gravity flow model ... 129

Table 5.5: The intervention in the network link at different budget levels based on P1

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(RRNM-2) ... 131

(RRNM-3) ... 132

(RRNM-4) ... 133

Table 6.1: Non-dominated solutions for budget level NRs 400 millions ... 149

Table 6.2: Non-dominated solutions for budget level NRs 600 millions ... 150

Table 6.3: Preferable solutions for budget level NRs 600 millions ... 154

Table 6.4: Comparisons of preferable solutions for budget level NRs 600 millions ... 155

Table D.1: Distance Matrix (km) for Network-Gorkha A ... 193

Table D.2: Distance Matrix (km) for Network-Gorkha B ... 194

Table D.3: Distance Matrix (km) for Network-Lamjung A ... 195

Table D.4: Distance Matrix (km) for Network-Lamjung B ... 196

Table E.1: The intervention in the network link at different level of budget based on P2 (RRNM-1) ... 197

Table E.4: The intervention in the network link at different level of budget based on P2( RRNM-2) ... 200

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Table F.1: Solutions for budget level NRs 400 millions ... 209

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List of Acronyms

B/C Benefit Cost Ratio

BB Backbone and Branch

BCA Benefit Cost Analysis

CEA Cost Efficiency Analysis

CRND Continuous Road Network Design

DDC District Development Committee

DM Decision Maker

DoLIDAR Department of Local Infrastructure Development and Agricultural Roads

DRND Discrete Road Network Design

GIS Geographical Information System

HDM Highway Development and Management

IMT Intermediate Means of Transport

IRAP Integrated Rural Accessibility Planning

IRR Internal Rate of Return

KP Knapsack Problem

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MCLP Maximal Covering Location Problem

MCT Main Central Thrust

MFT Main Frontal Thrust

MOP Multi Objective Problem

MPL Mathematical Programming Language

MST Minimum Spanning Tree

NDP Network Design Problem

NMT Non motorized transport

NPV Net Present Value

NRs Nepalese Rupees

O/D Origin and Destination

PR Priority Ranking

RED Road Economic Decision

RND Road Network Design

RRNM Rural Road Network Model

SAIPAL South Asian Institute for Policy Analysis and Leadership

SBD Suspension Bridge Division

STDS South Tibetan Detachment System

TRL Transport Research Laboratory

TU Traffic Unit

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VDC Village Development Committee

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Chapter 1

Introduction

1.1 Rational

Basic private and public infrastructures are essential for the operation and/or development of a society or enterprise. They are infrastructural facilities that support an entire structure of development. These infrastructural facilities refer to those basic services for functioning of primary, secondary and tertiary productive activities (Hirschman, 1958). The infrastructural facilities embrace all public services from law and order through education and public health to transportation, communications and water supply (Mabogunje, 1974; Kahn, 1979). In other words, these represent elements of basic needs, which communities would like to procure for better living. Regarding rural infrastructure facilities, Kahn (1979) asserts that they can be classified into three main types; namely,

(a) physical infrastructure – such as roads, water, rural electrification, storage and processing facilities;

(b) social infrastructure – health and educational facilities, community centres, fire and security services; and

(c) institutional infrastructure – credit and financial institutions, agricultural research facilities and social infrastructure.

The adequate provision of physical and social infrastructures will enhance the introduction and adoption of innovations offered by institutional infrastructure which is an important term used for judging the development level of a country.

Gramlich (1994) gives three different versions for the definition of infrastructures. From an economical standpoint, the first version consists in large capital-intensive natural monopolies such as highways and other transportation facilities, water and sewer lines, and communications systems. The second version focuses on ownership and is defined as the

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tangible capital stock owned by the public sector. The third version includes successive human capital formed by investment in research and development.

Generally, when the research community refers to infrastructure, they mean roads, telecommunications, electrifications, and irrigations that are named as hard infrastructures. Other types of infrastructures are institutional and are named as soft infrastructures.

Soft infrastructures refer to the institutional environment or way in which business is done. It includes various services such as those relating to transport, finance, animal husbandry, and marketing. Soft infrastructure and hard infrastructure are interlinked and interact with one another. Wanmali and Islam (1997) state that investments in infrastructure and the associated provision of services, are integral to the process of development. The key role of hard infrastructures investment in improving agriculture production and facilitating the growth of soft infrastructure in developing countries in particular, is emphasized (Ahmed and Donovan, 1992). Wanmali and Islam (1995, 1997) and Stern (1989) argue that limiting infrastructure to hard infrastructure such as roads, telecommunications, electrifications and irrigations is narrow, and that soft infrastructure (also named social infrastructure) are also very important. However, for efficient development and operation of soft infrastructures, the role of hard infrastructures is vital.

