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City-Scale Dark Fiber DAS Measurements of Infrastructure Use During the COVID-19 Pandemic.
Lindsey, Nathaniel J; Yuan, Siyuan; Lellouch, Ariel; Gualtieri, Lucia; Lecocq, Thomas; Biondi, Biondo.
Afiliação
  • Lindsey NJ; Geophysics Department Stanford University Stanford CA USA.
  • Yuan S; Geophysics Department Stanford University Stanford CA USA.
  • Lellouch A; Geophysics Department Stanford University Stanford CA USA.
  • Gualtieri L; Geophysics Department Stanford University Stanford CA USA.
  • Lecocq T; Seismology and Gravimetry Department Royal Observatory of Belgium Brussels Belgium.
  • Biondi B; Geophysics Department Stanford University Stanford CA USA.
Geophys Res Lett ; 47(16): e2020GL089931, 2020 Aug 28.
Article em En | MEDLINE | ID: mdl-32834188
ABSTRACT
Throughout the recent COVID-19 pandemic, real-time measurements about shifting use of roads, hospitals, grocery stores, and other public infrastructure became vital for government decision makers. Mobile phone locations are increasingly assimilated for this purpose, but an alternative, unexplored, natively anonymous, absolute method would be to use geophysical sensing to directly measure public infrastructure usage. In this paper, we demonstrate how fiber-optic distributed acoustic sensing (DAS) connected to a telecommunication cable beneath Palo Alto, CA, successfully monitored traffic over a 2-month period, including major reductions associated with COVID-19 response. Continuous DAS recordings of over 450,000 individual vehicles were analyzed using an automatic template-matching detection algorithm based on roadbed strain. In one commuter sector, we found a 50% decrease in vehicles immediately following the order, but near Stanford Hospital, the traffic persisted. The DAS measurements correlate with mobile phone locations and urban seismic noise levels, suggesting geophysics would complement future digital city sensing systems.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article