{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:05:42Z","timestamp":1776816342083,"version":"3.51.2"},"reference-count":33,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,18]],"date-time":"2022-12-18T00:00:00Z","timestamp":1671321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Starting funds from Zhejiang University","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Distributed acoustic sensing (DAS) is an emerging technology for recording vibration signals via the optical fibers buried in subsurface conduits. Its relatively easy-to-deploy and high spatial and temporal sampling characteristics make DAS an appealing tool to record seismic wavefields at higher quantity and quality than traditional geophones. Considering that the usage of optical fibers in the urban environment has drawn relatively less attention aside from its functionality as a telecommunication cable, we examine its ability to record seismic signals and investigate its preliminary application in city traffic monitoring. To solve the problems that DAS signals are prone to a variety of environmental noise and are generally of weak amplitude compared to noise, we propose a fast workflow for real-time DAS data processing, which can enhance the detection of regular car signals and suppress the other components. We conduct a DAS experiment in Hangzhou, China, a typical metropolitan area that can provide us with a rich data library to validate our DAS data-processing workflow. The well-processed data enable us to extract their slope and coherency attributes that can provide an estimate of real traffic situations. The one-minute (with video validations) and 24 h statistics of these attributes show that the speed and volume of car flow are well correlated demonstrates the robustness of the proposed data processing workflow and great potential of DAS for city traffic monitoring with high precision and convenience. However, challenges also exist in view that all the attributes are statistically analyzed based on the behaviors of a large number of cars, which is meaningful but lacking in precision. Therefore, we suggest developing more quantitative processing and analyzing methods to provide precise information on individual cars in future works.<\/jats:p>","DOI":"10.3390\/s22249976","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T09:31:01Z","timestamp":1671442261000},"page":"9976","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring"],"prefix":"10.3390","volume":"22","author":[{"given":"Hang","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunfeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Min","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6429-4261","authenticated-orcid":false,"given":"Yangkang","family":"Chen","sequence":"additional","affiliation":[{"name":"Bureau of Economic Geology, John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78712, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jiang, J.C., Kantarci, B., Oktug, S., and Soyata, T. (2020). Federated learning in smart city sensing: Challenges and opportunities. Sensors, 20.","DOI":"10.3390\/s20216230"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shatalin, S., Parker, T., and Farhadiroushan, M. (2021). High definition seismic and microseismic data acquisition using distributed and engineered fiber optic acoustic sensors. Distrib. Acoust. Sens. Geophys. Methods Appl., 1\u201332.","DOI":"10.1002\/9781119521808.ch1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1146\/annurev-earth-072420-065213","article-title":"Fiber-optic seismology","volume":"49","author":"Lindsey","year":"2021","journal-title":"Annu. Rev. Earth Planet. Sci."},{"key":"ref_4","unstructured":"Martin, E.R. (2018). Passive Imaging and Characterization of the Subsurface with Distributed Acoustic Sensing, Stanford University."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2020GL089931","DOI":"10.1029\/2020GL089931","article-title":"City-scale dark fiber DAS measurements of infrastructure use during the COVID-19 pandemic","volume":"47","author":"Lindsey","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1785\/0220200091","article-title":"Rose Parade seismology: Signatures of floats and bands on optical fiber","volume":"91","author":"Wang","year":"2020","journal-title":"Seismol. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1785\/0220190112","article-title":"Distributed acoustic sensing turns fiber-optic cables into sensitive seismic antennas","volume":"91","author":"Zhan","year":"2020","journal-title":"Seismol. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s43247-021-00234-3","article-title":"Ground vibrations recorded by fiber-optic cables reveal traffic response to COVID-19 lockdown measures in Pasadena, California","volume":"2","author":"Wang","year":"2021","journal-title":"Commun. Earth Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1038\/s41598-018-36675-8","article-title":"Distributed acoustic sensing using dark fiber for near-surface characterization and broadband seismic event detection","volume":"9","author":"Dou","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"20210812","DOI":"10.1098\/rspa.2021.0812","article-title":"The seismic wavefield as seen by distributed acoustic sensing arrays: Local, regional and teleseismic sources","volume":"478","author":"Kennett","year":"2022","journal-title":"Proc. R. Soc. A"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11620","DOI":"10.1038\/s41598-017-11986-4","article-title":"Distributed acoustic sensing for seismic monitoring of the near surface: A traffic-noise interferometry case study","volume":"7","author":"Dou","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1190\/tle39090646.1","article-title":"Near-surface characterization using a roadside