Influence of Urban Components on Travel Pattern of the people: A Case of Surat City of Gujarat

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1 Influence of Urban Components on Travel Pattern of the people: A Case of Surat City of Gujarat Amitkumar V. Rana 1, Ravin M. Tailor 2, Pankaj J. Gandhi 3, Niraj D. Shah 4 1 Civil Engineering Department, P P Savani University, India 2 Civil Engineering Department, S. V. National Institute of Technology (SVNIT), Surat, India. 3 Civil Engineering Department, P P Savani University, India 4 Civil Engineering Department, P P Savani University, India Abstract The urban land use of any metropolitan city consist Residential, Commercial, Industrial, Public space, Open space, Road network. The public space and open space at city level consist the components like Malls, Schools, Colleges, Hospitals, Theatres, Religious places, Gardens, Government offices etc. For the easy administration the city is divided in number of wards. So, these components attract the number of inter trip and intra trip from various wards of city. These paper focus on the rate of trip generate and trip attract due to the urban components which act as a urban magnet. The urban trip generation and urban trip attraction model is develop for finding the rate of trips by using multi linear regression analysis (MLRs) and Trans CAD software. Keywords Urban Components ; Travel Pattern ; Travel Scenario ; Multi Linear Regression Analysis (MLRs) ; Public Transport (PT) System I. INTRODUCTION: Surat also known as Suryapur, is the commercial capital of the Indian state of Gujarat. It is the administrative capital of Surat district and one of the fastest growing cities in India. In just last decade its population doubled in 2011 to that of the population of The city has been ranked 36th (Reference: Census-2011) in the list of world's largest cities. Surat Metropolitan Region is widely regarded as one of the cleanest Metropolitan Regions in the country with the city proper being the third cleanest city in India (Reference: Census-2011). Also Surat is number one resilient city of India. Surat was famous for its food and now it is the Textile City of India and is a Diamond City of the world. There are more than 150 dyeing and printing houses located in and around the city. The Surat city is one of the oldest historical trade centres of India. Surat city is located in well developed South Gujarat region. The city occupies a pivotal position on the Ahmedabad Mumbai regional corridor centrally located at a distance of 260 km North of Bombay and 224 km South of Ahmedabad, as well as on the 225 km long industrial belt, having direct linkages with the industrial urban centers of Vadodara, Ankleshwar and Vapi. The status of city was upgraded to Surat Municipal Corporation in 1963, with city area of 8.18sq.km. The expansion in city limits at a glance is chronicled in Table 1 indicating about 14 times increase in its area over the period of three decades. In 1978 Surat Urban Development Authority was established covering total area of 722 Sq.km. including S.M.C. area. Figure 1 shows the periodical expansion of the city limit for city of Surat. Page 86

2 TABLE 1: EXPANSION OF CITY LIMIT AT A GLANCE Year Area Area (sq.km) 1664 Inner Wall Area Outer Wall Area S.M.C. area S.M.C. area S.M.C. area S.M.C. area S.M.C. area S.M.C. area S.M.C. area (Source: City Development Plan ) Figure 1: Periodic Expansion of Surat City (Source: City Development Plan ) The demographic statistics of the population of the Surat city from census year 1901 to 2001 is contained in Table 2 highlighting centurial increase in city area by about 14 times coupled with about 20 times increase in the population. TABLE 2: DEMOGRAPHIC DETAILS OF SURAT CITY Census Status Area in Total Male Female Sex Density / Year sq.km Population Ratio sq.km 1901 M ,19,306 61,653 57, , M ,14,868 59,634 55, , M ,17,434 61,711 55, , M ,936 52,958 43, , M ,71,443 90,305 81, , M ,23,182 1,16,426 1,06, , M ,88,026 1,50,333 1,37, , M ,71,656 2,49,902 2,21, , MC ,76,656 4,18,114 3,58, , MC ,98,817 8,14,938 6,83, , MC ,33,785 13,72,307 10,61, , MC ,66,826 25,43,623 19,24, ,680 (Source: Surat Municipal Corporation) The land use pattern of any city consist Residential, Industrial, Commercial, Public purpose, and Recreational places. At micro level it consist factors such as Gardens, Malls, Page 87

