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Over the last decade, smartphones became a valuable source of traffic data. GNSS data and other data from smartphone sensors can be successfully used in travel mode classification. Travel mode classification data are a significant source of information for various applications such as travel planning, urban road operations or user behavior understanding. Today, the availability of access to real-time data streams makes fast and real-time classification of travel modes possible. Because of different characteristics of data streams, the applied classification method has to be adjusted to the particular data stream. In this paper two classification methods, k Nearest Neighbors and Random Forest, are compared with emphasis on accuracy. First, they are applied for classification of travel modes using a static GNSS dataset, and afterward using streaming GNSS data. For the purpose of classification, characteristic distribution of velocity and acceleration for different travel modes is determined. Regarding streaming GNSS data, the influence of the window size on the classification accuracy is analyzed. Obtained results show that both classification methods can be successfully applied for the classification of travel modes.
GNSS data ; data stream ; travel mode ; classification ; Random Forest ; k Nearest Neighbors
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