基于异质交通流的通行能力分析及CO_(2)排放影响

Analysis on Road Traffic Capacity and CO_(2) Emission Effect Based on Heterogeneous Traffic Flow

文中针对由智能网联车辆和人工驾驶车辆构成的异质交通流的通行能力问题与CO_(2)排放问题,分别选用协同自适应巡航控制模型和全速度差模型作为跟驰模型,根据所推导的基本图模型对最大限速和安全车距等参数作敏感性分析。引用CO_(2)排放模型,运用Python仿真分析不同智能网联车比例、车速等因素对CO_(2)排放量的影响。结果表明:增加车道限速值可显著提升道路通行能力;在一定仿真时间内,不论交通流是否处于平衡态,增大智能网联车辆比例,CO_(2)排放量整体均呈现降低趋势;随机改变车辆的加速度,当智能网联车辆比例大于60%,交通流恢复平衡态的时间在较小范围波动;降低最大车速、增加车头时距及增加车身长度可抑制CO_(2)排放。

In this paper,for the heterogeneous traffic flow and CO_(2) emission problem composed of intelligent and connected vehicles and human driven vehicles,the basic graph model is derived by selecting the cooperative adaptive cruise control model and the full velocity difference model as the following model respectively and sensitivity analysis of parameters such as maximum speed limit and safe distance from the vehicle is based on the derived basic graphical model.Next,the CO_(2) emission model is cited,and Python simulation is used to analyze the effects of different ratios of intelligent and connected vehicles and speeds on CO_(2) emission.The results show that:increasing lane speed limits can significantly improve road capacity;within a certain simulation time,an increase in the proportion of intelligent and connected vehicles results in an overall reduction in CO_(2) emission,regardless of whether the traffic flow is in equilibrium or not;randomly changing the acceleration of vehicles,when the proportion of intelligent and connected vehicles is greater than 60%,the time for traffic flow to return to a steady state fluctuates in a smaller range;reducing the maximum speed,increasing the time gap and length of vehicle can suppress CO_(2) emission.