3 Temples, Urban Squares, Schools, River Front, Religious places etc. which are known as Urban Components. These Components affect intra and inter urban travel. It affects mostly on the intra-urban travel behaviour, and it can have a profound influence on urban transport planning and management in general. Need for Study Literature Review Field Survey Data Inventory Survey Home Interview Survey Maps Data Analysis Demographic profile Existing urban components Develop generation Model UTGM 15 Distribution Analysis using Trans CAD software Develop Urban Component Attraction Model UCTAM 15 Conclusion Future Scope Figure 2: Methodology Page 88

4 The provision or presence of these urban components vary to a great extent and fluctuate the entire travel scenario of any city. These urban components attract number of trip and causes change in travel pattern of city. The study is carry out for assessment of urban components impact on the basis of trip attraction analysis for specific attraction like garden, theatre, mall, religious place, hospital, river front, aquarium, museum, auditorium, food court, water spot and other recreation place.. This study carried out for Surat city reflects that the sample survey technique with statistics base will help the urban planner to represent the sample data as over all city characteristics. The trip is highly depends on family size, family income, no. of working member, no. of school going member and no. of vehicle per house hold factors. The present study areas development of trip generation model using MLRA technique. Also the trip distribution is carried out in Trans CAD software for existing trip generation. Finally Urban Component Attraction Model UCTAM 15 is develop to correlate existence of urban components on trip attraction. II. LITERATURE REVIEW Schwartz, and Richard (1989) in them paper [1] on Developing Urban Office Generation Rates and Traffic Mitigation Measures in Cambridge, Massachusetts discussed on trip generation study of existing office buildings in the city. The study s intent was to develop person- and vehicle-trip generation rates that could accurately predict the level of office trip-making during the morning and evening peak hours of travel. The approach, which differs from the ITE trip generation rate methodology of measuring entering and exiting traffic volumes, is based on surveys of employees and visitors entering and exiting the offices of participating firms. Leck and Eran (2006) in his paper [2] The Impact of Urban Form on Travel Behavior: A Meta-Analysis had discussed on characteristics of the built environment, such as population density, mixed land use settings and street configuration, exert a strong influence on travel behavior. A common viewpoint held by many New-Urbanist and Neo-Traditional planners is that characteristics of the built environment, such as population density, mixed land use settings and street configuration, exert a strong influence on travel behavior. The empirical evidence for this relation, however, as portrayed in many primary studies, is somewhat mixed. La Paix, Monzón and Cherchi (2010) in them paper [3] on Location Effects on Generation: Evidence from Madrid Metropolitan Area had discussed effect on trip frequency, public transport and private vehicle dependency of socio-economic, transport and land use characteristics. In particular the land use is defined in terms of type of neighbourhoods and types of dwellers. They studied the effect on trip frequency, public transport and private vehicle dependency of socio-economic, transport and land use characteristics. In particular the land use is defined in terms of type of neighbourhoods and types of dwellers. Methodological attributes are also included to test the effect of the type of survey, namely trip-based versus activity-based survey. Shobeirinejad, Matthew and Sipe (2012) in them paper [4] on Analysing retail travel behaviour using an Australian data set Had discussed the impacts of substituting car trips with more sustainable alternatives such as walking, cycling and public transport, particularly focusing on travel to and from work and school. Retail is considered as a major Page 89

5 trip destination, for the non-peak hours. Australian cities are challenging with the expansion and changing form/structure of the retail sector which has a considerable potential to impact upon travel behaviour, air pollution and the amount of consumed fuel to access these destinations. Pradhan and Kockelman (1999) in their paper [5] on Uncertainty Propagation in Landuse transportation Models discussed The impact of uncertainty in the land use component of a partially integrated land use transportation modelling system called Urbanism. This study examines the impact of uncertainty in the land use component of two semi integrated land use-transportation models. Outputs from the land use models (LUM) act as inputs for travel demand models (TDM), and travel times from the traffic-assignment stage of the TDM are fed forward into the subsequent year s LUM. Panackel and Dr. Padmini (2013) in their paper [6] on Developing A Production Prediction Model Based On Residential Land Use Characteristics discussed on the formulation of a trip production model using multiple regression technique for the residential land use in medium sized towns of Kerala. The trip production model estimated the number of trips that will be produced from the residential land use of these medium sized towns. The Perinthalmanna, Tirur, and Ponnani towns of Kerala were selected as the study area based on certain criteria. The data on demographic and socio-economic characteristics these areas were collected through the administration of household interviews. The quantitatively and qualitatively analysis of the results were done using the correlation and multiple regression analysis. Based on the above mentioned research studies, it can be said that, it is essential for the urban planner to identify the urban components which can have an influence on the travel pattern of the people. The outcome of this study will be useful for the urban planner to design and develop public transport system for the emerging metro city like Surat of Gujarat III. STUDY AREA Area of Surat city is divided in 7 zones same as Surat Municipal Corporation (SMC). These 7 zones is further sub divided in 47 study wards. Figure 3: Zone and ward details of Surat city Page 90

