Development of Multiregime Speed–Density Relationships by Cluster Analysis
Development of Multiregime Speed–Density Relationships by Cluster Analysis
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DOI：
10.1177/0361198105193400107
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发表时间：
2005
影响因子：
1.7
通讯作者：
L. Sun;Jie Zhou
中科院分区：
文献类型：
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作者：
L. Sun;Jie Zhou
经验速度-密度关系之所以重要，不仅是因为它们在宏观交通流理论中发挥着核心作用，而且还因为它们与车辆跟驰模型的联系，而车辆跟驰模型是微观交通仿真的重要组成部分。多状态交通速度-密度关系比单状态模型更能代表整个密度范围内的交通流。然而，与多机制模型相关的一个主要困难是，机制的断点是以临时和主观的方式确定的。本文提出了使用聚类分析作为一种自然的工具，速度密度数据的分割。在数据分割之后，可以使用回归分析来单独拟合每个数据子集。三个真实的交通数据集的数值例子来说明这种方法。使用聚类分析，建模者可以灵活地指定制度的数量。结果表明，K-means算法（其中K表示聚类的数量）与原始（非标准化）的数据工作良好，为此目的，可以方便地在实践中使用。
Empirical speed–density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed– density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed–density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represents the number of clusters) with original (nonstandardized) data works well for this purpose and can be conveniently used in practice.