Available Online at Volume 5; Issue 4; July-August: 2017; Page No

ISSN: 2348 - 2117

International Journal of Engineering Technology and Computer Research (IJETCR) Available Online at www.ijetcr.org Volume 5; Issue 4; July-August: 2017; Page No. 100-107 Journal Approved by UGC

Rural Road Network Connectivity Analysis Based on GIS

Kamlesh Sah1, Ch. Ramesh Naidu2 PG Scholar, Dept. of Civil Engg, GVPCOE(A), Visakhapatnam, India Professor, Dept. of Civil Engg., GVPCOE(A), Visakhapatnam, India [email protected] Received 29 July 2017; Accepted 17 August. 2017

Abstract Rural roads are considered to be one of the important roads within a district, which connects areas of production with markets and with other villages or with the state and National Highways. Rural road network connectivity is perceived as one of the prime components in increasing the agricultural output and earning capacity of the rural population. Rural roads stimulate overall development by providing access to social infrastructure and facilities. The study area is located in Visakhapatnam district, Andhra Pradesh, India. An attempt has been made through the present study to develop Geographic Information System (GIS) based rural road database and also network connectivity analysis (Beta Index, Alpha Index, Gamma Index, Cyclomatic Number and Aggregate Transportation Score) has been performed. The analysis result several that good level of connectivity of roadways in the selected study area.

Keywords: Road network, Connectivity, Analysis, GIS route planning, transportation system modeling, INTRODUCTION healthcare accessibility planning, land cover The role of rural roads is very important in a country classification and even infrastructure management. like India where majority of the population resides in Road networks play an important role in many rural areas. Rural road connectivity is a key geospatial applications, such as cartographic, component of rural development by promoting infrastructure planning, etc. access to economic and social services. Rural roads OBJECTIVES provide the access to basic amenities and means of transporting agricultural products to nearest market • Collection of primary data, secondary data and centers. The rural roads can be classified as Other preparation of database inGIS. District Roads (ODR) and Village Roads (VR). • To develop spatial and attribute database for the A GIS database is quite suitable for planning, road network of the studyarea. constructing and monitoring of rural roads since all • To understand the existing rural road network of the relevant data in this case is geographically the municipality in terms of connectivity referenced. GIS makes it very easy to store, analyze anddevelopment. and present geographically referenced data. The THE STUDY AREA implementation of GIS can not only reduce the time needed for analyzing information but also can ensure Bheemunipatnam Municipality is considered for the a more efficient use of the resource with high study area which is located inVisakhapatnam district, flexibility in time and scale. It enables user to store Andhra Pradesh, India. Geographically it is situated and display large amount of data graphically to between 17.53N to 83.26E. It is the second oldest greatly enhance the interpretation and analysis. In municipality in India, established on 8 February 1861. the GIS platform, the database of transportation This Place is in the border of the Visakhapatnam network is normally extended by integrated with District and Vizianagaram District. It is 24 km far from spatial and attribute data. District headquarters Vishakhapatnam. Road data enables GIS applications to facilitate a The total geographical area is 18.88 Km2 and the variety of services which include satellite navigation, total population of the municipality is 51,984 (Census Report, 2011) with the population density of 2757

Corresponding author: Kamlesh Sah 100 Kamlesh Sah, International Journal of Engineering Technology and Computer Research (IJETCR)

persons per km2.There are about 27 thousand (52%) the most populous ward with population of 3486. It are female and about 25 thousand (48%) are male. has an average elevation of 51meters. 90% of the whole population are from general caste, Adorable Bay of Bengal on one side, riveting river 10% are from schedule caste and 0% are schedule Gosthani on the other and captivating Red Sand tribes. Child (aged under 6 years) population of dunes on the third gracing this magnificent piece of Bheemunipatnam Municipality is 10%, among them land Bhimili. It is surrounded by Padmanabham 51% are boys and 49% are girls. There are about 13 Mandal towards North, Anandapuram Mandal thousand households in the city and an average 4 towards west, Chinagadila Mandal towards South, persons live in every family. There are 28 wards in the Bhoghapuram Mandal towards East. city, among them Bheemunipatnam Ward No 24 is

