earticle

영어

The topics on human mobility model have long been researched by various academic and industrial elds. It also has been proven that human mobility has specic patterns and can be predicted up to the probability of 93%, since the mobility of a person cannot be random while peoples have their own frequent visiting places. As a basis of human mobility research, sets of positioning data is used widely. The positioning data of a human can be obtained by GPS or other similar positioning systems, however, it contains inherited environmental errors. It is clear that such position errors harm the correctness of human mobility re- lated results. In this paper we will present ltering method of erroneous positioning data of human mobility with the use of One-Class Support Vector Machine (OCSVM), and we adapted Radial Basis Function (RBF) as kernel function. Experimental values of the criti- cal parameter for RBF have been found for optimal ltering. With this optimal parameter, we ltered raw data set of human mobility trail in order to obtain accurate position data set for further research purpose. By iteratively applying the OCSVM based ltering, like hill climbing approach, we prove that researchers can lter arbitrary rate of raw data for their own purpose. With four sets of positioning data set from various sources, we demonstrate the usefulness of our ltering approach.