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首页> 外文期刊>Transportation Science >A Gaussian Kernel-Based Approach for Modeling Vehicle Headway Distributions
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A Gaussian Kernel-Based Approach for Modeling Vehicle Headway Distributions

机译:基于高斯核的车辆车距分布建模方法

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摘要

Headway distribution models are essential for studying traffic flow theory, roadway accidents, and microscopic traffic simulations. Previous work has focused on parametric models. Vehicle headways were considered to follow some known parametric distributions based on certain assumptions. However, these assumptions are not universally acceptable and, consequently, the reliability of those headway distribution models varies significantly when applied to different flow conditions. In this study, a nonparametric distribution model with Gaussian kernel functions is introduced and assessed for vehicle headways on urban multilane freeways. Without any assumptions, Gaussian kernel models can extract intrinsic patterns from observed headway data to describe the distributing attributes of headways. Experiments were conducted to evaluate the accuracy of Gaussian kernel models for modeling vehicle headways. Results from the experiments indicated that the proposed models outperformed traditional parametric methods in a wide range of flow rates. Furthermore, transferability tests of the nonparametric model were performed, and the results showed that the proposed models can be generalized for applications at other locations with similar traffic flow patterns.
机译:车距分布模型对于研究交通流理论,道路事故和微观交通模拟至关重要。先前的工作集中在参数模型上。基于某些假设,车辆行驶距离被认为遵循一些已知的参数分布。但是,这些假设不是普遍接受的,因此,这些车距分布模型的可靠性在应用于不同的流量条件时会发生很大的变化。在这项研究中,引入了具有高斯核函数的非参数分布模型,并评估了城市多车道高速公路上的车辆车距。在没有任何假设的情况下,高斯内核模型可以从观察到的车头数据提取内在模式,以描述车头的分布属性。进行了实验,以评估高斯核模型对车辆行驶距离进行建模的准确性。实验结果表明,所提出的模型在很大的流速范围内都优于传统的参数方法。此外,对非参数模型进行了可传递性测试,结果表明,所提出的模型可以推广到具有类似交通流模式的其他位置的应用。

著录项

  • 来源
    《Transportation Science》 |2014年第2期|206-216|共11页
  • 作者

    Guohui Zhang; Yinhai Wang;

  • 作者单位

    Department of Civil Engineering, University of New Mexico, Albuquerque, New Mexico 87131;

    The University of Washington and Harbin Institute of Technology Joint Laboratory on Advanced Transportation Technologies, Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    nonparametric models; headway distribution; Gaussian kernel functions; traffic flow;

    机译:非参数模型;进度分配;高斯核函数;交通流;

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