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首页> 外文学位 >Developing a methodology to account for commercial motor vehicles using microscopic traffic simulation models.
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Developing a methodology to account for commercial motor vehicles using microscopic traffic simulation models.

机译：使用微观交通仿真模型开发一种方法来解释商用汽车。

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摘要

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.
机译：数据的收集和解释是交通和运输工程的重要组成部分，用于建立基准绩效指标并预测未来状况。交通数据的重要来源之一是商用机动车辆（CMV）的重量和分类数据，用作交通设计，运营和规划中关键任务的输入。智能运输系统（ITS）技术的发展已为运输工程师和规划人员提供了更多的CMV数据可用性。这些数据的主要来源是自动车辆分类（AVC）和动态称重（WIM）。微观交通仿真模型已被广泛用于对包括车辆组成在内的运输系统的动态和随机性质进行建模。近年来，有效的微观交通模拟模型的一个方面是这些模型的校准，传统上，这些模型一直关注于从一系列可接受的值中识别“最佳”参数集。最近的研究已经开始了使校准过程自动化的过程，以准确地反映所分析的运输系统的组成部分。这项研究的目的是开发一种方法，在该方法中，可以将CMV的影响包括在微观交通模拟模型的校准中。该研究检查了有关CMV重量和运行特性的ITS数据，并将此数据纳入微观交通模拟模型的校准中。该研究开发了一种使用微观交通模拟模型对CMV建模的方法，然后利用这些模型的输出来生成必要的数据，以量化CMV对基础设施，行驶时间和排放的影响。该研究使用高级统计工具，包括主成分分析（PCA）和递归分区，以识别数据收集站点（即WIM，AVC）之间的关系，以便可以利用WIM站点收集的数据来估算AVC站点的重量和长度分布。该研究还研究了将校准趋势中的中心趋势和离散度（即均值，方差）的分布或度量包括在内的方法。该方法是使用CORSIM模型应用的，并使用自动遗传算法方法进行了校准。

著录项

  • 作者

    Schultz, Grant George.;

  • 作者单位

    Texas A&M University.;

  • 授予单位 Texas A&M University.;
  • 学科 Engineering Civil.; Transportation.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 350 p.
  • 总页数 350
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;综合运输;
  • 关键词

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