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http://dx.doi.org/10.9708/jksci.2021.26.12.133

Travel mode classification method based on travel track information  

Kim, Hye-jin (Dept. of General Education, Kookmin University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.12, 2021 , pp. 133-142 More about this Journal
Abstract
Travel pattern recognition is widely used in many aspects such as user trajectory query, user behavior prediction, interest recommendation based on user location, user privacy protection and municipal transportation planning. Because the current recognition accuracy cannot meet the application requirements, the study of travel pattern recognition is the focus of trajectory data research. With the popularization of GPS navigation technology and intelligent mobile devices, a large amount of user mobile data information can be obtained from it, and many meaningful researches can be carried out based on this information. In the current travel pattern research method, the feature extraction of trajectory is limited to the basic attributes of trajectory (speed, angle, acceleration, etc.). In this paper, permutation entropy was used as an eigenvalue of trajectory to participate in the research of trajectory classification, and also used as an attribute to measure the complexity of time series. Velocity permutation entropy and angle permutation entropy were used as characteristics of trajectory to participate in the classification of travel patterns, and the accuracy of attribute classification based on permutation entropy used in this paper reached 81.47%.
Keywords
Travel; Classification; Attribute; Extraction; Deep navigation;
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