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Assessment of Topological Pattern of Urban Road Transport System of Calicut City
Available online at www.sciencedirect.com
ScienceDirect Transportation Research Procedia 17 (2016) 253 – 262
11th Transportation Planning and Implementation Methodologies for Developing Countries, TPMDC 2014, 10-12 December 2014, Mumbai, India
Assessment of Topological Pattern of Urban Road Transport System of Calicut City M. G. Sreelekhaa*, K. Krishnamurthyb and M. V. L. R Anjaneyuluc a
Associate Professor, Department of Civil Engineering, Government Engineering College, Calicut, 673005, Kerala, India. Assosciate Professor, Department of Civil Engineering, National Institute of Technology-Calicut, 673601, Kerala, India. c Professor, Department of Civil Engineering, National Institute of Technology-Calicut, 673601, Kerala, India.
b
Abstract
A good road transport system can bring several benefits to a developing country since transportation is an engine for economic and social progress. The huge developmental cost of the transport system demands effective utilisation, which can be attained only when there is proper connectivity. Hence a great emphasis needs to be given to the layout and connectivity of the transport system. Urban road transport system has less theoretical research. Only some developed countries have performed urban transport system evaluation and hence it has great potential for development and application prospects. This study assessed the relationship between connectivity and development of the transport system of Calicut city using GIS. Road Network Development in kilometre per square kilometre varies from 1.20 to 16.53 with mean 5.99. Road development can be used as an indicator of urban development within a region, which in turn depends on the transport system connectivity. Based on this study it can be concluded that road transport system development is directly varying with transport system connectivity. This means that there is significant relationship between the level of road transport system connectivity and network development within the study area. On comparing the connectivity of the city as a whole with that of each of the zones, it is seen that the road transport system is not distributed in an even manner throughout the city. In fact the zones near the Central Business District have high connectivity and development, while the outer suburban zones have very less transport system development.
* Corresponding author. Tel.: 9544341060 E-mail address: [email protected]
2352-1465 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Department of Civil Engineering, Indian Institute of Technology Bombay doi:10.1016/j.trpro.2016.11.089
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M.G. Sreelekha et al. / Transportation Research Procedia 17 (2016) 253 – 262 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2015 The Authors. Published by Elsevier B. V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and peer-review under responsibility of the Department of Civil Engineering, Indian Institute of Technology Bombay.
Peer-review under responsibility of the Department of Civil Engineering, Indian Institute of Technology Bombay
Keywords: Transport System; GIS; Network Analysis; Connectivity; Coverage
1. Introduction The transport system enhances the movement of goods and services and consequently forms the source of development for the economic and social sectors because they link places together. Hence the transport system becomes one of the basic needs for any region in the world. Road transport is the major mode of transport in developing countries, as it allocates flexibility to the common dispersed spatial configuration. Improvement of the road transport system increases accessibility and mobility, thus reducing the travel cost and travel time. When we look at the transport planning and decision making strategies of cities of developing countries, most of the times, planning decisions are made based on pure speculation and it is hard to explain how decisions made affect the road network plan. Due to this fact cities grow in uncontrolled manner and evolve into more and more inefficient transport networks. Transport system and the spatial pattern of land use they serve are assumed to mutually influence each other. Changes to road transport system, such as the construction of a new link or expansion of an existing one eventually influence the location of investment in land, which in turn influences the demand for travel to and from a particular location. Thus a planned study of the urban road transport system is required to facilitate better transportation of man and materials, location of their activities, and hence to achieve overall growth of the city in an efficient manner. In short, it is necessary to reflect the problem and carry out urban road transport system evaluation and assess the system for identifying the topological pattern. Urban road transport system evaluation has less theoretical research, and relative project cases are also not enough. Only some developed countries have carried out the evaluation of urban transport system and hence it has great potential for development and application prospects. Extraction of the basic connectivity indices applying graph theory, which provide the fundamental information to delineate the character of the given transport system had been the focus of many studies. On the contrary, interaction between the connectivity pattern and development aspect of the road transport system is not clearly understood. Connectivity can be assessed in terms of the mobility the transport system provides and the opportunities that can be accessed within certain time of travel. The arrangement and connectivity of nodes and road segments within a system is referred to as the topology of the system. Generally, connectivity improves greatly when new roads connect to the existing road system. On the other hand, it remains unchanged or declines when development extends into the hinterland producing dead-end roads, rather than linking two existing roads. Patarasuk (2012) has examined the relationship between road network development and land-cover dynamics in Thailand through network connectivity, considering temporal variation. The result showed that even though the total length of roads in the study area increased, the connectivity did not do so. This means that, even though the purpose of road development is to facilitate the access to the different land use activities, the connectivity of roads cannot be ascertained. Indeed connectivity of the region depends on where roads are developed, whether linking existing roads or developed as dead-ends. Hence the research question is how the road connectivity change as the road transport system develops? Thus the main idea of this study is to examine the relationship between the road transport system connectivity pattern and road network coverage and development, considering spatial variation. Also to understand the road transport system configuration of the city as a whole and how it vary from that of the individual zones within the city. Calicut city region has been chosen as the study area for preparation of the road transport system basemap across the zonal tracts and hence for the characterisation of the transport system. In this study ArcGIS 9.3 software is used for digitising the transport network data, building network dataset as well as for analysing the network using graph theoretical measures for modelling the network configuration. The main benefit of using GIS is not merely the userfriendly visual access and display, but also the spatial analysis capability such as thematic mapping, network analysis, query, statistical analysis etc.. Regression analysis was used to infer the casual relationships between the variables and hence to understand how the typical value of the dependent variable changes with the explanatory variable.