Fig. 1: Location map of Bheemunipatnam

The Bheemunipatnam municipality consists of one town and five villages. They are: Town – Bheemili. Villages – Chittivalasa, K.V. Bhumulu, Kummaripalem, Sangivalasa and Thagarapuvalasa. There are total 73km roads in Bheemunipatnam municipality. Maximum temperature here reaches up to 32°C and minimum temperature goes down to 20°C. It is hot in summer. Average temperatures of January is 26 °C, February is 26 °C, March is 28 °C, April is 29 °C & May is 31 °C .Yearly average rainfall is 879.87 mm.

Methodology

Fig. 2: Research Methodology

Literature Review hospitals using GIS in that city. Bharati Gogoi (2013) worked on structural analysis of existing road Vinod R V et al. (2003) has calculated the network networks of Assam. The author has calculated the connectivity indices using GIS in Kasaragod taluk, network connectivity indices such as road density, Kerala. They identified poor connectivity of road alpha index, beta index, gamma index, cyclomatic network in that study area and worked for its number and aggregate transportation score of 27 improvement. R.Nijagunappaetal. (2007) worked districts of Assam and identified that study area has on various modules of Network analyst tool in ARC minimum road density and most of the districts are GIS software like network tracing, path analysis and characterized by minimum efficiency of road found solutions to different road network problems network in terms of connectivity. The efficiency of of Dehradun city. Arora.A et al. (2011) studied on road network is very low and higher spatial the analysis of road network for services like ATMS imbalance. of different banks and hospital to find solutions for routing problems related to accessibility, rate of Road Network Connectivity Analysis flow and network connectivity for south west part The connectivity of a network may be defined as of Delhi. Adesola.A et al. (2011) studied on road the degree of completeness of the links between network density and connectivity for Nigeria. They nodes of a network which are directly connected to identified that the study area was good in each other. Also, it means that the connectivity of a transportation facilities because of good network may be defined as the degree of connectivity index values and high road network connection between all vertices by arcs 'links' density. Sarkar, D. (2013) carried out structural (Robinson and Bamford 1978). analysis of existing road networks of Cooch Behar District, west Bengal. The author has calculated Kansky (1963) has studied the structure of the network connectivity indices such as alpha transportation networks and provided a number of index, beta index, gamma index, cyclomatic number indices which can be used for this purpose. There and aggregate transportation score of 12 blocks of are several methods that can be used to measure Cooch Behar District and identified that nine blocks the degree of connectivity; including Beta, Gamma, namely Mathabhanga II, Haldibari, Tufangang II, Alpha indices &Cyclomatic number (Taaffe and Sitai, Sitalkuchi, Mathabhanga I, Cooch Behar I and Gauthier, 1973 and Davis, 1974).There are many Cooch Behar II have lower development of Indices and every Index has its own equation for transportation in terms of connectivity and have different purposes. For extraction of connectivity least efficient road network in thedistrict. index,it requires a road network (line), junctions (nodes). These indices are useful to identify the Kumar et al. (2013) worked on road connectivity growth within network structure and for change and network analysis for Varanasi city. They found detection system. These may be calculated the optimal paths for providing services like asfollows.

Table 1: Formula for road network connectivity Indices

S. No. Indices Formula Correlation with Connectivity 1. Road Density Higher the density higher the

. development 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿ℎ 2. Beta Index β = Higher value indicates more connectivity 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑆𝑆𝑆𝑆𝑒𝑒 𝐾𝐾𝐾𝐾 3. Alpha Index +1 Higher value indicates more connectivity α= 2 𝑣𝑣 5 𝑒𝑒−𝑣𝑣 4. Gamma Index 𝑣𝑣− Higher value indicates more connectivity γ = 3( 2) 𝑒𝑒 5. Cyclomatic Number µ = 𝑣𝑣−+ 1 Higher value indicates more connectivity

6. Aggregate Transportation ATS = 𝑒𝑒β −+ α𝑣𝑣 + γ + µ Higher value indicates more connectivity Score (ATS) and efficiency

RESULT AND DISCUSSION Road Density Density of road network indicates the length of road per unit of geographical area, which discerns the degree of connectivity and accessibility of the road network system. The average density of the road network system of Bheemunipatnam municipality is 3.86 km/sq.km.