In the context of developing countries, the majority of population is concentrated in rural areas. Poverty is largely a rural phenomenon. Most of the rural residents are not integrated into the mainstream of national life. They barely participate in the economic and social activities and, most often, they are surviving with a low level of quality of life. One of the factors of their low quality of life is the absence, or poor quality, of infrastructures. There are wide gaps in the availability of physical and social infrastructure between rural and urban areas. This is considered as an important issue in developing countries. However, the development of infrastructure in rural areas of such countries has been hampered due to lack of funds and proper planning methodologies. Moreover, the quality of soft infrastructure has been suffered heavily due to absence or poor quality of hard infrastructures such as road, water supply, electricity, and telecommunication infrastructures.

Planners and Geographers alike tend to use rural infrastructural development as a strategy to address the problems of rural areas. The term ‘development’ refers to the conscious action of using, in a co-coordinated way, the resources available to a given political unit (Bernstein,

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1978). Accordingly, rural infrastructural development could imply the desirability of overcoming deprivation and low quality of rural life. It could also refer to the provision of bridges, hospitals, schools, electricity, and potable water, in areas where they are lacking. Rural infrastructural development is a positive action that aims to improve the welfare of people.

One of the major elements in infrastructure development in rural areas is related with accessibility and affordability of services to rural communities. The accessibility and affordability of services for the rural people is related to transport and communications infrastructure as there is a significant correlation between poverty and remoteness. Rural road construction is a intervention for raising living standards in poor rural areas (Gannon & Liu, 1997). Agricultural output from rural areas is a very significant component of the national economy in many developing countries. The rural transport systems require as much attention from transport planners as the inter-urban transport (Tolley & Turton, 1995). An adequate access to social services, such as medical and health services; proper nutritional care for the young; and education facilities to peasants, would determine to a large extent the improvement of social and economic welfare of the rural population (Howe & Richards (1984). These are also important determinants to ensure the continued self-sustaining momentum of the rural development efforts (Odoki et al., 2001). This can be improved only when the transport infrastructure is developed. Due to the lack of transport accessibility, basic goods and services do not reach to the majority of the rural population. This issue is more relevant in hilly regions of a developing country. With hilly and mountainous topography, better roads and optimal facility locations reduce isolation and economic vulnerability of rural residents. Enhancement of transport accessibility to settlements and various public facilities is important for the economic survival and welfare of rural communities.

In case of Nepal, the majority of the population is concentrated in mountainous hills of rural areas. The public facilities for the residents are scattered in different settlements. Many settlements of rural areas are not connected to the national road network due to the absence of road connectivity. It is difficult to get goods and public services and to participate in the economic and social activities because of the poor road connectivity, resulting in low quality of life. This has been happening due to lack of development of rural roads network covering the hill settlements and public facilities. The rural infrastructures which have been developed and are underdevelopment also have quality issues. A study (DoLIDAR, 2004) shows that

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only 30% of Nepal is accessible by roads. More than 39% percent of the population in the hills is out of reach to roads within 4-hours walk. The road networks are mostly developed in plain regions and in few parts of hilly regions. Hence, there is a need to extend and develop the rural road networks particularly in the hilly regions of Nepal to integrate public facilities and settlements, connecting rural residents to the national network.

One of the main constraints in the development of rural infrastructure is the lack of sufficient funds in developing countries. Apart from the limited funds to build rural infrastructures (roads, water supply, electricity, telecommunication network) and public facilities, the lack of proper planning methodologies for development, improvement and management of rural infrastructures is also a major problem. Optimal use of available funds is a necessity and may help to develop and improve the present situation.

A research on the planning of rural infrastructures in a comprehensive and integrated manner is a dire need for rural development. However, the major life line infrastructure in rural areas of developing countries is rural roads and also, vital for development and operations of other hard and soft infrastructures. In this way, the topic of this work is envisaged and devoted to a planning methodology for rural road networks in hilly regions that considers the public facilities location and the rural road networks simultaneously.

1.2 Objectives

The main objective of this work is to investigate a rural infrastructure planning methodology considering connectivity to public facility locations.

Based on this objective, the following specific objectives are to be obtained:

 find a planning methodology to locate nodal points for rural infrastructure networks in rural areas,

 find a planning methodology to define a rural infrastructure network, and

 develop models that optimize rural road networks considering public facilities.

There are many works that extensively studied the development of infrastructure in the past. They were however studied independently without consideration of the others. Often, infrastructures have impact on each other. For example, location of facilities, both private and public, in order to serve residents, are constrained by the structure of the designed transportation network. When the network is designed improperly, residents get poor service

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even when facilities are optimally located. As several evidences show there is significant interaction of the network with facility location, it is meaningful to determine the network design and facility locations simultaneously (Daskin and Owen, 1999; Melkote and Daskin, 2001). The study of these two issues together would assist decision makers on how to make an integrated choice, effectively, under limited fund constraints, namely, build schools, expand hospitals, or improve road links (Daskin and Owen, 1999). Therefore, it is important to investigate rural road network models where rural road networks are optimally designed considering existing and new public facility locations to achieve minimum cost comprising construction and operation costs.

