None Learning Transportation Modes with Two-Level Inference

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题名:	None Learning Transportation Modes with Two-Level Inference
作者:	謝承璋;Cheng-chang Hsieh
贡献者:	資訊工程研究所
关键词:	交通模式;transportation mode
日期:	2011-08-31
上传时间:	2012-01-05 14:57:51 (UTC+8)
摘要:	使用者的交通模式(例如：走路、公車、汽車)反映其戶外行為模式。隨著具GPS (Global Positioning System，全球衛星定位系統)功能的手機與行動上網的普及，即時預測交通模式成為下列三種應用的基礎：消費行為、旅行行程分享、智慧路徑推薦。預測交通模式是本篇論文的核心議題，我們使用一個兩層推論架構來處理。第一層以五種特徵推論更換點，也就是交通模式被改變地點。第二層以十種特徵推論交通模式，七種交通模式被考慮：走路、單車、公車、汽車、機車、捷運、火車。第一層的F-measure是0.753。第二層的實驗結果以兩種指標來評估：距離準確度(AL)與時間準確度(AD)。其中，距離準確度為 0.876，時間準確度為 0.693。我們的題目比相關文獻更具挑戰性，因為我們的交通模式更多，而更精細的分類是較困難的。 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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