The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on un- derlying infrastructure. However, in a subway environment, such positioning systems are not available for the po...The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on un- derlying infrastructure. However, in a subway environment, such positioning systems are not available for the position- ing 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 pro- pose to exploit multiple contextual features extracted from the mobile devices of subway riders to precisely detecting train arrivals. Following this line, we first investigate poten- tial contextual features which may be effective to detect train arrivals according to the observations from 3D accelerome- ters and GSM radio. Furthermore, we propose to explore the maximum entropy （MaxEnt） model for training a train ar- rival detector by learning the correlation between contextual features and train arrivals. Finally, we perform extensive ex- periments on several real-world data sets collected from two major subway lines in the Beijing subway system. Experi- mental results validate both the effectiveness and efficiency of the proposed approach.展开更多
文摘The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on un- derlying infrastructure. However, in a subway environment, such positioning systems are not available for the position- ing 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 pro- pose to exploit multiple contextual features extracted from the mobile devices of subway riders to precisely detecting train arrivals. Following this line, we first investigate poten- tial contextual features which may be effective to detect train arrivals according to the observations from 3D accelerome- ters and GSM radio. Furthermore, we propose to explore the maximum entropy （MaxEnt） model for training a train ar- rival detector by learning the correlation between contextual features and train arrivals. Finally, we perform extensive ex- periments on several real-world data sets collected from two major subway lines in the Beijing subway system. Experi- mental results validate both the effectiveness and efficiency of the proposed approach.
