Generating time-varying road network data using sparse trajectories

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dc.citation.epage1124en_US
dc.citation.spage1118en_US
dc.contributor.authorEser, Elifen_US
dc.contributor.authorKocayusufoğlu, F.en_US
dc.contributor.authorEravci, Bahaedden_US
dc.contributor.authorFerhatosmanoglu, Hakanen_US
dc.contributor.authorLarriba-Pey, J. L.en_US
dc.coverage.spatialBarcelona, Spain
dc.date.accessioned2018-04-12T11:46:07Z
dc.date.available2018-04-12T11:46:07Z
dc.date.issued2016-12en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 12-15 Dec. 2016
dc.descriptionConference name: IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016
dc.description.abstractWhile research on time-varying graphs has attracted recent attention, the research community has limited or no access to real datasets to develop effective algorithms and systems. Using noisy and sparse GPS traces from vehicles, we develop a time-varying road network data set where edge weights differ over time. We present our methodology and share this dataset, along with a graph manipulation tool. We estimate the traffic conditions using the sparse GPS data available by characterizing the sparsity issues and assessing the properties of travel sequence data frequency domain. We develop interpolation methods to complete the sparse data into a complete graph dataset with realistic time-varying edge values. We evaluate the performance of time-varying and static shortest path solutions over the generated dynamic road network. The shortest paths using the dynamic graph produce very different results than the static version. We provide an independent Java API and a graph database to analyze and manipulate the generated time-varying graph data easily, not requiring any knowledge about the inners of the graph database system. We expect our solution to support researchers to pursue problems of time-varying graphs in terms of theoretical, algorithmic, and systems aspects. The data and Java API are available at: http://elif.eser.bilkent.edu.tr/roadnetwork. © 2016 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:46:07Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017en
dc.identifier.doi10.1109/ICDMW.2016.0161en_US
dc.identifier.urihttp://hdl.handle.net/11693/37626
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICDMW.2016.0161en_US
dc.source.titleIEEE International Conference on Data Mining Workshops, ICDMW, 2016en_US
dc.subjectData generationen_US
dc.subjectDynamic road networksen_US
dc.subjectGraph databasesen_US
dc.subjectTime dependent shortest pathsen_US
dc.subjectTime-varying graphsen_US
dc.subjectData miningen_US
dc.subjectDatabase systemsen_US
dc.subjectFrequency domain analysisen_US
dc.subjectMotor transportationen_US
dc.subjectReal time systemsen_US
dc.subjectRoads and streetsen_US
dc.subjectTime varying networksen_US
dc.subjectTransportationen_US
dc.subjectData generationen_US
dc.subjectDynamic road networksen_US
dc.subjectGraph databaseen_US
dc.subjectTime-dependent shortest pathsen_US
dc.subjectTime-varying graphsen_US
dc.subjectGraph theoryen_US
dc.titleGenerating time-varying road network data using sparse trajectoriesen_US
dc.typeConference Paperen_US

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