Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Short-term forecast methodologies and case studies in traffic flow

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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.identifier.other CSE 2022 Y55
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/19694
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.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
dc.format.pages xiii, 74 leaves


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