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CHAPTER 6

NETWORK ANALYSIS

6.1 INTRODUCTION

Roads are essential elements on topographical maps, navigational

maps and other kinds of maps. In urban areas, supply has usually been unable

to keep pace with increasing demand, the only possibility is often to

reorganize the current supply configuration in order to use existing resources

efficiently. With the development of Geographic Information Systems (GIS)

technology, network and transportation analysis within a GIS environment

have become a common practice in many application areas.

The application of GIS to a diverse range of problems in

transportation engineering is now well established. It is a powerful tool for the

analysis of both spatial and non-spatial data and for solving complex

problems of networking. A key problem in network and transportation

analysis is the computation of shortest paths between different locations on a

network. Sometimes this computation has to be done in real time. Space

layout and urban road network design present a critical challenge as a result

of increasing levels of urbanisation and road traffic. Urban planners have

always aimed at optimizing the road network design to meet transportation

cost, safety, land use, aesthetic and environmental considerations. With the

rapid growth in traffic patterns and space utilisation, there is a growing need

for a tool to design and evaluate urban road networks Nagar and Tawfik

(2007).

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Having not considered such aspects as spatial distribution features

of transportation networks and resident areas, the spatial influence of outside

transportation modes on the features within the research regions as well as the

different transit capacity of different transportation modes, the conventional

measuring methods based on path analysis and network analysis is deficient

for the description of regional transportation situation Ma and Zhang (2005).

An attempt has been made to analyse the road network of

Coimbatore Corporation, Tamil Nadu with the following objectives:

1. Finding shortest path between any two destinations,

2. Finding the facilities that are available closer to the user

specified location, and

3. Identification of service area with user-specified criteria.

Dijkstra’s algorithm is used in the present study for Shortest path

and Closest facility. It is simply a step-by-step procedure that results in some

conclusion, like the least-cost path. Perhaps the most well known algorithm is

generally credited to Dijkstra (E.W. Dijkstra “A Note on Two Problems in

connexion (sic) with Graphs”, Numberiske Mathematik, 1 (1959)). Dijkstra’s

algorithm is one of the simplest path finding algorithms. NETWORK makes

use of Dijkstra’s algorithm. The classic Dijkstra's algorithm solves the single-

source, shortest-path problem on a weighted graph. To find a shortest path

from a starting location s to a destination location d, Dijkstra's algorithm

maintains a set of junctions, S, whose final shortest path from s has already

been computed. The algorithm repeatedly finds a junction in the set of

junctions that has the minimum shortest-path estimate, adds it to the set of

junctions S, and updates the shortest-path estimates of all neighbors of this

junction that are not in S. The algorithm continues until the destination

junction is added to S. Dijkstra’s algorithm finds the shortest path between

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two vertices in a weighted graph. It proceeds by finding the length of the

shortest path from a source to successive vertices and adding these vertices to

distinguished set of vertices. The algorithm terminates once it reaches the

final vertex. This algorithm maintains distance label d(i) with each node i,

which is the upper bound on the shortest path from node i. At any

intermediate step, the algorithm divides the nodes into two groups: those

which it designates as permanently labeled and those as temporarily labeled.

The distance label to any permanent node represents the shortest distance

from the source to that node. For any temporary node, the distance label is the

upper bound on the shortest path distance to that node.

function Dijkstra (Graph, source):

for each vertex v in Graph:

dist[v]:= infinity

previous[v] := undefined

dist[source] := 0

Q := the set of all nodes in while Q is not empty:

u := vertex in Q with smallest dist[]

if dist[u] = infinity:

break

remove u from Q

for each neighbor v of u:

alt := dist[u] + dist_between(u, v)

if alt < dist[v]: // Relax (u,v,a)

dist[v] := alt

previous[v] := u

return previous[]

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6.2 METHODOLOGY

The methodology that was followed is briefly given below, and is

also shown in Figure 6.1.

Road Map Junctions & Land

Network Data Building

Network Dataset

GIS Data Layers

Shortest Path Analysis Closest Facility Analysis Service Area Analysis

Selection of

Incident

Selection of

Facilities

Selection of

Incident

Selection of

Distance Service

Selection of

Destinations

Selection of

Series of Locations

Finding Closest Facility

to the Incident

Finding Service Area to

the Incident

Finding

Shortest Path

Figure 6.1 Methodology for Network Analysis

GPS Survey was conducted to capture the following major

features as point objects.