Basically, the rural infrastructure planning methodology will be developed based on the rural road networks. This works aims to explore road network patterns in hilly regions in order to cover most of rural settlements and public facilities. A number of case studies will be conducted to explore the network pattern. The applicability and validity of the proposed models will be tested in a real network considering financial and geographic constraints. Furthermore, the application of the developed methodology may be adoptable to the development of rural water supply distribution networks, rural electrification networks, and telecommunication line distribution networks in rural areas.

1.3 Outline

This thesis is organized in seven chapters. A brief introduction and general description of the studies made in this thesis is presented in Chapter 1. General background, the underlying objectives, and the outline of the thesis are described in this chapter.

In Chapter 2, existing rural road models is reviewed from the literature as well as topographical condition of rural areas. Basic concepts of rural road planning are presented in this chapter. The concepts so far developed are found for plain areas. Also, in this chapter the covering aspects of the rural roads are addressed. This concept has been used to develop the proposed rural road network model.

Development of rural roads in hilly regions of Nepal with local conditions is addressed in Chapter 3. The five zone mountain model is reviewed in the chapter in the context of rural road development. Route location and rural road alignment is also discussed.

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Covering based rural road network is presented in Chapter 4. The shortcomings of the existing methods have been addressed in this chapter. A new model for rural road planning, the covering based rural road network model, is proposed in the context of hilly regions. Public facilities (e.g. health centres, schools, and rural markets) have been considered in the study. The model is tested in the road network of 15 Village Development Centres (VDCs) in hilly region of Gorkha district in Nepal. The linkage pattern of rural roads in hilly regions, the backbone and branch (BB) network, one of the outcomes of the study has been presented in this chapter. The application of the methodology developed in this chapter can be extended to other rural engineering infrastructures, such as, development of rural water supply distribution network, rural electrification network, and telecommunication line distribution network in rural areas.

Rural road optimisation models are presented in Chapter 5. A total of four models for rural road network optimisation are proposed. The first model Rural Road Network Model (RRNM-1) is introduced for upgrading of rural road network in plain or hilly regions. The second model RRNM-2 is a general purpose rural road optimisation model for new networks which can be in both plain and hilly areas. The third model RRNM-3 is introduced for upgrading rural road network links in hilly regions and core networks in plain areas. The fourth model RRNM-4 is introduced for new rural road network in hilly regions and core networks in plain areas. Different options of road surface (e.g. earthen, gravel, and asphalt) have been considered in the models RRNM-1 and RRNM-3. The network models have been tested in the road network considered in Chapter 4. Four prioritisation procedures have been introduced for the selection of rural roads links for intervention. An extensive study has been conducted on the applicability of the models with the prioritisation methods.

A multi-objective analysis of the rural road network has been introduced in Chapter 6. A Multi-objective Rural Road Network Model is proposed for the upgrade of rural road networks. The model is an extension to model RRNM-1 and model RRNM-3 developed in Chapter 5. It is a general model and can also consider BB network for hilly regions. The interaction between decision makers (DM) with her/his preference of decision and Pareto optimal solutions has been presented in this chapter.

Main conclusions are drawn in chapter 7, summarising the main findings of each chapter. Moreover, limitations and future research directions are also presented.

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Chapter 2

Rural Road Network Problems

2.1 Introduction

The development of rural roads in developing countries has been increasingly realized by policy makers. For the systematic development of rural roads, some rural road planning models have bean used. There is a rich body of literature on transport network models, particularly focusing on urban problems. Hence, there are many advanced models for developments and improvement of urban transport networks. The urban transportation network planning models are mainly oriented towards choosing improvements or additions to an existing network, in order to reduce traffic congestion, energy consumption, and pollution control (Abdulaal & LeBlanc, 1978). Unlike urban transportation, rural transportation mainly deals with providing connectivity/accessibility to local settlements. As it is still in its developing phases, few papers deal with the subject of rural road network planning and development.

Transport for rural settlements typically encompasses the movement of rural people and their goods to meet their domestic, economic and social needs. This transport can be made by any means, along tracks, paths and roads. A research carried out by the World Bank and International Labour Organization (ILO) shows that transport in rural areas is carried out mostly on foot or with the aid of intermediate means of transport (IMT) (Edmonds et al. 1994). But, these people are generally isolated from the national network due to the absence of connectivity by a road to the main network. As a result, they have no or limited access to goods and services due to the absence or poor quality of physical access (transport) infrastructures.

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conditions. In such cases, connecting each and every settlements and facilities is impossible due to local conditions and the lack of resources. Therefore, the accessibility to the rural hill areas has to be defined in a different sense from the general term accessibility. The ground situation in the area is that the settlements and the facilities can be covered only within a specified distance from a road. Hence, the accessibility in those regions is to be understood in terms of coverage. This issue is emphasized in this study and the existing planning and development methods and models are reviewed on this basis.

The simplest approach to rural road planning is prioritisation of settlements based on their population and socioeconomic characteristics, and connecting them with the shortest road link. Most of the approaches are based on minimal spanning tree concept; inter settlement interaction approach, accessibility criteria, etc. These are said to be most appropriate and scientific approaches to rural road planning. Plenty of researches (e.g. Magnanti & Wong, 1984) consider road network design problem as a special case of network design problem (NDP). However, few works focus on rural road network developed for rural areas.

