Predicting the Effect of an Accident on Traffc Congestion

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dc.contributor.advisorDr. Grootjen, F.A.
dc.contributor.advisorDr. Hazenberg, S.J.
dc.contributor.authorSchreurs, Koert
dc.date.issued2018-07-12
dc.description.abstractIn this study a Multilayer Perceptron was applied to predict tra c jams based on accidents. Emergency service messages were used as an indication of an accident. The model uses emergency service messages and road tra c information as input features. A dataset was created that com- bines information about accidents with tra c data. The model predicts if an accident is followed by a tra c jam within the next 15 minutes. The results show that a higher AUC than the base- line of 0.5 is achievable. Furthermore two di erent techniques to deal with unbalanced data were explored, namely resampling and cost-sensitive learning.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/10829
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titlePredicting the Effect of an Accident on Traffc Congestionen_US
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