Method to use transport microsimulation models to create synthetic distributed acoustic sensing datasets

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dc.contributor.authorRobles Urquijo, Ignacio
dc.contributor.authorBenavente, Juan
dc.contributor.authorBlanco García, Javier
dc.contributor.authorDiego González, Pelayo
dc.contributor.authorLoayssa Lara, Alayn
dc.contributor.authorSagüés García, Mikel
dc.contributor.authorRodríguez Cobo, Luis
dc.contributor.authorCobo, Adolfo
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritzaeu
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.date.accessioned2025-07-16T08:36:41Z
dc.date.available2025-07-16T08:36:41Z
dc.date.issued2025-05-07
dc.date.updated2025-07-16T08:33:21Z
dc.description.abstractThis research introduces a new method for creating synthetic Distributed Acoustic Sensing (DAS) datasets from transport microsimulation models. The process involves modeling detailed vehicle interactions, trajectories, and characteristics from the PTV VISSIM transport microsimulation tool. It then applies the Flamant-Boussinesq approximation to simulate the resulting ground deformation detected by virtual fiber-optic cables. These synthetic DAS signals serve as large-scale, scenario-controlled, labeled datasets on training machine learning models for various transport applications. We demonstrate this by training several U-Net convolutional neural networks to enhance spatial resolution (reducing it to half the original gauge length), filtering traffic signals by vehicle direction, and simulating the effects of alternative cable layouts. The methodology is tested using simulations of real road scenarios, featuring a fiber-optic cable buried along the westbound shoulder with sections deviating from the roadside. The U-Net models, trained solely on synthetic data, showed promising performance (e.g., validation MSE down to 0.0015 for directional filtering) and improved the detectability of faint signals, like bicycles among heavy vehicles, when applied to real DAS measurements from the test site. This framework uniquely integrates detailed traffic modeling with DAS physics, providing a novel tool to develop and evaluate DAS signal processing techniques, optimize cable layout deployments, and advance DAS applications in complex transportation monitoring scenarios. Creating such a procedure offers significant potential for advancing the application of DAS in transportation monitoring and smart city initiatives.en
dc.description.sponsorshipThis research was funded by the Spanish Goverment 'Subprograma Ayudas Predoctorales 2020 Investigadores' program under Grant PRE2020-096336, assigned to the National Plan project PID2019-107270RB-C21, 'Photonic Devices and Systems Sensors for Intelligent Structures and Non Destructive Evaluation I'; the National 'Knowledge Generation Projects' project 'Photonic Sensors for Smart and Sustainable Cities (Performance)' under grant ID6448137269-137269-4-22; the project 'Integrated System for Traffic and Road Condition Monitoring Using Fiber-Optic Sensors (Ingestion)', under grant PDC2021-121172-C22 funded by MICIU/AEI/10.13039/501100011033 and by the European Union Next GenerationEU/PRTR). This work was supported in part by grant PID2022-137269OB-C21 by MICIU/AEI/10.13039/501100011033 and by ERDF 'A way of making Europe'; and grant PID2022-137269OB-C22 by MICIU/AEI/10.13039/501100011033 and by the European Union.
dc.format.mimetypeapplication/pdf
dc.identifier.citationRobles-Urquijo, I., Benavente, J., Blanco García, J., Diego Gonzalez, P., Loayssa, A., Sagües, M., Rodriguez-Cobo, L., Cobo, A. (2025) Method to use transport microsimulation models to create synthetic distributed acoustic sensing datasets. Applied Sciences, 15(9), 1-23. https://doi.org/10.3390/app15095203
dc.identifier.doi10.3390/app15095203
dc.identifier.issn1454-5101
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/54408
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofApplied Sciences, 15(9), 1-23
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107270RB-C21/ES/
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PDC2021-121172-C22/ES/
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137269OB-C21/ES/
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137269OB-C22/ES/
dc.relation.publisherversionhttps://doi.org/10.3390/app15095203
dc.rights© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDASen
dc.subjectDAS traffic monitoringen
dc.subjectDistributed acoustic sensingen
dc.subjectEnhanced spatial resolutionen
dc.subjectMicrosimulationen
dc.subjectSynthetic DASen
dc.subjectTransport engineeringen
dc.titleMethod to use transport microsimulation models to create synthetic distributed acoustic sensing datasetsen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublicationc694736c-d2bb-49e5-bf4f-496c976fbeff
relation.isAuthorOfPublication70a7fa6c-b9bf-4654-b2c0-3e834b8a56d1
relation.isAuthorOfPublication.latestForDiscoveryc694736c-d2bb-49e5-bf4f-496c976fbeff

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