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 socioeconomic 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.
Keywords: Trip Generation, Multiple linear regression, Cross-classification, Socio-economic parameters, Transportation planning.
INTRODUCTION
Background Transportation planning of any region plays an important role in the development of the transport infrastructure. To make efficient transportation infrastructure there is need to develop a model to understand the demand or pattern of traffic. One of the key parameter in transportation planning is travel demand forecasting. As the first step in the traditional four-step travel demand modelling, trip generation examines the prediction of future levels of a person or a vehicle travel and is studied with the help of TAZ (Traffic analysis zone) [1].

Keyworde: Trip Generation, Multiple linear regression, Cross-classification, Socio-economic parameters, Transportation planning.

Full Text PDF Enter a number greater than 18 Submit

Refrences:

1. 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).
2. 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.
3. 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.
4. Oyedepo, O.J., & Makinde, O. (2009). Regression model of household trip generation of AdoEkiti township in Nigeria. European Journal of scientific research, 28 (1), 132–140.
5. Everett, J.D. (2009). An investigation of the transferability of trip generation models and the
utilization of aSpatial context variable.
6. 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.
7. Rahman, M.H., Sadeek, S.N., Ahmed, A., Rifaat, S.M., & Abrar, M. (2021). Effect of socioeconomic and demographic factors on ride-sourcing services in Dhaka City, Bangladesh.
Transportation Research Interdisciplinary Perspectives, 12, 100492.
8. 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).
9. Papacostas, C.S., & Prevedouros, P.D. (1993). Transportation engineering and planning.
10. 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.