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摘要: 基于竞争-合作的群体决策机制，将单点信号优化构建为各相位的交叉口通行权的竞争过程，将多点协同构建为上下游相位之间的协作过程，提出了一种兼顾多交叉口协同效益和单交叉口控制优化的路网信号配时设计方法；利用车路协同环境下路网内车辆路径信息的可感知性，动态精准地量化解析上下游交通耦合关系；在此基础上建立了分层动态决策框架，在单层决策中剥离了上下游交叉口控制决策对本地决策的影响，解耦协同控制模型中路网交通状态和信号控制决策之间的复合关系；设计了基于交叉口内各交通流向竞争力的分布式信号配时决策算法，并通过仿真试验平台比较了群体决策协同控制方法与传统协同控制方法的控制效果。研究结果表明: 相较于传统协同控制方法，群体决策协同控制方法可动态适应路网交通需求，在交通效率和稳定性上具有显著优势，在不同饱和度的交通需求水平下可降低车均延误15%以上；在路网交通饱和度较高的情况下, 群体决策协同控制方法延误降低幅度可达19.2%，控制优势更加明显；由于群体决策协同控制方法可在下游交叉口进口道车辆排队过长时减少上游车辆流出，可降低路网最大排队长度超40%，有效规避路网溢流风险；通过对群体决策协同控制模型的分布式求解，可实现单次决策过程计算时间小于0.01 s，具有应用于大规模复杂路网的实时信号配时决策的潜力。Abstract: On the basis of the group decision-making mechanism with competition and cooperation, the isolated signal optimization was modeled as the right-of-way competition process of all phases at intersections, and the coordination among many intersections was modeled as the cooperation process between upstream and downstream phases. A signal timing design method for road networks was proposed under considering both the multi-intersection synergy and the optimal control of isolated intersections. The perceptibility of the vehicle route information in road networks under the vehicle-road cooperative environment was used to quantitatively analyze the coupling relationship between upstream and downstream traffic in a dynamic and accurate manner. On this basis, a hierarchical dynamic decision-making framework was established to avoid the impact of the control decisions of upstream and downstream intersections on local decisions in single-layer decision-making, and the composite relationship between the traffic states of road networks and the signal control decision in the cooperative control model was decoupled. A distributed decision-making algorithm for signal timing was designed based on the competitiveness of each traffic flow at intersections, and the performances of the proposed group decision-making cooperative control method and the traditional cooperative control method was compared by a simulation test platform. Research results show that compared with the traditional cooperative control method, the group decision-making cooperative control method can dynamically adapt to the traffic demand of the road network, and has significant advantages in traffic efficiency and stability. Under the traffic demand levels with different saturation degrees, the average vehicle delay can reduces by more than 15%. In the case of high traffic saturation, the delay can reduce by 19.2%, so the control advantage is more obvious. As the upstream outflow of the vehicles can be reduced by the group decision-making cooperative control method when the vehicle queues at downstream intersections for inflow are long, the maximum queue length in road networks can be cut by over 40%. In this way, the overflow risk in road networks can be avoided. Through the distributed solution of the group decision-making cooperative control method, the calculation time of a single decision-making process is less than 0.01 s, so the method has the potential to be applied to the real-time signal timing decision in large-scale complex road network. 1 tab, 7 figs, 31 refs.
