Extracting Traffic Information from Micro-Blog Based on D-S Evidence Theory
Zhang Hengca · Zhongwen xinxi xuebao · 2015
Micro-Blog messages usually contain a great amount of real-time traffic information which can be expected to become an important data source for city traffic.In this paper,we propose an approach for extracting traffic information from massive micro-blogs based on D-S evidence theory to solve the data fusion problem brought by microblog's characteristics of high dynamic,uncertainty and ambiguous narrating.Firstly,an evaluation index system for the traffic information collected from the mass micro-blog messages is built,whose accuracy is enhanced by use of a wikipedia semantic model.Secondly,a function of basic probability assignment is defined for the micro-blog messages with the help of word similarity.Finally,the D-S theory is adopted to judge and fuse the extracted traffic information,throught evidence composition and decision.An experiment on Beijing road networks and Sina Micro-blog platform shows the presented approach can effectively judge the reliability of the traffic information contained in mass micro-blog messages,and can utilize the message contents delivered by different micro-blog users at utmost.Meanwhile,compared with traditional text clustering algorithm,the proposed approach is more accurate.