Learning to detect subway arrivals for passengers on a train

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• 作者：Kuifei Yu (1) (3)
  Hengshu Zhu (2)
  Huanhuan Cao (3)
  Baoxian Zhang (1)
  Enhong Chen (2)
  Jilei Tian (3)
  Jinghai Rao (3)
  
• 关键词：subway arrival detection ; mobile users ; smart cities ; information storage and retrieval ; experimentation
• 刊名：Frontiers of Computer Science in China
• 出版年：2014
• 出版时间：April 2014
• 年：2014
• 卷：8
• 期：2
• 页码：316-329
• 全文大小：793 KB
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• 作者单位：Kuifei Yu (1) (3)
  Hengshu Zhu (2)
  Huanhuan Cao (3)
  Baoxian Zhang (1)
  Enhong Chen (2)
  Jilei Tian (3)
  Jinghai Rao (3)
  
  1. Research Center of Ubiqutious Sensor Networks, College of Engineering & Information Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
  3. Nokia Research Center, Nokia (China) Investment Corp. Ltd., Beijing, 100176, China
  2. Laboratory of Semantic Computing and Data Mining, Department of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China
  
• ISSN：1673-7466

文摘

The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on underlying infrastructure. However, in a subway environment, such positioning systems are not available for the positioning tasks, such as the detection of the train arrivals for the passengers in the train. An alternative approach is to exploit the contextual information available in the mobile devices of subway riders to detect train arrivals. To this end, we propose to exploit multiple contextual features extracted from the mobile devices of subway riders to precisely detecting train arrivals. Following this line, we first investigate potential contextual features which may be effective to detect train arrivals according to the observations from 3D accelerometers and GSM radio. Furthermore, we propose to explore the maximum entropy (MaxEnt) model for training a train arrival detector by learning the correlation between contextual features and train arrivals. Finally, we perform extensive experiments on several real-world data sets collected from two major subway lines in the Beijing subway system. Experimental results validate both the effectiveness and efficiency of the proposed approach.