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.