Information on real-time traffic events is necessary for many applications. With the rapid advancement of Internet, text published from network platforms has become an important data source for urban road traffic events. However, due to the complexity of massive multi-source web text and the diversity of spatial scenes in traffic events, existing methods are not enough to support accurate extraction and geographization of events from text, resulting in the information not being efficiently utilized. Therefore, in this study, we proposed a framework from the perspective of traffic events. First, the text is preprocessed, while road-related information is summarized. Next, a step-wise method for automatically extracting is proposed. Finally, we adopt methods for entity disambiguation and spatial computing. A case study of Shanghai is conducted, and the results show that our method is effective, which can make traffic events more complete and accurate in extraction, more multi-dimensional and fine-grained in geographization.