This study aims to estimate dynamics in mode choice decisions at different traffic states and transit congestion levels. Specifically, dynamics were examined over time of explanatory variables such as in-vehicle time and comfort of transit. The trips to the central business district (CBD) in Nanjing City of China were taken as a case study. Three travel modes were investigated: bus, metro, and car. Travelers’ socioeconomic characteristics and alternative specific attributes were collected through a reveal preference (RP) survey. A multinomial logit (MNL) model was proposed using RP Data1 from the questionnaire survey. It was found that in-vehicle time of cars and buses varied with traffic states. In addition, congestion level was divided by passengers per carriage to obtain the comfort of transit. Then, the mode choice decisions at different traffic states or congestion levels were estimated using a MNL model to analyze the dynamics. MNL analysis on the mode choice decisions revealed that those who own cars prefer auto trips. The income influence was also confirmed that individuals with high income prefer driving. The predicted mode choice decisions were compared with the actual choices to evaluate the model. Some possible reasons were explored to examine the mispredictions. Last, a comparison among different departure time with reference to their utilities of choosing modes revealed that traffic state and congestion level of transit took a significant effect on mode choice decisions. The proposed model had important implications to study travel behavior to improve the service of transit although some limitations in the model, such as only one mode determining rule, one transportation environment. The result of this prediction, however, can be viewed along with the results of other studies to obtain a development of dynamic travel behavior.