Travel time is valuable information for both drivers and traffic managers. While properly estimating the travel time of a single road section, an issue arises when multiple traffic streams exist. In highways, this usually occurs at the upstream of diverge bottleneck. The aim of this paper is to provide a new framework for travel time estimation of a diverging traffic stream using timestamp data only. While providing the framework, the main focus of this paper is on performing a few analyses on the stage of travel time data classification in the proposed framework. Three sequential steps with a few statistical approaches are provided in this stage: detection of data divergence, classification of divergent data, and outlier filtering. First, a divergence detection index (DDI) of data has been developed, and the analysis results show that this new index is useful in finding the threshold of determining data divergence. Second, three different methods are tested in terms of properly classifying the divergent data. It is found that our modified method based on the approach used by Korea Expressway Corporation shows superior performance. Third, a polynomial regression-based method is used for outlier filtering, and this shows reasonable performance even at a relatively low market penetration rate (MPR) of probe vehicles. Then, the overall performance of the travel time estimation framework is tested, and this test demonstrates that the proposed framework can show improved performance in distinctively estimating the travel times of two different traffic streams in the same road section.