Modeling Naturalistic Driver Behavior in Traffic Using Machine Learning

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dc.contributor.authorChong, Linsenen
dc.contributor.committeechairAbbas, Montasir M.en
dc.contributor.committeememberRamakrishnan, Narenen
dc.contributor.committeememberPasupathy, Raghuen
dc.contributor.departmentCivil Engineeringen
dc.date.accessioned2017-04-04T19:49:36Zen
dc.date.adate2011-08-14en
dc.date.available2017-04-04T19:49:36Zen
dc.date.issued2011-07-26en
dc.date.rdate2016-09-27en
dc.date.sdate2011-07-28en
dc.description.abstractThis research is focused on driver behavior in traffic, especially during car-following situations and safety critical events. Driving behavior is considered as a human decision process in this research which provides opportunities for an artificial driver agent simulator to learn according to naturalistic driving data. This thesis presents two mechine learning methodologies that can be applied to simulate driver naturalistic driving behavior including risk-taking behavior during an incident and lateral evasive behavior which have not yet been captured in existing literature. Two special machine learning approaches Backpropagation (BP) neural network and Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) are proposed to model driver behavior during car-following situation and safety critical events separately. In addition to that, as part of the research, state-of-the-art car-following models are also analyzed and compared to BP neural network approach. Also, driver heterogeneity analyzed by NFACRL method is discussed. Finally, it presents the findings and limitations drawn from each of the specific issues, along with recommendations for further research.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-07282011-000937en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-07282011-000937/en
dc.identifier.urihttp://hdl.handle.net/10919/76834en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDriver behavioren
dc.subjectcar-followingen
dc.subjectMachine learningen
dc.subjectsafety critical eventsen
dc.titleModeling Naturalistic Driver Behavior in Traffic Using Machine Learningen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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