A Capacity Model of Freeway Merging Areas with Partially Connected Automated Traffic
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摘要: 面向人类驾驶和具备协同自适应巡航功能的网联自动驾驶组成的新型混合交通流，考虑道路交通特性、道路结构以及匝道汇入前主线交通状态等因素的交互作用机理，基于概率统计理论解析网联自动驾驶渗透率和编队长度间的耦合关系，进一步基于间隙接受理论分析匝道汇入交通对合流区通行能力的折减效应，建立快速路合流区通行能力模型，定量描述不同道路条件下合流区通行能力如何随网联自动驾驶渗透率和编队长度变化。模型中的道路交通特性、道路结构及匝道汇入前部分交通状态参数根据实际道路交通环境标定，提升了模型的通用性与可迁移性。搭建内嵌车辆动力学模块的Vissim仿真平台进行模型评估，结果表明，模型精度在80%以上，且在不同网联自动驾驶渗透率和编队长度条件下皆表现良好。Abstract: A capacity model is developed to study freeway merging areas of a novel mixed traffic with human-driven vehicles and connected automated vehicles equipped with cooperative adaptive cruise control. The interaction mechanism of factors, such as road traffic characteristics, road structure, and mainline traffic state before the merge, is considered. The probability and statistics theory is used to analyze coupling relationships between penetration rate and platoon length. Furthermore, based on the gap acceptance theory, reduction effects of ramp merging traffic on the capacity of the merging area is analyzed. The capacity model of freeway merging areas with partially connected automated traffic is established to quantitatively describe how the capacity changes with the penetration rate and platoon length of connected automated vehicles under various road conditions. The parameters of road traffic characteristics, road structure, and part of the traffic state before the ramp merge are calibrated according to the actual traffic condition, which improves versatility and transferability of the model. A Vissim simulation platform with an embedded vehicle dynamics module is developed to evaluate the model. The results show that the accuracy of the model is generally over 80%. The model performed well under various penetration rates and platoon lengths of connected automated vehicles.
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图 2 新型混合交通流快速路合流区通行能力基本图
Figure 2. Capacity in freeway merging areas under partially CACC traffic
表 1 模型符号说明
Table 1. Model Notations
符号 含义 σ 具备CACC编队功能的CAVs渗透率 σe 匝道汇入后CAVs有效渗透率 λ 爱尔朗分布形状参数 ϕ 匝道交通需求，veh/h Cmix 新型混合交通流主线道路通行能力，veh/(h·ane) C 人类驾驶交通流道路通行能力, veh/(h·ane) ${\widetilde {{C_{{\mathop{\rm mix}\nolimits} }}}} $ 混有CACC编队的快速路合流区通行能力，veh/(h·ane) D 主线交通需求, veh/h ${\varepsilon \left( {\sigma , {N_{\rm{m}}}} \right)} $ 每个CAV的道路临界通行能力平均增益 ${\varepsilon \left( {{\sigma _e}, {N_{\rm{m}}}} \right)} $ 考虑匝道汇入后每个CAV的道路临界通行能力平均增益, s K 爱尔朗分布速率参数 Nm CACC最大编队长度(即编队长度限制), veh/platoon Na CACC实际编队长度, veh/platoon $ {{{\bar N}_{\rm{a}}}}$ CACC平均编队长度(即期望编队长度), veh/platoon $ {{{\tilde N}_{\rm{a}}}}$ 有效CACC编队长度, veh/platoon NG 时间段[0, T] 内匝道汇入主线的车辆数, veh N 合流区主线在时间段[0, T] 内通过车辆数, veh PG 匝道汇入概率 SN 编队长度分布标准差, veh/platoon ${t_{{\rm{HF}}}^{\min }} $ HVs跟随其他车辆的最小车头时距, s $ {t_{{\rm{AF}}}^{\min }}$ CAVs跟随其他车辆的最小车头时距, s $ {t_{{\rm{CF}}}^{\min }}$ CACC编队内部的最小车头时距, s PAF CAVs为跟随车的概率 PHF HVs为跟随车的概率 PCF CAVs处于CACC编队内部的概率 ${t_{{\rm{mix}}}^{\min }} $ 混合交通流平均最小车头时距, s m 主线车道数 $ {t_{\rm{G}}^{\min }}$ 匝道汇入可接受最小车头时距, s $ {\bar t}$ 主线外侧车道平均可穿越车头时距, s V 主线车头时距方差, s2 
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