Current Issue Cover

张恒才,陆锋,陈洁(中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京 100101)

摘 要
Extracting traffic information from massive micro-blog messages

Zhang Hengcai,Lu Feng,Chen Jie(State Key Lab of Resources and Environmental Information system, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

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.