The following sections review the existing methods/models which deal with rural road network development problems. The relevant concepts and methods to this study are briefly discussed. The shortcomings in the existing models and methods are identified to the context of rural areas particularly focusing for hilly regions and proposed to address the problem in this study.

2.2 Planning methodology

In this section, some existing methodologies for rural road planning and development are reviewed. The review of the existing methodologies will give some background in order to identify some lacking issues. These issues can be incorporated in further development of methodology that will be more rational in specific contexts. The methodologies identified in literature are discussed in the following sub sections.

2.2.1 Priority Ranking (PR)

PR methods (‘Sufficiency Rating’) were used in the early 1950’s for planning maintenance and improvement of U.S. highways (Highway Research Board, 1952). This method is one of the first methods for evaluation of road links. This has been used for planning and

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maintenance of highways. However, the concept has been proposed for adaptation for rural road projects (Carnemark et al., 1976).

PR is a weighted rating technique. An overall rating score Si is determined for each proposed

project i by

(2.1)

Where, Wj is the weight of the j-th considered factor or characteristic; Xij is the score of the

i-th project for i-the j-i-th factor; m is i-the number of factors. The higher i-the Si value, the more

urgent is the project.

However, the method is complicated when benefits of projects are not independent. This is usually a case in rural road planning. The benefit in this case accrues from connection of unconnected settlements by a road and its connection with other roads rather than in monetary terms.

2.2.2 Benefit/Cost Analysis (BCA)

The conventional planning techniques in road planning consider Benefit/Cost Analysis (BCA). The BCA methodology has been adapted to rural road projects by Carnemark et al. (1976). In BCA, benefits are expressed in monetary units and compared with costs; the higher the benefit/cost ratio, the better the project. The main difficulty of BCA is the correct evaluation of all benefits in monetary units.

In areas of sparse development, the guiding principle of network planning has been to enhance savings in crop-production costs and to satisfy access needs for those farmers who will benefit from network improvements (UNCHS, 1985). This concept is known as the "producers' surplus approach". Generally, in resource-constrained developing countries, an extensive low-quality (earth or gravel-surfaced) local rural road network (farm-to-market feeder roads and farm-access roads) is preferred over high-quality roads. The approach illustrates the policy of reducing road-building cost, by providing low-quality roads, while enhancing accessibility levels for rural communities based on the benefit from the construction of rural road. This approach may be suitable for plain areas with high agricultural production. However, there are many settlements located in very low economic

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potential (rural) areas and therefore it is difficult to estimate the benefit from the rural roads.

Shrestha (2003) has developed two computer-aided models for planning and prioritizing district transportation networks in Nepal for both developed and underdeveloped regions. The computer models use GIS for planning the district road network. A combination of the producer’s surplus and consumer’s surplus is used in the study. The prioritization of roads in the developed area is based on the economic net present value (ENPV), economic internal rate of return (EIRR), and the benefit cost ratio (B/C ratio); whereas the socio-economic criteria is used for underdeveloped area, supplemented with the economic analysis. In this study, most of the linkages to the rural settlements are not justified in economic grounds. Furthermore, the economic basis for selecting the rural road linkage avoids connecting the rural village settlements. This methodology is biased to choose links which connect heavily populated and economic centres.

In rural road planning, BCA also becomes more complicated when benefits of projects are not independent. Connection and accessibility are key concepts for the evaluation of rural road patterns (Oudheusden & Khan, 1987). Typically, in developing countries, a fixed budget for road development is allocated to a rural district. Hence, budget restrictions also should be considered in the evaluation of a rural road network. Usually, decision makers are expected to select the best road projects within the budget allocated.

2.2.3 Centrality index

The majority of the trips in rural areas are originated from one population centre and ended in another population centre. The centrality index can be used to assess the relative importance of settlements identified as transport nodes (Shrestha & Routray, 2002). Each settlement has different functions (service centres). The functions can be Education, Health, Business & Commerce, Industry institutions, and offices (Bank, Agriculture Service centre, Veterinary office, Post office, Telephone office, Electricity office, Cooperatives office). These functions attract the trips from other settlements hence, are included in the centrality index (DoLIDAR, 2010).

The centrality index of each settlement can be calculated as follows (Sarma, Routray & Singh, 1984; DoLIDAR, 2010):

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(2.2)

Where,

Cj = Centrality Index of the jth market centre,

Wi = Weight of the jth marketing functions,

Xij = Value of the ithfunction (number of establishments or shops at the jth market centre)

A settlement which has centres for marketing, clinics, schools and other commercial, social and welfare activities is called market centres.

The weight of a function can be obtained based on the median threshold population technique. According to the technique, the weight can be calculated as:

(2.3)

The median threshold population technique calculates the weight as follows. The forecast growth of the centrality index can be based on historical trend and opinion survey of knowledgeable persons. Also, an open ended discussion can be conducted with the informants on the development of market centres (Shrestha & Routray, 2002). Given the historical trend of urbanization pattern and evolving road networks, the prediction of urban growth can be pretty accurate. Nonetheless sometimes government institutions do not respond to the market dynamics fast enough. For example, relocation decisions of administrative offices entail serious political and social repercussions (Shrestha, 2003).

