| dc.description.abstract | The transportation mode of users, such as Walk, Bus, or Car, indicates the outdoor behavior pattern of the user. As the GPS (Global Positioning System) enabled phones and mobile internet accesses become pervasive, the prediction of transportation mode becomes fundamental in the area of shopping behaviors, travel itinerary sharing and smart route recommendation. Learning transportation modes is the central issue and a two-level inference architecture is used. The first level learns change-points, locations whose transportation mode differs from the previous location, with five features. The second level learns seven transportation modes, Walk, Bike, Bus, Car, Moto (Motorcycle), MRT (Mass Rapid Transit), and Train, with ten features. The F-measure is 0.753 in the first level. The results of second level are evaluated by Accuracy by by Length (AL) and Accuracy by Duration (AD), respectively. AL = 0.876 and AD = 0.693. Comparing to the related works, which contains four to five modes at the most, our work is more challenging since we have seven modes and the fine-grained classification is more difficult. The two main challenges in the classification of transportation modes, change-points and traffic congestions, are adressed and the combination of more sensors with GPS, such as 3-axis accelerometer, could be the future improvements.
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