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- W3148502221 abstract "This paper focuses on travel mode recognition based on Support Vector Machines(SVM) after analyzing the characters of different travel mode, which are extracted from travel trace information collected by smartphone. Firstly, we figure out which parameters can be detected and recorded by smartphone, and analyze the character of travel mode from two aspects which are travel trace and character parameters, to find which are the key parameters to divide each two kind of travel mode, so that we are able to make up the character vectors to recognize different travel mode, and eventually a classifier of Radial Basis Function based SVM can be established. This SVM is trained by travel mode samples from travel trace data in Dalian. As comparisons, Decision Tree and BP Neural Network are used. The result shows that the recognition accuracy rate of SVM is 89.6%, which of BP Neural Network is 85.5%, which of Decision Tree is 77.3%, and suggests that the SVM has better recognition performance." @default.
- W3148502221 created "2021-04-13" @default.
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- W3148502221 date "2014-01-01" @default.
- W3148502221 modified "2023-09-24" @default.
- W3148502221 title "Travel Mode Character Analysis and Recognition Based on SVM" @default.
- W3148502221 hasPublicationYear "2014" @default.
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