Behavioural Analysis of Out Going Trip Makers of Sabarkantha Region, Gujarat, India

DOI : 10.17577/IJERTV6IS040792

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Behavioural Analysis of Out Going Trip Makers of Sabarkantha Region, Gujarat, India

C. P. Prajapati


Civil Engineering Department Tatva Institute of Technological Studies

Modasa, Gujarat, India

  1. M. Shah

    Dr. H. R. Varia


    Tatva Institute of Technological Studies Modasa, Gujarat, India

    Lect.In Civil & Head (I/C),Civil Engineering Department, Government Polytechnic, Godhra

    Abstract Sabarkantha is the northeast region of Gujarat having low industrialization. The people of region moves out in search of better jobs and due to social and other reason they become frequent travellers. In absence of proper facility of comfortable and fast mode of transportation they choose to use private mode. So, knowledge of fundamental behavioural of out going trip makers of sabarkantha region is necessary for proper planning. The paper analyses impact of different mode selection parameters like travel time, travel cost, comfort, security for selecting particular mode of transport. Out of the analyzed 1205 observed samples impact of travel time is predominantly impacting factor for mode choice. The analysis shows around 60% traveler of long distance is adopting GSRTC bus for the travel choice.

    Keywords Behavioral Analysis, Modal Split, Trip Generation, Trip Distribution, Trip Assignment.


      In developing country like India, road traffic in general & urban roads traffic in particular, a variety of socio- demographic factors also influence travel patterns and behaviors. The factors such as household composition, age, gender, car ownership, and income all influence the choice of travel mode and the length and duration of the journey. All of these factors are significant but gender and household composition appear to be of particular significance in influencing travel behaviors. The occurrence of rapid urbanization in the world has created the migration of people from rural area to metropolitan cities. This has resulted in more people and goods making trips in urban areas, often over the long distances. Globally, people lives in urban areas are more compared to the people lives in rural areas. In 1950, there was about 30% of the worlds population was urban which increased to 54% in year 2014 and it is forecasted to increase about 66% of the worlds population is projected to be urban by the year 2050 and just three countries – India, China and Nigeria together are expected to account for 37% of the projected growth of the worlds urban population between 2014 and 2050 (World Urbanization Prospect, 2014).

      Indias urban population concentration in million plus cities has been steadily increasing. According to the census 2011, the total numbers of 468 cities is Class 1 urban

      agglomerations / cities and are believed to constitute more than 70% of countrys population are urban. Continuing population growth and urbanization accompanied challenges to urban transport.



      1. Behavioral analysis

        Behavioural analysis has taken significance as more and more policy initiatives are examined in regional areas to ease out the situation. Population being the end consumer, analysis of human behaviour and its inclusion in the modeling aspect has become essential. The case of transportation is one such sector. The commuters or users of facilities have to make various decisions like travel model to be used, the route selected, the time at which the trip should be made as per the purpose of trip, etc. It is difficult to forecast the decision of human being accurately; therefore, modeling the behaviour as accurately as possible is the key issue.

      2. Modal split

        Mode choice predicts the use of mode of transportation for number of trips from each origin to destination. Modal split has considerable implications for transportation policies, particularly in large metropolitan areas. The selection of the most appropriate travelling mode has always been a critical issue in mode choice modeling, since an individual have choice of modes available

        Figure 1: Mode Choice process

      3. Trip generation

        The trip generation is the first stage of classical transport model that aims to predict the total number of trips generated in and attracted towards each zone of the study area. after the trip generation analysis the transportation planner comes up with the vital figures about the total number of trips generated and attracted by each zone, purposes of these trips, and the travelling modes generally used for these trips.

      4. Trip distribution

        The trip distribution stage of the four-step model tends to provide a standard pattern of trip making by linking the trip ends with the origins. The trip distribution is essentially a table of trip generation and trip distribution, this trip table is commonly known as Origin-Destination Matrix (O-D Matrix), provides a comprehensive illustration of the number of trips generated between different zones of the study area.

      5. Trip assignment

      Trip assignment is the last stage of the four-step model, dealing with the allocation of a given set of trip interchanges to a specific transport network. Its main to objective is estimate the traffic volumes and the corresponding travel times or costs on each link of the transportation system by the help of inter-zonal or intra- zonal trip movements (determined by trip generation and distribution) and the travel behavior of the individuals (determined by modal split). The proportion of vehicles using each route between a particular origin-destination pair depends upon a number of attributes and the alternative routes including travel time, distance, number of stops / signals, aesthetic appeal etc. But travel time is the attribute most commonly considered in network assignment models.


