基于MoC解析法的宏观基本图估测研究

Approximation Estimation of Macroscopic Fundamental Diagram Based on MoC Analysis Method

宏观基本图(Macroscopic Fundamental Diagrams, MFD)描述了城市交通网络中平均网络流量、密度和速度三者之间的关系。本文基于MoC解析法,在考虑网联自动驾驶车辆功能退化的基础上,通过城市走廊的链路基本图模型、城市走廊的拓扑结构及其信号参数设置来获取城市走廊异质交通流的MFD。以旧金山的城市走廊为研究区域,通过Mathematica进行实证分析,对比不同网联自动驾驶车辆渗透率下异质交通流MFD。结果表明,考虑网联自动驾驶车辆功能退化,CAV车辆渗漏率的普及有利于异质交通流道路通行能力的提升,若网联自动驾驶车辆取代所有传统人工车辆,城市走廊通行能力的提升可达到3倍。

Macroscopic Fundamental Diagrams(MFD) describe the relationship between average network flow, density and speed in urban transportation network. Based on the MoC analysis method, and considering the functional degradation of connected autonomous vehicles, this paper obtains the MFD of heterogeneous traffic flow in urban corridors through the link basic graph model of urban corridors, the topology of urban corridors and the setting of signal parameters. Taking the urban corridor of San Francisco as the research area,numerical simulation analysis was carried out through Mathematica to compare the MFD of heterogeneous traffic flow under different penetration rates of CAV. The results show that, considering the functional degradation of connected autonomous vehicles, the popularization of the leakage rate of CAV is conducive to the improvement of the road capacity of heterogeneous traffic flow. If CAV replace all traditional manual vehicles, the traffic capacity of urban corridors will be improved up to 3 times.