6 Data of travel characteristics and urban components visits are collected by Home interview survey. Home interview survey is carried out within these 47 wards. Figure 3 shows the study wards map for surat city. IV. DATA COLLECTION In urban transport planning after study area delineation collection of data for studying the past and existing socio-economic, travel behaviour is major activity. This activity is the major in whole planning process and required a lot of time resources and efforts. Also accurate and large database is required to clearly assess the problem and formulate policy and plans for future, therefore proper data collection becomes very important, especially when the same kind of data is collected from the various sources. Surat city is considered as a case study which is divided into 47 wards from 7 zones of city. The Home Interview surveys of 1067 households from each ward are carried out for data collection. There are two types of surveys carried out to assess impact of urban component on travel behaviour. The first is known through the inventory search and the second is through the field survey. 1) Inventory Data: The inventory data of study area is collected from various recourses as listed below Surat Municipal Corporation (SMC) Statistical documents and figures related to ward wise population as per census 2001 and Various maps of Surat city Zones wise maps, Census ward maps, Road network map of Surat city. Presence of various urban components lists and numbers. 2) Website of census of India 3) Population details urbanization data and other relevant data. 4) Other references like Dissertation thesis, Technical paper, and journal paper etc. 5) Field Data: For the present work, home interview has been carried out in the Surat city. Home Interview Survey (HIS) method survey is one of the most reliable types of surveys for collection of socio-economic and travel data. The information on a travel pattern like number of trips made, purpose of trip, travel mode, and information on house hold characteristics like type of dwelling units, number of residents, age, sex, vehicle ownership, family income is collected in the HIS through Questionnaire. These data is useful to relate the amount of travel to various socio-economic, urban components attributes of household and zonal characteristics. To obtain the desired information from a part of the population is a sample. Survey has been carried out only for Surat city having an area of sq.km. Home interview survey is carried out in all 47 wards. The total 1067 samples are collect. TABLE 3: SAMPLE DISTRIBUTION Zone Study Ward Number Population No of samples Central 1, 2, 3, 4, 5, North 7, 8, 9, 33, Page 91

7 East 10, 11, 12, 13, 14, 35, 36, South 15, 19, 20, 21, 23, 39, 40, South West 24, 25, 26, 27, 28, 42, 43, 44, West 29, 30, 31, 32, 46, South East 16, 17, 18, 22, Total V. DATA ANALYSIS: 1) House hold Structure: Due to joint family in India the family size is higher. In the study area the family pattern is observed between 4 to 7. Table 4 shows the range of household size for various zones of Surat city TABLE 4: HOUSE HOLD STRUCTURE Zone Min H.H Size Max H.H Size Avg. H.H Size Avg. Working Member CZ NZ EZ SZ SWZ WZ SEZ Total Avg Avg. School /College going Member Figure 4: House hold Size Figure 5: Working and School Page 92

8 2) Income Structure: The highest average income observed in west zone i.e. 40,840/- per month. This zone consist study wards 29, 30, 31, 32, 46 and 47 in which most of the people are working in MNCs and also businessmen are live in this zone. TABLE 5: INCOME STRUCTURE Zone Min. Income (in thousand/month) Max. Income (in thousand/month) Avg. Income (in thousand/month) CZ NZ EZ SZ SWZ WZ SEZ Total Average Figure 6: Income Structure 3) Vehicle Ownership pattern: TABLE 6: VEHICLE OWNERSHIP DETAIL Zone Bicycle / HH 2W / HH 4W / HH Total Vehicle / HH CZ NZ EZ SZ SWZ WZ SEZ Total Page 93

