考虑安全间距的合流区可变限速协调控制方法

doi:10.13229/j.cnki.jdxbgxb20210028

摘要/Abstract

摘要：

针对匝道合流冲突的问题，分析了合流区车辆换道特性及运动轨迹，提出了满足车辆合流需求的车辆安全间距计算模型。首先，将其纳入可变限速的控制框架中，运用模型预测控制方法，建立了网联环境下合流区可变限速协调控制方法和模型。然后，利用元胞自动机搭建仿真环境，实现了网联车在上游控制区的速度协调控制。最后，将匝道与主道车辆到达率按照低、中、高进行27个场景的设置，选取交通量、速度、密度、行程时间作为评价指标，通过仿真分析了控制策略下的合流区交通状况。仿真结果表明：匝道车辆到达率会影响控制策略的实施效果，低密度时，效果不显著；中密度时，控制策略优势得以体现；高密度时，策略失效。

关键词: 交通运输系统工程, 可变限速, 模型预测控制, 安全间距, 网联车

Abstract:

To improve the problem of traffic congestion in merging area， the paper describes the lane changing characteristics and trajectories of vehicles in on-ramp area， the calculation model of minimum safe spacing for vehicles meeting the demand of vehicle merging is proposed. First, the calculation model is incorporated into the control framework of variable speed limit. The coordinated control method and model of variable speed limit in on-ramp area under the intelligent network environment are constructed by applying model predictive control method. Then，the simulation environment is built by using cellular automata to realize the speed control of connected vehicle in upstream control area. Finally,the ramp and main road vehicle arrival rate is set in 27 scenarios according to low， medium and high， and traffic volume， speed， density and travel time are selected as evaluation indexes. The traffic conditions of on-ramp area under control are analyzed through simulation. The simulation results show that the on-ramp vehicle arrival rate affects the implementation effect of the control strategy. When the density is low， the effect is not significant； when the density is medium， the advantage of the control strategy is reflected； when the density is high， the strategy is invalid.

Key words: engineering of communication and transportation system, variable speed limit, model predictive control, safe spacing, connected vehicle

中图分类号:

U491.4

吴文静,战勇斌,杨丽丽,陈润超. 考虑安全间距的合流区可变限速协调控制方法[J]. 吉林大学学报(工学版), 2022, 52(6): 1315-1323.

Wen-jing WU,Yong-bin ZHAN,Li-li YANG,Run-chao CHEN. Coordinated control method of variable speed limit in on⁃ramp area considering safety distance[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1315-1323.

图/表 13

图1

图2

图3

图4

图5

图6

图7

表1

匝道进车概率pin2=0.2"

主线进车概率 $p i n 1$	控制策略	流量/（veh·h^-1）	速度/（km·h^-1）	密度/（veh·km^-1·lane^-1）	行程时间/s
0.2	ICV-NC	1 534	96.77	8.39	47 267
0.2	ICV-C	1 546	97.81	7.67	46 682
0.5	ICV-NC	2 298	91.50	12.61	68 541
0.5	ICV-C	2 323	94.76	12.27	64 385
1	ICV-NC	3 208	87.69	21.65	76 553
1	ICV-C	3 280	90.32	19.75	75 526

表1

表2

匝道进车概率pin2=0.5"

主线进车概率 $p i n 1$	控制策略	流量/（veh·h^-1）	速度/（km·h^-1）	密度/（veh·km^-1·lane^-1）	行程时间/s
0.2	ICV-NC	2 310	91.52	9.89	53 561
0.2	ICV-C	2 477	93.91	9.21	52 688
0.5	ICV-NC	3 245	80.67	21.35	73 120
0.5	ICV-C	3 305	80.42	17.32	68 243
1	ICV-NC	3 438	66.98	37.95	86 423
1	ICV-C	3 596	71.42	32.96	85 547

表2

表3

匝道进车概率pin2=1"

主线进车概率 $p i n 1$	控制策略	流量/（veh·h^-1）	速度/（km·h^-1）	密度/（veh·km^-1·lane^-1）	行程时间/s
0.2	ICV-NC	3 059	85.65	11.87	62 468
0.2	ICV-C	3 131	84.32	12.43	68 618
0.5	ICV-NC	3 192	61.62	29.12	106 589
0.5	ICV-C	3 254	64.84	32.62	115 695
1	ICV-NC	3 017	63.47	35.35	125 638
1	ICV-C	3 161	58.58	34.32	142 033