distributed acoustic sensing array","volume":"39","author":"Yuan","year":"2020","journal-title":"Lead. Edge"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e2019GL086115","DOI":"10.1029\/2019GL086115","article-title":"Urban near-surface seismic monitoring using distributed acoustic sensing","volume":"47","author":"Fang","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e2020JB021004","DOI":"10.1029\/2020JB021004","article-title":"Aquifer monitoring using ambient seismic noise recorded with distributed acoustic sensing (DAS) deployed on dark fiber","volume":"126","year":"2021","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/2017GL075722","article-title":"Fiber-optic network observations of earthquake wavefields","volume":"44","author":"Lindsey","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2021AV000395","DOI":"10.1029\/2021AV000395","article-title":"Rapid response to the 2019 Ridgecrest earthquake with distributed acoustic sensing","volume":"2","author":"Li","year":"2021","journal-title":"AGU Adv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"219","DOI":"10.5194\/se-12-219-2021","article-title":"Sensing Earth and environment dynamics by telecommunication fiber-optic sensors: An urban experiment in Pennsylvania, USA","volume":"12","author":"Zhu","year":"2021","journal-title":"Solid Earth"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.1785\/0220200274","article-title":"Distributed acoustic sensing using a large-volume airgun source and internet fiber in an urban area","volume":"92","author":"Song","year":"2021","journal-title":"Seismol. Res. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"68968","DOI":"10.1109\/ACCESS.2018.2868418","article-title":"Traffic flow detection using distributed fiber optic acoustic sensing","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"van den Ende, M.P., Ferrari, A., Sladen, A., and Richard, C. (2021). Deep Deconvolution for Traffic Analysis with Distributed Acoustic Sensing Data. IEEE Trans. Intell. Transp. Syst.","DOI":"10.31223\/X5P345"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wiesmeyr, C., Coronel, C., Litzenberger, M., D\u00f6ller, H.J., Schweiger, H.B., and Calbris, G. (2021, January 19\u201322). Distributed Acoustic Sensing for Vehicle Speed and Traffic Flow Estimation. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564517"},{"key":"ref_22","unstructured":"Thulasiraman, P., and Liu, Y.Y. (2022, October 01). Traffic Monitoring with Distributed Acoustic Sensing. Available online: https:\/\/m2pi.ca\/project\/2020\/fotech-solutions\/Fotech-final.pdf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1190\/1.1527095","article-title":"Applications of plane-wave destruction filters","volume":"67","author":"Fomel","year":"2002","journal-title":"Geophysics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"F1","DOI":"10.1190\/geo2021-0266.1","article-title":"A Matlab code package for 2D\/3D local slope estimation and structural filtering","volume":"87","author":"Wang","year":"2022","journal-title":"Geophysics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1111\/1365-2478.13164","article-title":"Iterative Gaussian mixture model and multi-channel attributes for arrival picking in extremely noisy environments","volume":"70","author":"Wang","year":"2022","journal-title":"Geophys. Prospect."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1137\/05064182X","article-title":"Fast discrete curvelet transforms","volume":"5","author":"Candes","year":"2006","journal-title":"Multiscale Model. Simul."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"V261","DOI":"10.1190\/geo2015-0264.1","article-title":"Damped Multichannel Singular Spectrum Analysis for 3D Random Noise Attenuation","volume":"81","author":"Huang","year":"2016","journal-title":"Geophysics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1093\/gji\/ggw230","article-title":"Simultaneous denoising and reconstruction of 5D seismic data via damped rank-reduction method","volume":"206","author":"Chen","year":"2016","journal-title":"Geophys. J. Int."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1093\/gji\/ggac116","article-title":"Improving receiver function imaging with high-resolution Radon transform","volume":"230","author":"Zhang","year":"2022","journal-title":"Geophys. J. Int."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"WD1","DOI":"10.1190\/geo2014-0227.1","article-title":"Random noise attenuation using local signal-and-noise orthogonalization","volume":"80","author":"Chen","year":"2015","journal-title":"Geophysics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"V409","DOI":"10.1190\/geo2020-0151.1","article-title":"Nonstationary local signal-and-noise orthogonalization","volume":"86","author":"Chen","year":"2020","journal-title":"Geophysics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"V39","DOI":"10.1190\/geo2021-0243.1","article-title":"Retrieving the leaked signals from noise using a fast dictionary learning method","volume":"87","author":"Chen","year":"2022","journal-title":"Geophysics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"WA55","DOI":"10.1190\/geo2022-0168.1","article-title":"Retrieving useful signals from highly corrupted erratic noise using robust residual dictionary learning","volume":"88","author":"Chen","year":"2022","journal-title":"Geophysics"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9976\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:36Z","timestamp":1760147016000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9976"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,18]]},"references-count":33,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249976"],"URL":"https:\/\/doi.org\/10.3390\/s22249976","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,18]]}}}