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Description:
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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 . |