基于动态灵敏度的感应控制优化方法

doi:10.16097/j.cnki.1009-6744.2023.04.013

交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (4): 124-133.DOI: 10.16097/j.cnki.1009-6744.2023.04.013

• 智能交通系统与信息技术 • 上一篇下一篇

基于动态灵敏度的感应控制优化方法

管德永，徐越，王可^*

山东科技大学，交通学院，山东青岛 266000

收稿日期:2023-03-12 修回日期:2023-05-31 接受日期:2023-06-09 出版日期:2023-08-25 发布日期:2023-08-21
作者简介:管德永(1975- )，男，山东青岛人，副教授
基金资助:
山东省自然科学基金(ZR2020MG018)

Induction Control Optimization Method Based on Dynamic Sensitivity

GUAN De-yong, XU Yue, WANG Ke^*

College of Transportation, Shandong University of Science and Technology, Qingdao 266000, Shandong, China

Received:2023-03-12 Revised:2023-05-31 Accepted:2023-06-09 Online:2023-08-25 Published:2023-08-21
Supported by:
Natural Science Foundation of Shandong Province, China (ZR2020MG018)

摘要/Abstract

摘要： 为评估雷视一体检测方式在感应信号控制中的实际应用效果和价值，结合雷视一体检测特征，本文建立基于车型的初始绿灯配时模型，同时考虑实时等待车辆数及不同车型的启动损失时间和通过路口区域时间；通过分析雷视一体机在不同车头时距(灵敏度)检测响应条件下单位绿灯延长时间的变化，构建基于动态灵敏度的感应控制模型，并结合平均通过车辆数、平均车速、平均损失时间以及平均最大排队长度建立通行收益指数，使用强化学习方法确定动态灵敏度选择方案。最终，使用SUMO(Simulation of Urban MObility)建立仿真模型，对定时控制、固定灵敏度控制(传统感应控制)以及本文提出的动态灵敏度控制进行验证。仿真结果显示：动态灵敏度的选择与空间占有率有关；在15%最大交通量条件下，本文提出的动态灵敏度控制相对于定时控制和固定灵敏度控制，使交叉口平均车速分别提高了 31.69%和 5.05%，平均损失时间分别降低了36.19%和7.44%，平均最大排队长度分别缩短了45.17%和7.78%；且在交通量达到27%最大交通量之前，动态灵敏度的控制效果均为最优。仿真结果表明，本文提出的动态灵敏度感应控制模型性能良好，能为雷视一体在感应信号控制中的实际应用提供理论参考。

关键词: 交通工程, 动态感应控制, 强化学习, 非饱和交叉口, 雷视一体

Abstract: To evaluate the practical application effect and value of the integrated detection method in the induction signal control, this paper develops an initial green light timing model based on the characteristics of the integrated detection method, and takes into account the number of real-time waiting vehicles and the starting loss time of different models and the time passing through the intersection area. By analyzing the change of unit green light extension time under different headway (sensitivity) detection response conditions, the paper proposes the induction control model based on dynamic sensitivity. The dynamic sensitivity selection scheme is determined using the reinforcement learning method and the traffic benefit combining the average number of vehicles, the average speed, the average loss time and the average maximum queue length. At last, the simulation model is established by the SUMO(Simulation of Urban Mobility) to verify the timing control, fixed sensitivity control (traditional induction control) and the proposed dynamic sensitivity control. The simulation results show that: ① The selection of dynamic sensitivity is affected by the space occupancy. ② Under the condition of 15% maximum traffic volume, compared with the timing control and the fixed sensitivity control, the proposed dynamic sensitivity control increases the average speed of the intersection by 31.69% and 5.05%, reduces the average loss time by 36.19% and 7.44%, and shortens the average maximum queue length by 45.17% and 7.78% . Before the traffic volume reaches 27% of the maximum traffic volume, the control effect of dynamic sensitivity is optimal. It shows that the proposed dynamic sensitivity induction control model has good performance and can provide a theoretical reference for the practical application of radar and vision integration in induction signal control.

