MetroEye: Smart tracking your metro trips underground
ACM International Conference Proceeding Series, Vol: 28-November-2016, Page: 84-93
2016
- 9Citations
- 51Usage
- 20Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations9
- Citation Indexes9
- CrossRef5
- Usage51
- Downloads46
- Abstract Views5
- Captures20
- Readers20
- 17
Conference Paper Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85007543304&origin=inward; http://dx.doi.org/10.1145/2994374.2994381; https://dl.acm.org/doi/10.1145/2994374.2994381; https://ink.library.smu.edu.sg/sis_research/4744; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5747&context=sis_research
Association for Computing Machinery (ACM)
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