高速公路合流区可变限速和换道协同控制研究

doi:10.3969/j.issn.1674-0696.2022.02.06

摘要/Abstract

摘要： 针对高速公路合流区通行效率降低、车辆延误增加、整体服务水平下降等问题，提出了一种基于可变限速和换道控制的高速公路合流区车流密度优化模型。首先，换道控制通过预测瓶颈容量和交通需求为联网车辆（CAVs）提供变道建议，优化合流区上游的车流密度，减少瓶颈容量下降的影响；其次，基于换道控制下的合流区瓶颈容量以及匝道的交通流密度，确定修正前的可变限速值；再次，利用基于细胞传输模型的反馈进行变限速控制，实时控制交通瓶颈上游流量以保证换道区密度收敛到最优平衡点，得到修正后的可变限速值；最后，选择元胞传输模型作为基础交通流模型对合流区进行换道控制，采用中观多车道元胞传输模型模拟合流区主线换道行为及换道控制对合流区交通流运行的影响。仿真结果表明：与无控制方案和VSL控制方案相比，协同控制的平均旅行时间分别降低了58.55%、35.68%，平均流量分别提高了9.09%、2.35%，协同控制在通行效率、交通安全方面均有明显改善。

关键词: 智能交通；高速公路；可变限速控制；换道控制；CTM模型

Abstract: Aiming at the problems of reduced traffic efficiency, increased vehicle delay and reduced overall service level in expressway confluence area, an optimization model of vehicle flow density in expressway confluence area based on variable speed limit (VSL) and lane change control was proposed. Firstly, lane change control provided lane change suggestions for connected vehicles by predicting bottleneck capacity and traffic demand, to optimize the traffic flow density of upstream of the merging area and reduce the impact of the decline of bottleneck capacity. Secondly, the VSL before correction was determined based on the bottleneck capacity of the merging area under lane change control and the traffic flow density of the ramp. Thirdly, the feedback VSL control based on cell transmission model was used to the upstream flow of traffic bottleneck in real time to ensure that the density of lane changing area converged to the optimal equilibrium point, and the modified VSL was obtained. Finally, the cell transmission model was selected as the basic traffic flow model. For the lane change control in the confluence area, the meso multi-lane cell transmission model was adopted to simulate the lane change behavior of the main line in the confluence area and the impact of lane change control on the traffic flow operation in the confluence area. The simulation results show that compared with no control scheme and VSL control scheme, the average travel time of collaborative control is reduced by 58.55% and 35.68% respectively, and the average flow is increased by 9.09% and 2.35% respectively. Collaborative control has significantly improved traffic efficiency and traffic safety.

Key words: intelligent traffic; freeway; variable speed limit; lane change; CTM model

中图分类号:

U491

李巧茹，王少航，陈亮. 高速公路合流区可变限速和换道协同控制研究[J]. 重庆交通大学学报（自然科学版）, 2022, 41(02): 35-43.

LI Qiaoru, WANG Shaohang, CHEN Liang. Cooperative Control of Variable Speed Limit and Lane Change in Expressway Confluence Area[J]. Journal of Chongqing Jiaotong University(Natural Science), 2022, 41(02): 35-43.

参考文献

［1］贺玉龙，张鲁飞. 青藏高原双车道公路限速对运行效率的影响［J］. 重庆交通大学学报（自然科学版）, 2017,36(12): 82-90.
HE Yulong, ZHANG Lufei. The Influence of the speed limit on the operating efficiency of the two-lane highway on the Qinghai-Tibet plateau［J］. Journal of Chongqing
Jiaotong University (Natural Science), 2017,36(12): 82-90.
［2］ FREJO J R D, NEZ A, SCHUTTER D B, et al. Hybrid model predictive control for freeway traffic using discrete speed limit signals［J］. Transportation Research
Part C: Emerging Technologies, 2014, 46: 309-325.
［3］ IORDANIDOU G R, RONCOLI C, PAPAMICHAIL I, et al. Feedback-based mainstream traffic flow control for multiple bottlenecks on motorways［J］. IEEE
Transactions on Intelligent Transportation Systems, 2015, 16(2): 610-621.
［4］ RONCOLI C, PAPAMICHAIL I, PAPAGEORGIOU M. Hierarchical model predictive control for multi-lane motorways in presence of vehicle automation and
communication systems［J］. Transportation Research Part C: Emerging Technologies, 2016, 62: 117-132.
［5］ ZHANG Y, IOANNOU P A. Combined variable speed limit and lane change control for truck-dominant highway segment［C］∥ 2015 IEEE 18th International
Conference on Intelligent Transportation Systems. Las Palmas de Gran Canaria:［s.n.］, 2015: 1163-1168.
［6］ ZHANG C B, SABAR N R, CHUNG E, et al. Optimisation of lane-changing advisory at the motorway lane drop bottleneck［J］. Transportation Research Part C:
Emerging Technologies, 2019, 106: 303-316.
［7］田丽萍, 朱弘戈, 朱晓东, 等. 面向高速公路匝道合流区通行效率的车路协同限速方法［J］. 公路, 2019, 64(8): 310-316.
TIAN liping, ZHU Hongge, ZHU Xiaodong, at al. Vehicle road coordinated speed limit method for improving traffic efficiency in freeway ramp confluence area
［J］.Highway, 2019, 64(8): 310-316.
［8］ YE E, RAMEZANI M. Lane density optimisation of automated vehicles for highway congestion control［J］. Transportmetrica B: Transport Dynamics, 2019, 7(1):
1096-1116.
［9］ ZHANG Y, IOANNOU P A. Combined variable speed limit and lane change control for highway traffic［J］. IEEE Transactions on Intelligent Transportation Systems,
2017, 18(7): 1812-1823.
［10］ GUO Y, XU H, ZHANG Y, et al. Integrated variable speed limits and lane-changing control for freeway lane-drop bottlenecks［J］. IEEE Access, 2020, 8: 54710-
54721.
［11］陈亮, 冯延超, 李巧茹. 基于Multi-class SVm的车辆换道行为识别模型研究［J］. 安全与环境学报, 2020, 20(1): 193-199.
CHEN Liang, FENG Yanchao, LI Qiaoru, Probe into the Multi-class SVM-based recognition model for the vehicle lanealtering behaviors［J］Journal of Safety and
Environment, 2020, 20(1): 193-199.
［12］ PAN T L, LAM W H K, SUMALEE A, et al. Modeling the impacts of mandatory and discretionary lane-changing maneuvers［J］. Transportation Research Part C:
Emerging Technologies, 2016, 68: 403-424.
［13］陈亮, 何志超, 李巧茹, 等. 多车道城市快速路交织区拥堵形成机制［J］. 中国安全科学学报, 2018, 28(6): 73-78.
CHEN Liang, HE Zhichao, LI Qiaoru, et al. Study on congestion mechanism in multi-lane weaving section of urban expressway［J］. China Safety Science Journal, 2018, 28
(6): 73-78.
［14］ KEYVAN-EKBATANI M, KNOOP V L, DAAMEN W. Categorization of the lane change decision process on freeways［J］. Transportation Research Part C: Emerging
Technologies, 2016, 69: 515-526.
［15］ SALA M, SORIGUERA F. Lane-changing and freeway capacity: A bayesian inference stochastic model［J］. Computer-Aided Civil and Infrastructure Engineering,
2020, 35(7): 719-733.

