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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
114
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
116
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
121
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).
122
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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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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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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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.