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dc.contributor.advisorCaulfield, Brianen
dc.contributor.authorREZAEI, MOHAMMADAMINen
dc.date.accessioned2020-05-11T10:18:57Z
dc.date.available2020-05-11T10:18:57Z
dc.date.issued2020en
dc.date.submitted2020en
dc.identifier.citationREZAEI, MOHAMMADAMIN, Examining the efficiency of autonomous vehicles in highway transport, Trinity College Dublin.School of Engineering, 2020en
dc.identifier.otherYen
dc.descriptionAPPROVEDen
dc.description.abstractThis study examined the efficiency of Autonomous Vehicles (AVs) in highway transport. The study focused on the effects that AVs might have on factors associated with the adoption of AVs such as safety, traffic, cost, the environment and some other parameters. Then, it identified some gaps in the technology and research in this field, which need to be filled before the exploitation of AVs on public roads. In order to address the research gaps on AVs, this study assessed public perception and acceptance of these vehicles through a national survey in Ireland with 475 participants. Then the study conducted a survey amongst 300 international experts in related fields to gather their comments and thoughts on the substantial risks and advantages of the application of AVs in highway traffic from an expert point of view. The findings from the surveys answered a diverse range of questions regarding the application of AVs. However, it was deemed necessary to carry out experiments to determine the extent to which the results are in line with public and experts' perceptions in this matter. But, since the technologies used in AVs are new, and there is no previous experience of the application of AVs in highway transport, there is no information about the driving behaviours of these vehicles. So, the study optimised the parameters of human driving behaviours to find out what would happen if AVs could drive with modified human behaviours. Then, the study acquired the optimised driving behaviours to evaluate how efficient AVs can be within the context of the anticipated driving behaviours. The study simulated AVs in a conceptual model of the M50 motorway in Dublin under various traffic conditions of the year 2017. Also, the shared road of AVs and traditional vehicles was tested for where the proportion of the AVs increased by 10% from an entirely traditional network to a network fully occupied by AVs. Such an assessment of the shared road represented the transition periods between TVs and AVs in highway transport. In total, the modelling for this study was conducted over 340 simulation scenarios, with 5,446 hours of traffic simulation to address the impacts of adopting AVs in a diverse range of peak, normal and off-peak traffic conditions on the M50 motorway. Overall, the current study revealed that AVs could substantially improve the quality of traffic. However, they require more research, technology development, safety and security measures, improvements in juridical issues and legal liabilities related to their use, and the provision of acceptable conditions for vehicle ownership, especially in terms of the cost of the vehicle. In this way, many concerns regarding the adoption of these vehicles on the road would be resolved. However, according to the evaluations of this study, and based on the current status of AV research and development, the present study does not recommend adopting AVs until further studies confirm that the requirements for their adoption, mentioned above, are met.en
dc.publisherTrinity College Dublin. School of Engineering. Disc of Civil Structural & Environmental Engen
dc.rightsYen
dc.subjectAutonomous Vehiclesen
dc.subjectHighway Traffic Flowen
dc.subjectOptimisationen
dc.subjectSimulation Modellingen
dc.subjectStatistical Assessmenten
dc.titleExamining the efficiency of autonomous vehicles in highway transporten
dc.typeThesisen
dc.type.supercollectionthesis_dissertationsen
dc.type.supercollectionrefereed_publicationsen
dc.type.qualificationlevelDoctoralen
dc.identifier.peoplefinderurlhttps://tcdlocalportal.tcd.ie/pls/EnterApex/f?p=800:71:0::::P71_USERNAME:REZAEIMen
dc.identifier.rssinternalid216256en
dc.rights.ecaccessrightsopenAccess
dc.identifier.urihttp://hdl.handle.net/2262/92496


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