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Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

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dc.citedby5
dc.contributor.authorAl-Msari H.en_US
dc.contributor.authorKoting S.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-shafie A.en_US
dc.contributor.authorid57223256689en_US
dc.contributor.authorid55839645200en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2025-03-03T07:44:04Z
dc.date.available2025-03-03T07:44:04Z
dc.date.issued2024
dc.description.abstractExamining driving behaviour is crucial for traffic operations because of its influence on driver safety and the potential for increased risk of accidents, injuries, and fatalities. Approximately 95% of severe traffic collisions can be attributed to human error. With the progress in artificial intelligence in recent decades, notable advancements have been achieved in computer capabilities, communication systems and data collection technology. This increase has significantly influenced our capacity to replicate driver behaviour and comprehend underlying driving mechanisms in diverse situations. Traffic microsimulation facilitates an understanding of traffic performance inside a given road network. Among the microsimulation software packages, Verkehr In St�dten ? SIMulationsmodell (VISSIM) has garnered significant attention owing to its notable ability to accurately replicate traffic circumstances with high dependability in real-world scenarios. Given the diverse applicability of VISSIM-based schemes, this review systematically examines the applications of the VISSIM-based driving-behaviour models within different research contexts, revealing their utility. This review is designed to provide guidance for researchers in selecting the most suitable methodological approach tailored to their specific research objectives and constraints when utilising VISSIM. Five important aspects, including calibration, driving behaviour, incident, and heterogeneous traffic simulation, as well as utilisation of artificial intelligence with VISSIM, are assessed, which could yield substantial advantages in advancing more precise and authentic driving-behaviour modelling in VISSIM. ? 2024 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe25936
dc.identifier.doi10.1016/j.heliyon.2024.e25936
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85185266378
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185266378&doi=10.1016%2fj.heliyon.2024.e25936&partnerID=40&md5=4f791a5facde9eda370bfc74ce654cb7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36707
dc.identifier.volume10
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access; Gold Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitleHeliyon
dc.titleReview of driving-behaviour simulation: VISSIM and artificial intelligence approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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