Traffic Event Extraction and Geographization from Natural Language Web Text

<div class="section abstract"><div class="htmlview paragraph">Real-time traffic event information is essential for various applications, including travel service improvement, vehicle map updating, and road management decision optimization. With the rapid advancement of Internet, text published from network platforms has become a crucial data source for urban road traffic events due to its strong real-time performance and wide space-time coverage and low acquisition cost. Due to the complexity of massive, multi-source web text and the diversity of spatial scenes in traffic events, current methods are insufficient for accurately and comprehensively extracting and geographizing traffic events in a multi-dimensional, fine-grained manner, resulting in this information cannot be fully and efficiently utilized. Therefore, in this study, we proposed a “data preparation - event extraction - event geographization” framework focused on traffic events, integrating geospatial information to achieve efficient text extraction and spatial representation. First, the text data is preprocessed, with road-related information extracted and summarized to prepare for subsequent tasks. Next, a step-wise method for automated extraction is introduced. Trigger words and rules of spatial relationship are set to identify spatial elements within the text, then dictionaries of proper and general names are applied to further recognize candidate entities. Finally, we adopt a method for entity disambiguation by introducing spatial constraints such as direction. Based on spatial scenes, entities representing different elements are organized to perform spatial computing, realizing the multi-dimensional geographization of events. A case study in Shanghai demonstrated the effectiveness of the proposed method, showing that it improves the completeness and accuracy of traffic event extraction while enhancing the diversity and accuracy of geographization.</div></div>