Table 2: Ward wise Composite Score of Road Transportation in Bheemunipatnam Municipality

Ward No. Node (v) Edge (e) Beta Alpha Gamma Cyclomatic A.T.S Index (β) Index (α) Index (γ) Number (µ) 1 22 22 1 0.02 0.36 1 2.38 2 41 40 0.97 0 0.34 0 1.31 3 18 19 1.05 0 0.39 0 1.44 4 20 22 1.1 0.08 0.40 3 4.58 5 42 44 1.04 0.03 0.36 3 4.43 6 64 73 1.14 0.08 0.39 10 11.61 7 14 16 1.14 0.13 0.44 3 4.71 8 34 36 1.05 0.04 0.37 3 4.46 9 13 15 1.15 0.14 0.45 3 4.74 10 23 25 1.08 0.07 0.39 3 4.54 11 14 13 0.92 0 0.36 0 1.28 12 50 56 1.12 0.07 0.38 7 8.57 13 24 25 1.04 0.04 0.37 2 3.45 14 70 76 1.08 0.04 0.37 7 8.5 15 35 37 1.05 0.05 0.37 3 4.46 16 18 20 1.11 0.04 0.41 3 4.61 17 20 19 0.95 0.09 0.37 0 1.32 18 14 15 1.07 0 0.41 2 3.56 19 35 37 1.05 0.08 0.37 3 4.46 20 40 42 1.05 0.04 0.36 3 4.45 21 8 7 0.87 0 0.38 0 1.25 22 25 30 1.2 0 0.43 6 7.76 23 130 133 1.02 0.13 0.34 4 5.37 24 210 212 1 0.01 0.33 3 4.33 25 70 69 0.98 0 0.33 0 1.31 26 10 12 1.2 0 0.5 3 4.9 27 70 78 1.11 0.06 0.38 9 10.55 28 28 30 1.07 0.05 0.38 3 4.5

Beta Index (β) (0.92) & 17 (0.95) belong to the lower beta index value region. On the other hand, ward no's namely The beta index is a very simple measure of 2 (0.97), 25 (0.98), 1 (1.0), 5 (1.04), 8 (1.05), 13 connectivity, it can be found by dividing the total (1.04), 3 (1.05), 15 (1.05), 19 (1.05),20 (1.05), 23 number of arcs in a network by the total number of (1.02), 18 (1.07), 28 (1.07) & 24 (1.0) have moderate nodes. Beta index ranges from 0.0 to network which beta index value. The rest of the ward numbers consist just of nodes no arcs. If the value of beta namely 10 (1.08), 14 (1.08), 4 (1.1), 6 (1.14), 7 index is greater than 1, the networks are well (1.14), 9 (1.15), 12 (1.12), 16(1.11), 22 (1.2), 26 (1.2) connected and higher values indicate higher the & 27 (1.11) have the most complex &well complexities of the networks. connected road networks. The value of beta index in the study varies from 0.87 - 1.2. Ward numbers namely 21 (0.83), 11 © 2017 IJETCR. All Rights Reserved. 103 Kamlesh Sah, International Journal of Engineering Technology and Computer Research (IJETCR)

Table 3: Summary of Beta Index (β)

