Developing a methodology to account for commercial motor vehicles using microscopic traffic simulation models

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dc.contributor.advisor Rilett, Laurence R. en_US
dc.creator Schultz, Grant George en_US
dc.date.accessioned 2004-09-30T01:42:35Z
dc.date.available 2004-09-30T01:42:35Z
dc.date.created 2003-12 en_US
dc.date.issued 2004-09-30T01:42:35Z
dc.identifier.uri http://handle.tamu.edu/1969.1/111
dc.description.abstract The 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_US
dc.description.provenance Made available in DSpace on 2004-09-30T01:42:35Z (GMT). No. of bitstreams: 2 etd-tamu-2003C-CVEN-Schultz-1.pdf: 1869853 bytes, checksum: 265c390a5a42e0e5db09bbc7fc175dcc (MD5) etd-tamu-2003C-CVEN-Schultz-1.pdf.txt: 682527 bytes, checksum: 4b5f5dab251de6153fe9426ab6f3e632 (MD5) en
dc.format.extent 1869853 bytes
dc.format.extent 682527 bytes
dc.format.medium electronic en_US
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso en_US en_US
dc.publisher Texas A&M University en_US
dc.subject commercial motor vehicles en_US
dc.subject microscopic traffic simulation models en_US
dc.subject weigh-in-motion en_US
dc.subject WIM en_US
dc.subject intelligent transportation systems en_US
dc.subject ITS en_US
dc.subject car-following en_US
dc.subject genetic algorithm en_US
dc.subject automatic vehicle classification en_US
dc.subject AVC en_US
dc.subject CORSIM en_US
dc.subject principal component analysis en_US
dc.subject recursive partitioning en_US
dc.subject CART en_US
dc.subject calibration en_US
dc.subject emissions en_US
dc.title Developing a methodology to account for commercial motor vehicles using microscopic traffic simulation models en_US
thesis.degree.department Civil Engineering en_US
thesis.degree.discipline Civil Engineering en_US
thesis.degree.grantor Texas A&M University en_US
thesis.degree.name PHD en_US
thesis.degree.level Doctoral en_US
dc.contributor.committeeMember Burris, Mark W. en_US
dc.contributor.committeeMember Spiegelman, Clifford H. en_US
dc.contributor.committeeMember Lomax, Timothy J. en_US
dc.type.genre Electronic Dissertation en_US
dc.type.material text en_US
dc.format.digitalOrigin born digital en_US

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