MetroEye: Smart tracking your metro trips underground

Weixi Gu; Ming Jin; Zimu Zhou; Costas J. Spanos; Lin Zhang

doi:10.1145/2994374.2994381

MetroEye: Smart tracking your metro trips underground

Weixi Gu
, Ming Jin
, Zimu Zhou
, Costas J. Spanos
, Lin Zhang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

9 Citations (Scopus)

Abstract

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. © 2016 ACM.

Original language	English
Title of host publication	Proccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016
Publisher	Association for Computing Machinery
Pages	84-93
Volume	28-November-2016
ISBN (Print)	9781450347501, 9781450347815
DOIs	https://doi.org/10.1145/2994374.2994381
Publication status	Published - 28 Nov 2016
Externally published	Yes
Event	13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016 - Hiroshima, Japan Duration: 28 Nov 2016 → 1 Dec 2016

Publication series

Name	ACM International Conference Proceeding Series
Volume	28-November-2016

Conference

Conference	13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016
Place	Japan
City	Hiroshima
Period	28/11/16 → 1/12/16

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Research Keywords

Crowdsourcing
Location-based service
Smartphone
Underground public transport

Access Output

10.1145/2994374.2994381

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Cite this

Gu, W., Jin, M., Zhou, Z., Spanos, C. J., & Zhang, L. (2016). MetroEye: Smart tracking your metro trips underground. In Proccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016 (Vol. 28-November-2016, pp. 84-93). (ACM International Conference Proceeding Series; Vol. 28-November-2016). Association for Computing Machinery. https://doi.org/10.1145/2994374.2994381

@inproceedings{b78a40c9c45448b898b7b4a44b033446,

title = "MetroEye: Smart tracking your metro trips underground",

abstract = "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. {\textcopyright} 2016 ACM.",

keywords = "Crowdsourcing, Location-based service, Smartphone, Underground public transport",

author = "Weixi Gu and Ming Jin and Zimu Zhou and Spanos, \{Costas J.\} and Lin Zhang",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected]. ; 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016 ; Conference date: 28-11-2016 Through 01-12-2016",

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doi = "10.1145/2994374.2994381",

language = "English",

isbn = "9781450347501",

volume = "28-November-2016",

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Gu, W, Jin, M, Zhou, Z, Spanos, CJ & Zhang, L 2016, MetroEye: Smart tracking your metro trips underground. in Proccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016. vol. 28-November-2016, ACM International Conference Proceeding Series, vol. 28-November-2016, Association for Computing Machinery, pp. 84-93, 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016, Hiroshima, Japan, 28/11/16. https://doi.org/10.1145/2994374.2994381

MetroEye: Smart tracking your metro trips underground. / Gu, Weixi; Jin, Ming; Zhou, Zimu et al.
Proccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016. Vol. 28-November-2016 Association for Computing Machinery, 2016. p. 84-93 (ACM International Conference Proceeding Series; Vol. 28-November-2016).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - MetroEye

T2 - 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016

AU - Gu, Weixi

AU - Jin, Ming

AU - Zhou, Zimu

AU - Spanos, Costas J.

AU - Zhang, Lin

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2016/11/28

Y1 - 2016/11/28

N2 - 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. © 2016 ACM.

AB - 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. © 2016 ACM.

KW - Crowdsourcing

KW - Location-based service

KW - Smartphone

KW - Underground public transport

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85007543304&origin=recordpage

U2 - 10.1145/2994374.2994381

DO - 10.1145/2994374.2994381

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781450347501

SN - 9781450347815

VL - 28-November-2016

T3 - ACM International Conference Proceeding Series

SP - 84

EP - 93

BT - Proccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016

PB - Association for Computing Machinery

Y2 - 28 November 2016 through 1 December 2016

ER -

MetroEye: Smart tracking your metro trips underground

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Research Keywords

Access Output

Other Access Options

Permanent Link

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