Category Range Ward No. (Beta Index Value) Lower value 0.87 - 0.95 11 (0.92), 17 (0.95) & 21 (0.87) Moderate value 0.95 - 1.07 1 (1.0), 2 (0.97), 3 (1.05), 5 (1.04), 8 (1.05), 13 (1.04), 15 (1.05), 18(1.07), 19 (1.05), 20 (1.05), 23 (1.02), 24 (1.0), 25 (0.98) & 28(1.07) Higher value 1.07 - 1.2 4 (1.1), 6 (1.14), 7 (1.14), 9 (1.15), 10 (1.08), 12 (1.12), 16 (1.11), 22 (1.2), 26 (1.2) & 27 (1.11)

alpha index value '0'. Thirteen ward numbers Alpha Index (α) namely 23 (0.01), 1 (0.02), 5 (0.03), 8 (0.04), 13 The alpha index is a ratio of circuits to the number (0.04), 14 (0.05), 15 (0.04), 19 (0.04), 20 (0.04), of maximum possible circuits in the network. The 28 (0.05), 27 (0.06), 10 (0.07) & 12 (0.07) belong range of the index is from a value of 0 for a to the lower alpha index region having lower minimally connected network to a value of 1 for a maximally connected one. connected road network. While ward numbers 18 (0.08), 4 (0.08), 6 (0.08), 16 (0.09), 7 (0.13) & 22 The ward numbers has been divided in to four (0.13) belong to the moderate connected road regions on the basis of obtained alpha values. network. On the other hand, only two ward no's Tables 2 and 4 indicate that ward numbers2 (0), 3 namely 9 (0.14) & 26 (0.2) belong to (0), 11 (0), 17 (0), 21 (0), 24 (0) & 25 (0) have comparatively higher connected road network. minimally connected road network having the

Table 4: Summary of Alpha Index (α)

Category Range Ward No. (Alpha Index Value) Lowest value 0 2 (0), 3 (0), 11 (0), 17 (0), 21 (0), 24 (0) & 25 (0) Lower value 0.01 - 0.07 1 (0.02), 5 (0.03), 8 (0.04), 10 (0.07), 12 (0.07), 13 (0.04), 14 (0.05), 15 (0.04), 19 (0.04), 20 (0.04), 23 (0.01), 27 (0.06) & 28 (0.05) Moderate value 0.07 - 0.13 4 (0.08), 6 (0.08), 7 (0.13), 16 (0.09), 18 (0.08) & 22 (0.13) Higher value 0.13 - 0.2 9 (0.14) & 26 (0.2)

Gamma Index (γ) index region. On the other hand, ward numbers3 The Gamma Index is a ratio of actual number of (0.39), 6 (0.39), 10 (0.39), 4 (0.40), 16 (0.41), 18 edges to the maximum possible number of edges (0.41), 21(0.41), 22 (0.43) & 7 (0.44) belongs to in the network. Its value ranges from 0.0 – the moderate gamma index region. Rest eighteen indicates no connection between nodes, to 1.0 – ward numbers namely 24 (0.33), 25 (0.33), 2 maximum number of connection with direct link (0.34), 23 (0.34), 1 (0.36), 5 (0.36), 11 (0.36), 20 to all nodes. (0.36), 8(0.37), 13 (0.37), 14 (0.37), 15 (0.37), 17 Tables 2 and 5 shows that ward numbers namely (0.37), 19 (0.37), 28 (0.38), 12 (0.38) & 27(0.38) 9 (0.45) & 26 (0.5) belong to the higher gamma belong to the lower gamma index region.

Table 5: Summary of Gamma Index (γ)

Category Range Ward No. (Gamma Index Value) Lower value 0.33 - 0.38 1 (0.36), 2 (0.34), 5 (0.36), 8 (0.37), 11 (0.36), 12 (0.38), 13 (0.37), 14 (0.37), 15 (0.37), 17 (0.37), 19 (0.37), 20 (0.36), 23 (0.34), 24 (0.33), 25 (0.33), 27 (0.38) & 28 (0.38) Moderate value 0.38 - 0.44 3 (0.39), 4 (0.40), 6 (0.39), 7 (0.44), 10 (0.39), 16 (0.41), 18 (0.41), 21 (0.41) & 22 (0.43) Higher value 0.44 - 0.5 9 (0.45) & 26 (0.5)

