Managing sensor traffic data and forecasting unusual behaviour propagation

dc.creatorMedeiros, CB
dc.creatorJoliveau, M
dc.creatorJomier, G
dc.creatorDe Vuyst, F
dc.date2010
dc.dateJUL
dc.date2014-11-19T13:35:11Z
dc.date2015-11-26T18:03:30Z
dc.date2014-11-19T13:35:11Z
dc.date2015-11-26T18:03:30Z
dc.date.accessioned2018-03-29T00:45:24Z
dc.date.available2018-03-29T00:45:24Z
dc.identifierGeoinformatica. Springer, v. 14, n. 3, n. 279, n. 305, 2010.
dc.identifier1384-6175
dc.identifierWOS:000275105300002
dc.identifier10.1007/s10707-010-0102-7
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/53175
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/53175
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/53175
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1292557
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionSensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embedded) sensors, generating large and complex spatio-temporal series. This scenario presents several research challenges, in spatio-temporal data management and data analysis. Management issues involve, for instance, data cleaning and data fusion to support queries at distinct spatial and temporal granularities. Analysis issues include the characterization of traffic behavior for given space and/or time windows, and detection of anomalous behavior (either due to sensor malfunction, or to traffic events). This paper contributes to the solution of some of these issues through a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and data management strategies to query these data. The first aspect is geared towards supporting pattern matching. This leads to a model to study and predict unusual traffic behavior along an urban road network. The second aspect deals with spatio-temporal database issues, taking into account information produced by the model. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with tests conducted on 1,000 sensors, during 3 years, in a large French city.
dc.description14
dc.description3
dc.descriptionSI
dc.description279
dc.description305
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFrench Research Program
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.languageen
dc.publisherSpringer
dc.publisherDordrecht
dc.publisherHolanda
dc.relationGeoinformatica
dc.relationGeoinformatica
dc.rightsfechado
dc.rightshttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dc.sourceWeb of Science
dc.subjectIntelligent Transportation Systems
dc.subjectTraffic sensor data
dc.subjectTraffic modelling
dc.subjectSensor networks
dc.subjectTime series
dc.subjectMoving-objects
dc.subjectTrajectories
dc.subjectNetwork
dc.titleManaging sensor traffic data and forecasting unusual behaviour propagation
dc.typeArtículos de revistas


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