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2. Review of Literature A road transport network is a framework of a set of nodes possessing spatial locations and a set of links representing connections between the nodes. A network possess many different structural properties, displaying both topological and geometrical variations. The arrangement and connectivity of nodes and links within a network is referred to as its topology (Xie & Levinson, 2007). There are various measures and indices including the graph theoretic measures for assessing the topology, proposed in earlier studies which can be used for evaluating the configuration of transport system. Selected indicators used in this paper can be classified under connectivity and coverage. 2.1 Connectivity The connectivity measures evaluate the quality of a transport system based on intensity of connections between the road segments. The various indices used for evaluating the connectivity pattern are Alpha Index, Beta Index, Gamma Index and Eta Index proposed by Kansky (1963) and Grid Tree Pattern Index by Noda (1996). Alpha index is the ratio of the number of fundamental circuits to the maximum possible number of circuits in the network. The alpha index gives the range values possible from 0 to 1. Simple networks, such as trees, have nil values. A value of 1 is indicative of a highly integrated network in which every possible link exists between the various nodes. Beta index compares the number of links with the number of nodes in a network. Beta index takes 0 value when no edges, 1 when the network has one circuit, and exceeds 1 for a complicated network with several circuits. Gamma index compares the actual number of links with the maximum possible number of links in the network. This index measures the theoretical maximum connectivity of a network. The value range from 0 to 1, with a higher value indicating a more connected network. Eta index measures the average length of a link in the network. This index is used as a measure of speed in the network with the assumption that longer the segment, the better it is to ensure maximum speed in the segment concerned. Grid Tree Pattern index is a measure for identifying the pattern of the network, varying from 0 in the case of tree pattern to 1 in the case of grid pattern. 2.2 Coverage Coverage measure describe the density aspect of the elements of a transport system, such as road links. The indices which are used for attributing the level of coverage and development are Network Length (Levinson, 2012) and Network Density (Bento, Cropper, Mobarak & Vinha, 2003). Higher the measures, more the system is developed. Network Length indicates total length of the road network within each of the zonal region. Network Density indicates the length of network per square kilometre of the surface, which measures the transport network development. A higher value indicates more road network development. The equations used for calculating the indices are given in table 1. Table 1. Transport System Connectivity and Coverage Measures
Index
Formula
Notation e the number of edges
Alpha index
αൌ
െ ʹ െ ͷ
v the number of vertices p the number of subgraphs
Gamma index
e the number of edges
͵ሺ െ ʹሻ
e the number of edges
βൌ
Beta index
γൌ
v the number of vertices
v the number of vertices
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Eta index Grid Tree Proportion index Network Density
ηൌ
ൌ
L the length of network E the observed number of edges
െͳ
e the number of edges
ሺξ െ ͳሻ²
v the number of vertices
L the length of network
ൌ
A the area of zone
Dill (2004) evaluated various measures of network connectivity relating walking and biking. Zhang, Bigham, Li, Ragland and Chen (2012) studied the associations between connectivity and pedestrian-bicyclist accidents and determined that higher connectivity relate to fewer crashes for non-motorized road users. The coverage measures can be utilized to understand settlement patterns. Levinson (2012) focused on how network scale and connectivity vary with city size considering metro system, in which it has been indicated that connectivity grows with network size and network density. Sarkar (2013) made an attempt to explore the existing pattern and spatial variation of the road network of West Bengal with the help of structural measures including road density. Vinod, Sukumar and Sukumar (2003), Nagne, Vibhute and Gawali (2013) and Nagne and Gawali (2013) are some other Indian studies which applied GIS technology for the assessment of transportation network using connectivity indices.
3. Objectives a) To study the existing road transport system configuration of the city in terms of connectivity, coverage and development. b) To examine the relationship between road transport system connectivity and road network development. c) To understand the variation of the topology pattern of the road transport system at zonal level and city level. 4. Methodology Urban road transport system evaluation using GIS involves the establishment of evaluation system, collecting the data resources, digitising the urban road transport system, building the road network database, extracting the network structure etc.. This study made use of the satellite imagery of Calicut corporation obtained from Google map in order to update the existing road network. All the roads including National Highway, State Highway, Collector Streets and Local Streets were considered. The zone wise corporation boundary was derived from the Autocad drawing obtained from Calicut Corporation office. The imagery as well as the Autocad ward map were geo-rectified in ArcGIS 9.3 to geographic co-ordinates for which the ground control points were used. In ArcGIS, the ward boundary and the roads were converted into polygon and polyline features respectively. The ward boundaries are overlayed on the Calicut city road transport system to extract the road transport system within each of the seventy five zones for studying the topology and development pattern of the system. Alpha Index, Beta Index, Gamma Index, Eta index and Grid Tree Pattern Index were used for attributing the topology and Network Density for attributing the coverage and development and hence to identify the variation and the relationship between connectivity and development.
5. Study Area The area selected for this study is the corporation of Calicut district. Calicut city is situated in the South-West corner of the district and extends between 75º 47' 23" E and 76º 26' 40" E longitudes and 11º 30' 08" N and 11º 58' 40" N latitudes. Calicut city has an area of 84.29 sq km and has a population of 4,32,097 as per 2011 data with gross
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density 5126 persons per sq.km. Figure 1 gives the zone map of the Calicut Corporation. The total length of the road transport system within the Calicut city urban area is about 335 kilometers. The road density is 3.97 km per sq.km. The roads in the entire network can be broadly divided into categories based on their functions, namely, the roads facilitating intercity trips and roads facilitating intra city trips. The National Highways and State Highways are the major roads facilitating intercity trips. The city roads, which connect the residential neighbourhood with the major road or with the CBD and other work centres, are meant for intra city movements. Figure 2 shows the digitised road network map of Calicut Corporation.
Fig. 1. Zone Map of Calicut City
Fig. 2. Digitised Road Map of Calicut City
6. Analysis and Results The zone-wise road transport system extracted by overlaying the corporation boundary of each zone on the road transport system was quantified with respect to the various aspects of characterisation as connectivity and coverage. 6.1 Connectivity The road transport system within each of the seventy five zones were quantified with respect to the indices measuring connectivity as Alpha, Beta, Gamma, Grid Tree Pattern Index and Eta Index. For this the road transport system digital map within each zone was converted into Network Dataset using ArcGIS Network Analyst extension. From the Network Dataset, the attribute details of the network such as the count of edges and vertices within the network were obtained from Network Dataset properties available from ArcCatalogue. Using the edge and node count of each zone, the connectivity indices were evaluated for each zone. Sample calculation of the various connectivity indices are shown in table 2 and the descriptive statistics of the various connectivity indices are listed in table 3.
