This study aims to estimate travel behaviors by dividing individual travelers into several groups based on their personal characteristics. The individual grouping was achieved using the cluster analysis with the aid of the statistical analysis system (SAS) software. The trips to the central business district (CBD) in Nanjing City of China were taken as a case study. Two travel mode choices were investigated: the transit (bus and metro) and car. Travelers’ personal information and travel information were collected through a reveal preference (RP) survey and a stated preference (SP) survey. The personal information include gender, occupation, income, and car ownership, while the travel information include the mode choice, walking time, waiting time, in-vehicle time, fare, comfort, etc. There were 524 valid respondents in the RP/SP survey. These 524 individuals were categorized into three groups using cluster analysis based on their personal information. It was found that people from the three groups had very different characteristics, indicating the cluster analysis worked well. In addition, six travel scenarios were designed for each respondent to ask their travel mode choice. Then, the travel mode choices were estimated using a discrete choice model and compared with the mode choices in the RP/SP survey for each group. It was found that the accuracy rate of the mode choice estimation using individual grouping were remarkably higher than that without grouping, indicating that the individual grouping improved the travel behavior estimation.