Incentive-based strategies tailored to individual preferences can motivate commuters to adopt public transit, potentially easing road congestion and fostering ecofriendly urban travel. However, understanding diverse responses to these incentives has been challenging due to low survey participation and certain homogeneity assumptions, limiting our knowledge of individuals’ preferences for using public transit in different cities. To address this, our study employs a latent class choice model and mixed logit model to analyze individual responses to incentives and identify key factors that influence the effectiveness of these incentives. Data for this analysis was sourced from a mobile navigation application, covering 34 cities within China, thereby enabling the analysis of individuals within each latent class to reveal their diverse preferences for using public transit within different cities. Our findings indicate significant individual differences in response to incentives, categorized into three main latent classes: Class 1 individuals exhibit minimal influence from incentives; those in Class 2 demonstrate moderate responsiveness, especially to food and shopping coupons; and Class 3 individuals, whose decision-making is significantly affected by education level, gender, and travel mode preference, show a high degree of responsiveness to incentives. These insights are invaluable for policymakers seeking to design more effective, tailored incentive schemes to encourage public transit adoption.