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As a famous step in the transportation planning process, the mode choice problem has been traditionally studied by employing the concept of random utility optimization in the last 50 years. More recently and especially in the past decade, the emergence of big data and the computational power and insights brought by machine learning techniques has resulted in incorporating data-driven approaches that can lead to more accurate predictions. In this study, we developed travel mode choice models for commute trips in Chicago. Our data set is obtained from the Chicago Metropolitan Agency for Planning (CMAP) travel tracker survey conducted in 2018–2019, including approximately 12,000 households’ activity and travel records for 24 h. We also employed Google Maps Direction API to calculate travel times. We first constructed the home-based tour of each person’s trips to detect the main choice and eliminate the …