This index was also used for determining the hierarchy of the nodal points in Shrestha (2003) as network module for district road network planning and prioritisation.

2.2.4 Intensity of interaction

Settlements in a region interact among each other. If the intensity of interaction between two urban centres can be calculated, we can find the importance of a link between them. Furthermore, the hierarchy of settlements can also be fixed based on the settlement interaction. In addition to the population, the functions in a market centre play vital role to generate or attract trips. Normally educational institutions, hospitals and private clinics, wholesale shops and other industries should be included since those functions attract trips

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(Shrestha & Routray, 2002). A gravity model can be used to find the interaction between two nodal points. The population is multiplied with the centrality index. The centrality index can be considered as the weight of the population.

The distance between the urban centres plays an important role for generating trips following a distance decay function. The force of interaction between two settlements can be obtained by gravity model equation in the following form (Isard, 1960):

(2.4)

Where,

Iij = Interaction between two nodal points i and j

Wi = Weight/Centrality index of the node i

Wj = Weight/Centrality index of the node j

Pi = Population of the node i

Pj = Population of the node j

d = Road distance between i and j

b = exponent of d

The Iij provides the preliminary indicative desire lines among the settlements. For the sake of

simplicity the value of b can be considered as 1 (Shrestha & Routray, 2002).

Shrestha (2003) used Equation 2.4 to calculate the intensity of interaction between two nodal points and named it transport demand estimation model. However, the author sets the value of b as 2 in the study (Shrestha, 2003). Education, health related, commercial and industrial functions are included when calculating the index, as these functions attract trips from the hinterland settlements. In terms of trip generating capacity, the government institutions like administrative offices, district level court, and police station play an insignificant role in comparison to the other functions (Shrestha, 2003). However, from the accessibility point of view connectivity to these service/institutions may be important.

Mahendru et al. (1983, 1985) used the concept of settlement interaction, link efficiency, route efficiency, and network efficiency to generate, analyse, and evaluate alternative rural road linkage pattern. An Integrated area development approach was considered to develop the road network so that it serves the studied area in a balanced way. Gravity hypothesis was used to quantify the inter-settlement interaction based on level of socio-economic development, population, and spatial separation between settlements. Centrality score was

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used as the composite index to quantify the level of socio-economic development. The interaction between two settlements was considered proportional to the difference in their centrality scores. Alternative networks were generated using various criteria like maximum link efficiency, minimum total link length, minimum total operating cost, and fully developed network. These were then compared regarding their total cost (which consisted of construction and operating costs) to arrive at the optimal network. In spite of its rational treatment of various aspects of rural road planning, there are some deficiencies in this approach. The Gravity hypothesis, used to model the hidden pattern of inter-settlement interaction, gives erroneous results when the centrality scores of interacting settlements are the same (in that case the interaction computed through the model is zero). The deterrence parameter, used in the model, was taken as the direct distance between the settlements, (Mahendru et al. 1985) and the one obtained from basic connectivity matrix (Srivastava 1989), which is not true for new road links in hilly and irregular topographical conditions.

2.2.5 Road density

In practice, road networks form a grid, and roads deviate to follow the best alignments for economical and simple construction. Roads also tend to concentrate near areas of high agricultural yield and market places. Moreover, road densities vary according to the access requirements of various crop types. Furthermore, the densities can vary depending on terrain conditions of rural areas.

Table 2.1: Road density and distance to roads (United Nations, 1979)

Density (km/sq km) Average distance to road (km) Maximum distance to road (km)

0.500 0.50 1.0

0.200 1.25 2.5

0.100 2.50 5.0

0.050 5.00 10.0

0.025 10.00 20.0

UNCHS (1985) guideline has used the road density concept which is expressed as the number of kilometres of road per square kilometre to reflect the degree of difficulty of any given journey. The guidelines take it as a measure to indicate the average distance that a crop on a farm or a person travelling to a town must move before reaching a road (to obtain any mechanized mode of transportation). The concept is frequently used when planning the distance of farm-to-market roads in local-road networks. For a network in form of parallel roads, straight and evenly distributed in an area, the average distance from a road to the farms

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can be as indicated in Table 2.1.

2.2.6 Accessibility Indicators (AI)

An Integrated Rural Accessibility Planning (IRAP, 2002) methodology is also being used for planning rural roads in developing countries. The IRAP is a local-level planning tool aiming to optimise the infrastructure investment on the basis of the most urgent needs of local communities (Howe, 1996). IRAP is based on the accessibility-activity approach and takes into account the access needs of households and the activities fulfilling these needs. In its basic sense, IRAP addresses household accessibility problems. It provides input to the rural accessibility planning process by the rating household need requirements for access as well rating the infrastructure on the basis of its ability to provide access. These inputs are useful to formulate strategies to reduce access problems at different budget requirements.

