Traffic prediction framework for OpenStreetMap using deep learning based complex event processing and open traffic cameras

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dc.contributor.authorYaduv, Piyush
dc.contributor.authorSarkar, Dipto
dc.contributor.authorSalwala, Dhaval
dc.contributor.authorCurry, Edward
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2021-02-23T16:46:29Z
dc.date.available2021-02-23T16:46:29Z
dc.date.issued2020-09-25
dc.description.abstractDisplaying near-real-time traffic information is a useful feature of digital navigation maps. However, most commercial providers rely on privacy-compromising measures such as deriving location information from cellphones to estimate traffic. The lack of an open-source traffic estimation method using open data platforms is a bottleneck for building sophisticated navigation services on top of OpenStreetMap (OSM). We propose a deep learning-based Complex Event Processing (CEP) method that relies on publicly available video camera streams for traffic estimation. The proposed framework performs near-real-time object detection and objects property extraction across camera clusters in parallel to derive multiple measures related to traffic with the results visualized on OpenStreetMap. The estimation of object properties (e.g. vehicle speed, count, direction) provides multidimensional data that can be leveraged to create metrics and visualization for congestion beyond commonly used density-based measures. Our approach couples both flow and count measures during interpolation by considering each vehicle as a sample point and their speed as weight. We demonstrate multidimensional traffic metrics (e.g. flow rate, congestion estimation) over OSM by processing 22 traffic cameras from London streets. The system achieves a near-real-time performance of 1.42 seconds median latency and an average F-score of 0.80.en
dc.description.sponsorshipScience Foundation Ireland (grants SFI/13/RC/2094 and SFI/12/RC/2289_P2)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid17en
dc.identifier.citationPiyush, Y., Sarkar, D., Salwala, D. and Curry, E. (2020) 'Traffic Prediction Framework for OpenStreetMap Using Deep Learning Based Complex Event Processing and Open Traffic Cameras', 11th International Conference on Geographic Information Science (GIScience 2021) - Part I, Poznan, Poland (online), 27-30 Sept., Leibniz International Proceedings in Informatics, 17. doi: 10.4230/LIPIcs.GIScience.2021.I.17en
dc.identifier.doi10.4230/LIPIcs.GIScience.2021.I.17en
dc.identifier.endpage17en
dc.identifier.isbn978-3-95977-166-5
dc.identifier.issn1868-8969
dc.identifier.journaltitleLeibniz International Proceedings in Informaticsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/11099
dc.identifier.volume177en
dc.language.isoenen
dc.publisherSchloss Dagstuhl--Leibniz-Zentrum für Informatik GmbHen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2094/IE/Lero - the Irish Software Research Centre/en
dc.relation.urihttps://drops.dagstuhl.de/opus/volltexte/2020/13052/
dc.rights© Piyush Yadav, Dipto Sarkar, Dhaval Salwala, and Edward Curry; licensed under Creative Commons License CC-BYen
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/legalcodeen
dc.subjectTraffic estimationen
dc.subjectOpenStreetMapen
dc.subjectComplex event processingen
dc.subjectTraffic camerasen
dc.subjectVideo processingen
dc.subjectDeep learningen
dc.titleTraffic prediction framework for OpenStreetMap using deep learning based complex event processing and open traffic camerasen
dc.typeConference itemen
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