过饱和状态的潮汐车道交叉口群协同控制及仿真
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摘要
可变车道技术是潮汐式交通拥堵问题的有效解决手段之一,杭州西湖区潮汐走廊是连接城西居民区和市区的主干道。为了解决潮汐车道在早高峰通勤时过饱和状态下的拥堵问题,深入分析了潮汐走廊的实际交通数据并判断出潮汐车道的瓶颈路段。提出基于群体动力学的交叉口群协同控制算法,建立了双交叉口协同控制模型,并研究了潮汐走廊过饱和状态下瓶颈路段两端双交叉口的信号实时协同配时方案。利用VISSIM+MATLAB的自适应仿真控制技术对双交叉口配时方案进行了对比分析。仿真结果表明:在满足了交通流量的前提下,协同配时方案使得瓶颈路段的流速由22.7 km/h提高至28.2 km/h,提速24.23%,由中度拥堵改善为轻度拥堵,行车时间、平均延误时间等参数也均达到了不同程度的优化。然而,在双交叉口的协同配时控制达到一定效果的同时,下一路段的车辆流速却降低了12.35%,平均延误时间增加了8.96%。这说明:基于群体动力学算法的多交叉口协同配时方案设计,必须强调上下游交通流的关联关系,从全局角度进行全路段的交叉口群的协同控制。上述成果为城市潮汐车道的定量分析和实时智能交通控制的研究提供了一定的理论和实践基础。
Variable lane technology is one of the effective solutions to the problem of "tidal traffic congestion",the Tidal Corridor in Hangzhou West Lake District is the main road connecting the western suburbs and urban areas. In order to solve the commuter traffic congestion of over-saturated tidal lane in morning rush,the actual traffic data is deeply analyzed and the bottleneck section of the tidal lane is determined. Based on group dynamics,the cooperative control algorithm is put forward,and a doubleintersection cooperative control model is established,the real-time signal cooperative timing scheme under the supersaturated situation at both ends of the bottleneck road is researched. VISSIM + MATLAB adaptive simulation control technology is used to compare the signal timing schemes for intersections at both ends. The simulation results show that( 1) The speed in the bottleneck section increased from 22. 7 km/h to 28. 2 km/h due to the cooperative timing scheme,which has been improved by 24. 23%,The congestion level is improved from moderate to mild. Meanwhile,driving time,average delay time and other parameters also reached a certain degree of optimization.( 2) although the cooperative traffic control in the 2 intersections achieved a certain effect,the traffic flow in the next section decreased by 12. 35% while the average delaytime increased by 8. 96%. It means that the multi-intersection cooperative timing scheme design based on group dynamics algorithm must emphasize the correlation of upstream and downstream traffic flows. It is necessary to carry out cooperative control over the intersections of all road sections from the overall view. The above works provided a theoretical and practical basis for the quantitative analysis and real-time intelligent control of urban tidal lanes.
Variable lane technology is one of the effective solutions to the problem of "tidal traffic congestion",the Tidal Corridor in Hangzhou West Lake District is the main road connecting the western suburbs and urban areas. In order to solve the commuter traffic congestion of over-saturated tidal lane in morning rush,the actual traffic data is deeply analyzed and the bottleneck section of the tidal lane is determined. Based on group dynamics,the cooperative control algorithm is put forward,and a doubleintersection cooperative control model is established,the real-time signal cooperative timing scheme under the supersaturated situation at both ends of the bottleneck road is researched. VISSIM + MATLAB adaptive simulation control technology is used to compare the signal timing schemes for intersections at both ends. The simulation results show that( 1) The speed in the bottleneck section increased from 22. 7 km/h to 28. 2 km/h due to the cooperative timing scheme,which has been improved by 24. 23%,The congestion level is improved from moderate to mild. Meanwhile,driving time,average delay time and other parameters also reached a certain degree of optimization.( 2) although the cooperative traffic control in the 2 intersections achieved a certain effect,the traffic flow in the next section decreased by 12. 35% while the average delaytime increased by 8. 96%. It means that the multi-intersection cooperative timing scheme design based on group dynamics algorithm must emphasize the correlation of upstream and downstream traffic flows. It is necessary to carry out cooperative control over the intersections of all road sections from the overall view. The above works provided a theoretical and practical basis for the quantitative analysis and real-time intelligent control of urban tidal lanes.