Key words: traffic engineering, dynamic induction control, reinforce learning, unsaturated intersection, thunder-vision machine

中图分类号:

U491.54

管德永, 徐越, 王可. 基于动态灵敏度的感应控制优化方法[J]. 交通运输系统工程与信息, 2023, 23(4): 124-133.

GUAN De-yong, XU Yue, WANG Ke. Induction Control Optimization Method Based on Dynamic Sensitivity[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(4): 124-133.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2023.04.013

http://www.tseit.org.cn/CN/Y2023/V23/I4/124

参考文献

[1] 毛程远, 王琴, 钱俊, 等. 基于短周期预测的感应信号控制方法研究[J]. 浙江师范大学学报(自然科学版), 2022, 45(2): 210-217. [MAO C Y, WANG Q, QIAN J, et al. Research on actuated signal control based on short�period prediction[J]. Journal of Zhejiang Normal University, 2022, 45(2): 210-217.]

[2] WANG X B, YIN K, LIU H. Vehicle actuated signal performance under general traffic at an isolated intersection[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 582-598.

[3] 谭真, 黄志义, 梅振宇. 主路协调下单点TSP策略关键控制参数仿真优化[J]. 交通运输系统工程与信息, 2012, 12(4): 64-71. [TAN Z, HUANG Z Y, MEI Z Y. Simulation of key control parameters with TSP strategies and artery coordination on isolated intersections[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(4): 64-71.]

[4] 刘佳佳, 左兴权. 交叉口交通信号灯的模糊控制及优化研究[J]. 系统仿真学报, 2020, 32(12): 2401-2408. [LIU J J, ZUO X Q. Research on fuzzy control and optimization for traffic lights at single intersection[J]. Journal of System Simulation, 2020, 32(12): 2401-2408.]

[5] 安娜. 基于多目标跟踪雷达的单点全感应式信号控制方法[D]. 大连: 大连理工大学, 2020. [AN N. Fully�actuated signal control at isolated intersections based on multi-target tracking radar[D]. Dalian: Dalian University of Technology, 2020.]

[6] 周敬龙. 基于视频检测交通流数据的感应信号控制评估与参数优化[D]. 武汉: 武汉理工大学, 2020. [ZHOU J L. Evaluation and parameter optimization of actuated signal[D]. Wuhan: Wuhan University of Technology, 2020.]

[7] 吴兵, 李晔. 交通管理与控制(第3版)[M]. 北京: 人民交通出版社, 2007. [WU B, LI Y. Traffic management and control (the third edition) [M]． Beijing: People's Transportation Press, 2007.]

[8] 万绪军, 陆化普. 实时自适应交通信号控制优化理论模型[J]. 交通运输工程学报, 2001(4): 60-66. [WAN X J, LU H P. A real time self-adaptive traffic signal setting optimization theory[J]. Journal of Traffic and Transportation Engineering, 2001(4): 60-66.]

[9] SHAMS A, DAY C M. Advanced gap seeking logic for actuated signal control using vehicle trajectory data: Proof of concept[J]. Transportation Research Record, 2022: 03611981221108147.

[10] FENG S, CI Y, WU L, et al. Vehicle delay estimation for an isolated intersection under actuated signal control[J]. Mathematical Problems in Engineering, 2014(1): 1-7 .

[11] LU C, HUANG J, DENG L, et al. Coordinated ramp metering with equity consideration using reinforcement learning[J]. Journal of Transportation Engineering, Part A: Systems, 2017, 143(7): 04017028.

[12] 罗小芹, 王殿海, 金盛. 面向混合交通的感应式交通信号控制方法[J]. 吉林大学学报(工学版), 2019, 49(3): 695-704. [LUO X Q, WANG D H, JIN S. Traffic signal actuated control at isolated intersections for heterogeneous traffic[J]. Journal of Jilin University (Engineering and Technology), 2019, 49(3): 695-704.]

[13] 徐建闽, 周湘鹏, 首艳芳. 基于深度强化学习的自适应交通信号控制研究[J]. 重庆交通大学学报(自然科学版), 2022, 41(8): 24-29. [XU J M, ZHOU X P, SHOU Y F. Adaptive traffic signal control based on deep reinforcement learning[J]. Journal of Chongqing Jiaotong University (Natural Science), 2022, 41(8): 24-29.]