Cyclomatic Number (µ) Tables 2 and 6 indicate that ward numbers 27 (9) & 6 (10) are having the highest cyclomatic Cyclomatic number is based upon the condition number, represents its more closeness and more that as soon as a connected network has enough connected state of road network. The cyclomatic arcs or links to for a tree, then additional arcs will number '0' of ward numbers 2, 3, 11, 17, 21, & 25 result in the formation of circuits. The cylomatic indicates tree type graph. On the other hand only number '0' indicates a tree type graph, whereas three ward numbers namely 22 (6), 12 (7), & 14 the graph closer and closer to the completely (7)belong to the moderate cyclomatic index region connected state, the cyclomatic number increases. having moderate value. Rest seventeen ward A disadvantage of this method is that networks numbers namely 1 (1), 4 (3), 5 (3), 7 (3), 8 (3), 9 with very different forms may have the same (3), 10 (3),13 (20), 15 (3), 16 (3), 18 (2), 19 (3), 20 cyclomatic number (Waugh, 1995). (3), 23 (4), 24 (3), 26 (3) & 28 (3) have low value ofcyclomatic number region.

Table 6: Summary of Cyclomatic Number (µ)

Category Range Ward No. (Cyclomatic Index Value) Lowest value 0 2 (0), 3 (0), 11 (0), 17 (0), 21 (0) & 25 (0) Lower value 1 - 4 1 (1), 4 (3), 5 (3), 7 (3), 8 (3), 9 (3), 10 (3), 13 (2), 15 (3), 16 (3), 18 (2), 19 (3), 20 (3), 23 (4), 24 (3), 26 (3) & 28 (3) Moderate value 4 - 7 12 (7), 14 (7) & 22 (6) Higher value 7 - 10 6 (10) & 27 (9)

A.T.S (Aggregate Transportation Score) (4.43), 28 (4.5) & 18 (3.56) have almost equal and lower level of connectivity ranging from 1.24 - 4.69. It is the summation of alpha index (α), Beta Index Again, the ward numbers like 7 (4.71), 9 (4.74), 16 (β), Gamma Index (γ) and Cyclomatic Number (µ). (4.61), 22 (7.76) & 23 (5.37) are having slightly Higher the ATS value means good connectivity in better level of connectivity if compared with the the network. previous category. Moreover, four wards i.e. 12 It is clear that, the ward numbers namely 21 (1.24), (8.57), 27 (10.55), 6 (11.61) and 14 (8.5) have higher 11 (1.28), 2 (1.31), 25 (1.31), 17 (1.32), 3 (1.44), A.T.S value which results higher degree of 1(2.38), 4 (4.58), 5 (4.43), 8 (4.46), 10 (4.54), 13 connectivity. (3.45), 15 (4.46), 16 (4.61), 19 (4.46), 20 (4.45),24

Table 7: Summary of A.T.S

Category Range Ward No. (A.T.S Value) Lower value 1.24 - 4.69 1 (2.38), 2 (1.31), 3 (1.44), 4 (4.58), 5 (4.43), 8 (4.46), 10 (4.54), 11 (1.28), 13 (3.45), 15 (4.46), 16 (4.61), 17 (1.32), 18 (3.56), 19 (4.46), 20 (4.45), 21 (1.24), 24 (4.33), 25(1.31) & 28 (4.5) Moderate value 4.69 - 8.14 7 (4.71), 9 (4.74), 16 (4.61), 22 (7.76) & 23 (5.37) Higher value 8.14 - 11.61 6 (11.61), 12 (8.57), 14 (8.5) & 27 (10.55)

Table 8: Overall Network Connectivity Indices of the Study Area

Node Edge Length Area Road density Alpha Beta Gamma Cyclomatic A.T.S (Km) (Km2) Km/sq.Km Index Index Index Number

