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dc.contributor.authorElizondo, Daviden
dc.contributor.authorOrun, A.en
dc.date.accessioned2017-04-24T15:51:54Z
dc.date.available2017-04-24T15:51:54Z
dc.date.issued2017-06
dc.identifier.citationElizondo, D. and Orun, A. (2017) An Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollution, TPM- Transport Practitioner’s Meeting Conference, 28-29 June 2017, Nottinghamen
dc.identifier.urihttp://hdl.handle.net/2086/14084
dc.descriptionCCI Group has contributed to the researchen
dc.description.abstractTraffic flow related air pollution is one of the major problems in urban areas, and is often difficult to avoid it if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our introduced work here, an artificial intelligence technique such as Bayesian networks are used for a robust traffic data analysis and modelling. The most common challenge in traditional data analysis is a lack of capability of unveiling the hidden links between the distant data attributes (e.g. pollution sources, dynamic traffic parameters, geographic location characteristics, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long term basis.en
dc.language.isoenen
dc.publisherPTRC Education and Research Services Limiteden
dc.subjecttraffic network designen
dc.subjectoptimisationen
dc.subjectair pollutionen
dc.subjectBayesian networksen
dc.titleAn Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollutionen
dc.typeConferenceen
dc.researchgroupDIGITSen
dc.explorer.multimediaNoen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-02-22en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteLeicester Institute for Pharmaceutical Innovation - From Molecules to Practice (LIPI)en


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