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dc.contributor.authorAnwar, T
dc.contributor.authorLiu, C
dc.contributor.authorVu, HL
dc.contributor.authorIslam, MS
dc.contributor.editorC. Aggarwal, M. de Rijke, R. Kumar, V. Murdock, T. Sellis, J. Yu
dc.date.accessioned2020-03-26T04:48:43Z
dc.date.available2020-03-26T04:48:43Z
dc.date.issued2015
dc.identifier.isbn9781450337946
dc.identifier.doi10.1145/2806416.2806588
dc.identifier.urihttp://hdl.handle.net/10072/370088
dc.description.abstractWith the rapidly growing population in urban areas, these days the urban road networks are expanding at a faster rate. The frequent movement of people on them leads to traffic congestions. These congestions originate from some crowded road segments, and diffuse towards other parts of the urban road networks creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this work, we propose RoadRank, an algorithm to compute the influence scores of each road segment in an urban road network, and rank them based on their overall influence. It is an incremental algorithm that keeps on updating the influence scores with time, by feeding with the latest traffic data at each time point. The method starts with constructing a directed graph called influence graph, which is then used to iteratively compute the influence scores using probabilistic diffusion theory. We show promising preliminary experimental results on real SCATS traffic data of Melbourne.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for Computing Machinery (ACM)
dc.publisher.placeUnited States
dc.relation.ispartofconferencename24th ACM International on Conference on Information and Knowledge Management (CIKM 2015)
dc.relation.ispartofconferencetitleCIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
dc.relation.ispartofdatefrom2015-10-19
dc.relation.ispartofdateto2015-10-23
dc.relation.ispartoflocationMelbourne, Australia
dc.relation.ispartofpagefrom1671
dc.relation.ispartofpageto1674
dc.relation.ispartofvolumeOctober 2015
dc.subject.fieldofresearchData Structures
dc.subject.fieldofresearchDatabase Management
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080403
dc.subject.fieldofresearchcode080604
dc.subject.fieldofresearchcode080109
dc.titleRoadRank: Traffic Diffusion and Influence Estimation in Dynamic Urban Road Networks
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ISBN: 978-1-4503-3794-6, https://doi.org/10.1145/2806416.2806588
gro.hasfulltextFull Text
gro.griffith.authorIslam, Saiful


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