Machine Learning Modeling of Household Trip Generation by State Using NHTS Data

This study investigates the factors that influence household trip generation across the United States using the National Household Travel Survey (NHTS) dataset. Recognizing the limits of a one-size-fits-all modeling approach, we conduct a two-stage analysis to investigate spatial heterogeneity within travel behavior. Stage one creates a benchmark analysis, comparing advanced machine learning models (CatBoost and random forest) to a traditional linear regression model. Contrary to prevailing trends in predictive modeling, the results reveal that linear regression not only delivers competitive overall performance but also emerges as the best performing model in the majority of states. Providing optimal balance between predictive accuracy and interpretability. Building on these findings, the second stage applies state specific linear models to uncover geographic differences in trip generation drivers. The findings highlight extensive spatial heterogeneity: while core demographic variables like household size and the presence of young children show consistent effects across the US, the influence of socio-economic factors such as income and vehicle ownership are highly context-dependent and spatially volatile. These findings highlight the importance of moving beyond black box modeling and instead implementing place based, context sensitive techniques in the promotion of more effective and equitable transportation plans.