Analyzing commuting-time satisfaction could help to improve the subjective well-being of society. This study aimed to explore the nonlinear relationship between commuting satisfaction and commuting times in different groups and its influencing factors. An empirical study was conducted in Kunming, China. Firstly, applying a random forest algorithm revealed that there was a nonlinear relationship between commuting satisfaction and commuting time. Secondly, the k-means clustering algorithm was used to divide the respondents into three types of commuter: short-duration-tolerant (group 1), medium-duration-tolerant (group 2), and long-duration-tolerant (group 3). It was found that the commuting-time satisfaction of these three clustered groups had different threshold effects. Specifically, the commuting satisfaction of group 1 showed a nonlinear downward trend, which decreased significantly at 12 and 28 min, respectively; the commuting satisfaction of group 2 rapidly decreased at 35 min; the commuting satisfaction of group 3 first increased in the range of 20–30 min, decreased significantly after 45 min, and decreased sharply above 70 min. These time thresholds were consistent with the ideal commuting times (ICTs) and tolerance thresholds of the commuting times (TTCTs) of the three clustered groups, which indicates that the ICT and TTCT had significant effects on commuting satisfaction. Lastly, the results of the multinominal logistic model showed that variables such as the commuting mode, job–housing distance, income, and educational background had significant effects on the three clustered groups. The policy implications of the study are that commuting circles should be planned with the TTCT as a constraint boundary and ICT as the optimal goal; in addition, different strategies should be adopted for different commuting groups to improve commuting satisfaction.
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