9 No of Vehicle/HH International Journal Of Advancement In Engineering Technology, Management and Applied Average 4) Purpose wise trip generation: The purpose wise trip generation at the Zonal level. Survey states that the total s/ HH/Day are ranging between 5 to 6 at Zonal level. The average trip generated/household/day is 5.62 out of which 4.96 trips/household/day are for work and education purpose while 0.65 trips/household/day is for shopping, recreation, and social purposes Figure 7 shows the minimum trip generated from east zone i.e TPHD and maximum trip generated from south west zone i.e TPHD. The maximum purpose wise trips generated are nearly 52 % for work purpose in Study area. The maximum 63 % of work purpose trips are observed in 1st Zone i.e. central Zone CZ NZ EZ SZ SWZ WZ SEZ Zone Bicycle / HH 2W / HH 4W / HH Figure 7: Vehicle Ownership Detail TABLE 7: PURPOSE WISE TRIP GENERATION Zone No of Work School/College Shopping Recreation Social Total % of No of % of No of CZ NZ EZ SZ SWZ WZ SEZ Total Average % of No of % of No of % of No of Page 94

10 Figure 8: Purpose wise 5) Urban Components : The urban components trip generation at the Zonal level. Survey states that the total urban components s/ HH/Day are ranging between 0.4 to 0.5 at Zonal level. The average urban components trip generated/ household/ day is out of which 0.103, 0.094, trips/ household/ day are for food court, religious place and garden respectively. TABLE 8: URBAN COMPONENTS TRIP/HH/DAY Zone Garden Theatre Mall Religious place Hospital River Front Aqua rium Museum Audito rium Food court Other place Total CZ NZ EZ SZ SWZ WZ SEZ Total Ave rage Zone Garden Theatre Mall TABLE 9: PERCENTAGE OF URBAN COMPONENTS TRIP/HH/DAY Religious place Hospital River Front Aqua rium Museum Audito rium CZ NZ EZ SZ SWZ WZ SEZ Total Ave rage Food court Other place Total Page 95

11 Figure 9: Urban Components s/hh/day The maximum urban components trips generated are nearly 21 % for food court in Study area. VI. DEVELOPMENT OF URBAN TRIP GENERATION MODEL UTGM 15 The Urban Generation Model UTGM 15 in the present context is a Multiple Linear Regression model which correlates the total Numbers of trips generated/ HH / Day with different travel indicators. It correlates s generation with various Travel indicators. Five important attributes like family size, family income, no. of school going member, no. of working member and no. of vehicle have been incorporated as they have considerable bearing on the trip generation rate. The model is developed by considering the 1067 data sets. Page 96

12 generation model developed using regression method. The input variable of family size, family income, no. of working member, no. of school going member were consider to predict output of trip generation. Total 1067 dataset is used to developed model. T g = F S F I W M S M V O (R 2 =0.964) (i) Where, T g = Generated per HH per Day F S = Family Size F I = Family Income / Month in Thousand W M = Number of Working Member S M = Number of School Going Member V O = Number of Vehicle Ownership The family income contributes less value to the trip generation. All factors have the additive effect on the rate of trips generated. R 2 value for the present model is 0.96, which is acceptable for a travel forecasting model for strategic transportation planning. VII. TRIP DISTRIBUTION USING TRANS-CAD The trip distribution has been carried out using Growth factor methods & Synthetic methods. In growth factor methods the present trip pattern is considered for future projection. While in Synthetic models the distribution is done on the basis of present trip making behaviour with respect to relative attract ability of a zone and the influence of travel link between the zones of origin and destination of a trip. The Gravity model is synthetic model based on the gravity concept propounded by Newton. Accordingly, the interchange of trips between zones in an area is considered to be direct to the relative attraction between the zones and the inversely proportional to the spatial separation between them as measured by an appropriate function of distance popularly known as travel impedance. The simplest form of gravity model is KAj Pi Tij = di j n Where, T i-j = s between zones i and j Pi = s produced in zone i A j = s attracted to zone j d i-j = Distance between zone i and zone j, or the time or cost of travelling between them k = A constant, usually independent of i and j n = An exponential constant whose value is usually found to lie between 1&3. Distribution is carried out in Trans-CAD software on the basis of Gravity model, which is of the following form Pi Aj f (dij ) All Zone Pz f (djz )...Constraint to Attraction Page 97

13 Pi Aj f (dij ) All Zone Az f (diz )...Constraint to Production VIII. INPUT VARIABLE FOR TRANS CAD 1) Production With the help of UTGM 15 the number of trip produce from each study ward is predict for the projected population Tabular format of Production and Attraction from different study wards obtain through Trans CAD software shown in Table- 10. TABLE 10: RESULT OF TRIP ATTRACTION IN TRANS CAD AT WARD LEVEL Study Ward Production Attraction Study Production Attraction No Ward No Page 98

