智能网联车借用公交专用道的轨迹与信号协同优化

doi:10.13229/j.cnki.jdxbgxb.20230385

摘要/Abstract

摘要：

为改善智能网联车与人工驾驶汽车的混行交通流运行状态，本文提出了一种智能网联车借用公交专用道通行的轨迹与信号的协同优化方法。先在信号和轨迹双层优化模型中，为轨迹变量与信号变量建立线性数学关系，并对整体模型进行时空上的分段求解。然后应用于实际数值案例，对模型进行可行性分析，对比协同优化与非协同优化的数值结果，证明了协同优化相比于非协同优化对于智能网联车的运行效率平均有7.7%的提升。

关键词: 交通信息工程及控制, 智能网联车, 公交专用道, 轨迹优化, 协同优化

Abstract:

In order to improve the mixed traffic of connected automated vehicles and human-driving vehicles， this paper proposed a collaborative optimization method for signals and trajectories of connected automated vehicles on dedicated bus lanes. In the double-layer optimization model of signal and trajectory， this paper builds linear mathematical relations between trajectory variables and traffic signal variables， and compute the solutions in sections of time and space. Consequently， this model is used in real number case to verify the validity and operate comparative experiments between collaborative and non-collaborative optimization. It proves that efficiency of connected automated vehicles in collaborative optimization is 7.7% higher.

Key words: traffic information engineering and control, connected automated vehicle, dedicated bus lane, trajectory optimization, collaborative optimization

中图分类号:

U491.5

金盛,李博林,薛炜. 智能网联车借用公交专用道的轨迹与信号协同优化[J]. 吉林大学学报(工学版), 2025, 55(2): 566-576.

Sheng JIN,Bo-lin LI,Wei XUE. Collaborative optimization for signals and trajectories of connected automated vehicles on dedicated bus lanes[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(2): 566-576.

