Open Access Open Access  Restricted Access Subscription Access

Understanding Role of Socio-economic Parameters Using Trip Generation

Aayushi Barot, Pankaj Prajapati

Abstract


Transportation infrastructure is backbone of development for any region. Travel demand forecasting plays importance role in transportation infrastructure need, which includes road network, terminals, traffic controls and management devices. For effective transportation infrastructure, planner needs to find out
travel behaviour and characteristics of road users. The Aim of the study is to discover socio-economic parameters in trip generation rate and develop model to estimate trip generation by household data of Vadodara, Gujarat. It is based on household interview from 1072 households, with 5777 person-trips. The
survey includes household characteristics, socio-economic characteristics of household, types of trips made by individuals, trip characteristics, status of trips maker, and individual characteristics of trip maker are collected. The highest trips are made for work with share is of 48% followed by educational trips
(30%). The private and public transport modes used in the sample is 80% and 20% respectively. The analysis is conducted by multiple regression and cross classification methods to predict the trip generation for various purposes. In multiple regression influencing parameters observed are purpose trip rate, income of household, household size, number of children, vehicle ownership per household and rate of employment. Another method used for analysis is cross-classification method. In this model main variables taken are household income and number of vehicles (i.e. two-wheeler and four-wheeler) owned by household in respective category. This study is useful for estimating future trips by travel purpose either for individual or for each zone. i.e. aggregate and disaggregate approach for similar cities. Finally, it helps in developing future transportation infrastructure of similar kind of region having similar characteristics to analyze travel demand.


Full Text:

PDF

References


Al-Masaeid, H. R., & Fayyad, S. S. (2018). Estimation of trip generation rates for residential areas in Jordan. Jordan Journal of Civil Engineering, 12(1).

Etu, J. E., & Oyedepo, O. J. (2018). Forecasting Trip Generation for High Density Residential Zones of Akure, Nigeria: Comparability of Artificial Neural Network and Regression Models. Journal of Civil Engineering, Science and Technology, 9(2), 2-2.

Everett, J. D. (2009). An investigation of the transferability of trip generation models and the utilization of aSpatial context variable.

Mousavi, A., Bunker, J., & Lee, J. B. (2021). Exploring Socio-Demographic and Urban Form Indices in Demand Forecasting Models to Reflect Spatial Variations: Case Study of Childcare Centres in Hobart, Australia. Buildings, 11(10), 493.

Oyedepo, O. J., & Makinde, O. (2009). Regression model of household trip generation of Ado- Ekiti township in Nigeria. European Journal of scientific research, 28(1), 132-140.

Papacostas, C. S., & Prevedouros, P. D. (1993). Transportation engineering and planning.

Quintero, P. A., Diaz, G. M., & Moreno, E. G. (2016). Trip generation by transportation mode of private school, semi-private and public. Case study in Merida-Venezuela. Transportation

Research Procedia, 18, 73-79.

Rahman, M. H., Sadeek, S. N., Ahmed, A., Rifaat, S. M., & Abrar, M. (2021). Effect of socio- economic and demographic factors on ride-sourcing services in Dhaka City, Bangladesh. Transportation Research Interdisciplinary Perspectives, 12, 100492.

Rahul, T. M., Megha, T. M., Meenakshi, S., Madhavendra, K., & Verma, A. (2020). Gender differences in work trip generation: evidences from Bangalore. Transportation research procedia, 48, 2800-2810.

SIDE, S. Z. A. A. K. (2011). Modeling Household Trip Generation for selected Zones at Al- Karkh Side of Baghdad City. Journal of Engineering, 17(6).




DOI: https://doi.org/10.37628/jrrpd.v8i2.981

Refbacks

  • There are currently no refbacks.