Understanding conditions and situations causing sudden braking is important for preventing traffic accidents. Previous studies have used probe vehicle data to detect risky positions where sudden braking frequently occurred. However, they have mainly focused on vehicle-related factors.In this paper, we propose a novel method for discriminating sudden braking. Unlike previous studies, the method exhaustively explores probe data including temporal factors, constructs a large number of features combining pairs of feature names and their values, and applies the Support Vector Machine classifier and Feature Selection method to the features. To conduct the experiments, we used probe data provided by the Aizu-Wakamatsu City Open Data Utilization Verification Project. The proposed method discriminated sudden braking quite accurately, with a discrimination performance averaging an F1 measure of 93.2%. We also found that the probability of the occurrence of sudden braking is not always high at locations where sudden braking frequently occurred, but rather, temporal factors such as date and time, or day of week are strongly related to performance in discriminating sudden braking with high probability.