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参考文献 20

1	Ye L, Yamamoto T. Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput[J]. Physica A: Statistical Mechanics and its Applications, 2018, 512: 588-597.
2	Almeida C G H, Menendez M. Automated and connected vehicles: effects on traffic, mobility and urban design[J]. International Journal of Transportation Science and Technology, 2017, 6(1): 3-4.
3	NHTSA. Preliminary statement of policy concerning automated vehicles[R]. Washington: NHTSA, 2013.
4	Zhou J, Zhu F. Modeling the fundamental diagram of mixed human-driven and connected automated vehicles[J]. Transportation Research Part C: Emerging Technologies, 2020, 115: 102614.
5	Mohajerpoor R, Ramezani M. Mixed flow of autonomous and human-driven vehicles: analytical headway modeling and optimal lane management[J]. Transportation Research Part C: Emerging Technologies, 2019, 109: 194-210.
6	Chen Z, He F, Zhang L H, et al. Optimal deployment of autonomous vehicle lanes with endogenous market penetration[J]. Transportation Research Part C: Emerging Technologies, 2016, 72: 143-156.
7	Conceição L, Correia G, Tavares J P. The deployment of automated vehicles in urban transport systems: a methodology to design dedicated zones[J]. Transportation Research Procedia, 2017, 27: 230-237.
8	毛向阳, 尚世亮, 崔海峰. 自动驾驶汽车安全影响因素分析与应对措施研究[J]. 上海汽车, 2018, 2018(1): 33-37.
	Mao Xiang-yang, Shang Shi-liang, Cui Hai-feng. Analysis of safety influencing factors and countermeasures of autonomous vehicles[J]. Shanghai Auto, 2018, 2018(1): 33-37.
9	Wirasinghe S C, Kattan L, Rahman M, et al. Bus rapid transit-a review[J]. International Journal of Urban Sciences, 2013, 17(1): 1-31.
10	Hoonsiri C, Chiarakorn S, Kiattikomol V. Using combined bus rapid transit and buses in a dedicated bus lane to enhance urban transportation sustainability[J]. Sustainability, 2021, 13(6): No. 13063052.
11	He S L, Ding F, Lu C R, et al. Impact of connected and autonomous vehicle dedicated lane on the freeway traffic efficiency[J]. European Transport Research Review, 2022, 14(1): 102374.
12	陆化普, 孙煦, 吴娟. 公交专用道优化设计的双层规划模型[J]. 中国公路学报, 2015, 28(2): 87-94.
	Lu Hua-pu, Sun Xu, Wu Juan. Bi-level programming model for optimization design of eclusive bus lane[J]. China Journal of Highway and Transport, 2015, 28(2): 87-94.
13	徐志刚, 李金龙, 赵祥模, 等. 智能公路发展现状与关键技术[J]. 中国公路学报, 2019, 32(8): 1-24.
	Xu Zhi-gang, Li Jin-long, Zhao Xiang-mo, et al. A review on intelligent road and its related key technologies[J]. China Journal of Highway and Transport. 2019, 32(8): 1-24.
14	Eichler M, Daganzo C F. Bus lanes with intermittent priority: strategy formulae and an evaluation[J]. Transportation Research Part B: Methodological, 2006, 40(9): 731-744.
15	赵鑫. 基于元胞自动机的联网车辆专用车道设置研究[D]. 哈尔滨: 哈尔滨工业大学交通科学与工程学院, 2020.
	Zhao Xin. Researchon dedicated lane setting ointernet of vehicles based oncellular automata[D]. Harbin: School of Transportation Science and Engineering, Harbin Institute of Technology, 2020.
16	Winsor M. Influence of networked and cooperative vehicles on virtual right-of-way performance in mixed traffic[D]. Munich: Technical University of Munich, 2020.
17	Chen X D, Lin X, He F, et al. Modeling and control of automated vehicle access on dedicated bus rapid transit lanes[J]. Transportation Research Part C: Emerging Technologies, 2020, 120: 102795.
18	庞明宝, 柴紫欣, 巩丹阳. 混合交通下智能网联车借道公交专用车道控制[J]. 交通运输系统工程与信息, 2021, 21(4): 118-124.
	Pang Ming-bao, Chai Zi-xin, Gong Dan-yang. Control of connected and automated vehicles driving on dedicated bus lane under mixed traffic[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(4): 118-124.
19	Brociek R, Wajda A, Słota D. Comparison of heuristic algorithms in identification of parameters of anomalous diffusion model based on measurements from sensors[J]. Sensors, 2023, 23(3): 23031722.
20	Luo Q, Rao Y Q. Heuristic algorithms for the special knapsack packing problem with defects arising in aircraft arrangement[J]. Expert Systems with Applications, 2023, 215: 119392.

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流量位置	低流量		中流量		高流量
流量位置	进	出	进	出	进	出
1	140	0	280	0	420	0
2	30	50	60	100	89	140
3	100	20	200	40	300	60
4	120	63	240	120	357	190
5	119	160	240	320	355	480
6	66	83	140	170	199	250

公交停靠时间/s	轨迹优化 /（m·s^-1）	协同优化4 s /（m·s^-1）	优化幅度/%	协同优化7 s /（m·s^-1）	优化幅度	协同优化10 s /（m·s^-1）	优化幅度/%
30	9.30	9.58	3.01	9.87	6.11%	10.10	8.60
45	9.80	10.24	4.45	10.43	6.43%	10.77	9.90
60	11.20	11.42	1.93	11.63	3.86%	11.80	5.36
75	11.70	11.97	2.27	12.12	3.59%	12.30	5.13
90	12.00	12.31	2.60	12.56	4.67%	12.80	6.67
105	11.00	11.40	3.64	11.80	7.27%	12.00	9.09
120	10.40	10.76	3.46	11.03	6.06%	11.30	8.65
135	9.70	10.05	3.63	10.36	6.80%	10.80	11.34
150	9.80	10.00	2.04	10.09	2.96%	10.30	5.10