Short-term forecast methodologies and case studies in traffic flow

dc.contributor	Graduate Program in Computational Science and Engineering.
dc.contributor.advisor	Işlak, Ümit.
dc.contributor.advisor	Arslan, İlker.
dc.contributor.author	Yılmaz, Elif.
dc.date.accessioned	2023-10-15T06:41:00Z
dc.date.available	2023-10-15T06:41:00Z
dc.date.issued	2022
dc.description.abstract	This thesis gives case studies on short-term traffic flow forecasting strategies within a time series framework. After discussing the traditional, machine learning and deep learning methods, one of main goals is to experiment on the uses of hybrid methods. Besides analyzing approaches that were already used in the traffic flow literature, we also introduce and test distinct strategies. Further, we supplement our point forecast results with interval forecasts. In particular, quantiles regression based intervals such as quantile regression averaging and quantile regression neural network are implemented. Both point and interval forecasts are evaluated via several evaluation metrics, and an extensive comparison is provided among the methodologies studied.
dc.format.pages	xiii, 74 leaves
dc.identifier.other	CSE 2022 Y55
dc.identifier.uri	https://digitalarchive.library.bogazici.edu.tr/handle/123456789/19694
dc.publisher	Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022.
dc.subject.lcsh	Traffic flow.
dc.subject.lcsh	Traffic estimation.
dc.title	Short-term forecast methodologies and case studies in traffic flow

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