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基于主成分分析和FCM聚类的行驶工况研究

石琴 仇多洋 吴靖

石琴, 仇多洋, 吴靖. 基于主成分分析和FCM聚类的行驶工况研究[J]. 环境科学研究, 2012, 25(1): 70-76.
引用本文: 石琴, 仇多洋, 吴靖. 基于主成分分析和FCM聚类的行驶工况研究[J]. 环境科学研究, 2012, 25(1): 70-76.
SHI Qin, QIU Duo-yang, WU Jing. Study on Driving Cycles based on Principal Component Analysis and Fuzzy C-Means Clustering[J]. Research of Environmental Sciences, 2012, 25(1): 70-76.
Citation: SHI Qin, QIU Duo-yang, WU Jing. Study on Driving Cycles based on Principal Component Analysis and Fuzzy C-Means Clustering[J]. Research of Environmental Sciences, 2012, 25(1): 70-76.

基于主成分分析和FCM聚类的行驶工况研究

基金项目: 国家自然科学基金项目(71071044)

Study on Driving Cycles based on Principal Component Analysis and Fuzzy C-Means Clustering

  • 摘要: 在定义了运动学片段的基础上,对典型道路上采集的大量工况试验数据进行划分,从而获得大量运动学片段.用主成分分析法对12个表征道路运行特征的参数(包括加速比例、减速比例、匀速比例、怠速比例、平均速度、平均运行速度、最大速度、平均加速度、最大加速度、最小加速度、速度标准偏差和加速度标准偏差)进行压缩,得到4个主成分.利用模糊C均值聚类技术对所有运动学片段的第一和第二主成分得分进行分类,根据相关系数的大小及各类别的时间长度比选取合适片段,最终拟合出代表性工况.通过对特征参数和速度、加速度联合概率分布的误差分析可知,所提出的构建方法精度较高,拟合工况能综合反映合肥市实际道路的交通状况.

     

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  • 刊出日期:  2012-01-25

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