The IRAP is a process oriented approach that has the flexibility to solve traditionally considered transport-sector problems by either transport or non-transport means. For example, if water collection is a severe access need, the problem can be solved either by providing better footpaths or roads leading to the facility, or by bringing the water collection points closer to the users. In this way, the IRAP incorporates the mobility and sitting of service into the same framework.

The main features of the IRAP framework are (Howe 1996):

- It is needs-based in the sense that it covers all aspects of household needs

- It is comprehensive in the sense of its ability to suggest solutions to the access problems, not just transport problems

- It is sustainable because it is intended to be managed by local-level participation.

The IRAP methodology is based on household needs for access (Edmonds 1998, Dixon- Fyle 1998). The methodology starts by obtaining household data concerning accessibility for services like water and firewood collection, healthcare, education, etc. Various indicators are used to define the access needs for these services. Time or distance to the facilities offering these services is usually the main. The IRAP methodology provides two main outputs, the accessibility indicators and the accessibility profile (Figure 2.1), as explained as follows.

The first output of the household data collection exercise within the IRAP framework is the development of Accessibility Indicators (AI) for each of the access needs. AI is given as

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(IRAP, 2002):

Figure 2.1: The accessibility planning cycle (Dixon-Fyle, 1998).

AI = number of households × time (or distance) to the facility (2.5)

Table 2.2 Examples of Accessibility Indicators (AI) (Edmonds, 1998)

VILLAGE DISTRICT NATIONAL

Water Number of

households × Average collection time in the dry season

%age of households with no direct access to a water supply × Average collection time in dry season

%age of households with no direct access to a water supply × Average

collection time in the dry season Health Number of households × time to a health centre or clinic %age of households in villages with no health centre × Average time to a health centre

%age of households living in villages with no health centre × Average time to a health centre

Education Number of primary school age children × time taken to get to the school

%age of households with no primary school in their village × Pupil/classroom ratio (or Pupil/teacher ratio)

In the above equation, the number of households is representative of the population affected. The time (or distance) to the facility is representative of the burden to be borne by the population. The higher the value of AI the least will be the accessibility of a particular facility to a given population. In this way, the AI defines, in empirical terms, the inaccessibility of the

1. Data Collection 2. Data Encoding 3. Preparation of:

Accessibility profiles Accessibility indicators Accessibility maps 4. Identification & Prioritisation of Access problems

5. Defining Objectives and Targets 7. Project Implementation 8. Monitoring & Evaluation 6. Plan formulation The AP Cycle

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activities. Parameters other than number of households can also be used in the AI. Table 2.2 explains the range and importance of the parameters defining AI (IRAP, 2002). It contains the definition of AI at various levels of rural accessibility planning.

From Table 2.2 it is clear that the definition of AI is based on two factors:

a) the affected population; for example households or number of school age children b) the burden; for example time taken to the facility or the distance to be covered

In this way, the AI defines two possible ways of solving the accessibility problem: - by reducing the size of the affected population; this can be done by improving the

capacity of the facilities (increasing number of classes, etc.)

- by reducing the distance or time for access; this can be done by improving the

infrastructure (provision of roads) or enhancing supply of transport vehicles (IMT, NMT, etc.)

The accessibility indicators AI are used to develop accessibility profiles of the areas covered in the IRAP study. These are the maps of the whole area under study redrawn to highlight the access problem of locations (for example villages) with reference to the burden they face. The accessibility profiles act as the planning tool in guiding the decision-makers regarding the best use of the resources.

Integrated Rural Accessibility Planning (IRAP, 2002) is a local level planning tool to prioritise rural infrastructure investments by looking at the access of rural household to basic services and facilities such as health services, schools, markets, and water supplies based on

AI. This is a general method for a village level planning works of infrastructures. It is

important to note that rural roads are one of the components only in planning exercise. This method is suitable for very low volume roads and village tracks. It requires heavy data collection and is usually very time consuming. However, presently, this method has been applied to many projects in developing countries in Asia such as Cambodia, Laos, Thailand, Philippines, Nepal, India, and Indonesia.

2.2.7 Costs

The costs associated with each link/network provide basis for comparison and selection of the link/network to upgrade/choose (Kumar & Tilloston, 1985; Makarchi & Tilloston, 1991). Each link/network will provide different levels of construction and travel costs. The optimum

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network is the one for which the total cost, i.e. construction plus travel costs, are minimum (Kumar & Tilloston, 1985).

In Kumar and Tilloston (1985), construction costs have been taken as proportional to the lengths of links. Similarly, travel costs have been taken as proportional to a factor called "person-kilometre (km)". The person-km for a village node is defined as the product of population connected by the village node to its root node and the distance between that village node and its root node (root nodes are generally the settlements connected by a road or a point in the road). The factor has the following assumptions:

 The number of trips generated by a village node is proportional to its population

 The travel costs are proportional to the distance travelled.

Thus the factor, person-km (multiplication of population and the distance travelled), will be proportional to the total travel costs.