The study has been conducted by the Department of Civil Engineering, Tatva Institute of Technological Studies, Modasa. The study area and datad collection was carried out from Sabarkantha (Old Sabarkantha including Arravali dist.).and find out the current population of the Sabarkantha region by the average growth factor method. The first step in the methodology is to identify the problem; it covers the subject of work. The next is review of literature, in this step the terms related to mode choice along with the previous case studies on mode choice are collected and has been studied carefully. The third step is to select the study area for implementing through of work and it should be suitable for objective for the present study the data is collected from the Sabarkantha Region. The fourth step of the study is collection of secondary and primary data for the study, the secondary data consists the population data, vehicle ownership data and existing public transport details collected from the government and private offices of Sabarkantha Region. The collection of primary data is done by conducting Personal interview survey of peripheral areas of Sabarkantha using revealed and stated

preference questionnaire by conducting Personal interview survey using random sampling technique the formats of Personal interview survey.

Fig.2: Methodology chart for study

  1. Population of Gujarat (Dist. Wise)



    Sabarkantha region current population

    Yearly Population Increase Between to 2001 to 2011


    10 year population Increase = 346058

    Avg. 1 year population Increase= 34605.80

    Current population = 2011 population + (1year population*5)

    = 2428589+(34605.80*5)

    = 2428589+173029

    = 26,01,618 avg

    So Current(2016) Population of Sabarkantha Region (Old Sabarkantha District) = 26,01,618 average

    Figure 3. Sabarkantha District Taluka map


    Data collection is carried out at Sabarkantha region of Gujarat state. For the outgoing long trip makers of Sabarkantha region various higher demanded talukas like as Himatnagar, Modasa, Idar. Vadali, Bhiloda, Khedbrahma are our main locations for data collection. For each long trip makers of Sabarkantha Region the following data field should be included as,

    • Mode Choice

    • Origin of outgoing Long trip makers

    • Destination of outgoing Long makers

    • Frequency of modes

    • Travel Time (Min.)

    • Travel Cost(Rs.)

    • Traveling Distance(Km.)

    • Comfort level

    • Security of mode .

    Figure.4: Model Split for Out of Total Trip base observation modes of

    Sabarkantha Region

    Figure.5: Model Split for Out of Total Trip base observation modes of


    Figure.6: Model Split for Out of Total Trip base observation modes of


    Figure.7: Model Split for Out of Total Trip base observation modes of



  1. Trip length Frequency Distribution

    Trip length frequency distribution wise mode choice category analysis for all over region and main origins of the Sabarkantha region.

    Figure.8: Trip length wise mode choice analysis for all over Sabarkantha Region.

    Figure.9: Trip length wise mode choice analysis for Himmatnagar

    Figure.10: Trip length wise mode choice analysis for Modasa

    Figure. 11: Trip length wise mode choice analysis for Ider

  2. Travel Time Frequency Distribution

    Travel Time frequency distribution analysis for all over region and main origins of the Sabarkantha region .

    Figure.12: Total Travel Time Frequency Distribution for all over Sabarkantha Region

    Figure.13: Total Travel Time Frequency Distribution for Himatnagar Origin

    Figure.14: Total Travel Time Frequency Distribution for Modasa Origin

    Figure.15: Total Travel Time Frequency Distribution for Ider Origin

  3. Travel Cost Frequency Distribution

Travel Cost frequency distribution analysis for all over region and main origins of the Sabarkantha region .

Figure.16: Total Travel Cost Frequency Distribution for All over Sabarkantha Region

Figure.17: Total Travel Cost Frequency Distribution for Himatnagar origin

Figure.18: Total Travel Cost Frequency Distribution for Modasa origin

Figure.19: Total Travel Cost Frequency Distribution for Ider origin


From the survey we find out that Sabarkantha region is the top ten developing district of the Gujarat Hence, there are frequently long trip makers but at the Sabarkantha region level there is no fastest transportation modes for the frequent long trip makers. from the survey analysed that there is G.S.R.T.C. buses service, Luxury buses(travels ) services, and private cars are use to catch fastest mode like air line or railway for long trip makers. We can solve by the providing that fastest mode at the Sabarkantha region. There is no air transport facility at the Sabarkantha District, but the nearest airport in Ahmedabad is 80 km away from Himatnagar (District headquarter).We can establish the Air port and also connect the fastest rail network to the Sabarkantha region.


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Book References

Gundaliya P.J. and Varia H.R. (2014), "Urban Transportation system", 3rd edition, Mahajan Publishing House.

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