Extracting traffic information from massive micro-blog messages
微博客蕴含交通信息的提取

Micro-blog messages usually contain a great deal of traffic information such as traffic conditions, traffic events and traffic controls, which can be useed as a complement to conventional traffic information collection technologies like fixed sensors and floating cars. However, due to ambiguous narrating, uncertainty, and the unstructured characteristics of micro-blog messages, extracting traffic information from micro-blog messages is rather difficult. In this paper, we propose an approach for extracting traffic information from a large amount of micro-blog messages. First, we build a traffic information table by semantically extracting traffic related words from micro-blog messages and matching each word onto the corresponding road segment of the road networks. Then, according to the traffic information table, we evaluate the highest confidence level of traffic condition for each road segment by using a neural network based Fuzzy-C-Means (FCM) clustering method, to obtain the most confident road conditions. Experiments on Beijing road networks with a large number of Sina micro-blog messages verify the effectiveness of the presented approach.