摘要
首先对自行车的跟驰行为进行描述,并通过视频实验对信号交叉口自行车流的相关参数进行采集和分析。在此基础上,综合考虑自行车流运行过程中相对距离、相对速度、前车加速度、横向干扰和骑车人属性等因素对后车加速度的影响,先后构建了具有三种不同BP网络结构的跟驰模型。通过对不同BP网络训练误差的比较分析,提出了一种可以较好地反映信号交叉口处自行车群内前后车之间刺激-反应关系的跟驰模型。这对于分析混合交通流的微观运行特性,开展与混合交通相关的微观模拟等研究具有一定借鉴意义。
The bicycle following behavior was described firstly, and the parameters about the bicycle traffic were collected by the video experiments at the signalized intersection. A series of bicycle following models with 3 different BP neural network structures were built considering the influences of some critical parameters in the bicycle traffic, such as the distance between bicycles, the relative speed, the acceleration of the leading bicycle, the transversal interference, and the character of the bicycle rider, on the acceleration of the following bicycle. Based on the comparative analysis of the training error of the different BP neural networks, a bicycle following model was proposed. This model can better reflect the stimulus-response relationship between the leading and the following bicycles in the bicycle colony at the signalized intersection. This is of reference for analyzing the microscopic characteristics of the mixed urban traffic and performing corresponding microscopic simulation.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2008年第1期53-56,共4页
Journal of Jilin University:Engineering and Technology Edition
基金
'973'国家重点基础研究发展规划项目(2006CB705500)
'十五'国家科技攻关计划项目(2005BA414B02)
北京交通大学校基金项目(2004SM022)
关键词
交通运输安全工程
自行车流
跟驰行为
自行车跟驰
BP算法
信号交叉口
engineering of communication and transportation safety
bicycle traffic
following behavior
bicycle-following
BP algorithm
signalized intersection
