biostudies-literatureSong HProject of Humanities and Social Sciences Research Planning Fund, Ministry of Education of ChinaNational Natural Science Foundation of China6270https://www.ebi.ac.uk/biostudies/studies/S-EPMC12905192biostudies-literatureUnknown16(1)This study was conducted to investigate the travel behavior of residents in a medium-sized Chinese city, with the goal of exploring travel characteristics and identifying the key factors influencing urban travel mode choices. While traditional discrete choice models are known for their strong interpretability, their predictive accuracy remains limited. In contrast, machine learning models are recognized for offering higher predictive accuracy but are frequently criticized for their lack of interpretability. To address this issue, a CART-Apriori predictive model was constructed through the integration of the Classification and Regression Tree (CART) model and the Apriori algorithm. Accuracy, the Kappa coefficient, and the Macro-F1 score were utilized as performance metrics for the quantitatScientific reportsStudy on urban residents' travel mode choice based on the CART-Apriori method.PMC129051927247111524YJAZH168Shi LLi STian WWang XSong HfalseStudy on urban residents' travel mode choice based on the CART-Apriori method.This study was conducted to investigate the travel behavior of residents in a medium-sized Chinese city, with the goal of exploring travel characteristics and identifying the key factors influencing urban travel mode choices. While traditional discrete choice models are known for their strong interpretability, their predictive accuracy remains limited. In contrast, machine learning models are recognized for offering higher predictive accuracy but are frequently criticized for their lack of interpretability. To address this issue, a CART-Apriori predictive model was constructed through the integration of the Classification and Regression Tree (CART) model and the Apriori algorithm. Accuracy, the Kappa coefficient, and the Macro-F1 score were utilized as performance metrics for the quantitat2026-01-01T00:00:00Z2026 Jan2026-07-15T22:17:05.885Z2026-07-09T10:19:05.34ZS-EPMC129051924159319510.1038/s41598-026-37216-4