File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Measuring fine-grained metro interchange time via smartphones

TitleMeasuring fine-grained metro interchange time via smartphones
Authors
KeywordsLocation-based service
Underground public transport
Smartphone
Crowdsourcing
Issue Date2017
Citation
Transportation Research Part C: Emerging Technologies, 2017, v. 81, p. 153-171 How to Cite?
Abstract© 2017 High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agent-operated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a two-tier classifier to infer interchange states during a metro trip, and further distinguishes 10 fine-grained cases during interchanges. Experimental results within 6 months across over 14 subway lines in 3 major cities demonstrate that our approach yields an overall interchange state inference F1-measurement of 91.0% and an average time error of less than 2 min at an inference interval of 20 s, and an average accuracy of 89.3% to distinguish the 10 fine-grained interchange cases. We also conducted a series of case studies using measurements collected from crowdsourced users during 3 months, which reveals findings previously unattainable without fine-grained interchange time measurements, such as portions of waiting time during interchange, interchange directions, usage of facilities (stairs/escalators/lifts), and the root causes of long interchange times.
Persistent Identifierhttp://hdl.handle.net/10722/296148
ISSN
2019 Impact Factor: 6.077
2015 SCImago Journal Rankings: 2.062
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGu, Weixi-
dc.contributor.authorZhang, Kai-
dc.contributor.authorZhou, Zimu-
dc.contributor.authorJin, Ming-
dc.contributor.authorZhou, Yuxun-
dc.contributor.authorLiu, Xi-
dc.contributor.authorSpanos, Costas J.-
dc.contributor.authorShen, Zuo Jun (Max)-
dc.contributor.authorLin, Wei Hua-
dc.contributor.authorZhang, Lin-
dc.date.accessioned2021-02-11T04:52:56Z-
dc.date.available2021-02-11T04:52:56Z-
dc.date.issued2017-
dc.identifier.citationTransportation Research Part C: Emerging Technologies, 2017, v. 81, p. 153-171-
dc.identifier.issn0968-090X-
dc.identifier.urihttp://hdl.handle.net/10722/296148-
dc.description.abstract© 2017 High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agent-operated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a two-tier classifier to infer interchange states during a metro trip, and further distinguishes 10 fine-grained cases during interchanges. Experimental results within 6 months across over 14 subway lines in 3 major cities demonstrate that our approach yields an overall interchange state inference F1-measurement of 91.0% and an average time error of less than 2 min at an inference interval of 20 s, and an average accuracy of 89.3% to distinguish the 10 fine-grained interchange cases. We also conducted a series of case studies using measurements collected from crowdsourced users during 3 months, which reveals findings previously unattainable without fine-grained interchange time measurements, such as portions of waiting time during interchange, interchange directions, usage of facilities (stairs/escalators/lifts), and the root causes of long interchange times.-
dc.languageeng-
dc.relation.ispartofTransportation Research Part C: Emerging Technologies-
dc.subjectLocation-based service-
dc.subjectUnderground public transport-
dc.subjectSmartphone-
dc.subjectCrowdsourcing-
dc.titleMeasuring fine-grained metro interchange time via smartphones-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.trc.2017.05.014-
dc.identifier.scopuseid_2-s2.0-85020243664-
dc.identifier.volume81-
dc.identifier.spage153-
dc.identifier.epage171-
dc.identifier.isiWOS:000405762200009-
dc.identifier.issnl0968-090X-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats