Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/67009
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Wang, YQ | - |
dc.creator | Cao, JN | - |
dc.creator | Li, WG | - |
dc.creator | Gu, T | - |
dc.date.accessioned | 2017-05-22T02:27:43Z | - |
dc.date.available | 2017-05-22T02:27:43Z | - |
dc.identifier.isbn | 978-1-5090-0898-8 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/67009 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Wang, Y., Cao, J., Li, W., & Gu, T. (2016, May). Mining traffic congestion correlation between road segments on GPS trajectories. In Smart Computing (SMARTCOMP), 2016 IEEE International Conference on (pp. 1-8). IEEE is available at https://doi.org/10.1109/SMARTCOMP.2016.7501704 | en_US |
dc.subject | Traffic congestion | en_US |
dc.subject | Congestion correlation | en_US |
dc.subject | GPS trajectories | en_US |
dc.subject | Classification | en_US |
dc.title | Mining traffic congestion correlation between road segments on GPS trajectories | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 131 | en_US |
dc.identifier.epage | 138 | en_US |
dc.identifier.doi | 10.1109/SMARTCOMP.2016.7501704 | en_US |
dcterms.abstract | Traffic congestion is a major concern in many cities around the world. Previous work mainly focuses on the prediction of congestion and analysis of traffic flows, while the congestion correlation between road segments has not been studied yet. In this paper, we propose a three-phase framework to study the congestion correlation between road segments from multiple real world data. In the first phase, we extract congestion information on each road segment from GPS trajectories of over 10,000 taxis, define congestion correlation and propose a corresponding mining algorithm to find out all the existing correlations. In the second phase, we extract various features on each pair of road segments from road network and POI data. In the last phase, the results of the first two phases are input into several classifiers to predict congestion correlation. We further analyze the important features and evaluate the results of the trained classifiers. We found some important patterns that lead to a high/low congestion correlation, and they can facilitate building various transportation applications. The proposed techniques in our framework are general, and can be applied to other pairwise correlation analysis. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | 2016 2nd IEEE International Conference on Smart Computing (SMARTCOMP), May 18-20, 2016, St Louis, MO, p. 131-138 | - |
dcterms.issued | 2016 | - |
dc.identifier.isi | WOS:000390715200033 | - |
dc.relation.conference | International Conference on Power System Technology [PowerCon] | en_US |
dc.identifier.rosgroupid | 2015001791 | - |
dc.description.ros | 2015-2016 > Academic research: refereed > Refereed conference paper | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Wang_Multiple_Faulty_GNSS.pdf | Pre-Published version | 2.27 MB | Adobe PDF | View/Open |
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