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Table 2 Calculation of Connectivity Indices Number of Nodes (v) : 14 Number of Edges (e) : 18 Number of Subgraphs (p) : 1 Total Length of Network : 5.813 km
Ward No : 15 Ward Name : Chelavur
݁ െ ݒ ͳ ͺ െ ͳͶ ͳ ൌ ൌ ͲǤʹʹ ʹ ݒെ ͷ ʹ ͳ כͶ െ ͷ ݁ ͳͺ ߚൌ ൌ ൌ ͳǤʹͻ ͳ ݒͶ ݁ ͳͺ ߛൌ ൌ ൌ ͲǤͷͲ ͵ሺ ݒെ ʹሻ ͵ כሺͳͶ െ ʹሻ ܴ ݄ݐ݈݃݊݁݀ܽͷǤͺͳ͵ ߟൌ ൌ ൌ ͲǤ͵ʹʹ ݊Ǥ ݏ݈݂݇݊݅ ͳͺ ݁ െ ݒ ͳ ͳͺ െ ͳͶ ͳ ܲܶܩൌ ൌ ൌ ͲǤͷͻ ሺξ ݒെ ͳሻ² ሺξͳͶ െ ͳሻ²
Alpha index:
ߙൌ
Beta index: Gamma index: Eta index: GTP index:
Table 3 Descriptive Statistics of Connectivity Indices
Minimum
Maximum
Mean
Std. Deviation
Alpha Index
0.090
0.760
0.437
0.163
Beta Index
1.000
2.490
1.769
0.354
Gamma Index
0.430
0.840
0.633
0.104
Eta Index
0.050
0.340
0.140
0.071
GTP Index
0.150
0.870
0.570
0.181
6.2 Coverage The road transport system development of each of the zones were evaluated with respect to the attributes as Network Size and Network Density. The total length of the transport system and area of each zonal boundary were extracted from the attribute table of the road transport system and that of the zonal boundary respectively. Making use of these data, Network Density was calculated for all the zones as shown in table 4. The descriptive statistics of the various coverage indices are listed in table 5. Table 4 Calculation of Coverage index
Ward No : 15 Ward Name : Chelavur
Network Density:
Area of Zone : 3.1 km2 Total Length of Network : 5.813 km
ܰ ܦൌ
ܮ ͷǤͺͳ͵ ൌ ൌ ͳǤͺͺ݇݉Ȁ݇݉ଶ ܣ ͵Ǥͳ
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Table 5 Descriptive Statistics of Coverage Indices
Minimum
Maximum
Mean
Std. Deviation
Network Size (km)
2.370
25.540
8.550
4.470
Network Density (km/sq km)
1.198
16.533
5.997
3.351
7. Data Analysis Effect of the explanatory variables as connectivity indices on the road network development in terms of Network Size and Network Density were separately studied. For exploring the causal relationship between road network development and connectivity, correlation analysis was performed. Spearman’s rho correlation coefficient, which identifies both linear and non-linear relationship, is used to assess the association between the variables and the corresponding values are shown in table 6. Table 6 Spearman’s Rho Correlation Coefficients
Network Density Network Size
Alpha
Beta
Gamma
Eta
GTP
0.803**
0.832**
0.769**
- 0.737**
0.746**
0.666**
0.750**
0.650**
- 0.529**
0.554**
**Correlation is significant at 0.01 level (2-tailed) The variables that are identified to be positively correlated to Network Density are Alpha, Beta, Gamma and GTP index, while Eta index is seen to be negatively correlated. The correlation value comes in the range 0.74 to 0.80, which shows strong correlation of each of the connectivity indices with Network Density. The correlation of the connectivity indices with Network Size comes in the range 0.50 to 0.75. Scatter plots were prepared for examining the relationship of each of the indices as Alpha Index, Beta Index, Gamma Index, Eta Index and Grid Tree Pattern Index attributing the road transport system connectivity with the road network development. From the scatter plots, the variables were seen to follow nonlinear relationship. Hence non-linear regressions were tested with several types of mathematical formulations as logarithmic, exponential and inverse. R2 the coefficient of determination, which gives the proportion of variation in the response variable that is attributed by the explanatory variable was obtained. While analysing the explanatory variables taken individually to study their effect on road network development, the logarithmic formulation showed the highest R 2 for most of the indices. Hence logarithmic models were prepared for analysing the effect of connectivity on Network size as well as Network Density. 7.1 Connectivity Indices with Network Density To examine the variation of the connectivity indices with Network Density, logarithmic plots which showed the highest R2 are plotted and is shown in figure 4.
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a) Alpha Index vs Network Density
b) Beta Index vs Network Density
c) Gamma Index vs Network Density
d) Eta Index vs Network Density
e) GTP Index vs Network Density Fig. 4 Connectivity Indices with Network Density
From the plot it is seen that Alpha Index, Beta Index, Gamma Index and GTP index are positively varying with Network Density while Eta Index declines with Network Density. Table 7 shows the parameter estimates of the logarithmic model when Network Density used for predicting connectivity indices. Table 7 Regressions: Independent Variable = ln(Network Density) Dependent variable Alpha Beta Gamma Eta GTP
Explanatory variable: ln(Network Density) Model Std Error y = 0.057 + 0.232 ln(x) y = 0.913 + 0.523 ln(x) y = 0.404 + 0.141 ln(x) y = 0.272 - 0.080 ln(x) y = 0.181+ 0.239 ln(x)
0.034 0.068 0.024 0.019 0.043
T-Statistic
Sign.