表3

图8

图9

图10

参考文献 15

1	Kang K P, Chang G L, Zou N. Optimal dynamic speed-limit control for highway work zone operations[C]∥Venue: Hilton DFW Lakes Executive Conference Center, Grapevine, Texas, USA, 2004: 77-84.
2	Zhang Yi-hang, Sirmatel I I, Alasiri F. Comparison of feedback linearization and model predictive techniques for variable speed limit control[C]∥2018 21st International Conference on Intelligent Transportation Systems, Maui, HI, USA, 2018: 3000-3005.
3	Zhang C B, Nasser R. Optimisation of lane-changing advisory at the motorway lane drop bottleneck[J]. Transportation Research Part C, 2019, 106(7): 303-316.
4	Zhang Yao-zong, Ma Ming-hui, Liang Shi-dong. Dynamic control cycle speed limit strategy for improving traffic operation at freeway bottlenecks[J]. KSCE Journal of Civil Engineering, 2021, 25(2): 692-704.
5	陈永恒,刘鑫山,熊帅,等. 冰雪条件下快速路汇流区可变限速控制[J]. 吉林大学学报: 工学版, 2018, 48(3): 677-687.
	Chen Yong-heng, Liu Xin-shan, Xiong Shuai, et al. Variable speed limit control under snow and ice conditions for urban expressway in junction bottleneck area[J]. Journal of Jilin University (Engineering and Technology Edition), 2018, 48(3): 677-687.
6	Hu Xiang-wang, Sun Jian. Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area[J]. Transportation Research Part C: Emerging Technologies, 2019, 101(2): 111-125.
7	Ntousakis I A, Nikolos I K, Papageorgiou M. Optimal vehicle trajectory planning in the context of cooperative merging on highways[J]. Transportation Research Part C: Emerging Technologies, 2016, 71: 464-488.
8	Xu Ling-hui, Lu Jia, Wang Chong, et al. Cooperative merging control strategy of connected and automated vehicles on highways[J]. Journal of Southeast University(English Edition), 2019, 35(2): 220-227.
9	Hyun W, Jorge A. Combined ramp-metering and variable speed limit system for capacity drop control at merge bottlenecks[J]. Journal of Transportation Engineering, Part A: Systems, 2020, 146(6): No. 04020033.
10	杨敏, 王立超, 张健, 等. 面向智慧高速的合流区协作车辆冲突解脱协调方法[J].交通运输工程学报, 2020, 20(3): 217-224.
	Yang Min, Wang Li-chao, Zhang Jian, et al. Collaborative method of vehicle conflict resolution in merging area for intelligent expressway[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 217-224.
11	Du W D, Abbas-Turki A, Koukam A, et al. On the V2X speed synchronization at intersections: Rule based System for extended virtual platooning[J]. Procedia Computer Science, 2018, 141: 255-262.
12	Yang M M, Wang X S, Quddus M. Examining lane change gap acceptance, duration and impact using naturalistic driving data[J]. Transportation Research, Part C: Emerging Technologies, 2019, 104(7): 317-331.
13	张良,陈诗慧,张伟. 驾驶员换道执行持续时间研究[J]. 工业工程与管理,2014,19(4):109-114.
	Zhang Liang, Chen Shi-hui, Zhang Wei. Study on lane change duration[J]. Industrial Engineering and Management, 2014, 19(4):109-114.
14	马明辉, 杨庆芳, 梁士栋. 高速公路主线可变限速控制方法[J]. 哈尔滨工业大学学报, 2015, 47(9): 107-111.
	Ma Ming-hui, Yang Qing-fang, Liang Shi-dong.A method of variable speed limit control for traffic flow on freeway mainline[J]. Journal of Harbin Institute of Technology, 2015, 47(9): 107-111.
15	Ye L H, Yamamoto T. Modeling connected and autonomous vehicles in heterogeneous traffic flow[J]. Physica A: Statistical Mechanics and its Applications, 2018, 490: 269-277.

相关文章 15

[1]	冯天军,孙学路,黄家盛,田秀娟,宋现敏. 基于三种过街方式的两相位信号交叉口延误[J]. 吉林大学学报(工学版), 2022, 52(3): 550-556.
[2]	李兴华,冯飞宇,成诚,王洧,唐鹏程. 网约拼车服务选择偏好分析及建模[J]. 吉林大学学报(工学版), 2022, 52(3): 578-584.
[3]	李先通,全威,王华,孙鹏程,安鹏进,满永兴. 基于时空特征深度学习模型的路径行程时间预测[J]. 吉林大学学报(工学版), 2022, 52(3): 557-563.
[4]	尹超英,邵春福,黄兆国,王晓全,王晟由. 基于梯度提升决策树的多尺度建成环境对小汽车拥有的影响[J]. 吉林大学学报(工学版), 2022, 52(3): 572-577.
[5]	贾洪飞,邵子函,杨丽丽. 终点不确定条件下网约车合乘匹配模型及算法[J]. 吉林大学学报(工学版), 2022, 52(3): 564-571.
[6]	贾彦峰,曲大义,林璐,姚荣涵,马晓龙. 基于运行轨迹的网联混合车流速度协调控制[J]. 吉林大学学报(工学版), 2021, 51(6): 2051-2060.
[7]	薛锋,何传磊,黄倩,罗建. 多式轨道交通网络的耦合协调度[J]. 吉林大学学报(工学版), 2021, 51(6): 2040-2050.
[8]	陆文琦,周天,谷远利,芮一康,冉斌. 基于张量分解理论的车道级交通流数据修复算法[J]. 吉林大学学报(工学版), 2021, 51(5): 1708-1715.
[9]	卢凯,吴蔚,林观荣,田鑫,徐建闽. 基于KNN回归的客运枢纽聚集人数组合预测方法[J]. 吉林大学学报(工学版), 2021, 51(4): 1241-1250.
[10]	彭博,张媛媛,王玉婷,唐聚,谢济铭. 基于自动编码机-分类器的视频交通状态自动识别[J]. 吉林大学学报(工学版), 2021, 51(3): 886-892.
[11]	张健,吴坤润,杨敏,冉斌. 智能网联环境下交叉口双环自适应控制模型[J]. 吉林大学学报(工学版), 2021, 51(2): 541-548.
[12]	王殿海,沈辛夷,罗小芹,金盛. 车均延误最小情况下的相位差优化方法[J]. 吉林大学学报(工学版), 2021, 51(2): 511-523.
[13]	何德峰,罗捷,舒晓翔. 自主网联车辆时滞反馈预测巡航控制[J]. 吉林大学学报(工学版), 2021, 51(1): 349-357.
[14]	贾超,徐洪泽,王龙生. 基于多质点模型的列车自动驾驶非线性模型预测控制[J]. 吉林大学学报(工学版), 2020, 50(5): 1913-1922.
[15]	宋现敏,张明业,李振建,王鑫,张亚南. 动态公交专用道的设置及其仿真分析评价[J]. 吉林大学学报(工学版), 2020, 50(5): 1677-1686.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed