摘要
现有的微观仿真模型中,车辆跟驰模型的构建多数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提取典型实验数据的有用信息,寻求能够最大程度反映跟车信息的内生变量作为跟驰模型的输入变量。
Most of car-following models of microscopic traffic simulation were developed on the basis of the perceptual knowledge of traffic phenomena, and some have made statistical analysis of the field data, without consideration 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 endogenous variables with higher information as the input variables of car-following model.
出处
《公路交通科技》
CAS
CSCD
北大核心
2004年第1期81-84,共4页
Journal of Highway and Transportation Research and Development
基金
国家自然科学基金资助项目(70371022)
关键词
交通流
输入变量
因子分析
车辆跟驰模型
Traffic flow
Input variable
Factor analysis
Car-following model
