The follow up time is an important parameter for estimating the entry capacity of roundabouts. However, its variability and contributing factors have long been ignored in the literatures. In this study, 171 follow up ...The follow up time is an important parameter for estimating the entry capacity of roundabouts. However, its variability and contributing factors have long been ignored in the literatures. In this study, 171 follow up samples and contributing factors （traffic volume, vehicle position, waiting vehicles behind, vehicle type, and drivers＇ gender） are collected at a roundabout in Pacific Pines, Australia. It is found that the follow up time is indeed significantly affected by traffic volume, waiting vehicles behind, vehicle type, and drivers＇ gender. In order to establish the relationship between the follow up time and its contributing factors, an inverse Gaussian regression model is further developed. This relationship could be applied to estimate the entry capacities by taking into account the variability of follow up samples. According to the model, the traffic volume and vehicle types are the most important contributing factors.展开更多
针对智能网联车辆(intelligent and connected vehicle,ICV)跟驰建模中基于恒定车头时距的问题,文中在考虑可变车头时距下对期望车头间距做出了新的描述,优化ICV跟驰模型的车头间距模型结构.拟定了车辆跟驰行驶中期望车头时距的一般函...针对智能网联车辆(intelligent and connected vehicle,ICV)跟驰建模中基于恒定车头时距的问题,文中在考虑可变车头时距下对期望车头间距做出了新的描述,优化ICV跟驰模型的车头间距模型结构.拟定了车辆跟驰行驶中期望车头时距的一般函数形式及适用范围,论证了基于所提模型的参数物理含义,理论推导了稳定态与动态两种交通流下的稳定性条件.在以前车为干扰条件下通过MATLAB数值仿真实验对比模型改进前后稳定性.结果表明:新的ICV模型具备比原ICV模型更良好的稳定性,且在0~33.3 m/s速度范围内均保持稳定,同时可以比原ICV模型更有效地抑制不稳定流的传播,更好地描述期望车头间距与车速的非线性关系,更加真实地反映实际交通流跟驰特征.展开更多
基金supported by CIEM Seed Fund Scheme and GU NRG/ITF Scheme
文摘The follow up time is an important parameter for estimating the entry capacity of roundabouts. However, its variability and contributing factors have long been ignored in the literatures. In this study, 171 follow up samples and contributing factors （traffic volume, vehicle position, waiting vehicles behind, vehicle type, and drivers＇ gender） are collected at a roundabout in Pacific Pines, Australia. It is found that the follow up time is indeed significantly affected by traffic volume, waiting vehicles behind, vehicle type, and drivers＇ gender. In order to establish the relationship between the follow up time and its contributing factors, an inverse Gaussian regression model is further developed. This relationship could be applied to estimate the entry capacities by taking into account the variability of follow up samples. According to the model, the traffic volume and vehicle types are the most important contributing factors.
文摘针对智能网联车辆(intelligent and connected vehicle,ICV)跟驰建模中基于恒定车头时距的问题,文中在考虑可变车头时距下对期望车头间距做出了新的描述,优化ICV跟驰模型的车头间距模型结构.拟定了车辆跟驰行驶中期望车头时距的一般函数形式及适用范围,论证了基于所提模型的参数物理含义,理论推导了稳定态与动态两种交通流下的稳定性条件.在以前车为干扰条件下通过MATLAB数值仿真实验对比模型改进前后稳定性.结果表明:新的ICV模型具备比原ICV模型更良好的稳定性,且在0~33.3 m/s速度范围内均保持稳定,同时可以比原ICV模型更有效地抑制不稳定流的传播,更好地描述期望车头间距与车速的非线性关系,更加真实地反映实际交通流跟驰特征.
