Coordination control method of regional traffic flow
Article Text (Baidu Translation)
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摘要: 根据Agent技术和模糊控制方法, 提出了区域交通流协调控制方法。以路段拥挤度和绿灯持续时间为输入变量, 以绿灯修正延长时间为输出变量, 确定了变量数据的获取方式以及变量之间的对应关系, 设计了协调控制器。根据下游交叉口配时的不同方案, 制定了不同的模糊控制规则, 修正了控制策略, 并运用MATLAB进行仿真。仿真结果表明: 采用Agent技术和模糊控制方法后, 平均总延误为127.431 s·km-1, 下降了约9.9%;路段平均流量密度为18.828 veh·km-1; 路段平均流量为9 597 veh·h-1; 平均车速为17.798 km·h-1, 提高了约6.3%。可见, 路网密度明显降低, 交通状况明显改善。Abstract: On the basis of agent technology and fuzzy control method, a coordination control method of regional traffic flow was proposed. Section congestion degree and green light duration were taken as input variables, green light's correction prolong time was taken as output variable, the acquiring methods of variable data and the corresponding relationships among the variables were determined, and a coordination controller was designed. According to different schemes of time assignments at downstream intersections, various fuzzy control rules were formulated, control strategy was corrected, and corresponding simulation was carried out by MATLAB. Simulation result shows that after using the control method, average total delay is 127.431 s·km-1, and decreases by about 9.9%. Average section density is 18.828 veh·km-1. Average section flow is 9 597 veh·h-1. Average speed is 17.798 km·h-1, and rises by about 6.3%. It can be significantly seen that with the method section density decreases, and traffic condition is improved.
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Key words:
- traffic control /
- regional traffic flow /
- coordination control /
- fuzzy control /
- agent technology
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表 1 路段拥挤度参数值
Table 1. Parameter values of road congestion degrees
参数 Z VS S M L VL σ1 0.11 0.12 0.11 0.11 0.09 0.10 a1 0.00 0.17 0.37 0.58 0.80 1.00 
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表 2 绿灯持续时间参数值
Table 2. Parameter values in green light duration
参数 VS S M L VL σ2 0.136 0.100 0.120 0.100 0.100 a2 0.000 0.250 0.500 0.750 1.000
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表 3 绿灯修正延长时间参数值
Table 3. Parameter values of correction prolong times for green light
参数 Z VS S M L VL σ3 0.95 0.96 0.96 0.95 0.95 0.85 a3 0.00 2.00 4.00 6.00 8.00 10.00
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表 4 绿灯时模糊规则
Table 4. Fuzzy rules in green time
T X Z VS S M L VL VS Z Z VS VS S M S Z Z VS VS S M M Z Z VS S S M L Z Z S S M M VL Z VS S M M L
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表 5 红灯时模糊规则制定
Table 5. Fuzzy rule in red time
T X Z VS S M L VL VS Z Z VS S S M S Z Z S S M M M Z VS S M M L L Z VS S M L L VL Z Z M L L VL
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表 6 仿真结果
Table 6. Simulation result
指标 小汽车 货车 公交车 总体 均值 标准差 均值 标准差 均值 标准差 均值 标准差 延误/(s·km-1) 定时控制 139.771 6.347 152.951 8.493 174.329 9.119 141.355 6.443 模糊控制 125.739 4.395 142.773 6.633 159.364 3.411 127.431 4.353 密度/(veh·km-1) 定时控制 17.264 0.783 1.007 19.054 模糊控制 17.062 0.759 1.007 18.828 流量/(veh·h-1) 定时控制 9 057 398 295 9750 模糊控制 8 917 392 288 9597 行程车速/(km·h-1) 定时控制 16.815 0.489 14.951 0.519 14.922 0.544 16.666 0.488 模糊控制 17.979 0.357 15.606 0.428 15.889 0.237 17.798 0.346
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表 7 分析结果
Table 7. Analysis result
评价指标 定时控制 模糊控制 总路程/km 21 266.4 20 872.7 总行程时间/h 1 167.2 1 055.4 停车次数/[次·(veh·km)-1] 2.7 2.6 停车时间/min 144.7 112.7
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