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图 1 多交叉口协同控制的群体决策
Figure 1. Group decision-making of multi-interaction coordinated control
图 4 基于竞争的信号配时决策过程
Figure 4. Decision-making process of signal timing based on competition
表 1 交叉口车均延误
Table 1. Average vehicle delays at intersection
交通需求饱和度 0.6 1.0 1.2 绿波协同控制/s 34.1 52.1 113.9 群体决策控制/s 28.7 43.6 92.0 延误降低幅度/% 15.8 16.3 19.2 
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[1] YAO Han-dong, CUI Jian-xun, LI Xiao-peng, et al. A trajectory smoothing method at signalized intersection based on individualized variable speed limits with location optimization[J]. Transportation Research Part D: Transport and Environment, 2018, 62: 456-473. doi: 10.1016/j.trd.2018.03.010 [2] AIISLAM S M A B, HAJBABAIE A. Distributed coordinated signal timing optimization in connected transportation networks[J]. Transportation Research Part C: Emerging Technologies, 2017, 80: 272-285. doi: 10.1016/j.trc.2017.04.017 [3] BEAK B, HEAD K L, FENG Yi-heng. Adaptive coordination based on connected vehicle technology[J]. Journal of the Transportation Research Board, 2017, 2619(1): 1-12. doi: 10.3141/2619-01 [4] LITTLE J D C, KELSON M D, GARTNER N H. MAXBAND: a program for setting signals on arteries and triangular networks[J]. Journal of the Transportation Research Board, 1981, 795: 40-46. [5] GARTNER N H, ASSMAN S F, LASAGA F, et al. A multi-band approach to arterial traffic signal optimization[J]. Transportation Research Part B: Methodological, 1991, 25(1): 55-74. doi: 10.1016/0191-2615(91)90013-9 [6] 曲大义, 万孟飞, 王兹林, 等. 干线协调控制优化及其应用[J]. 交通运输工程学报, 2016, 16(5): 112-121. doi: 10.3969/j.issn.1671-1637.2016.05.013QU Da-yi, WAN Meng-fei, WANG Zi-lin, et al. Arterial coordination control optimization and application[J]. Journal of Traffic and Transportation Engineering, 2016, 16(5): 112-121. (in Chinese) doi: 10.3969/j.issn.1671-1637.2016.05.013 [7] YAN Hui-min, HE Fang, LIN Xi, et al. Network-level multiband signal coordination scheme based on vehicle trajectory data[J]. Transportation Research Part C: Emerging Technologies, 2019, 107: 266-286. doi: 10.1016/j.trc.2019.08.014 [8] 刘芹, 徐建闽. 交通区域协调控制模型[J]. 交通运输工程学报, 2012, 12(3): 108-112. doi: 10.3969/j.issn.1671-1637.2012.03.018LIU Qin, XU Jian-min. Coordinated control model of regional traffic signals[J]. Journal of Traffic and Transportation Engineering, 2012, 12(3): 108-112. (in Chinese) doi: 10.3969/j.issn.1671-1637.2012.03.018 [9] LI P F, MIRCHANDANI P, ZHOU X S. Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork[J]. Transportation Research Part B: Methodological, 2015, 81: 103-130. doi: 10.1016/j.trb.2015.08.011 [10] WADA K, USUI K, TAKIGAWA T, et al. An optimization modeling of coordinated traffic signal control based on the variational theory and its stochastic extension[J]. Transportation Research Part B: Methodological, 2018, 117: 907-925. doi: 10.1016/j.trb.2017.08.031 [11] WANG P R, LI P F, CHOWDHURY F R, et al. A mixed integer programming formulation and scalable solution algorithms for traffic control coordination across multiple intersections based on vehicle space-time trajectories[J]. Transportation Research Part B: Methodological, 2020, 134: 266-304. doi: 10.1016/j.trb.2020.01.006 [12] LEE S, WONG S C. Group-based approach to predictive delay model based on incremental queue accumulations for adaptive traffic control systems[J]. Transportation Research Part B: Methodological, 2017, 98: 1-20. [13] 李冰. 基于随机交通需求预测的主动分布式信号控制研究[D]. 昆明: 昆明理工大学, 2019.LI Bing. Research on proactive distributed signal control based on stochastic traffic demand prediction[D]. Kunming: Kunming University of Science and Technology, 2019. (in Chinese) [14] GOKULAN B P, SRINIVASAN D. Distributed geometric fuzzy multiagent urban traffic signal control[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3): 714-727. doi: 10.1109/TITS.2010.2050688 [15] EL-TANTAWY S, ABDULHAI B, ABDELGAWAD H. Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC): methodology and large-scale application on downtown Toronto[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3): 1140-1150. doi: 10.1109/TITS.2013.2255286 [16] ZHU F, AZIZ H M A, QIAN X W, et al. A junction-tree based learning algorithm to optimize network wide traffic control: a coordinated multi-agent framework[J]. Transportation Research Part C: Emerging Technologies, 2015, 58: 487-501. doi: 10.1016/j.trc.2014.12.009 [17] CHU Tian-shu, WANG Jie, LARA C, et al. Multi-agent deep reinforcement learning for large-scale traffic signal control[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(3): 1086-1095. doi: 10.1109/TITS.2019.2901791 [18] WANG Xing-min, YIN Ya-feng, FENG Yi-heng, et al. Learning the max pressure control for urban traffic networks considering the phase switching loss[J]. Transportation Research Part C: Emerging Technologies, 2022, 140: 103670. doi: 10.1016/j.trc.2022.103670 [19] 杨文臣, 张轮, ZHU Feng. 多智能体强化学习在城市交通网络信号控制方法中的应用综述[J]. 计算机应用研究, 2018, 35(6): 1613-1618. doi: 10.3969/j.issn.1001-3695.2018.06.003YANG Wen-chen, ZHANG Lun, ZHU Feng. Multi-agent reinforcement learning based traffic signal control for integrated urban network: survey of state of art[J]. Application Research of Computers, 2018, 35(6): 1613-1618. (in Chinese) doi: 10.3969/j.issn.1001-3695.2018.06.003 [20] LI Zhen-ning, YU Hao, ZHANG Guo-hui, et al. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning[J]. Transportation Research Part C: Emerging Technologies, 2021, 125: 103059. doi: 10.1016/j.trc.2021.103059 [21] LE T, KOVÁCS P, WALTON N, et al. Decentralized signal control for urban road networks[J]. Transportation Research Part C: Emerging Technologies, 2015, 58: 431-450. doi: 10.1016/j.trc.2014.11.009 [22] UKKUSURI S, DOAN K, AZIZ H M A. A bi-level formulation for the combined dynamic equilibrium based traffic signal control[J]. Procedia—Social and Behavioral Sciences, 2013, 80: 729-752. [23] FENG Yi-heng, HEAD K L, KHOSHMAGAHAM S, et al. A real-time adaptive signal control in a connected vehicle environment[J]. Transportation Research Part C: Emerging Technologies, 2015, 55: 460-473. doi: 10.1016/j.trc.2015.01.007 [24] 王正武, 罗大庸, 黄中祥. 基于CTM的信号优化设计及求解[J]. 交通运输工程学报, 2007, 7(4): 84-88. http://transport.chd.edu.cn/article/id/200704018WANG Zheng-wu, LUO Da-yong, HUANG Zhong-xiang. Optimization designing and solving of signal based on CTM[J]. Journal of Traffic and Transportation Engineering, 2007, 7(4): 84-88. (in Chinese) http://transport.chd.edu.cn/article/id/200704018 [25] MOHEBIFARD R, HAJBABAIE A. Optimal network-level traffic signal control: a benders decomposition-based solution algorithm[J]. Transportation Research Part B: Methodological, 2019, 121: 252-274. [26] AI ISLAM S M A B, HAJBABAIE A, AZIZ H M A. A real- time network-level traffic signal control methodology with partial connected vehicle information[J]. Transportation Research Part C: Emerging Technologies, 2020, 121: 102830. [27] ZAIDI A A, KULCSÁR B, WYMEERSCH H. Back-pressure traffic signal control with fixed and adaptive routing for urban vehicular networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(8): 2134-2143. [28] YU Hao, LIU Pan, FAN Yue-yue, et al. Developing a decentralized signal control strategy considering link storage capacity[J]. Transportation Research Part C: Emerging Technologies, 2021, 124: 102971. [29] GARTNER N H, LITTLE J D C, GABBAY H. Optimization of traffic signal settings by mixed-integer linear programming: Part Ⅱ: the network synchronization problem[J]. Transportation Science, 1975, 9(4): 321-343. [30] MA Cheng-yuan, YU Chun-hui, YANG Xiao-guang. Trajectory planning for connected and automated vehicles at isolated signalized intersections under mixed traffic environment[J]. Transportation Research Part C: Emerging Technologies, 2021, 130: 103309. [31] YU C H, FENG Y H, LIU H X, et al. Corridor level cooperative trajectory optimization with connected and automated vehicles[J]. Transportation Research Part C: Emerging Technologies, 2019, 105: 405-421. -
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