A Cyberphysical System for Data-Driven Real-Time Traffic Prediction on the Las Vegas I-15 Freeway

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dc.contributor.authorGuzman, Jose A.
dc.contributor.authorMorris, Brendan T.
dc.contributor.authorNunez, Felipe
dc.date.accessioned2025-01-20T20:18:38Z
dc.date.available2025-01-20T20:18:38Z
dc.date.issued2023
dc.description.abstractMobility and transportation services in modern large-scale cities face traffic congestion as one of the main sources of discomfort and economic losses. In this context, taking preventive measures based on traffic predictions looks like an appealing alternative to mitigate congestion. The increasing availability of detectors in the transportation infrastructure has allowed tackling the traffic prediction problem by using a purely data-driven approach, where deep learning models have excelled. Unfortunately, the implementation of these techniques in real time is still under development. This work presents the implementation of a real-time traffic prediction application in the Las Vegas, NV, USA, urban area, built as a cyberphysical system with real-time data streaming from field sensors to a cloud-like environment where deep learning-based traffic predictors are hosted. Implementation results show the feasibility of doing traffic prediction in real time with the current technology and the usefulness of periodic retraining to maintain prediction accuracy.
dc.description.funderChilean National Agency for Research and Development
dc.fuente.origenWOS
dc.identifier.doi10.1109/MITS.2022.3211996
dc.identifier.eissn1941-1197
dc.identifier.issn1939-1390
dc.identifier.urihttps://doi.org/10.1109/MITS.2022.3211996
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92474
dc.identifier.wosidWOS:000917748100001
dc.issue.numero1
dc.language.isoen
dc.pagina.final35
dc.pagina.inicio23
dc.revistaIeee intelligent transportation systems magazine
dc.rightsacceso restringido
dc.subjectDetectors
dc.subjectPredictive models
dc.subjectTraffic control
dc.subjectData models
dc.subjectReal-time systems
dc.subjectSimple object access protocol
dc.subjectUrban areas
dc.subject.ods11 Sustainable Cities and Communities
dc.subject.odspa11 Ciudades y comunidades sostenibles
dc.titleA Cyberphysical System for Data-Driven Real-Time Traffic Prediction on the Las Vegas I-15 Freeway
dc.typeartículo
dc.volumen15
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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