AbstractThe importance of analyzing travel mode choice to understand travel behavior in urban areas is crucial in the formulation of mobility policies, especially considering the growing concern to reduce the use of private transport modes in favor of sustainable ones. Although multinomial logit models have been widely used in travel behavior research, machine learning models are becoming an interesting alternative to perform this task, in which tree-based ensemble models, such as random forest and gradient boosting models, have demonstrated superiority in accomplishing this goal, although they both have not been compared using household survey data. This paper compares different logit and machine learning models, with a specific emphasis on gradient boosting, random forest, and multinomial logit models to predict travel mode choice and to identify the determinants in travel behavior in an urban area for three transport modes (public transport, private transport, and walking/bike), using household survey data. Although the methodology is defined following the case and features of the metropolitan area of the Aburrá Valley—Colombia (MAAV), it can be applied to any urban area. The results show that an optimized gradient boosting model is able to predict travel mode choice in an urban area using household survey data, outperforming the other compared models. In addition, travel time, parking type at the destination, the number of motorized vehicles in the household (cars and motorbikes), age, and gender are features that explain the travel mode choice in the MAAV. The optimized gradient boosting model presented in this paper can be employed as a policy tool to study and analyze strategies to promote the reduction of the use of private transport modes in the MAAV and increase the use of more sustainable transport modes.