R2- value
F-value
RMSE
2.66 13.36 16.85 14.01 4.24
0.001 0.000 0.000 0.000 0.000
0.66 0.71 0.59 0.41 0.56
138.70 176.18 104.22 51.34 94.02
0.097 0.193 0.067 0.055 0.121
The table shows that indices as Alpha, Beta, Gamma and GTP grow with Network Density while Eta index declines
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with Network Density, with R2 values varying between 0.41 to 0.71. This again supports the fact that more the road length per unit area, more will be the connections. Eta index reduces with Network Density because of the fact that more the connections, less will be the average link length. To verify the significance of the relationship between the dependent and explanatory variables, t- test can be applied. t- ratio shows whether coefficient of each variable is significantly different from zero. Usually a value greater than 1.96 (corresponding to 95 per cent confidence) indicates that the coefficient is significantly different from zero. From the table 7, it can be seen that all the models are significant as the t values are greater than 1.96. The effect of connectivity on Network Size was also studied. From the plot of the connectivity indices with Network Size; Alpha Index, Beta Index, Gamma Index and GTP index were seen to positively correlate with total Road Length while Eta Index declined with total Road Length. In the case of the logarithmic model prepared for examining the effect of the connectivity indices on Network Size, R2 value ranges from 0.312 to 0.593 suggesting the fact that more the road length within a zone, more will be the connections, increasing the edges and vertices of the network. Eta index reduces with Network Size, which supports the fact that more the connections, less will be the average link length. Because of the low values of R 2, the models prepared for analysing the effect on Network Size is not shown here. 7.2 Topology pattern of the city as a whole Attempt has been done to obtain an overall idea of the topology pattern of the whole city, and hence to scrutinize the network connectivity pattern at two different spatial scales, zonal level and city level. The connectivity and coverage values of the various indices of the whole city were obtained and is shown in table 8. A comparison between the topology values of the whole city and that of each individual zone is also shown in the table. Table 8 Network topology indices of Calicut city Variable Alpha Beta Gamma Eta (km) GTP Network Density (km/sq km)
Index Value 0.480 1.830 0.650 0.078 0.460 5.381
Number of zones having values Lesser Greater 44 31 44 31 41 34 41 34 43 32 41 34
The study shows that the levels of connectivity and development differ according to the scales employed. The topology pattern of the whole city quantified in terms of the various connectivity and coverage indices have values in between the minimum and maximum values of each of the indices calculated at the zonal level. The connectivity values of the whole city approximately approach the mean of the values of the indices calculated for each of the seventy five zones of the city. This means the road transport system is not distributed in an even manner throughout the city. In fact some zones, particularly the zones near the Central Business District have high values of connectivity and road transport development because of the intense development of activities. While the outer suburban zones have very less transport system development. On an average, fifty five percent of the zones have transport system with topology level above that of the city as a whole, while forty five percent of zones have lesser connectivity and development pattern. 8. Conclusion The road transport system is a major source of development for any country in the world. One of the important factors contributing to the performance of a road transport system is the availability of connectivity within the network. This paper intended to study the topological characteristics of the road transport system of Calicut city region using ArcGIS 9.3 and hence identify its variation with respect to the network development. Existing road transport system was examined for the variation of connectivity and coverage level for each zones as well as for whole city. Also the interrelationship between the connectivity pattern and the road network development is presented. In this study, the correlation between each of the connectivity indices with road network development is seen to
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M.G. Sreelekha et al. / Transportation Research Procedia 17 (2016) 253 – 262 follow nonlinear logarithmic relationship with R2 value varying from 0.41 to 0.71. The significance of the relationship between the variables were verified using t-ratio test, which tests the significance of each of the variable coefficients. This means that there is significant relationship between the level of road network connectivity and network development within the study area, suggesting that the level of road network connectivity could explain the network coverage significantly. The road network development can be used as an indicator of urban development within a region, which in turn depends on the transport system connectivity. The main conclusion that can be drawn from this study is that more the network development and coverage of the road transport system, more the network inter-connectivity. This means the road transport system of Calicut city has been developed so as to link the existing roads, thus facilitating mobility and accessibility. That is, the transport system of Calicut city shows better performance and spatial distribution with respect to the arrangement and connectivity of the road segments. Thus, the result fully support the initial hypothesis that as road transport system is developed, connectivity increases. On comparing the connectivity of the city as a whole with that of each of the zones, it is seen that the road transport system is not distributed in an even manner throughout the city. In fact the zones near the Central Business District have high connectivity and development, while the outer suburban zones have very less transport system development. The indicators undertaken for evaluating and analysing the transport system of a region can be applied to identify its effect on the pattern of land use and socio-demographic activities in the area. The study point to the importance of developing measures to quantify road network connectivity and extends to the relation between connectivity and development. The measures presented here consider certain aspects of connectivity of network structure. Future extension to current analysis include looking at other measures that could capture the topology aspect of network. Also, considering the topology measures of other transport networks as transit or non-motorised network could help in testing the validity of the result. Estimating similar network measures for other cities could help to test the regional transferability of the model. References 1.
Bento, A. M., Cropper, M. L., Mobarak, A. M., & Vinha, K. (2003). The Impact of Urban Spatial Structure on Travel Demand in the United States, World Bank Policy, Research Paper No. 3007. Kansky, K. (1963). Structure of Transportation Networks: Relationships between Network Geometry and Regional Characteristics, Ph. D. thesis, University of Chicago, Research Paper No. 84. 3. Levinson, D. (2012). Network Structure and City Size. Plos One 7(1): e29721. doi:10.1371/journal.pone.0029721 4. Nagne, A. D., & Gawali, B. W. (2013). Transportation Network Analysis by Using Remote Sensing and GIS A review, International Journal of Engineering Research and Applications Vol. 3, Issue 3, 70-76. 5. Nagne, A. D., Vibhute, A. D., & Gawali, B. W. (2013). Spatial Analysis of Transportation Network for Town Planning of Aurangabad City by using Geographic Information System, International Journal of Scientific & Engineering Research, Volume 4, Issue 7. 6. Noda, H. (1996). A Quantitative Analysis on the Patterns of Street Networks using Mesh Data System, City Planning Review, 202, 64-72 (in Japanese). 7. Patarasuk, R. (2012). Road Network Connectivity and Land-Cover Dynamics in Lop Buri Province, Thailand. Journal of Transport Geography 28, 111–123 8. Sarkar, D., (2013). Structural Analysis of Existing Road Networks of Cooch Behar District, West Bengal, India: A Transport Geographical Appraisal, Ethiopian Journal of Environmental Studies and Management Vol. 6 No.1 9. Vinod, R. V., Sukumar, B., & Sukumar, A. (2003). Transport Network Analysis of Kasaragod Taluk, Kerala Using GIS, Indian Cartographer. 10. Xie, F., & Levinson, D. (2007). Measuring the Structure of Road Networks. Geographical Analysis 39 (3), 336–356. 11. Zhang, Y., Bigham, J, Li, Z., Ragland, D., & Chen, X. (2012). Associations between Road Network Connectivity and Pedestrian-Bicyclist Accidents, Paper presented at TRB Annual Meeting. 2.
