基于跟驰特性的智能网联车混合交通流轨迹重构

蒋阳升; 刘梦; 王思琛; 高宽; 姚志洪

doi:10.3969/j.issn.0258-2724.20200735

基于跟驰特性的智能网联车混合交通流轨迹重构

doi: 10.3969/j.issn.0258-2724.20200735

蒋阳升^{1, 2,},
刘梦^{1, 2},
王思琛^{1, 2},
高宽^{1, 2},
姚志洪^{1, 2}

1.
西南交通大学交通运输与物流学院，四川成都 611756
2.
西南交通大学综合交通大数据应用技术国家工程实验室，四川成都 611756

基金项目: 国家自然科学基金（52002339）；四川省科技计划（2021YJ0535，2020YFH0026）；广西壮族自治区科技计划（2021AA01007AA）；中央高校基本科研业务费专项资金（2682021CX058）

详细信息

作者简介:
蒋阳升（1976—），男，教授，博士，研究方向为交通系统优化，E-mail：jiangyangsheng@swjtu.cn

中图分类号: U491
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- 被引次数: 14
出版历程
- 收稿日期: 2020-11-10
- 修回日期: 2021-01-13
- 网络出版日期: 2021-04-15
- 刊出日期: 2021-04-15

Trajectory Reconstruction for Traffic Flow Mixed withConnected Automated Vehicles Based on Car-Following Characteristics

JIANG Yangsheng^{1, 2
,},
LIU Meng^{1, 2},
WANG Sichen^{1, 2},
GAO Kuan^{1, 2},
YAO Zhihong^{1, 2}

1.
School of Transportation and Logistics，Southwest Jiaotong University，Chengdu 611756，China
2.
National Engineering Laboratory of Integrated Transportation Big Data Application Technology，Southwest Jiaotong University，Chengdu 611756，China

摘要

摘要:
车辆轨迹数据蕴含着丰富的时空交通信息，是交通状态估计的基础数据之一. 为解决现有数据采集环境难以获得全样本车辆轨迹的问题，面向智能网联环境，构建了混合交通流全样本车辆轨迹重构模型. 首先，分析了智能网联环境下混合交通流的车辆构成及其轨迹数据采集环境；然后，提出了基于智能驾驶员跟驰模型的车辆轨迹重构模型，实现了对插入轨迹数量、轨迹位置和速度等参数的估计；最后，设计仿真试验验证了模型在不同交通流密度和智能网联车（connected automated vehicle，CAV）渗透率条件下的适用性. 试验结果表明：CAV和网联人工驾驶车（connected vehicle，CV）的渗透率为8%和20%时，该车辆轨迹重构模型在不同交通流密度下均能重构84%以上的车辆轨迹；重构轨迹准确性随着CAV和CV渗透率的增加而提高；当交通密度为70辆/km，且CAV渗透率仅为4%的情况下，模型也能重构82%的车辆轨迹.
- 智能交通 /
- 跟驰模型 /
- 智能网联车 /
- 混合交通流
Abstract:
Vehicle trajectory data contains massive spatial-temporal traffic information, which is one of the necessary data for traffic state estimation. To solve the problem that it's difficult to obtain the fully sampled vehicular trajectory in the existing data collection environment, oriented to the connected and automated environment, a fully sampled trajectory reconstruction model of mixed traffic flow is proposed . Firstly, vehicle composition and trajectory data collection environment of mixed traffic flow with the connected automated vehicle (CAV) are analyzed. Then, a vehicle trajectory reconstruction model is proposed based on intelligent driver car-following model. Based on this, the number of inserted trajectories, trajectory position and speed are estimated. Finally, numerical simulation is designed to investigate the influence of traffic density and penetration rate of CAVs. Results show that, when the penetration rates of CAV and connected vehicle (CV) at 8% and 20%, respectively, the model can reconstruct more than 84% vehicular trajectories under different traffic densities. The accuracy of the reconstructed trajectories increases with the increase in penetration rates of CAV and CV. Besides, when the traffic density is 70 veh/km and the penetration rate of CAV at a low level of 4%, the proposed model can reconstruct 82% vehicular trajectories.
- intelligent transportation system (ITS) /
- car-following model /
- connected automated vehicle /
- mixed traffic flow

