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dc.contributor.advisorRilett, Laurence R.
dc.creatorSchultz, Grant George
dc.date.accessioned2004-09-30T01:42:35Z
dc.date.available2004-09-30T01:42:35Z
dc.date.created2003-12
dc.date.issued2004-09-30
dc.identifier.urihttps://hdl.handle.net/1969.1/111
dc.description.abstractThe collection and interpretation of data is a critical component of traffic and transportation engineering used to establish baseline performance measures and to forecast future conditions. One important source of traffic data is commercial motor vehicle (CMV) weight and classification data used as input to critical tasks in transportation design, operations, and planning. The evolution of Intelligent Transportation System (ITS) technologies has been providing transportation engineers and planners with an increased availability of CMV data. The primary sources of these data are automatic vehicle classification (AVC) and weigh-in-motion (WIM). Microscopic traffic simulation models have been used extensively to model the dynamic and stochastic nature of transportation systems including vehicle composition. One aspect of effective microscopic traffic simulation models that has received increased attention in recent years is the calibration of these models, which has traditionally been concerned with identifying the "best" parameter set from a range of acceptable values. Recent research has begun the process of automating the calibration process in an effort to accurately reflect the components of the transportation system being analyzed. The objective of this research is to develop a methodology in which the effects of CMVs can be included in the calibration of microscopic traffic simulation models. The research examines the ITS data available on weight and operating characteristics of CMVs and incorporates this data in the calibration of microscopic traffic simulation models. The research develops a methodology to model CMVs using microscopic traffic simulation models and then utilizes the output of these models to generate the data necessary to quantify the impacts of CMVs on infrastructure, travel time, and emissions. The research uses advanced statistical tools including principal component analysis (PCA) and recursive partitioning to identify relationships between data collection sites (i.e., WIM, AVC) such that the data collected at WIM sites can be utilized to estimate weight and length distributions at AVC sites. The research also examines methodologies to include the distribution or measures of central tendency and dispersion (i.e., mean, variance) into the calibration process. The approach is applied using the CORSIM model and calibrated utilizing an automated genetic algorithm methodology.en
dc.format.extent1869853 bytesen
dc.format.extent682527 bytesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherTexas A&M University
dc.subjectcommercial motor vehiclesen
dc.subjectmicroscopic traffic simulation modelsen
dc.subjectweigh-in-motionen
dc.subjectWIMen
dc.subjectintelligent transportation systemsen
dc.subjectITSen
dc.subjectcar-followingen
dc.subjectgenetic algorithmen
dc.subjectautomatic vehicle classificationen
dc.subjectAVCen
dc.subjectCORSIMen
dc.subjectprincipal component analysisen
dc.subjectrecursive partitioningen
dc.subjectCARTen
dc.subjectcalibrationen
dc.subjectemissionsen
dc.titleDeveloping a methodology to account for commercial motor vehicles using microscopic traffic simulation modelsen
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentCivil Engineeringen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberBurris, Mark W.
dc.contributor.committeeMemberSpiegelman, Clifford H.
dc.contributor.committeeMemberLomax, Timothy J.
dc.type.genreElectronic Dissertationen
dc.type.materialtexten
dc.format.digitalOriginborn digitalen


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