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ScienceDirect Transportation Research Procedia 17 (2016) 253 – 262
11th Transportation Planning and Implementation Methodologies for Developing Countries, TPMDC 2014, 10-12 December 2014, Mumbai, India
Assessment of Topological Pattern of Urban Road Transport System of Calicut City M. G. Sreelekhaa*, K. Krishnamurthyb and M. V. L. R Anjaneyuluc a
Associate Professor, Department of Civil Engineering, Government Engineering College, Calicut, 673005, Kerala, India. Assosciate Professor, Department of Civil Engineering, National Institute of Technology-Calicut, 673601, Kerala, India. c Professor, Department of Civil Engineering, National Institute of Technology-Calicut, 673601, Kerala, India.
b
Abstract
A good road transport system can bring several benefits to a developing country since transportation is an engine for economic and social progress. The huge developmental cost of the transport system demands effective utilisation, which can be attained only when there is proper connectivity. Hence a great emphasis needs to be given to the layout and connectivity of the transport system. Urban road transport system has less theoretical research. Only some developed countries have performed urban transport system evaluation and hence it has great potential for development and application prospects. This study assessed the relationship between connectivity and development of the transport system of Calicut city using GIS. Road Network Development in kilometre per square kilometre varies from 1.20 to 16.53 with mean 5.99. Road development can be used as an indicator of urban development within a region, which in turn depends on the transport system connectivity. Based on this study it can be concluded that road transport system development is directly varying with transport system connectivity. This means that there is significant relationship between the level of road transport system connectivity and network development within the study area. On comparing the connectivity of the city as a whole with that of each of the zones, it is seen that the road transport system is not distributed in an even manner throughout the city. In fact the zones near the Central Business District have high connectivity and development, while the outer suburban zones have very less transport system development.
* Corresponding author. Tel.: 9544341060 E-mail address: [email protected]
2352-1465 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Department of Civil Engineering, Indian Institute of Technology Bombay doi:10.1016/j.trpro.2016.11.089
254
M.G. Sreelekha et al. / Transportation Research Procedia 17 (2016) 253 – 262 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2015 The Authors. Published by Elsevier B. V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and peer-review under responsibility of the Department of Civil Engineering, Indian Institute of Technology Bombay.
Peer-review under responsibility of the Department of Civil Engineering, Indian Institute of Technology Bombay
Keywords: Transport System; GIS; Network Analysis; Connectivity; Coverage
1. Introduction The transport system enhances the movement of goods and services and consequently forms the source of development for the economic and social sectors because they link places together. Hence the transport system becomes one of the basic needs for any region in the world. Road transport is the major mode of transport in developing countries, as it allocates flexibility to the common dispersed spatial configuration. Improvement of the road transport system increases accessibility and mobility, thus reducing the travel cost and travel time. When we look at the transport planning and decision making strategies of cities of developing countries, most of the times, planning decisions are made based on pure speculation and it is hard to explain how decisions made affect the road network plan. Due to this fact cities grow in uncontrolled manner and evolve into more and more inefficient transport networks. Transport system and the spatial pattern of land use they serve are assumed to mutually influence each other. Changes to road transport system, such as the construction of a new link or expansion of an existing one eventually influence the location of investment in land, which in turn influences the demand for travel to and from a particular location. Thus a planned study of the urban road transport system is required to facilitate better transportation of man and materials, location of their activities, and hence to achieve overall growth of the city in an efficient manner. In short, it is necessary to reflect the problem and carry out urban road transport system evaluation and assess the system for identifying the topological pattern. Urban road transport system evaluation has less theoretical research, and relative project cases are also not enough. Only some developed countries have carried out the evaluation of urban transport system and hence it has great potential for development and application prospects. Extraction of the basic connectivity indices applying graph theory, which provide the fundamental information to delineate the character of the given transport system had been the focus of many studies. On the contrary, interaction between the connectivity pattern and development aspect of the road transport system is not clearly understood. Connectivity can be assessed in terms of the mobility the transport system provides and the opportunities that can be accessed within certain time of travel. The arrangement and connectivity of nodes and road segments within a system is referred to as the topology of the system. Generally, connectivity improves greatly when new roads connect to the existing road system. On the other hand, it remains unchanged or declines when development extends into the hinterland producing dead-end roads, rather than linking two existing roads. Patarasuk (2012) has examined the relationship between road network development and land-cover dynamics in Thailand through network connectivity, considering temporal variation. The result showed that even though the total length of roads in the study area increased, the connectivity did not do so. This means that, even though the purpose of road development is to facilitate the access to the different land use activities, the connectivity of roads cannot be ascertained. Indeed connectivity of the region depends on where roads are developed, whether linking existing roads or developed as dead-ends. Hence the research question is how the road connectivity change as the road transport system develops? Thus the main idea of this study is to examine the relationship between the road transport system connectivity pattern and road network coverage and development, considering spatial variation. Also to understand the road transport system configuration of the city as a whole and how it vary from that of the individual zones within the city. Calicut city region has been chosen as the study area for preparation of the road transport system basemap across the zonal tracts and hence for the characterisation of the transport system. In this study ArcGIS 9.3 software is used for digitising the transport network data, building network dataset as well as for analysing the network using graph theoretical measures for modelling the network configuration. The main benefit of using GIS is not merely the userfriendly visual access and display, but also the spatial analysis capability such as thematic mapping, network analysis, query, statistical analysis etc.. Regression analysis was used to infer the casual relationships between the variables and hence to understand how the typical value of the dependent variable changes with the explanatory variable.