引文
[1]王勇.城市交通网络可变车道设置方案研究[D].成都:西南交通大学,2014.WANG Yong.Research on Plan of Reversible Lane in Urban Traffic Network[D].Chengdu:Southwest Jiaotong University,2014.
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[10]裴玉龙,蒋贤才.饱和交通状态下的绿信比优化及其应用研究[J].哈尔滨工业大学学报,2005,37(11):1499-1502.PEI Yu-long,JIANG Xian-cai.Green Ratio Optimizing and Application in Saturated Traffic Flow[J].Journal of Harbin Institute of Technology,2005,37(11):1499-1502.
[11]王莉,王明哲,周丰.实时自适应交通信号控制CPN建模分析[J].公路交通科技,2008,25(6):115-119.WANG Li,WANG Ming-zhe,ZHOU Feng,et al.Modeling and Analyzing of Real-time Adaptive Traffic Signal Control with CPN[J].Journal of Highway and Transportation Research and Development,2008,25(6):115-119.
[12]ABU-LEBDEH G,CHEN H,BENEKOHAL R.F.Modeling Traffic Output for Design of Dynamic Mu1ti-cycle Control in Congested Conditions[J].Journal of Intelligent Transportation Systems,2007,11(1):25-40.
[13]BEDE Z,SZABOG,PETER T.Optimalization of Road Traffic with the Applied of Reversible Direction Lanes[J].Periodica Polytechnica Transportation Engineering,2010,38(1):31-38.
[14]陈东静,李林波,朱锐,等.信号交叉口可变车道主预信号配时协调关系研究[J].重庆交通大学学报:自然科学版,2013,32(2):252-256.CHEN Dong-jing,LI Lin-bo,ZHU Rui,et al.Coordinate Relationship between Main Signals and Pre-Signals of the Variable Lane at Signalized Intersection[J].Journal of Chongqing Jiaotong University:Natural Science Edition,2013,32(2):252-256.
[15]王曦,祝付玲.基于高斯混合分布的交通拥堵评价模型[J].公路交通科技,2011,28(2):127-132.WANG Xi,ZHU Fu-ling.Evaluation Model of Traffic Congestion Based on Gaussian Mixture Distribution[J].Journal of Highway and Transportation Research and Development,2011,28(2):127-132.
[16]邵娟,程琳.基于Synchro系统的干线绿波控制优化技术[J].公路交通科技,2013,30(9):116-121.SHAO Juan,CHENG Lin.Green-wave Control Optimization Technique of Arterial Roads Based on Synchro[J].Journal of Highway and Transportation Research and Development,2013,30(9):116-121.
[17]南天伟,敖梦雅,魏丽英.基于模糊神经网络的城市干道信号协调控制[J].公路交通科技,2012,29(1):145-149.NAN Tian-wei,AO Meng-ya,WEI Li-ying.Traffic Signal Coordination for Urban Arterial Roads Based on Fuzzy Neural Network[J].Journal of Highway and Transportation Research and Development,2012,29(1):145-149.
[18]谢正全.基于VISSIM的实时数据交通仿真技术的应用研究[D].成都:西南交通大学,2010.XIE Zheng-quan.Research on Application of Real Time Data Traffic Simulation Technology Based on VISSIM[D].Chengdu:Southwest Jiaotong University,2010.
[2]祝付玲.城市道路交通拥堵评价指标体系研究[D].南京:东南大学,2006.ZHU Fu-ling.Research on Index System of Urban Traffic Congestion Evaluation[D].Nanjing:Southeast University,2006.
[3]杨莹莹.杭州市道路交通高峰拥堵成因分析与对策研究[J].城市道桥与防洪,2012(7):15-18.YANG Ying-ying.Study on Cause Analysis and Countermeasure of Traffic Jam in Peak Time in Hangzhou[J].Urban Roads Bridges&Flood Control,2012(7):15-18.
[4]代磊磊,姜桂艳,裴玉龙.饱和信号交叉口排队长度预测[J].吉林大学学报:工学版,2008,38(6):1287-1290.DAI Lei-lei,JIANG Gui-yan,PEI Yu-long.Prediction of Queue Length at Saturate Signalized Intersection[J].Journal of Jilin University:Engineering and Technology Edition,2008,38(6):1287-1290.
[5]楚天广,杨正东,邓魁英,等.群体动力学与协调控制研究中的若干问题[J].控制理论与应用,2010,27(1):86-93.CHU Tian-guang,YANG Zheng-dong,DENG Kui-ying,et al.Problems in Swarm Dynamics and Coordinated Control[J].Control Theory&Applications,2010,27(1):86-93.
