MetroEye: Smart tracking your metro rips underground

• Main Authors: GU, Weixi, JIN, Ming, ZHOU, Zimu, SPANOS, Costas J., ZHANG, Lin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: underground public transport; location-based service; smartphone; crowdsourcing; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4744; https://ink.library.smu.edu.sg/context/sis_research/article/5747/viewcontent/mobiquitous16_gu.pdf
• Institution: Singapore Management University

Metro has become the first choice of traveling for tourists and citizens in metropolis due to its efficiency and convenience. Yet passengers have to rely on metro broadcasts to know their locations because popular localization services (e.g. GPS and wireless localization technologies) are often inaccessible underground. To this end, we propose MetroEye, an intelligent smartphone-based tracking system for metro passengers underground. MetroEye leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and infers the state of passengers (Stop, Running, and Interchange) during an entire metro trip using a Conditional Random Field (CRF) model. MetroEye further provides arrival alarm services based on individual passenger state, and aggregates crowdsourced interchange durations to guide passengers for intelligent metro trip planning. Experimental results within 6 months across over 14 subway trains in 3 major cities demonstrate that MetroEye yields an overall accuracy of 80.5% outperforming the state-of-the-art.