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2. Review of Literature A road transport network is a framework of a set of nodes possessing spatial locations and a set of links representing connections between the nodes. A network possess many different structural properties, displaying both topological and geometrical variations. The arrangement and connectivity of nodes and links within a network is referred to as its topology (Xie & Levinson, 2007). There are various measures and indices including the graph theoretic measures for assessing the topology, proposed in earlier studies which can be used for evaluating the configuration of transport system. Selected indicators used in this paper can be classified under connectivity and coverage. 2.1 Connectivity The connectivity measures evaluate the quality of a transport system based on intensity of connections between the road segments. The various indices used for evaluating the connectivity pattern are Alpha Index, Beta Index, Gamma Index and Eta Index proposed by Kansky (1963) and Grid Tree Pattern Index by Noda (1996). Alpha index is the ratio of the number of fundamental circuits to the maximum possible number of circuits in the network. The alpha index gives the range values possible from 0 to 1. Simple networks, such as trees, have nil values. A value of 1 is indicative of a highly integrated network in which every possible link exists between the various nodes. Beta index compares the number of links with the number of nodes in a network. Beta index takes 0 value when no edges, 1 when the network has one circuit, and exceeds 1 for a complicated network with several circuits. Gamma index compares the actual number of links with the maximum possible number of links in the network. This index measures the theoretical maximum connectivity of a network. The value range from 0 to 1, with a higher value indicating a more connected network. Eta index measures the average length of a link in the network. This index is used as a measure of speed in the network with the assumption that longer the segment, the better it is to ensure maximum speed in the segment concerned. Grid Tree Pattern index is a measure for identifying the pattern of the network, varying from 0 in the case of tree pattern to 1 in the case of grid pattern. 2.2 Coverage Coverage measure describe the density aspect of the elements of a transport system, such as road links. The indices which are used for attributing the level of coverage and development are Network Length (Levinson, 2012) and Network Density (Bento, Cropper, Mobarak & Vinha, 2003). Higher the measures, more the system is developed. Network Length indicates total length of the road network within each of the zonal region. Network Density indicates the length of network per square kilometre of the surface, which measures the transport network development. A higher value indicates more road network development. The equations used for calculating the indices are given in table 1. Table 1. Transport System Connectivity and Coverage Measures
Index
Formula
Notation e the number of edges
Alpha index
αൌ
െ ʹ െ ͷ
v the number of vertices p the number of subgraphs
Gamma index
e the number of edges
͵ሺ െ ʹሻ
e the number of edges
βൌ
Beta index
γൌ
v the number of vertices
v the number of vertices
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Eta index Grid Tree Proportion index Network Density
ηൌ
ൌ
L the length of network E the observed number of edges
െͳ
e the number of edges
ሺξ െ ͳሻ²
v the number of vertices
L the length of network
ൌ
A the area of zone
Dill (2004) evaluated various measures of network connectivity relating walking and biking. Zhang, Bigham, Li, Ragland and Chen (2012) studied the associations between connectivity and pedestrian-bicyclist accidents and determined that higher connectivity relate to fewer crashes for non-motorized road users. The coverage measures can be utilized to understand settlement patterns. Levinson (2012) focused on how network scale and connectivity vary with city size considering metro system, in which it has been indicated that connectivity grows with network size and network density. Sarkar (2013) made an attempt to explore the existing pattern and spatial variation of the road network of West Bengal with the help of structural measures including road density. Vinod, Sukumar and Sukumar (2003), Nagne, Vibhute and Gawali (2013) and Nagne and Gawali (2013) are some other Indian studies which applied GIS technology for the assessment of transportation network using connectivity indices.
3. Objectives a) To study the existing road transport system configuration of the city in terms of connectivity, coverage and development. b) To examine the relationship between road transport system connectivity and road network development. c) To understand the variation of the topology pattern of the road transport system at zonal level and city level. 4. Methodology Urban road transport system evaluation using GIS involves the establishment of evaluation system, collecting the data resources, digitising the urban road transport system, building the road network database, extracting the network structure etc.. This study made use of the satellite imagery of Calicut corporation obtained from Google map in order to update the existing road network. All the roads including National Highway, State Highway, Collector Streets and Local Streets were considered. The zone wise corporation boundary was derived from the Autocad drawing obtained from Calicut Corporation office. The imagery as well as the Autocad ward map were geo-rectified in ArcGIS 9.3 to geographic co-ordinates for which the ground control points were used. In ArcGIS, the ward boundary and the roads were converted into polygon and polyline features respectively. The ward boundaries are overlayed on the Calicut city road transport system to extract the road transport system within each of the seventy five zones for studying the topology and development pattern of the system. Alpha Index, Beta Index, Gamma Index, Eta index and Grid Tree Pattern Index were used for attributing the topology and Network Density for attributing the coverage and development and hence to identify the variation and the relationship between connectivity and development.
5. Study Area The area selected for this study is the corporation of Calicut district. Calicut city is situated in the South-West corner of the district and extends between 75º 47' 23" E and 76º 26' 40" E longitudes and 11º 30' 08" N and 11º 58' 40" N latitudes. Calicut city has an area of 84.29 sq km and has a population of 4,32,097 as per 2011 data with gross
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density 5126 persons per sq.km. Figure 1 gives the zone map of the Calicut Corporation. The total length of the road transport system within the Calicut city urban area is about 335 kilometers. The road density is 3.97 km per sq.km. The roads in the entire network can be broadly divided into categories based on their functions, namely, the roads facilitating intercity trips and roads facilitating intra city trips. The National Highways and State Highways are the major roads facilitating intercity trips. The city roads, which connect the residential neighbourhood with the major road or with the CBD and other work centres, are meant for intra city movements. Figure 2 shows the digitised road network map of Calicut Corporation.
Fig. 1. Zone Map of Calicut City
Fig. 2. Digitised Road Map of Calicut City
6. Analysis and Results The zone-wise road transport system extracted by overlaying the corporation boundary of each zone on the road transport system was quantified with respect to the various aspects of characterisation as connectivity and coverage. 6.1 Connectivity The road transport system within each of the seventy five zones were quantified with respect to the indices measuring connectivity as Alpha, Beta, Gamma, Grid Tree Pattern Index and Eta Index. For this the road transport system digital map within each zone was converted into Network Dataset using ArcGIS Network Analyst extension. From the Network Dataset, the attribute details of the network such as the count of edges and vertices within the network were obtained from Network Dataset properties available from ArcCatalogue. Using the edge and node count of each zone, the connectivity indices were evaluated for each zone. Sample calculation of the various connectivity indices are shown in table 2 and the descriptive statistics of the various connectivity indices are listed in table 3.
