基于样条曲线的多域交通流模型（英文）

为了建立最优化多域交通流模型,提出了一种将聚类分析和B-样条回归分析相结合的新方法.首先,为了确定合适的域数,采用K-means和模糊c-means算法对实际交通流数据进行了聚类分析,分析表明将交通流数据分为2类或3类最能反映交通流的固有类型.然后,将此信息用于指导B-样条回归分析,可显著减少样条模型参数估计的计算量.用样条回归分析来估计结点位置和模型参数可使得整体误差最小.模型分析结果表明,提出的样条曲线模型比传统模型具有更好的拟合能力和通用性.此外,新方法在数据拟合方面更加灵活,且能提供更加光滑的交通流模型曲线.

Spline-based multi-regime traffic stream models

In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and the fuzzy c-means methods are applied in cluster analysis to actual traffic data, which suggests that dividing the traffic flow into two or three clusters can best reflect intrinsic patterns of traffic flows. Such information is then taken as guidance in spline regression, thus significantly re...