[14] 尚春琳, 刘小明, 田玉林, 等. 基于深度强化学习的综合干线协调控制方法[J]. 交通运输系统工程与信息, 2021, 21(3): 64-70. [ SHANG C L, LIU X M, TIAN Y L, et al. Priority of dedicated bus arterial control based on deep reinforcement learning[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 64-70.]

[15] WANG J, LV W, JIANG Y, et al. A multi-agent based cellular automata model for intersection traffic control simulation[J]. Physica A: Statistical Mechanics and Its Applications, 2021, 584: 126356.

基于动态灵敏度的感应控制优化方法

Induction Control Optimization Method Based on Dynamic Sensitivity

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张铎, 饶红玉, 刘佳琦, 王俊骅, 孙剑. 基于自然驾驶数据的驾驶人跟驰行为内在异质性预测与建模[J]. 交通运输系统工程与信息, 2023, 23(5): 33-44.
[2]	陈信强, 王美琳, 李朝锋, 杨洋, 梅骁峻, 周亚民. 基于深度学习与多级匹配机制的港区人员轨迹提取[J]. 交通运输系统工程与信息, 2023, 23(4): 70-79.
[3]	高云峰, 席建伟, 孙科. 面向客货分离交叉口的网联货车队列车速引导与信号优先组合优化[J]. 交通运输系统工程与信息, 2023, 23(4): 88-101.
[4]	巫威眺, 周霄, 朱彦辰, 李鹏, 邹弘辉, 李余. 考虑乘客时空灵活性的需求响应客货联运动态调度[J]. 交通运输系统工程与信息, 2023, 23(4): 211-227.
[5]	柏强, 江瑛, 李毅帆, 徐誌蔓. 基于改进时空轨迹理论的机场出发层车道边通行能力评估方法研究[J]. 交通运输系统工程与信息, 2023, 23(4): 228-236.
[6]	郝嘉田, 吴忠广, 田万利, 韩峰, 刘博文. 高速公路改扩建中央分隔带开口长度通用计算模型构建与验证[J]. 交通运输系统工程与信息, 2023, 23(4): 262-269.
[7]	秦严严, 肖腾飞, 贺正冰. 雾天场景下高速公路通行能力分析及提升策略[J]. 交通运输系统工程与信息, 2023, 23(3): 39-47.
[8]	王殿海, 黄莉莎, 曾佳棋, 蔡正义. 降雨对城市路网交通状态的影响机理[J]. 交通运输系统工程与信息, 2023, 23(3): 48-55.
[9]	韩磊, 张轮, 郭为安. 混合交通流环境下基于改进强化学习的可变限速控制策略[J]. 交通运输系统工程与信息, 2023, 23(3): 110-122.
[10]	葛慧敏, 臧文铠, 董磊, 周礼军. 大型多环道涡轮环交车道级信号控制方法[J]. 交通运输系统工程与信息, 2023, 23(3): 123-132.
[11]	付锐, 许清津, 葛振振, 陈桃, 吴付威, 袁伟. 弯道路侧标志梯度提示策略研究[J]. 交通运输系统工程与信息, 2023, 23(3): 133-144.
[12]	李思贤, 邢增勇, 钱大琳, 李鹏程, 袁梦. 危险货物道路运输车辆驾驶人风险倾向性分类研究[J]. 交通运输系统工程与信息, 2023, 23(3): 161-173.
[13]	杨洋, 贺昆, 王云鹏, 陈垚, 袁振洲. 面向动态交通流的高速公路事故风险模型空间移植研究[J]. 交通运输系统工程与信息, 2023, 23(3): 174-186.
[14]	康柳江, 李浩, 孙会君, 吴建军. 复杂山区工程建设物资运输无人机巡航模型构建与实证研究[J]. 交通运输系统工程与信息, 2023, 23(3): 290-299.
[15]	钱国敏, 任璐, 季青原, 冯远静, 章立辉, 王殿海. 考虑车流通行效率的混行交叉口时空资源配置[J]. 交通运输系统工程与信息, 2023, 23(2): 67-73.