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图 1 混合交通流中不同车辆组成

Figure 1. Composition of different types of vehicles in mixed traffic flow

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图 2 混合交通流轨迹时空图

Figure 2. Spatio-temporal diagram of mixed traffic flow trajectories

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图 3 车辆轨迹示意

Figure 3. Schematic diagram of vehicle trajectories

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图 4 插入车辆轨迹示意

Figure 4. Schematic diagram of inserted vehicle trajectories

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图 5 流量-密度-速度关系

Figure 5. Relationship between volume，density and speed of traffic flow

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图 6 交通流密度为60辆/km时轨迹时空图

Figure 6. Spatio-temporal diagram of the trajectories when traffic flow density is 60 veh/km

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图 7 CAV渗透率为14%时轨迹时空图

Figure 7. Spatio-temporal diagram of the trajectories when CAV penetration rate is 14%

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图 8 均方根误差热力图

Figure 8. Heat map of RMSE

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表 1 不同交通流密度条件下插入车辆数估计

Table 1. Estimation of the numbers of inserted vehicles under different traffic densities

交通流密度/ （辆•km⁻¹）	CAV/ 辆	插入车辆/辆	实际车辆/辆	插入误差/辆	插入误差百分比/%
20	2	5.80	6.40	0.60	9.38
30	2	14.05	13.80	0.35	2.54
40	3	15.80	16.05	0.25	1.56
50	4	21.75	21.38	0.31	1.50
60	5	27.85	25.75	2.10	8.16
70	6	31.40	27.25	4.30	15.78
注：表中插入车辆数为多次仿真试验的平均值，因此会出现不是整数的情况；插入误差百分比为插入辆数的误差与实际插入车辆数的百分比.

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表 2 不同交通流密度条件下轨迹重构误差

Table 2. Trajectory reconstruction error under different traffic density conditions

交通流密度/ （辆•km⁻¹）	CAV/ 辆	MAE/ m	MAPE/ %	RMSE/ m
20	2	15.02	0.36	18.28
30	2	11.90	0.29	14.30
40	3	12.14	0.31	18.68
50	4	12.30	0.35	20.39
60	5	13.09	0.42	22.19
70	6	13.39	0.52	23.47
平均值		12.97	0.37	19.55

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表 3 不同渗透率条件下插入车辆数估计

Table 3. Estimation of the numbers of inserted vehicles under different CAV penetration rates

CAV 渗透率/%	CAV/辆	插入车辆/辆	实际车辆/辆	插入误差/辆	插入误差百分比/%
4	3	45.50	38.70	6.80	17.57
6	4	41.50	35.20	5.80	16.48
8	6	31.40	27.25	4.30	15.78
10	7	29.44	25.44	4.00	15.72
12	8	26.80	23.20	3.60	15.52
14	10	19.60	17.00	2.60	15.29

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表 4 CAV不同渗透率条件下轨迹重构误差

Table 4. Trajectory reconstruction error under different CAV penetration rates

CAV渗透率/%	CAV/辆	MAE/m	MAPE/%	RMSE/m
4	3	16.76	0.68	33.77
6	4	15.69	0.63	28.95
8	6	13.39	0.52	23.47
10	7	12.98	0.51	21.86
12	8	12.23	0.49	19.90
14	10	10.45	0.42	16.45

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表 5 CV不同渗透率轨迹重构结果

Table 5. Reconstruction results of different CV penetration rates

CV 渗透率/%	CV/辆	插入误差/辆	插入误差百分比/%	MAE/m	MAPE/ %	RMSE/ m
16	11	5.40	0.18	17.46	0.69	32.43
18	13	4.33	0.16	13.98	0.55	24.27
20	14	4.30	0.16	13.39	0.52	23.47
22	15	4.20	0.15	12.93	0.52	21.50
24	17	3.80	0.14	11.50	0.48	19.11

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