Bus Stand

Flyover Bridge

Important Landmark

Road Junction

Park

Hospital

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Govt. Office

Industry

Pumping Station

Railway Station

Educational Institution

Theater, and

Market

Table 6.1 GPS Survey Data

S.No GPS_DATA

1 Decan Industries, Ganapathy

2 Government Higher Secondary School, Ganapathy

3 Roots Industries Ltd, Ganapathy

4 Ganapathy Bridge

5 Sri RamaKrishna Hospital, AvaramPalayam

6 Sheela Hospital, 100 Feet Road

7 Kongunadu Hospital, Cross Cut Road

8 Nort Coimbatore Bridge

9 Tamil Nadu Agriculture University, Laly Road

10 Avinasilingam Deemed University

11 Tamil Nadu Government Automobile Workshop

12 Gandhi Park, Roundana

13 Tratment Plant Pumping Station, Selvapuram

14 Vetenery Hospital, Town Hall Road

15 Tretment Plant (Storage Station), Vellaladul

16 Parsn Appartments ,Nangundapuramp Road

17 Waste Water Pumping Station, Nacchundapurpam SouthZone -3

18 Shanthi Gears E Unit, Singanallur

19 Ondipudur Bridge

20 Singanallur Bus Stand, Singanallur

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Table 6.1 (Continued)

S.No GPS_DATA

21 Coimbatore Medical College, Avinasi Road

22 PSG Institue of Managemen,t Avinasi Road

23 Lakshmi Mills ,Avinasi Road

24 Anna Statue, Avinasi Road

25 Regional Transport Office, Balasundarm Road

26 Gandhipuram Town Busstand

27 Ukkadam Bus stant, Ukkadam

28 Sewage Pumping Station (Treatment Plant), Ukkadam

29 Ukkadam Bridge, Trichy Raod

30 Sungam, Roundana

31 Railway Station

32 Commissinor of Police Roundana

33 District Court ,Roundana

34 Race Cource, Roundana

35 Upilipalayam Bridge (Over fy)

36 Central Prison, Coimbatore

For the above spatial features, corresponding non-spatial data

were collected from various sources. The non-spatial data are:

Names of the roads and streets,

Names of the bus stands,

Names of the locations

Names of the schools

Type of school

Names of industries, and

Type of industries

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Table 6.2 Names of Important Industries, places and Government Offices

S No Places

1 Saw Mills Ltd

2 Peelamedu

3 Head Post Office

4 Telite Theatre

5 City MPL School

6 New MLP High School

7 Government Polytechnic

8 Telungupalayam

9 Chettipalayam

10 Poosari Palayam

11 Karumpukadai

12 Nanjunathapuram

13 Pulianthoppu

14 Selvapuram South

15 Kumarapalayam

16 Priya Nagar

17 Telengupalayam

18 Chocka Puram

19 Ponnayarajapuram

20 Gandhi Park

21 Rice Mill

22 UKK I

23 Old Market

24 MPL Office PWD

25 Women Training College

26 Royal Theatre

27 ENG Club

28 Europian Convent Girls School

29 Highways Departments

30 SLM II

31 SLM I

32 LGP

33 Air India

34 Ramanathapuram

35 Marudur

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Table 6.2 (Continued)

S No Places

36 Thiruvalluvar Nagar

37 Central Studio

38 Rajalakshmi Mills

39 Kallimalai

40 Singanallur

41 RV layout

42 Weavers Colony

43 Ondipudur

44 Varatharajapuram

45 Uppilipalayam

46 Singanallur

47 Sowripalayam

48 Udavampalayam

49 Pankaja Mill

50 Appusamy Layout

51 Red Field

52 Railway Station

53 State Bank

54 Collectrate Office

55 YMC Club

56 LM Chruch

57 DSP Office

58 Uppilipalayam

59 Murugen Theatre

60 Somasundra Mill

61 Staines High School

62 Chithabaram Park

63 Stadium

64 Srinivasapuram

65 Kaleswara

66 POC Market

67 Shanmuga Theatre

68 Kikani High School

69 Seeranaikam Playam

70 Paranaicken Pudur

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Table 6.2 (Continued)

S No Places

71 Velandipalayam

72 Kokulam Colony Road

73 Madathur

74 Kumastha Layout

75 Indira Nagar

76 Venkatapuram

77 Sakilivattam

78 Ambilipudur

79 Marutha

80 TVS Nagar

81 TNSTC Head Office

82 Tatabat

83 Power House

84 Tatabat Street1

85 Murugan Mills

86 Bharani Park

87 Mal Colony

88 Tatabat Street

90 Central Theatre

91 North CBE

92 Pykara Power House

93 RTO Office

94 Thiru valluver Bus Stand

95 Sida Pudur

96 PRS Ground

97 Kuppsamy Naidu Hospital

98 Papanaickenpalayam

99 Akshiya Mills

100 Puliyakulam

101 Bharathipuram

102 Ramakrishnapuram

103 Chinna Sowripalayam

104 Masakapalayam

105 Pudur

106 PSG

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Table 6.2 (Continued)

S No Places

107 Hope College

108 Ramanujan Nagar

109 Singanallur

110 Vallurvar Nagar

111 Balarenganathapuram

112 Villankurichi Part

113 Krishnarayapuram

114 Sakilaiar Nattam

115 Ganapathi

116 Sanganur

117 Parachery

118 Sanganur

119 Pusaripalayam

120 Maniyakarampalayam

121 Nallampalayam

122 Kempatty Colony

123 UKK Bus Stant

124 Union High School

125 Government Arts College

126 KG Complex

127 CMC Hospital

128 Race Course

All non-spatial data were typed in MS-Access database with

suitable structure.