In a rural road planning model, Makarchi and Tillotson (1991) also divided cost into two types. One type has been called construction costs and other type of cost has been called travel costs. The construction costs can be estimated with reasonable accuracy. However, the travel costs cannot be estimated with a satisfactory degree of accuracy. In a district of reasonably uniform topography, it seems reasonable to assume that the construction costs will be proportional to the lengths of the links (Makarchi & Tilloston, 1991). The travel costs are likely to be proportional to (i) the number of people connected by the link, and (ii) the distance travelled through the link to reach the destination (Makarchi & Tilloston, 1991). It is therefore argued that whatever the travel costs may be, they will be proportional to a factor called 'person-km' which is defined as the product of population connected by the link, and the distance between the village and the destination through the link, as defined by Kumar and Tilloston (1985).

These methodologies can be applied when the population of the settlements and the linear distance between them are known. This has a special advantage for rural areas where this simple data is often easily available. This method can still be used with actual costs where these are known.

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2.2.8 Accessibility index

In developing countries, the system of roads connecting rural settlements may be new construction, the up-gradating of existing paths/tracks and fair weather roads. Due to budget constraint, all these cannot be built/upgraded to a desired level. Therefore, there is a need to select the most efficient network which provides minimum basic access to all the settlements. The road links are required to be evaluated for their efficiency in terms of the level of accessibility provided. The accessibility provided by a road link is inversely proportional to the total amount of travel distance required for satisfying the missing functions in the settlement. The total travel can be computed by identifying various missing functions, per capita trips for these functions, location of functions in the region, length of connecting road link and the population of the settlement. Total travel through the connecting road link is the summation of travel, in terms of person-km, for various missing functions in the unconnected settlement. The lesser the amount of travel required, the more accessible will be the settlement (Singh, 2010).

Using this procedure, the road link offering maximum accessibility for each unconnected settlement can be determined. The developed network (based on person-km as the only criteria) seems to evolve maximum accessibility connectivity pattern for unconnected settlements. Settlements should be connected one at a time by considering and evaluating all the possible road link options in a given order. If this order is not taken into account then the possibility of inter connectivity among various unconnected settlements gets ignored. An indicator of accessibility is introduced in Singh (2010) which considers the settlements should be connected one at a time considering and evaluating all the possible road link options in an orderly manner. The indicator of accessibility can be obtained by dividing the total person-km of travel with the population of the unconnected settlement. The indicator now represents the average person lead for an unconnected settlement to access all its missing functions through the connecting road link. It can be used to compare the accessibility offered by various connecting road link options and the one which offers the maximum accessibility should be chosen first in the process of network development. Singh (2010) has formulated the index of accessibility as follows.

In rural road planning, the road links are added to the existing network of road system so that each unconnected settlement gets connected to at least one road link. The connecting road link, emanating from the unconnected settlement, either joins a nearby

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connected settlement or any intermediate node on the links of the existing road network. Therefore, the entire system of nodes can be divided into two categories: unconnected nodes (settlements) and connected nodes (i.e. connected settlements and intermediate nodes on the links of the existing road network). A link option is a road link between the unconnected and connected nodes (Figure 2.2). The unconnected settlements can be connected by upgrading the existing paths, tracks or un-surfaced roads to connected settlements.

If the total number of connected nodes in the existing road network is m then, theoretically, there can be these many link options offering connectivity to each unconnected settlement. However, many of these link options will be redundant as they will be intersecting the existing road network at many points and excessively large in length. Among these link options t h e one which offers maximum accessibility to the unconnected settlement should be chosen. The accessibility of a link option can be calculated by identifying the missing functions (services) k in the unconnected settlement, the total travel requirement of the unconnected settlement PK in terms of person-km to satisfy all its missing functions and the length of new road link option d as given below.

Unconnected settlement Connected settlement Existing road network m3 m4 m1 m2 Existing road network Intermediate nodes of link Intermediate nodes of link Link options j i

Figure 2.2: Link options from unconnected settlement (Singh, 2010).

The total person–km of travel i.e. PKi(l), for an unconnected settlement i to access

missing functions k through the link option l can be calculated from Equation (2.6).

(2.6)

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missing function k and dik(l) is the distance between the unconnected settlements i to the

nearest function k through the link option l.

The dik(l) can be calculated using Equation (2.7).

(2.7)

Here, diml is the length of link option l (i.e. the ground distance between the unconnected

settlement l and connected node m on the network) and dmjk is the minimum distance between

node m and the nearest node j on the network where the function k is present. If the function

k is present at node m then dmjk will be zero.

The accessibility index of link option l (i.e. Ail) is calculated by dividing the total person-

kilometre PKi(l) with the population of the settlement Pi as given in Equation (2.8).

(2.8)

Here, Pi is the population of the unconnected settlement i .

It is assumed that the unconnected settlement access the first nearest function in its neighbourhood, through the link option, to satisfy its missing functions. Higher value of Ail

indicates lower accessibility of link option l, and vice-versa.

