Generating time-varying road network data using sparse trajectories
dc.citation.epage | 1124 | en_US |
dc.citation.spage | 1118 | en_US |
dc.contributor.author | Eser, Elif | en_US |
dc.contributor.author | Kocayusufoğlu, F. | en_US |
dc.contributor.author | Eravci, Bahaedd | en_US |
dc.contributor.author | Ferhatosmanoglu, Hakan | en_US |
dc.contributor.author | Larriba-Pey, J. L. | en_US |
dc.coverage.spatial | Barcelona, Spain | |
dc.date.accessioned | 2018-04-12T11:46:07Z | |
dc.date.available | 2018-04-12T11:46:07Z | |
dc.date.issued | 2016-12 | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.description | Date of Conference: 12-15 Dec. 2016 | |
dc.description | Conference name: IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016 | |
dc.description.abstract | While 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.provenance | Made 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: 2017 | en |
dc.identifier.doi | 10.1109/ICDMW.2016.0161 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/37626 | en_US |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/ICDMW.2016.0161 | en_US |
dc.source.title | IEEE International Conference on Data Mining Workshops, ICDMW, 2016 | en_US |
dc.subject | Data generation | en_US |
dc.subject | Dynamic road networks | en_US |
dc.subject | Graph databases | en_US |
dc.subject | Time dependent shortest paths | en_US |
dc.subject | Time-varying graphs | en_US |
dc.subject | Data mining | en_US |
dc.subject | Database systems | en_US |
dc.subject | Frequency domain analysis | en_US |
dc.subject | Motor transportation | en_US |
dc.subject | Real time systems | en_US |
dc.subject | Roads and streets | en_US |
dc.subject | Time varying networks | en_US |
dc.subject | Transportation | en_US |
dc.subject | Data generation | en_US |
dc.subject | Dynamic road networks | en_US |
dc.subject | Graph database | en_US |
dc.subject | Time-dependent shortest paths | en_US |
dc.subject | Time-varying graphs | en_US |
dc.subject | Graph theory | en_US |
dc.title | Generating time-varying road network data using sparse trajectories | en_US |
dc.type | Conference Paper | en_US |
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