摘要
为了给降雪天气下高速公路交通管理者提供合理、准确的交通状态信息,提出了一种可用于高速公路降雪天气下交通状态划分方法。考虑到高速公路交通状态分类存在模糊性的特点,以速度、密度作为样本数据的特征属性,提出了改进的模糊C均值聚类(FCM)的交通状态分类方法。针对传统算法要求事先确定交通状态划分的数目c存在的缺陷,利用自适应函数求得聚类数c。以乌鲁木齐市机场高速公路为实例进行分析,获得降雪天气下交通流状态的划分结果。
To provide reasonable and accurate traffic condition information for highway traffic man- agers under snowfall weather, a classified method for traffic status expressway is proposed. Taking into ac- count the fuzziness of expressway traffic classification state, speed and density are regarded as character- istic property of sample data, the improved fuzzy c-means clustering （FCM） method of traffic state classi- fication is set up. Due to the defects that the traditional algorithm require a pre-determined the number c of traffic state division , the cluster number c is obtained by forming adaptive function. Urumqi airport ex- pressway is used as an example to gain the divided results of traffic flow state under snowy weather.
作者
朱兴琳
王宇
邵学恒
ZHU Xinglin WANG Yu SHAO Xueheng(School of Machinery and Traffic, Xinjiang Agricultural University, Uru, mchi, Xinjiang 830052, Chin)
出处
《公路工程》
北大核心
2017年第1期101-105,135,共6页
Highway Engineering
基金
新疆维吾尔自治区自然科学基金(2014211A031)
新疆农业大学国家大学生创新创业训练计划项目(201310758007)
关键词
交通状态判别
模糊C均值聚类
降雪天气
高速公路
traffic state identification
fuzzy c-means clustering
snowfall weather
expressway