14 Study Ward No Production Attraction Study Ward No Production Attraction Total ) Friction Factor This is travel impedance affecting the trip attraction. This is given as Where, d i-j = Aerial Distance between Zone I and j n = Constant (1.75) Distance from one zone to other zone consider as Impedance. In Trans CAD software first generate Friction Factor Matrix then it use in attraction model. IX. TRIP GENERATION-ATTRACTION AND DESIRE LINE DIAGRAM IN TRAN CAD The Trans CAD software calculating s generated from each study wards and find the trip production and attraction data. For this software use aerial distance as a Impedance matrix and from impedance matrix it generate Friction Factor matrix. And then by giving the value of trip generated in each study ward the software give the value of trip attract in each study ward on the basis of impedance value and friction factor value for each ward. The result of trip production and attraction then represent graphically by the Desire Line diagram as shown in Figure Page 99

15 Figure 10: Desire Line Diagram of Production and Attraction Study Ward No TABLE 11: RESULT OF TRIP ATTRACTION IN TRAN-CAD IN /CAPITA/DAY Generated Attracted Study Ward No Generated Attracted Total X. DEVELOPMENT OF URBAN COMPONENT TRIP ATTRACTION MODEL UCTAM 15 The Urban Attraction Model UCTAM 15 in the present context is a Multiple Linear Regression model which correlates the total Numbers of trips attracted/ capita / Day with different attraction components. It correlates s attraction with various components. Five important urban trip attraction components like School/Colleges, Gardens, Theatres-Malls- Food Courts, Religious Places and Hospitals have been incorporated. The model is developed by considering the result of trip attraction which is obtained by using the Trans CAD software and Number of components presence in various study ward. The input variables for Page 100

16 trip attraction model are School/College, Garden, Theatre/Mall/Food-Court, Religious Place, Hospital, Government Offices, Land use (Residential / Commercial/ Industrial) XI. REGRESSION BASED URBAN COMPONENT TRIP ATTRACTION MODEL: (UCTAM 15) attraction model developed using regression method. The input variable of Schools/Colleges, Gardens, Theatres-Malls-Food Courts, Religious Places, Hospitals Government offices, Residential/Commercial/Industrial were considered to predict output of trip attraction. Total 47 study ward s dataset is used to developed model. T au = N S/C N G N T-M-FC N RP N H N GO R A C/I A (R 2 =0.88)...(ii) Where, Ta U = attracted per capita per day N S/C = Number of Schools/Colleges N G = Number of Gardens N T-M-FC = Number of Theatres, Malls, Food Courts N RP = Number of Religious Places N H = Number of Hospitals N G = Number of Government Offices R A = Residential Area C/I A = Commercial / Industrial Area All factors have the additive effect on the rate of trips attracted. R2 value for the present model is 0.88, which is acceptable for a travel forecasting model for strategic transportation planning. XII. CONCLUSION The urban component attraction model can be used for deciding the future urban components to be developed in existing area with its impact on trip attraction. Also new developing area can be assessed for the trip attraction analysis using the proposed composition of urban components for the developing area. The same model can be easily customized for any city for provision of urban components planning and development. The outcome of this paper is very much useful for Urban Planners, ULBs and Transport companies for the development of public transport system, such as metro train, and BRTS. Also the outcome and model developed here in this paper can also be used for the scheduling of existing public transport system to improve the efficiency and productivity and reduce the operating cost. REFERENCES [1] W. L. Schwartz and R. A. Easier, Developing Urban Office Generation Rates and Traffic Mitigation Measures in Cambridge, Compendium of Technical Papers, ITE, pp , [2] Leck, Eran and Technion, The Impact of Urban Form on Travel Behavior: A Meta-Analysis, Berkeley Planning Journal, vol. 19, pp.37-58, [3] L. Paix, A. Monzon and E. Cherchi, Location Effects on Generation: Evidance From Madrid Metropolitan Area, WCTR-Lisbon, vol. 12, pp.1-23, [4] M. Shobeirinejad, M. Burke and N. Sipe, Analysing Retail Travel Behaviour using an Australian Data Set, Australian Transport Research Forum, Perth, Australia, [5] A. Pradhan and K. Maria, Uncertainty Propagation in Land Use-Transportation Model, pp.11-18, Page 101

17 [6] L. Samual and Dr. A.K Padmini, Developing a Production Model based on Residential Land Use Characteristics, IJIRSET, vol. 2, pp , [7] Dr. L. R. Kadiyali, Traffic Engineering and Transport Planning by Khanna Publishers New Delhi. Page 102