Collective data forecasting in dynamic transport networks

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buir.advisorKörpeoğlu, İbrahim
dc.contributor.authorGüvercin, Mehmet
dc.date.accessioned2021-11-17T12:41:44Z
dc.date.available2021-11-17T12:41:44Z
dc.date.copyright2021-09
dc.date.issued2021-09
dc.date.submitted2021-11-16
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 68-72).en_US
dc.description.abstractForecasting is a crucial tool for intelligent transportation systems and passengers of these systems and critical for transportation planning and management, as the transportation variable (e.g. delay, traffic speed) are among major costs in transportation. Each transportation variable may cause a further propagation in dynamic transport network. Hence, the transportation variable pattern of a node and the location of the node in the transport network can provide useful information for other nodes. We address the problem of forecasting transportation variable of a transport network node, utilizing the network information as well as the transportation variable patterns of similar nodes in the network. We propose ECFM, Exploratory Clustered Forecasting Modeling, on both static and dynamic transportation network which makes use of graph based fea-tures for time-series estimation. ECFM approach builds a representative time-series for each group of nodes in the transport network and fits a common model like Seasonal Autoregressive Integrated Moving Average (SARIMA), Long-Short Term Memory (LSTM), Regression with Autoregressive Integrated Moving Av-erage errors (REG-ARIMA), Regression with Long-Short Term Memory errors (REG-LSTM) for each, using the network based features as regressors. The models are then applied individually to each node data for predicting the node’s transportation variable. We perform a network based analysis of the transport network and identify graph-based features and we represent nodes as vectors that are used for both grouping nodes and as regressors in forecasting models. We evaluate proposed ECFM, Exploratory Clustered Forecasting Modeling, on two datasets (flight de-lay dataset, traffic speed dataset). The experiments show that ECFM provides accurate forecasts of delays/traffics compared to individual forecasting models. Centrality measure of nodes such as betweenness centrality score is found to be an effective regressor in the clustered modeling. Clustered models built on dynamic networks performs better compared to static networks. ECFM, Exploratory Clustered Forecasting Modeling, is an conceptual ap-proach and it is domain independent. Our proposed approach tries to incorporate information, related to estimated variable, exist in similar nodes of the network. Thus, we can achieve to build robust estimation models on enriched data.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2021-11-17T12:41:44Z No. of bitstreams: 1 Collective data forecasting in dynamic transport networks.pdf: 4541563 bytes, checksum: 07938abb7a9839797f78136de3f4f08b (MD5)en
dc.description.provenanceMade available in DSpace on 2021-11-17T12:41:44Z (GMT). No. of bitstreams: 1 Collective data forecasting in dynamic transport networks.pdf: 4541563 bytes, checksum: 07938abb7a9839797f78136de3f4f08b (MD5) Previous issue date: 2021-09en
dc.description.statementofresponsibilityby Mehmet Güvercinen_US
dc.format.extentxiv, 72 leaves : illustrations (color), tables, charts (color) ; 30 cm.en_US
dc.identifier.itemidB125980
dc.identifier.urihttp://hdl.handle.net/11693/76687
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTime series forecastingen_US
dc.subjectClustered forecasting modelsen_US
dc.subjectDelay esti-mationen_US
dc.subjectTraffic forecastingen_US
dc.subjectNetwork clusteringen_US
dc.subjectDynamic transport networksen_US
dc.titleCollective data forecasting in dynamic transport networksen_US
dc.title.alternativeDinamik ulaştırma ağlarında kolektif veri tahminlemesien_US
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelDoctoral
thesis.degree.namePh.D. (Doctor of Philosophy)

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