[6]黄超.杭州西湖“潮汐走廊”交通模型及评价研究[D].杭州:杭州电子科技大学,2016.HUANG Chao.Research on Traffic Model and Evaluation of“Tidal Corridor”in West Lake,Hangzhou[D].Hangzhou:Hangzhou Dianzi University,2016.
[7]张敏捷.基于群体动力学的交通协调控制理论与方法研究[D].广州:华南理工大学,2013.ZHANG Min-jie.Research on Traffic Coordination Control Theories and Methods Based on Group Dynamics[D].Guangzhou:South China University of Technology,2013.
[8]JAHANGIRI A,AFANDIZADEH S,KALANTARI N.The Optimization of Traffic Signal Timing for Emergency Evacuation Using the Simulated Annealing Algorithm[J].Transport,2011,26(2):133-140.
[9]张邻,吴伟明,黄选伟.基于动态信号配时的非线性规划模型[J].公路交通科技,2014,31(8):134-137.ZHANG Lin,WU Wei-ming,HUANG Xuan-wei.A Nonlinear Programming Model Based on Dynamic Signal Timing[J].Journal of Highway and Transportation Research and Development,2014,31(8):134-137.
[10]裴玉龙,蒋贤才.饱和交通状态下的绿信比优化及其应用研究[J].哈尔滨工业大学学报,2005,37(11):1499-1502.PEI Yu-long,JIANG Xian-cai.Green Ratio Optimizing and Application in Saturated Traffic Flow[J].Journal of Harbin Institute of Technology,2005,37(11):1499-1502.
[11]王莉,王明哲,周丰.实时自适应交通信号控制CPN建模分析[J].公路交通科技,2008,25(6):115-119.WANG Li,WANG Ming-zhe,ZHOU Feng,et al.Modeling and Analyzing of Real-time Adaptive Traffic Signal Control with CPN[J].Journal of Highway and Transportation Research and Development,2008,25(6):115-119.
[12]ABU-LEBDEH G,CHEN H,BENEKOHAL R.F.Modeling Traffic Output for Design of Dynamic Mu1ti-cycle Control in Congested Conditions[J].Journal of Intelligent Transportation Systems,2007,11(1):25-40.
[13]BEDE Z,SZABOG,PETER T.Optimalization of Road Traffic with the Applied of Reversible Direction Lanes[J].Periodica Polytechnica Transportation Engineering,2010,38(1):31-38.
[14]陈东静,李林波,朱锐,等.信号交叉口可变车道主预信号配时协调关系研究[J].重庆交通大学学报:自然科学版,2013,32(2):252-256.CHEN Dong-jing,LI Lin-bo,ZHU Rui,et al.Coordinate Relationship between Main Signals and Pre-Signals of the Variable Lane at Signalized Intersection[J].Journal of Chongqing Jiaotong University:Natural Science Edition,2013,32(2):252-256.
[15]王曦,祝付玲.基于高斯混合分布的交通拥堵评价模型[J].公路交通科技,2011,28(2):127-132.WANG Xi,ZHU Fu-ling.Evaluation Model of Traffic Congestion Based on Gaussian Mixture Distribution[J].Journal of Highway and Transportation Research and Development,2011,28(2):127-132.
[16]邵娟,程琳.基于Synchro系统的干线绿波控制优化技术[J].公路交通科技,2013,30(9):116-121.SHAO Juan,CHENG Lin.Green-wave Control Optimization Technique of Arterial Roads Based on Synchro[J].Journal of Highway and Transportation Research and Development,2013,30(9):116-121.
[17]南天伟,敖梦雅,魏丽英.基于模糊神经网络的城市干道信号协调控制[J].公路交通科技,2012,29(1):145-149.NAN Tian-wei,AO Meng-ya,WEI Li-ying.Traffic Signal Coordination for Urban Arterial Roads Based on Fuzzy Neural Network[J].Journal of Highway and Transportation Research and Development,2012,29(1):145-149.
[18]谢正全.基于VISSIM的实时数据交通仿真技术的应用研究[D].成都:西南交通大学,2010.XIE Zheng-quan.Research on Application of Real Time Data Traffic Simulation Technology Based on VISSIM[D].Chengdu:Southwest Jiaotong University,2010.