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dc.contributor.authorJagannathan, Ramanujanen_US
dc.date.accessioned2011-08-06T16:06:48Z
dc.date.available2011-08-06T16:06:48Z
dc.date.issued2003-06-27en_US
dc.identifier.otheretd-04242004-151851en_US
dc.identifier.urihttp://hdl.handle.net/10919/10144
dc.description.abstractAlthough concepts of the XDL intersection or CFI (Continuous Flow Intersection) have been around for approximately four decades, users do not yet have a simplified procedure to evaluate its traffic performance and compare it with a conventional intersection. Several studies have shown qualitative and quantitative benefits of the XDL intersection without providing accessible tools for traffic engineers and planners to estimate average control delays, and queues. Modeling was conducted on typical geometries over a wide distribution of traffic flow conditions for three different design configurations or cases using VISSIM simulations with pre-timed signal settings. Some comparisons with similar conventional designs show considerable savings in average control delay, and average queue length and increase in intersection capacity. The statistical models provide an accessible tool for a practitioner to assess average delay and average queue length for three types of XDL intersections. Pre-timed signal controller settings are provided for each of the five intersections of the XDL network. In this research, a "real-time" traffic signal control strategy is developed using genetic algorithms and neural networks to provide near-optimal traffic performance for XDL intersections. Knowing the traffic arrival pattern at an intersection in advance, it is possible to come up with the best signal control strategy for the respective scenario. Hypothetical cases of traffic arrival patterns are generated and genetic algorithms are used to come up with near-optimal signal control strategy for the respective cases. The neural network controller is then trained and tested using pairs of hypothetical traffic scenarios and corresponding signal control strategies. The developed neural network controller produces near-optimal traffic signal control strategy in "real-time" for all varieties of traffic arrival patterns.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.relation.haspartram-thesisaug27final.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectGenetic Algorithmsen_US
dc.subjectCFIen_US
dc.subjectXDLen_US
dc.subjectNeural Networksen_US
dc.subjectContinuous Flow Intersectionsen_US
dc.subjectReal-time Signal Controlen_US
dc.titleEvaluation of Crossover Displaced Left-turn (XDL) Intersections and Real-time Signal Control Strategies with Artificial Intelligence Techniquesen_US
dc.typeThesisen_US
dc.contributor.departmentCivil Engineeringen_US
dc.description.degreeMSen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeechairTeodorovic, Dusanen_US
dc.contributor.committeememberCollura, Johnen_US
dc.contributor.committeememberTignor, Samuel C.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-04242004-151851en_US
dc.date.sdate2004-04-24en_US
dc.date.rdate2007-10-12
dc.date.adate2004-10-12en_US


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