In a physical sense, Ail represents the average distance the population has to cross to

access all the missing functions in the unconnected settlement when it gets connected through the road link l. The link option with minimum index value (i.e. maximum accessibility) is the most preferred choice in developing the maximum accessibility network.

The estimation of accessibility index, using Equation (2.8), requires the determination of Tik

and dik(l). Tik can be estimated by multiplying the settlement size P with the appropriate trip

rate tk for each function, as given in Equation (2.9).

(2.9)

Here, the settlement size Pi can be the settl em ent ’s i population, number of

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function k in the unconnected settlement.

For rural areas, appropriate trip rate models can be developed by correlating it with the socio-economic factors. The model form can be represented as shown in Equation (2.10).

(2.10)

In absence of any such model, tk can be estimated for a few of the functions. For example, if the entire rural population is to be provided education up to high school standard by the end of planning horizon, then the trip rates to primary, middle and high school will be the proportion of the school going population in their corresponding age groups. However, the trip rates for marketing, health purpose etc will depend on the expected level of development in the area. These trip rates will be less in underdeveloped areas and more for the developed areas.

The term dik(l), has two components i.e. dmjk and diml. Here, the term dmjk can be

estimated quite easily, as the location of facilities on the existing road network is known. The other term diml, which is the length of new link option, will depend on its actual alignment on

the ground. This alignment need not always to be a direct one and will primarily depend on the topography of the area. Topographic features such as water bodies, hills, costly structures, land type etc may cause deviation from straight alignment. Even for the network planning of a small number of unconnected settlements there will be many possible road link options and their alignment, depending on topography of the area. These link options needs to be analysed for their alignment, length, construction cost etc so that the least cost options could be selected (Singh, 2010). Furthermore, there is need of huge data for many functions to determine accessibility index of each link.

2.2.9 Facility based approach

In addition to market centres, in many rural areas additional access may be needed to various facilities like health, education, banks and administrative headquarters. To incorporate the access to these facilities, Kumar and Kumar (1999) have developed a facility based model to consider accessibility to those facilities in addition to the market centres. The model considers access of the village to market centres and some user friendly facility centres. About 93 percent of the rural trips are terminated either at the nearest market centre or at the nearest education facility. Thus, if road connections are provided to the nearest market centres and

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nearest education centres, then about 93 percent of the travel demands of the rural population can be met (Kumar & Kumar, 1999).

2.2.10 Traffic Flow

The conventional transport planning process is based on the principle of subdividing the study area into smaller zones and subzones and studying the inter-zonal trip frequencies through OD matrix to decide linkage needs between various origins and destinations. This method may not be suitable for planning rural roads. Even planning rural roads at the district level, there will be around 1500 to 2000 rural settlements and it will require enormous amount of resource to collect trip frequency data for all the settlements to form the O-D matrix. Even if, the O-D pattern is made available, the observed trip frequencies will be very low and that would hardly justify the links in a network. The use of such data will be only to fix the relative priorities of the linkages (Mahendru et al. 1985). This shows that the use of conventional methods is not practical.

Furthermore, it is very difficult to estimate the traffic that will be served by a rural road involving construction of bridges and major upgrade. Population is considered as a good proxy for traffic in rural areas because traffic data generally is difficult to get (Kumar & Kumar, 1999).

Most of the literature on transport network design is concerned with the choice of improvements to, or additions of, links to an existing network either to reduce traffic congestion, energy consumption, pollution or other appropriate objective. However, the network design problem for rural road network in developing countries is somewhat different from that of developed countries. The networks are being planned around existing main roads and very few rural roads may still exist. Furthermore, in the rural areas of developing countries traffic flows are low and congestion may be assumed to have no effect on travel time (Makarchi & Tilloston, 1991). The specific objective is to connect all villages to the network regardless of their sizes (Makarchi & Tilloston, 1991).

A traffic simulation model was used by Athanasenas (1997) to evaluate rural road network design and alternative rural road investment strategies in the United States. An approach for cost-effective rural road management was identified by examining both deterministic and probabilistic traffic simulation models. The authors find difficulty in acquiring traffic data from the rural settlements and an estimation of trip generation from the rural villages.

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Typically, acquiring traffic data and analysis is very difficult for rural areas and, moreover, the output of the effort is also less significant. Hence, rural road planning models circumvent this by using population (Kumar & Kumar, 1999) or person-km (Kumar & Tilloston, 1985; Makarchi & Tilloston, 1991; Singh, 2010).

2.3 Rural road network generation

One of the first works in rural road network generation is due to Swaminathan et al. (1982) which develop a system approach for rural road development by using the concept of graph theory (Figure 2.3). The market centres and main roads were considered as high-intensity electrical charges attracting the smaller charges at the nearby villages. This model used the minimum spanning tree (MST) for connecting the settlements to existing nearby roads or to the nearest market. In this model, weight was calculated for different alternative road connections for a village by using the gravity model to develop a complete road network plan that connects all the villages to nearby market centres by selecting links of maximum weight.

Figure 2.3: System approach to rural road development (Kumar & Kumar, 1999).