1162 1223 73 18.88 3.86 0.026 1.05 0.35 62 63.42

Fig.2: Existing road network of Bheemunipatnam Municipality

CONCLUSIONS the road as perIRC.  A well-maintained transport route promotes 1. The Bheemunipatnam Municipality has 3.86 socio-economic and infrastructural development. Km/sq.Km road density, which is satisfactory Furthermore, roads should be constructed to link up inposition. the numerous roads to eachother. 2. There is a little variation among the wards in  The efficiency of a GIS is clearly evident in this terms of Beta Index and GammaIndex. study in terms of road network analysis. The road 3. Fourward numbers namely11, 17 and 21 have network analysis using GIS should be widely extended lower development of transportation in terms of to many rural and urban areas so that it provides connectivity (Beta Index) and have the least efficient enormous benefits for efficient rural and road network in these wards. urbanplanning. 4. In case ofalltheconnectivityindices(betaindex, alphaindex, gammaindex, cyclomaticnumber and REFERENCES ATS) only nine wards namely 6, 7, 9, 12, 14, 16, 22, 23 1. A. M. Rao, Durai B.K., Jain P.K. &SikdarP.K.(2004) and 27 represent highest value and higher degree of “Geographical Information System for Planning road connectivity in these wards. and Management of Rural Roads”, GIS 5. The Aggregate Transportation Score (ATS) result Development,Noida. obtained from the summation of various connectivity 2. Ajay, D. Nagne and Bharti W.Gawali(2013) indices is 63.42. This value indicates that overall “Transportation Network Analysis by Using network connectivity in the study area isgood. Remote Sensing and GIS,” International Journal of RECOMMENDATIONS Engineering Research and Applications, Vol. 3, Issue 3,pp.70-76.  Width deficiency is main problem to road 3. Bharati Gogoi (2013) “Structural Analysis of network.Therefore, we should increase the width of © 2017 IJETCR. All Rights Reserved. 106 Kamlesh Sah, International Journal of Engineering Technology and Computer Research (IJETCR)

Existing Road Networks of Assam” International 10. Praveen Kumar Rai, P. K. Singh, A. K. Singh and K. Journal of Scientific & Engineering Research, Vol. Mohan (2013) “Network Analysis Using GIS” 4, Issue 12, pp. 1817- 1823. International Journal of Emerging Technologies in 4. Dnyaneshwar S Puri (2016) “Latur Town Computational and Applied Sciences, Vol. 5, Issue Roadways Network Analysis and Planning By 3, pp.289-292. Using GIS and RS,” International Journal of 11. R. Nijagunappa, et al. (2007). “Road Network Advance Research, IJOAR,Vol. 4, Issue 7. Analysis of Dehradun City using High Resolution 5. Dr. K. M. Lakshmana Rao and K. Jayasree(2003) Satellite Data and GIS”, Journal of The Indian “Rural Infrastructure Planning with Emphasis on Society of Remote Sensing, Vol. 35, No.3. Road Network Connectivity by Coplanar 12. R.V.Vinod, et al. (2003) “Transport Network Concurrent Theory”, Map India conference. Analysis of Kasragod Taluk, Kerala Using GIS” 6. Goplala Raju S.S.S.V, Durga Rani K and Balaji IndianCartographer. K.V.G.D (2012) Analysis of road network in 13. Sarkar, D. (2013) “Structural Analysis of Existing Visakhapatnam city using Road Networks of Cooch Behar District, West geographicalinformation susyems. Indian Journal Bengal, India: A Transport Geographical of Innovations Dev., Vol. 1, No.5, pp. 395-399. Appraisal.” Ethopian Journal of Environmental 7. K. Ilayaraja(2013) “Road Network Analysis Studies and Management, Vol. 6, No.1, pp.74-81. inNeyveli Township, Cuddalore District by Using 14. Sikdar, P.K, Kanagadurai, B, Prasada Rao, I., and Quantum GIS”, Indian Journal of Computer Jain, P.K.(2002) “GIS Based Functional Science andEngineering, Vol. 4, No.1. pp.56-61. Accessibility Approach for Rural Road Network 8. Kadiyali L.R., “Traffic Engineering and Planning –A Case Study”, IIT kharagpur, vol. Transportation Planning”, Khanna Publishers, 2,pp.57-69. 1999. 15. Singh, A.K. (2004) “GIS Based Rural Road Network 9. Khanna S.K., and Justo C.E.G., “Highway Planning for Developing Countries”. Journal of Engineering”, Nem Chand and Bros, Roorkee, Transportation Engineering. 1991.