GIS database building and data linking with non-spatial data

were done after digitization and geo referencing. Using the

database, network analysis was carried out to find out,

Shortest path between any two destinations,

Closest facility, and

Service area

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Different GIS layers such as road network, different landmark

(location) layer containing industries, schools, hospitals were created using

the data obtained through GPS survey and Corporation office in Coimbatore.

6.3 NETWORK ANALYSIS

Road pricing theory and analysis suggest that savings in travel time

and related resources could be achieved if drivers were directed to travel

along minimum marginal time routes. Equivalently, tolls based on the

difference between marginal and average travel times might be used to induce

drivers to shift to routes that have shortest travel time. This theory has been

well known from the works such as those by Beckmann et al (1956), Small

(1992) and McDonald et al (1999) for recent findings and syntheses of the

road pricing literature.

Accessibility is an important element in evaluating existing land

use patterns and transportation services, predicting travel demands and

programming transportation investments in urban transportation planning.

The movement of people, the transportation and distribution of goods and

services, the delivery of resources and energy and the communication of

information, all occur through definable network systems. Networks form the

infrastructure of the modern world. The capacity and efficiency of these

networks have a substantial impact on the standard of living and affect our

perception of the world around us.

Network algorithms provide tools to find paths, and the shortest or

minimum impedance path within a network. Finding the most efficient path to

a series of locations is possible through network analysis. Path finding is the

fundamental problem addressed by network analysis. It finds a shortest path

or locates a series of places of visit in a network, at the least cost. The cost

may be determined using the attribute of the network components that is

expressed in numeric terms. A path is a minimum impedance course through

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a network where the stops are visited in predetermined order. Least cost path

may be worked out based on the distance that is to be covered by a traveller.

Minimum the distance that one travels, less the cost he has to spend.

Impedance is the cost associated with the utilization of the supplied

resource through a network. However, distance and travel time are commonly

used impedances in network analysis. Networks can consist of a system of

roads, a railroad system, city bus routes and so forth. A network can also be a

pipeline system, city sewerage system or some other utility network.

The computation of the shortest paths between different sources

and destination nodes on a network is a central and computationally intensive

task in many transportation and network analysis problems. With the

advancement of Geographic Information System technology and the

availability of high quality network data, application of network analysis

within a GIS environment has become a common practice for many

transportation problems. Hence, it is attempted here to find the shortest path

between any two destinations, closest facility from specified location and

service area demarcation for the road network of Coimbatore Corporation.

6.4 NETWORK DATASET

Spatial data from Geographic Information System database is being

used more and more in transportation planning due to the convenient structure

they provide for entering, viewing and manipulating spatially-oriented data.

Applications of GIS in the traffic safety area has been limited mostly to visual

representation of accident locations.

Network dataset is built mainly from two GIS data layers. They are

major road network which is captured as line features, and junctions and

important landmarks that are captured as point features. These two are playing

prominent role in keeping network alive at all time. Road network is properly

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connected in GIS with junctions and important landmarks. Necessary attribute

data such as name of roads, length of roads, name of the junctions and

important landmarks have been given as input in network dataset. Exclusive

module available in ArcGIS software was used to build network data from the

above line and point features. While building the network dataset, proper

connections between road and junctions were checked and any violations are

flagged for rectification of errors. After achieving error free data, the dataset

was used to find out ‘shortest path’, ‘closest facility’ and ‘service area

demarcation’ within the Coimbatore Corporation limits.

Table 6.3 List of Major Roads

S.No Major Roads

1 Mettupalayam Road

2 Sathiamangam Road

3 Siruvani Road

4 Avinashi Road

5 Tiruchi Road

6 Pollachi Road

7 Palaghat Road

6.5 SHORTEST PATH ANALYSIS

A shortest path problem is finding a path with minimum travel

distance from one or more origins to one or more destinations through a

connected network. It is an important issue because of its wide range of

applications in transportation networks. In some applications, it is also

beneficial to know the second or third shortest paths between two nodes. For

instance, in order to improve the effectiveness of travel information provision,

there is a need to provide some rational alternative paths for road users

driving in real road network (Yongtaek and Kim 2005).

