Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/453987
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dc.coverage.spatialGrey wolf and bald eagle search based hybrid algorithm for short term traffic prediction
dc.date.accessioned2023-01-30T04:35:53Z-
dc.date.available2023-01-30T04:35:53Z-
dc.identifier.urihttp://hdl.handle.net/10603/453987-
dc.description.abstractAt present, traffic congestion is one of the critical challenges faced newlineby metropolitan cities across the globe. In a fast-moving world, transportation newlineconsumes most of the time and resources. Traffic congestion delays transit newlineand has some drastic indirect effects on time consumption, fuel consumption, newlineair, noise, and environmental pollution. To solve this global issue, it is newlinenecessary to execute a systematic well-organized solution. newlineProper analysis of traffic demand is needed to estimate traffic newlineconditions in the future. This analytic data can be used to optimize routes newlineopted by vehicles. More specifically, this road traffic data and its analysis newlinehave significant impact on the development of autonomous vehicles as we are newlinein the era of moving into smart transportation. newlineApplication of statistical models and machine learning algorithms newlinehave been proved successful in a wide range of applications. Traffic newlineprediction also becomes a thrust application for machine learning algorithms newlineto overcome hurdles faced by congestion. newlineA well-known prediction mechanism, Support Vector Regression newline(SVR), has been employed for short-term traffic forecasting. newline
dc.format.extentxiii,118p.
dc.languageEnglish
dc.relationp.108-117
dc.rightsuniversity
dc.titleGrey wolf and bald eagle search based hybrid algorithm for short term traffic prediction
dc.title.alternative
dc.creator.researcherAngayar Kannii S A
dc.subject.keywordTraffic forecasting
dc.subject.keywordGrey Wolf Optimization
dc.subject.keywordBald Eagle Search
dc.description.note
dc.contributor.guideSivakumar R
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2021
dc.date.awarded2021
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File248.53 kBAdobe PDFView/Open
02_prelim pages.pdf3.16 MBAdobe PDFView/Open
03_content.pdf95.66 kBAdobe PDFView/Open
04_abstract.pdf98.89 kBAdobe PDFView/Open
05_chapter 1.pdf1.63 MBAdobe PDFView/Open
06_chapter 2.pdf1.47 MBAdobe PDFView/Open
07_chapter 3.pdf1.93 MBAdobe PDFView/Open
08_chapter 4.pdf2.96 MBAdobe PDFView/Open
09_annexures.pdf137.72 kBAdobe PDFView/Open
80_recommendation.pdf539.9 kBAdobe PDFView/Open


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