Development of urban trip generation model based on residential area: a case study of ward no. 09 under khulna city corporation, Bangladesh
International Journal of Development Research
Development of urban trip generation model based on residential area: a case study of ward no. 09 under khulna city corporation, Bangladesh
Received 19th April, 2017; Received in revised form 24th May, 2017; Accepted 26th June, 2017; Published online 22nd July, 2017
Copyright©2017, Bijoy Dash Gupta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The aim of this paper is to formulate a trip generation model using Cross-Classification method for the ward no. 09 under Khulna City Corporation based on Boyra Residential Area. Therefore, five key variables (households size, monthly income, car ownership, trip number for specific purposes and trip cost) were used to prepare four basic models of Cross-Classification method i.e. Income sub-model, Car ownership sub-model, Trip production sub-model and Trip purpose sub-model. A household is considered as a sampling unit for primary data collection process. Households interview were used through questionnaire survey on travel behavior, travel characteristics and socio-economic characteristics of the residents. The collected data were analyzed by using the Statistical Package for Social Science (SPSS) software. The result of the analysis shows that trip generation is dominated by the impact of medium income group and produced most of the trips (45.81%) in the study area. On the other hand, most of the trips are produced from low and medium income groups those who do not have any car though the people with higher income have more car ownership (94.41%).The result also indicates that the home based education trips (59.71%) contribute higher among the different trip purposes. The number of trips generated from the study area is strongly influenced by the population, household’s size, income level, active workers and students of the area. By using this method trip production of other part of the city will be predicted and will provide a guideline to the city development authority for the smooth operation of transportation management plan.