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Having built the network GIS data layers, shortest path between

any two destinations can be efficiently found. The shortest path takes into

account road distance and not radial distance. It is also possible to find out a

route which has the least travel time. In the case of travel time based shortest

path, the impedance is to be given based on the road condition, vehicle type

and other influencing parameters. In the present case, the best way to get from

one location to another was worked out based on road distance where the

destinations can be chosen by the user interactively. It has also been built in

such a way that due to any reason if the road is closed, the user can introduce

a barrier interactively. In such situations, the best possible alternative route is

identified and made available to the user along with the distance he has to

travel.

For example if one wishes to go from TVS Nagar to the office of

the District Collector located in the centre of the Coimbatore city, he has to

travel 5.90 km through Kikani High School (Figure 6.2). In case Kikani High

School is to be avoided to reach District Collectorate from TVS Nagar, then

one has to travel 5.95 km through Sampandam road. It is about 50 m. more in

terms of road distance (Figure 6.3). In this way, any two destinations can be

chosen and shortest path can be derived by this network analysis. In this way

any two destinations can be chosen to find shortest path between them. The

user can also select number of destinations and shortest path from one

location to another location could be derived optimally, but without change

the sequence. It is also possible to optimally work out the shortest path

between multiple destinations with the advantage of changing the sequence

but without altering the origin and end destinations. This shortest path finding

analysis is more useful when applied on-line by interacting with the software.

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Figure 6.2 Shortest Path between T.V.S Nagar and District Collector’s Office

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Figure 6.3 Alternate Route from T.V.S. Nagar to District Collector’s Office

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6.6 CLOSEST FACILITY ANALYSIS

Closest Facility works on two different entities such as facility and

incident. Incident is considered as place of interest from where system

searches facilities within the user-specified road distance. For one incident,

there may be many facilities from which, closest facility may be picked up for

the purpose. At the same time, it continues to search for the next closest

facility and this process is repeated and all the facilities are identified within

the user- specified maximum road distance.

For example, finding a closest facility from a particular location

such as closest hospital to an accident site, closest police station to a crime

incident, closest ATM Centre from user- specified location, etc. can be well

retrieved in Coimbatore Corporation through the present network closest

facility analysis.

To demonstrate the capability of identifying nearest facility, office

of the Deputy Superintendent of Police (DSP) is selected as incident while the

hospitals are considered as facilities. The network model identified the nearest

hospital as CMC (Coimbatore Medical College) hospital which is located at a

distance of 950 m. The next closest hospital is Kuppusamy Naidu Hospital

which is located at a distance of 1.99 km. Network module in the present

study searches for facilities within 2 km road distance and two hospitals are

retrieved and the closest hospital is CMC Hospital at a road distance of 0.95

km while Kuppusamy Naidu Hospital is located at a farther distance but

within 2.00 km road distance. The network model not only finds out the

nearby facility but also indicates the path to follow from the incident to the

facility along with actual distance to be travelled by the user. The route to be

followed from the office of the DSP to the nearest facility such as CMC

Hospital is shown in Figure 6.4.

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Figure 6.4 Route to nearest Facility

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6.7 SERVICE AREA ANALYSIS

One can find service area coverage around any particular location.

If a hospital has certain norm of servicing only up to 1.00 km road distance,

the service area around selected hospital can be demarcated at all directions

through road network model. The same concept can also be applied to find

out the service area of schools, fair price shops, police stations, etc. based on

selected norms.

Service area demarcation is done with respect to the facility that is

chosen by the user. For example, if the Head Post Office (HPO) located in

Railway Feeder Road can serve only up to 1.00 km. distance in the urban

area, the network immediately demarcates the areas that are serviced by that

Head Post Office (Figure 6.5). This kind of demarcation will be useful to

service-oriented departments who can take decisions about their service

jurisdictions for general public based on certain criteria.

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Figure 6.5 Service Area of a Typical Facility

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6.8 CONCLUSION

The present study demonstrates the various network applications

such as shortest path finding, finding out closest facility and service area

demarcation with regard to Coimbatore Corporation. Three types of network

analyses have been conducted in the present study. Shortest path analysis has

brought out optimal path between two destinations based on travel distance.

However, this may also be improved by incorporating time and cost based

impedances in the network dataset which were not the scope of the present

study.

Second type of network analysis namely closest facility has been

carried out to identify number of facilities available around a place of interest.

The searching criteria was employed based on road distance and facilities

available at nearby distance have been identified for taking further decisions

by the user. When it is web-enabled, online queries can be made by the users

from any part of the globe. On giving more impedance parameters, there are

possibilities to expand the scope of the study to a larger extent with the same

spatial dataset.

In the Service area analysis, it has been done by giving distance

impedance for every service features such as post office, police station, etc.

This has not only given the geographic perspective of the area of jurisdiction,

but also displays distribution of other features in the selected service area

limit. When it is web-enabled, online queries can be made by the users from

any part of the globe. On giving more impedance parameters, there are

possibilities to expand the scope of the study to a larger extent with the same

spatial dataset.