Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6135
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dc.contributor.authorÖzhayoğlu, Mural-
dc.contributor.authorKüçükayan, Gökhan-
dc.contributor.authorDoğdu, Erdoğan-
dc.date.accessioned2021-09-11T15:35:02Z-
dc.date.available2021-09-11T15:35:02Z-
dc.date.issued2016en_US
dc.identifier.citation4th IEEE International Conference on Big Data (Big Data) -- DEC 05-08, 2016 -- Washington, DCen_US
dc.identifier.isbn978-1-4673-9005-7-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6135-
dc.description.abstractDue to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives, provides quicker road openings, hence decreases wasted time and resources, and increases efficiency. In this study, we propose a preliminary real-time autonomous accident-detection system based on computational intelligence techniques. Istanbul City traffic-flow data for the year 2015 from various sensor locations are populated using big data processing methodologies. The extracted features are then fed into a nearest neighbor model, a regression tree, and a feed-forward neural network model. For the output, the possibility of an occurrence of an accident is predicted. The results indicate that even though the number of false alarms dominates the real accident cases, the system can still provide useful information that can be used for status verification and early reaction to possible accidents.en_US
dc.description.sponsorshipIEEE, IEEE Comp Soc, Natl Sci Fdn, Cisco, Huawei, Elsevier, Navigant, Johns Hopkins Whiting Sch Engnen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2016 IEEE International Conference On Big Data (Big Data)en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTraffic flowen_US
dc.subjectbig dataen_US
dc.subjectaccident detectionen_US
dc.subjectintelligent transportation systemsen_US
dc.subjectneural networksen_US
dc.subjectnearest neighboren_US
dc.subjectregression treeen_US
dc.subjectcomputational intelligenceen_US
dc.subjectmachine learningen_US
dc.subjectIoTen_US
dc.subjectsensorsen_US
dc.titleA Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligenceen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage1807en_US
dc.identifier.endpage1813en_US
dc.authorid0000-0001-7998-5735-
dc.authorid0000-0001-5987-0164-
dc.identifier.wosWOS:000399115001105en_US
dc.identifier.scopus2-s2.0-85015154422en_US
dc.institutionauthorDoğdu, Erdoğan-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference4th IEEE International Conference on Big Data (Big Data)en_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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