Publication:
Review of data fusion methods for real-time and multi-sensor traffic flow analysis

Simple item page
dc.citedby40
dc.contributor.authorKashinath S.A.en_US
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorMustapha A.en_US
dc.contributor.authorMahdin H.en_US
dc.contributor.authorLim D.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorMohammed M.A.en_US
dc.contributor.authorAl-Rimy B.A.S.en_US
dc.contributor.authorFudzee M.F.M.en_US
dc.contributor.authorYang T.J.en_US
dc.contributor.authorid57222712288en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid57200530694en_US
dc.contributor.authorid35759460000en_US
dc.contributor.authorid57222715023en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid57192089894en_US
dc.contributor.authorid57200494876en_US
dc.contributor.authorid34971036200en_US
dc.contributor.authorid57222713457en_US
dc.date.accessioned2023-05-29T09:11:56Z
dc.date.available2023-05-29T09:11:56Z
dc.date.issued2021
dc.descriptionHighway planning; Highway traffic control; Intelligent systems; Intelligent vehicle highway systems; Real time systems; Travel time; Data fusion methods; Decision level fusion; Feature level fusion; Intelligent transportation systems; Research and application; Testing and evaluation; Traffic controllers; Traffic flow analysis; Sensor data fusionen_US
dc.description.abstractRecently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9389771
dc.identifier.doi10.1109/ACCESS.2021.3069770
dc.identifier.epage51276
dc.identifier.scopus2-s2.0-85103782712
dc.identifier.spage51258
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85103782712&doi=10.1109%2fACCESS.2021.3069770&partnerID=40&md5=6a6594a5ec94d3973e3f529ac3015f60
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26558
dc.identifier.volume9
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleReview of data fusion methods for real-time and multi-sensor traffic flow analysisen_US
dc.typeReviewen_US
dspace.entity.typePublication
Files
Collections
SCOPUS