[1]	陈昱光1, 2, 胡山1, 林弘灏1, 黄金涛2, 郭凤香1. 基于改进YOLOv8-DeepSORT的城市交叉口交通冲突自动检测方法[J]. 重庆交通大学学报（自然科学版）, 2025, 44(10): 35-42.
[2]	朱政泽, 熊宇恒. 基于多时空特征和图注意力网络的交通流预测模型研究[J]. 重庆交通大学学报（自然科学版）, 2025, 44(10): 51-59.
[3]	崔铁军1, 2, 李莎莎1, 2, 王鑫阳1, 2. 地铁站乘客使用设备故障及其导致的人员伤亡过程研究[J]. 重庆交通大学学报（自然科学版）, 2025, 44(10): 60-64.
[4]	陈红1, 刘洋1, 梁子君2, 肖赟2, 李琛3. 青年货车驾驶人危险驾驶行为影响因素分析[J]. 重庆交通大学学报（自然科学版）, 2025, 44(10): 65-73.
[5]	何保红，谢维凯，杨夕蕊. 基于居住偏好视角的城市居民通勤与居住再选择研究[J]. 重庆交通大学学报（自然科学版）, 2025, 44(7): 83-90.
[6]	陈鲁川1，张姝玮2，王亮1，苏东兰3，郭忠印4. 基于演化博弈的高速公路诱导分流预测模型[J]. 重庆交通大学学报（自然科学版）, 2025, 44(7): 91-98.
[7]	王连震，周铭，程国柱. 基于SEM与fsQCA的出租车驾驶人危险驾驶行为多致因组态分析[J]. 重庆交通大学学报（自然科学版）, 2025, 44(7): 99-109.
[8]	朱震军1，张芮嘉1，韩吉1，唐超2，过秀成3. 不同出行目的下城市建成环境对自行车出行时间的影响[J]. 重庆交通大学学报（自然科学版）, 2025, 44(3): 88-95.
[9]	李晓伟1，刘倩1，石兰馨1,2，李昊田1，陈君1，时宗琦3. 天气和建成环境对乘客公交通勤时间的非线性影响[J]. 重庆交通大学学报（自然科学版）, 2025, 44(3): 96-104.
[10]	张旭1，杨晓光2，庞聿皓3,李英帅3. 交通语言综述化研究[J]. 重庆交通大学学报（自然科学版）, 2024, 43(1): 39-45.
[11]	邵长桥，陈艳清. 高速公路收费站ETC/MTC混合收费车道通行能力计算模型研究[J]. 重庆交通大学学报（自然科学版）, 2024, 43(1): 52-58.
[12]	王登忠1,2，马东方3，方博3，王如杰3，袁超1. 基于移动性导向的公交网络可达性研究[J]. 重庆交通大学学报（自然科学版）, 2024, 43(1): 59-66.
[13]	赵欣，李瑞，酆磊. 基于公交优先的干线协调信号控制改进模型[J]. 重庆交通大学学报（自然科学版）, 2024, 43(1): 67-74.
[14]	周和平，李文杰. 考虑鲁棒成本与绝对后悔的最短路径问题研究[J]. 重庆交通大学学报（自然科学版）, 2024, 43(1): 91-98.
[15]	赵红专, 李林, 周旦, 陈建鹏, 展新. 车联网环境下的可变单向交通控制算法研究[J]. 重庆交通大学学报（自然科学版）, 2023, 42(6): 111-118.