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Table 2 Calculation of Connectivity Indices Number of Nodes (v) : 14 Number of Edges (e) : 18 Number of Subgraphs (p) : 1 Total Length of Network : 5.813 km
Ward No : 15 Ward Name : Chelavur
݁ െ ݒ ͳ ͺ െ ͳͶ ͳ ൌ ൌ ͲǤʹʹ ʹ ݒെ ͷ ʹ ͳ כͶ െ ͷ ݁ ͳͺ ߚൌ ൌ ൌ ͳǤʹͻ ͳ ݒͶ ݁ ͳͺ ߛൌ ൌ ൌ ͲǤͷͲ ͵ሺ ݒെ ʹሻ ͵ כሺͳͶ െ ʹሻ ܴ ݄ݐ݈݃݊݁݀ܽͷǤͺͳ͵ ߟൌ ൌ ൌ ͲǤ͵ʹʹ ݊Ǥ ݏ݈݂݇݊݅ ͳͺ ݁ െ ݒ ͳ ͳͺ െ ͳͶ ͳ ܲܶܩൌ ൌ ൌ ͲǤͷͻ ሺξ ݒെ ͳሻ² ሺξͳͶ െ ͳሻ²
Alpha index:
ߙൌ
Beta index: Gamma index: Eta index: GTP index:
Table 3 Descriptive Statistics of Connectivity Indices
Minimum
Maximum
Mean
Std. Deviation
Alpha Index
0.090
0.760
0.437
0.163
Beta Index
1.000
2.490
1.769
0.354
Gamma Index
0.430
0.840
0.633
0.104
Eta Index
0.050
0.340
0.140
0.071
GTP Index
0.150
0.870
0.570
0.181
6.2 Coverage The road transport system development of each of the zones were evaluated with respect to the attributes as Network Size and Network Density. The total length of the transport system and area of each zonal boundary were extracted from the attribute table of the road transport system and that of the zonal boundary respectively. Making use of these data, Network Density was calculated for all the zones as shown in table 4. The descriptive statistics of the various coverage indices are listed in table 5. Table 4 Calculation of Coverage index
Ward No : 15 Ward Name : Chelavur
Network Density:
Area of Zone : 3.1 km2 Total Length of Network : 5.813 km
ܰ ܦൌ
ܮ ͷǤͺͳ͵ ൌ ൌ ͳǤͺͺ݇݉Ȁ݇݉ଶ ܣ ͵Ǥͳ
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Table 5 Descriptive Statistics of Coverage Indices
Minimum
Maximum
Mean
Std. Deviation
Network Size (km)
2.370
25.540
8.550
4.470
Network Density (km/sq km)
1.198
16.533
5.997
3.351
7. Data Analysis Effect of the explanatory variables as connectivity indices on the road network development in terms of Network Size and Network Density were separately studied. For exploring the causal relationship between road network development and connectivity, correlation analysis was performed. Spearman’s rho correlation coefficient, which identifies both linear and non-linear relationship, is used to assess the association between the variables and the corresponding values are shown in table 6. Table 6 Spearman’s Rho Correlation Coefficients
Network Density Network Size
Alpha
Beta
Gamma
Eta
GTP
0.803**
0.832**
0.769**
- 0.737**
0.746**
0.666**
0.750**
0.650**
- 0.529**
0.554**
**Correlation is significant at 0.01 level (2-tailed) The variables that are identified to be positively correlated to Network Density are Alpha, Beta, Gamma and GTP index, while Eta index is seen to be negatively correlated. The correlation value comes in the range 0.74 to 0.80, which shows strong correlation of each of the connectivity indices with Network Density. The correlation of the connectivity indices with Network Size comes in the range 0.50 to 0.75. Scatter plots were prepared for examining the relationship of each of the indices as Alpha Index, Beta Index, Gamma Index, Eta Index and Grid Tree Pattern Index attributing the road transport system connectivity with the road network development. From the scatter plots, the variables were seen to follow nonlinear relationship. Hence non-linear regressions were tested with several types of mathematical formulations as logarithmic, exponential and inverse. R2 the coefficient of determination, which gives the proportion of variation in the response variable that is attributed by the explanatory variable was obtained. While analysing the explanatory variables taken individually to study their effect on road network development, the logarithmic formulation showed the highest R 2 for most of the indices. Hence logarithmic models were prepared for analysing the effect of connectivity on Network size as well as Network Density. 7.1 Connectivity Indices with Network Density To examine the variation of the connectivity indices with Network Density, logarithmic plots which showed the highest R2 are plotted and is shown in figure 4.
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a) Alpha Index vs Network Density
b) Beta Index vs Network Density
c) Gamma Index vs Network Density
d) Eta Index vs Network Density
e) GTP Index vs Network Density Fig. 4 Connectivity Indices with Network Density
From the plot it is seen that Alpha Index, Beta Index, Gamma Index and GTP index are positively varying with Network Density while Eta Index declines with Network Density. Table 7 shows the parameter estimates of the logarithmic model when Network Density used for predicting connectivity indices. Table 7 Regressions: Independent Variable = ln(Network Density) Dependent variable Alpha Beta Gamma Eta GTP
Explanatory variable: ln(Network Density) Model Std Error y = 0.057 + 0.232 ln(x) y = 0.913 + 0.523 ln(x) y = 0.404 + 0.141 ln(x) y = 0.272 - 0.080 ln(x) y = 0.181+ 0.239 ln(x)
0.034 0.068 0.024 0.019 0.043
T-Statistic
Sign.
