Citation :

Adnya S. Manjarekar, Anand V. Shivapur, Vilas V. Karjinni, "Household-Based Trip Generation Modelling Using Multi-Model Statistical Approaches: Evidence from a Tier-2 Indian City," International Journal of Civil Engineering, vol. 13, no. 7, pp. 276-296, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I7P118

Abstract

Trip generation modelling is a fundamental part of transportation planning, especially in fast-growing Tier 2 cities where there is a limited amount of data available. This study introduces and compares household trip generation models for Sangli–Miraj–Kupwad Municipal Corporation (SMKMC), India, based on the household survey data collected from 1,294 households. A comparative modelling framework: Multiple Linear Regression (MLR), Generalized Linear Model (GLM), Generalized Additive Model (GAM), and Quantile Regression was done in R to test linear, nonlinear, and distributional behavior of travel. The most important factors contributing to household trip generation were found to be household size and household vehicle ownership. Based on the results of the performance evaluation, the Generalized Additive Model (GAM) showed the best performance (AIC = 4329.25; RMSE = 1.26), which is highly capable of capturing the nonlinear relationship while keeping the model interpretation. Quantile regression additionally showed behavioural differences between the trip generation levels. The results from the sensitivity analysis indicated that household size is more influential on the generation of trips by households than vehicle ownership, and the results of the scenario analyses showed that the proposed model is applicable to forecast future travel demand under different scenarios of urban growth. The results suggest that the combination of interpretable statistical models with nonlinear modelling offers a solid and practical way to model trip making on the household level in data-limited urban settings. Though the study has been limited to considering a single year data set and selected socio-economic data, the proposed framework gives valuable support to the forecasting of travel demand and transportation planning in Tier-2 cities of the Indian scenario.

Keywords

Household size, Multiple Linear Regression, Trip generation, Urban transportation planning.

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