城市快速路速度引导预测控制模型

陈大山; 孙剑; 李克平

doi:10.19818/j.cnki.1671-1637.2012.01.016

城市快速路速度引导预测控制模型

doi: 10.19818/j.cnki.1671-1637.2012.01.016

同济大学交通运输工程学院，上海 201804

基金项目:

国家自然科学基金项目 61004113

高等学校博士学科点专项科研基金项目 200802471072

详细信息

作者简介:
陈大山（1983 -），男，江苏宿迁人，同济大学工学博士研究生，从事交通信息控制研究

李克平（1960 -），男，上海人，同济大学教授，工学博士

中图分类号: U491.112
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出版历程
- 刊出日期: 2012-02-25

Speed guidance predictive control model on urban expressway

School of Transportation Engineering, Tongji University, Shanghai 201804, China

More Information

Author Bio:
CHEN Da-shan(1983-), male, doctoral student, +86-21-69583656, shyits@126.com

LI Ke-ping(1960-), male, professor, PhD, +86-21-69583656, keping_li@vip.163.com

摘要

摘要: 在城市快速路控制系统中, 将速度引导作为控制变量, 建立了宏观动态交通流模型。以车辆总行程时间与速度引导为目标函数, 计算了城市快速路入口区域流量和匝道入口区域流量, 建立了快速路速度引导预测控制模型, 对速度引导进行优化设计, 利用MATLAB软件对下游交通流突变进行仿真分析。分析结果表明: 通过速度引导控制, 交通流平均速度由72.704 6km.h^-1上升到74.167 6km.h^-1, 交通流平均密度由23.011 2veh.km^-1下降到21.156 7veh.km^-1, 波动均小于8%;速度方差下降, 且最大值仅为420(km.h^-1)²; 速度引导控制前后的速度方差与密度方差之比分别为3.57、1.91;在交通流突变时段内, 速度引导控制前后的速度方差与密度方差之比分别为4.56、2.34。可见, 速度引导控制模型有效。
- 城市快速路 /
- 交通控制 /
- 宏观动态交通流模型 /
- 速度引导 /
- 预测控制模型
Abstract: In the control system of urban expressway, speed guidance was taken as control variable, and the macro dynamic traffic flow of urban experssway model was established. Total vehicle traveling time and speed guidance were taken as objective functions, and the entrance flows of urban expressway and ramp were calculated. The speed guidance predictive control model of urban expressway was established to optimize speed guidance, and the mutation of downstream traffic flow was simulated by using MATLAB. Simulation result shows that because of speed guidance control, the average speed of traffic flow increases from 72. 704 6 km~ h^-1 to 74. 167 6 km ~ h^-1, the average density of traffic flow decreases from 23. 011 2 veh ~ km^-1 to 21. 156 7 veh ~ km^-1, and their fluctuations are less than 8%. The speed variance of traffic flow decreases, and the maximum value is only 420 (kin ~ h^-1)². The speed and density deviation ratios between before and after speed guidance control are 3.57 and 1.91 respectively. During traffic flow's mutation period, the speed and density deviation ratios between before and after speed guidance control are 4. 56 and 2. 34 respectively. So the control model is effective.
- urban expressway /
- traffic control /
- macro dynamic traffic flow model /
- speed guidance /
- predictive control model

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图 1 控制原理

Figure 1. Control principle

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图 2 预测流程

Figure 2. Predictive flow

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图 3 仿真路段

Figure 3. Simulation sections

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图 4 无速度引导控制下的密度

Figure 4. Densities without speed guidance control

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图 5 无速度引导控制下的速度

Figure 5. Speeds without speed guidance control

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图 6 速度引导

Figure 6. Speed guidances

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图 7 速度引导控制下的密度

Figure 7. Densities under speed guidance control

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图 8 速度引导控制下的速度

Figure 8. Speeds under speed guidance control

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图 9 平均速度比较

Figure 9. Comparison of average speeds

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图 10 平均密度比较

Figure 10. Comparison of average densities

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图 11 密度方差

Figure 11. Variances of densities

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图 12 速度方差

Figure 12. Variances of speeds

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表 1 模型参数

Table 1. Model parameters

表 2 参数比较

Table 2. Parameter comparison

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