R2- value
F-value
RMSE
2.66 13.36 16.85 14.01 4.24
0.001 0.000 0.000 0.000 0.000
0.66 0.71 0.59 0.41 0.56
138.70 176.18 104.22 51.34 94.02
0.097 0.193 0.067 0.055 0.121
The table shows that indices as Alpha, Beta, Gamma and GTP grow with Network Density while Eta index declines
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with Network Density, with R2 values varying between 0.41 to 0.71. This again supports the fact that more the road length per unit area, more will be the connections. Eta index reduces with Network Density because of the fact that more the connections, less will be the average link length. To verify the significance of the relationship between the dependent and explanatory variables, t- test can be applied. t- ratio shows whether coefficient of each variable is significantly different from zero. Usually a value greater than 1.96 (corresponding to 95 per cent confidence) indicates that the coefficient is significantly different from zero. From the table 7, it can be seen that all the models are significant as the t values are greater than 1.96. The effect of connectivity on Network Size was also studied. From the plot of the connectivity indices with Network Size; Alpha Index, Beta Index, Gamma Index and GTP index were seen to positively correlate with total Road Length while Eta Index declined with total Road Length. In the case of the logarithmic model prepared for examining the effect of the connectivity indices on Network Size, R2 value ranges from 0.312 to 0.593 suggesting the fact that more the road length within a zone, more will be the connections, increasing the edges and vertices of the network. Eta index reduces with Network Size, which supports the fact that more the connections, less will be the average link length. Because of the low values of R 2, the models prepared for analysing the effect on Network Size is not shown here. 7.2 Topology pattern of the city as a whole Attempt has been done to obtain an overall idea of the topology pattern of the whole city, and hence to scrutinize the network connectivity pattern at two different spatial scales, zonal level and city level. The connectivity and coverage values of the various indices of the whole city were obtained and is shown in table 8. A comparison between the topology values of the whole city and that of each individual zone is also shown in the table. Table 8 Network topology indices of Calicut city Variable Alpha Beta Gamma Eta (km) GTP Network Density (km/sq km)
Index Value 0.480 1.830 0.650 0.078 0.460 5.381
Number of zones having values Lesser Greater 44 31 44 31 41 34 41 34 43 32 41 34
The study shows that the levels of connectivity and development differ according to the scales employed. The topology pattern of the whole city quantified in terms of the various connectivity and coverage indices have values in between the minimum and maximum values of each of the indices calculated at the zonal level. The connectivity values of the whole city approximately approach the mean of the values of the indices calculated for each of the seventy five zones of the city. This means the road transport system is not distributed in an even manner throughout the city. In fact some zones, particularly the zones near the Central Business District have high values of connectivity and road transport development because of the intense development of activities. While the outer suburban zones have very less transport system development. On an average, fifty five percent of the zones have transport system with topology level above that of the city as a whole, while forty five percent of zones have lesser connectivity and development pattern. 8. Conclusion The road transport system is a major source of development for any country in the world. One of the important factors contributing to the performance of a road transport system is the availability of connectivity within the network. This paper intended to study the topological characteristics of the road transport system of Calicut city region using ArcGIS 9.3 and hence identify its variation with respect to the network development. Existing road transport system was examined for the variation of connectivity and coverage level for each zones as well as for whole city. Also the interrelationship between the connectivity pattern and the road network development is presented. In this study, the correlation between each of the connectivity indices with road network development is seen to
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M.G. Sreelekha et al. / Transportation Research Procedia 17 (2016) 253 – 262 follow nonlinear logarithmic relationship with R2 value varying from 0.41 to 0.71. The significance of the relationship between the variables were verified using t-ratio test, which tests the significance of each of the variable coefficients. This means that there is significant relationship between the level of road network connectivity and network development within the study area, suggesting that the level of road network connectivity could explain the network coverage significantly. The road network development can be used as an indicator of urban development within a region, which in turn depends on the transport system connectivity. The main conclusion that can be drawn from this study is that more the network development and coverage of the road transport system, more the network inter-connectivity. This means the road transport system of Calicut city has been developed so as to link the existing roads, thus facilitating mobility and accessibility. That is, the transport system of Calicut city shows better performance and spatial distribution with respect to the arrangement and connectivity of the road segments. Thus, the result fully support the initial hypothesis that as road transport system is developed, connectivity increases. On comparing the connectivity of the city as a whole with that of each of the zones, it is seen that the road transport system is not distributed in an even manner throughout the city. In fact the zones near the Central Business District have high connectivity and development, while the outer suburban zones have very less transport system development. The indicators undertaken for evaluating and analysing the transport system of a region can be applied to identify its effect on the pattern of land use and socio-demographic activities in the area. The study point to the importance of developing measures to quantify road network connectivity and extends to the relation between connectivity and development. The measures presented here consider certain aspects of connectivity of network structure. Future extension to current analysis include looking at other measures that could capture the topology aspect of network. Also, considering the topology measures of other transport networks as transit or non-motorised network could help in testing the validity of the result. Estimating similar network measures for other cities could help to test the regional transferability of the model. References 1.
Bento, A. M., Cropper, M. L., Mobarak, A. M., & Vinha, K. (2003). The Impact of Urban Spatial Structure on Travel Demand in the United States, World Bank Policy, Research Paper No. 3007. Kansky, K. (1963). Structure of Transportation Networks: Relationships between Network Geometry and Regional Characteristics, Ph. D. thesis, University of Chicago, Research Paper No. 84. 3. Levinson, D. (2012). Network Structure and City Size. Plos One 7(1): e29721. doi:10.1371/journal.pone.0029721 4. Nagne, A. D., & Gawali, B. W. (2013). Transportation Network Analysis by Using Remote Sensing and GIS A review, International Journal of Engineering Research and Applications Vol. 3, Issue 3, 70-76. 5. Nagne, A. D., Vibhute, A. D., & Gawali, B. W. (2013). Spatial Analysis of Transportation Network for Town Planning of Aurangabad City by using Geographic Information System, International Journal of Scientific & Engineering Research, Volume 4, Issue 7. 6. Noda, H. (1996). A Quantitative Analysis on the Patterns of Street Networks using Mesh Data System, City Planning Review, 202, 64-72 (in Japanese). 7. Patarasuk, R. (2012). Road Network Connectivity and Land-Cover Dynamics in Lop Buri Province, Thailand. Journal of Transport Geography 28, 111–123 8. Sarkar, D., (2013). Structural Analysis of Existing Road Networks of Cooch Behar District, West Bengal, India: A Transport Geographical Appraisal, Ethiopian Journal of Environmental Studies and Management Vol. 6 No.1 9. Vinod, R. V., Sukumar, B., & Sukumar, A. (2003). Transport Network Analysis of Kasaragod Taluk, Kerala Using GIS, Indian Cartographer. 10. Xie, F., & Levinson, D. (2007). Measuring the Structure of Road Networks. Geographical Analysis 39 (3), 336–356. 11. Zhang, Y., Bigham, J, Li, Z., Ragland, D., & Chen, X. (2012). Associations between Road Network Connectivity and Pedestrian-Bicyclist Accidents, Paper presented at TRB Annual Meeting. 2.
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