Please use this identifier to cite or link to this item: http://dspace.lib.uom.gr/handle/2159/28909
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorKalampokis, Evangelosen
dc.contributor.authorBrimos, Petrosen
dc.date.accessioned2023-05-15T10:49:28Z-
dc.date.available2023-05-15T10:49:28Z-
dc.date.issued2023el
dc.identifier.urihttp://dspace.lib.uom.gr/handle/2159/28909-
dc.descriptionΔιπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2023.el
dc.description.abstractIn recent years, there has been a growing interest in traffic forecasting using Graph Neural Networks (GNNs). These models manage to capture the complex topology in the non – Euclidean space by using Graph convolutions, while dynamic temporal dependencies are captured by recurrent networks. Moreover, the efficiency of such models mostly depends on the availability of cleansed and trustworthy mobility data. However, the access to such data is limited or restricted in most cases. Over the last decades, governments and the public sector publish their data, including dynamic traffic data, freely accessible by all citizens without any restrictions. These vast amounts of Open Government Data (OGD) have the potential to be exploited by data intelligence applications, such as Graph Neural Networks for traffic forecasting. To that end, this study focuses on a single case using open traffic data of the Greek OGD portal for predicting future traffic flows. More precisely, data exploration and analysis demonstrated that the OGD dataset contains many missing values and anomalies for the first two years of operation. The quality of the traffic data improves significantly after August 2022 where the experiments of this thesis were conducted. Two GNN models are implemented for the selected time window namely: Temporal Graph Convolutional Network (TGCN) and Diffusion Convolutional Recurrent Neural Network (DCRNN). The two GNN – based models achieved approximately 50% decrease on all error metrics compared with other baseline models, demonstrating high prediction precision. The TGCN model specifically achieved the best performance in traffic flow prediction among all prediction horizons and all metrics compared with all other models. To the author’s knowledge this is the first time a study exploited OGD traffic data for traffic forecasting with Graph Neural Networks.en
dc.format.extent127el
dc.language.isoenen
dc.publisherΠανεπιστήμιο Μακεδονίαςel
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Διεθνέςel
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectGraph Neural Networksen
dc.subjectOpen Government Dataen
dc.subjectTraffic forecastingen
dc.subjectSpatial – temporal predictionen
dc.subjectDeep learning on graphsen
dc.subjectOpen traffic dataen
dc.subjectDeep learningen
dc.titleTraffic forecasting with graph neural networksen
dc.title.alternativeΠρόβλεψη κυκλοφορίας δρόμων με χρήση νευρωνικών δικτύων γράφωνel
dc.typeElectronic Thesis or Dissertationen
dc.typeTexten
dc.contributor.departmentΠρόγραμμα Μεταπτυχιακών Σπουδών Αναλυτική των Επιχειρήσεων και Επιστήμη των Δεδομένωνel
Appears in Collections:ΠΜΣ Αναλυτική των Επιχειρήσεων και Επιστήμη των Δεδομένων (Μ)

Files in This Item:
File Description SizeFormat 
BrimosPetrosMsc2023.pdf3.15 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons