城市交通疏散救援的元胞自动机模型

杨兆升; 高学英; 孙迪

doi:10.19818/j.cnki.1671-1637.2011.02.019

城市交通疏散救援的元胞自动机模型

doi: 10.19818/j.cnki.1671-1637.2011.02.019

杨兆升^{1, 2,},
高学英²,
孙迪²

1.
吉林大学汽车动态模拟国家重点实验室, 吉林长春 130022
2.
吉林大学交通学院, 吉林长春 130022

基金项目:

国家863计划项目 2009AA11Z218

详细信息

作者简介:
杨兆升(1941-), 男, 辽宁大连人, 吉林大学教授, 从事智能交通系统理论与技术研究

中图分类号: U491.54
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- 文章访问数: 680
- HTML全文浏览量: 143
- PDF下载量: 433
- 被引次数: 0
出版历程
- 收稿日期: 2010-10-18
- 刊出日期: 2011-04-25

Cellular automata model of urban traffic emergency evacuation and rescue

1.
State Key Laboratory of Automobile Dynamic Simulation, Jilin University, Changchun 130022, Jilin, China
2.
School of Transportation, Jilin University, Changchun 130022, Jilin, China

More Information

Author Bio:
YANG Zhao-sheng(1941-), male, professor, + 86-431-85095891, yangzs@jlu.edu.cn

摘要

摘要: 分析了城市内部各向车流冲突造成的车辆延误对疏散与救援决策的影响, 将交叉口控制参数引入到现有的基于元胞传输模型的应急疏散救援仿真模型中, 建立灾时交叉口紧急控制下的应急疏散救援仿真模型。改进后的模型以规划周期内疏散与救援加权行程时间最小为目标, 并引入反流策略。仿真结果表明: 由于低优先级的车流G1和G2让行高优先级车流G3和G4而产生延误, 其平均行程时间分别增加了10.0、11.1 s, 符合城市内部疏散与救援的实际情况; 反流策略实施后, 受益的车流G2和G4行程时间分别下降了6.5、6.0 s。可见, 提出的模型及反流策略有效。
- 元胞传输模型 /
- 动态交通分配 /
- 交叉口紧急控制 /
- 整数规划 /
- 反流策略
Abstract: The effects of vehicle delay on the emergency evacuation and rescue decision-making of all traffic flow conflicts within cities were analyzed.An intersection control parameter was introduced into the exciting simulation model of emergency evacuation and rescue, and the model was based on cell transmission model.A simulation model of emergency evacuation and rescue was built under intersection emergency control.In the improved model, the weighted travel time of emergency evacuation and rescue in planning horizon was minimized, and contraflow strategy was introduced.Simulation result shows that because giving way to high-priority traffic flows G3 and G4, the average travel times of low-priority traffic flows G1 and G2 increase by 10.0 s and 11.1 s respectively, which accords with the actual circumstance of urban emergency evacuation and rescue.After the implementation of contraflow strategy, the travel times of beneficial traffic flows G2 and G4 decrease by 6.5 s and 6.0 s respectively.So the model and contraflow strategy are feasible.
- cell transmission model /
- dynamic traffic assignment /
- intersection emergency control /
- integer programming /
- contraflow strategy

HTML全文

图 1 原始简单网络节点-弧段表达方法

Figure 1. Node-arc representation of original simple network

下载: 全尺寸图片幻灯片

图 2 反流后简单网络节点-弧段表达方法

Figure 2. Node-arc representation of simple network after contraflow

下载: 全尺寸图片幻灯片

图 3 CTM扩展网络

Figure 3. Augmented network of CTM

下载: 全尺寸图片幻灯片

表 1 G1组最优解

Table 1. Optimal solution of G1

表 2 G2组最优解

Table 2. Optimal solution of G2

表 3 G3组最优解

Table 3. Optimal solution of G3

表 4 G4组最优解

Table 4. Optimal solution of G4

表 5 理想行程时间

Table 5. Ideal travel times

表 6 实际行程时间对比

Table 6. Comparison of real travel times

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