Vehicle Trajectory Reconstruction Model of Signalized Intersection in Connected Automated Environments
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摘要:
车辆轨迹数据提供了大量的时空交通流信息，可用于各类交通研究. 传统车辆轨迹模型多以人工驾驶环境为研究对象，普遍未考虑由常规车（RV）、网联人工驾驶车（CV）以及智能网联车（CAV）组成的混合交通流的影响. 为解决该问题，构建智能网联环境下信号交叉口全样本车辆轨迹重构模型. 首先，介绍并分析智能网联环境下城市道路交叉口处车辆组成及排队通过情况；然后，构建城市道路混合交通流轨迹数量估计模型，并针对前后车的排队情况提出虚拟车的概念，用于估计不同车辆的交通状态；最后，设计数值仿真实验分析交通流密度和网联车渗透率对模型的影响，并基于NGSIM数据进行实例验证. 结果表明：轨迹重构模型的数量误差和位置误差均随着交通流密度和网联车渗透率的增大而减小，如交通流密度由20 veh/km增大至50 veh/km的过程中，模型数量误差和位置误差均呈现下降趋势，且最大误差分别不超过6.88%和8.02 m；与网联人工驾驶车渗透率相比，智能网联车的渗透率对模型结果影响更大.
Abstract:Vehicle trajectory data provides abundant spatial-temporal traffic flow information, which can be used for traffic research. Traditional vehicle trajectory models mostly focus on the artificial driving environment and fail to consider the impact of mixed traffic flows composed of regular vehicles (RVs), connected vehicles (CVs), and connected automated vehicles (CAVs). To solve this problem, a full sample vehicle trajectory reconstruction model of signalized intersections in connected automated environments was proposed. Firstly, the composition of vehicles at signalized intersections of urban roads and the passage of queues in connected automated environments were analyzed. Secondly, a model for estimating the number of trajectories of mixed traffic flows on urban roads was constructed, and the concept of virtual vehicles was further proposed to estimate the traffic status of different vehicles according to the queuing of front and rear vehicles. Finally, a numerical simulation test was designed to analyze the influence of traffic flow density and penetration rate of CAVs and CVs on the model, and the model was verified by NGSIM data. The results show that the error of the number and position of the model decreases with the increase in traffic flow density and the penetration rate of CAVs and CVs. For example, when the traffic flow density increases from 20 veh/km to 50 veh/km, both the error of the number and position of the model shows a decreasing trend, and the maximum error is no more than 6.88% and 8.02 m. Compared with that of CVs, the penetration rate of CAVs has a greater impact on the model results.
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图 4 混合交通流下信号交叉口处时空轨迹
Figure 4. Spatial-temporal trajectories at signalized intersection under mixed traffic flow
图 7 交通流密度为40 veh/km时重构轨迹
Figure 7. Trajectory reconstruction with a traffic flow density of 40 veh/km
图 8 智能网联车渗透率为14%时重构轨迹
Figure 8. Trajectory reconstruction with a CAV penetration rate of 14%
图 10 Lankershim Boulevard街道重构轨迹
Figure 10. Trajectory reconstruction of Lankershim Boulevard Street
表 1 车辆速度与视野和注视距离关系
Table 1. Relationship among vehicle speed, horizon, and gaze distance
车辆速度/（km·h−1） 视野/（°） 注视距离/m 0 170 80 40 100 180 60 86 355 80 60 377 100 40 564 120 22 710 
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表 2 IDM跟驰模型参数
Table 2. Parameters of IDM car-following model
模型 An/ （m·s−2） bn/ （m·s−2） vf/ （m·s−1) Tn/s s0/m IDM 1.00 2.00 33.30 1.50 2.00 虚拟 IDM 2.00 1.68 28.92 1.45 2.00
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表 3 不同交通流密度条件下轨迹重构结果
Table 3. Trajectory reconstruction results under different traffic density conditions
交通流
密度/
（veh·km−1）插入 RV 的数量 插入 RV 的位置 实际车
辆/vehMAEm/
vehMAPEm/
%MAEx/
mRMSEx/
m20 29.05 2.00 6.88 4.15 8.02 30 29.09 1.85 6.36 3.18 7.45 40 29.00 1.82 6.27 2.93 7.06 50 23.50 1.33 5.67 2.88 6.61
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表 4 不同CAV渗透率条件下轨迹重构结果
Table 4. Trajectory reconstruction results under different CAV penetration rates
CAV 渗透率/
%CAV 数量/
veh插入 RV 的数量 插入 RV 的位置 实际车辆/veh MAEm/
vehMAPEm/
%MAEx/
mRMSEx/
m2 1 34.04 2.41 7.07 3.46 8.37 4 2 29.85 2.04 6.83 3.01 7.63 6 3 28.03 2.00 7.14 3.31 8.01 8 4 23.50 1.33 5.67 2.88 6.61 10 5 22.05 1.18 5.37 2.80 6.51 12 6 18.93 0.67 3.54 2.21 5.04 14 7 16.48 0.58 3.52 2.19 4.85
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表 5 不同CV渗透率条件下轨迹重构结果
Table 5. Trajectory reconstruction results under different CV penetration rates
CV 渗透率/% CV 辆数/veh 插入 RV 的数量 插入 RV 的位置 实际车辆/veh MAEm/
vehMAPEm/
%MAEx/
mRMSEx/
m8 4 27.33 2.25 8.23 3.74 8.72 12 6 26.61 1.74 6.54 3.55 8.23 16 8 24.86 1.57 6.32 3.07 6.96 20 10 23.50 1.33 5.67 2.88 6.61 24 12 23.30 1.23 5.26 2.71 6.43 28 14 21.41 0.91 4.26 2.49 6.06
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表 6 Lankershim Boulevard街道轨迹重构误差
Table 6. Trajectory reconstruction errors of Lankershim Boulevard Street
插入 RV 的数量 插入 RV 的位置 本文方法 已有方法 实际车
辆/vehMAEm/
vehMAPEm/
%MAEx/
mRMSEx/
mMAEx/
mRMSEx/
m5.63 0.38 6.75 4.35 5.94 6.53 7.60
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