摘要
在已有的车辆跟驰微观仿真模型中,多数模型的构建是基于对交通现象的感性认识,有些模型对数据进行了统计分析,却未直接涉及到对输入变量选择的研究。本文运用因子分析非线性多元统计方法榨取典型实验数据的有用信息从而寻求车辆跟驰模型的基本结构,寻求能够最大程度反映跟车信息的内生变量作为建立微观仿真模型的基础。
For the existed car-following models for microscopic traffic flow simulation, most of them were developed on the basis of the sensible understanding to traffic phenomena, some of them have made statistical analysis of the field data, but have not come down to the question of the choice of input variables. In this paper, the nonlinear statistical method of factor analysis is used to extract the useful information from the representative field data to seek the basic structure of the car-following model and the inner variable with higher information capacity for car-following process.
出处
《软科学》
CSSCI
北大核心
2004年第2期16-19,共4页
Soft Science
基金
国家自然科学基金项目(50178033)
山东理工大学博士基金项目
关键词
车辆跟驰
微观仿真模型
交通流
影响因子
traffic flow
input variable
factor analysis
car-following model
