基本信息来源于合作网站,原文需代理用户跳转至来源网站获取       
摘要:
Regional cities in Japan are at the risk of experiencing big fire accidents or earthquakes every day.However,neither the number nor the capacity of shelters has increased because local governments might not consider them owing to budget shortfall.By contrast,wide-area evacuation simulations can easily provide an antagonizing image of regional urban disasters.After a disaster,the city collapses and the evacuation routes are closed;consequently,evacuees feel anxious and they cannot move as usual.This anxiety behavior has not been considered in previous related studies and simulations.In this study,a wide-area evacuation simulation is developed;this model can not only calculate the possibility of blocking escape routes when the city is broken but also provide safe and more realistic evacuation plans before a disaster occurs by incorporating into the simulation the risk avoidance behaviors of evacuees from road blockage,such as“the route re-seeking behavior”and“the shelter re-selecting behavior”.
推荐文章
Agent及Multi-Agent System的理论和应用
多Agent系统
Agent
人工智能
Multi-Agent在工控系统中的应用研究
Agent
Multi-Agent
现场总线
基于Multi-Agent 的主动式ESS设计
智能体
主管支持系统
主动式
基于Multi-Agent的图像理解
multi-agent
图像理解
色直方图
纹理直方图
区域抽取
内容分析
关键词云
关键词热度
相关文献总数  
(/次)
(/年)
文献信息
篇名 A Fundamental Study on Multi-agent Pedestrian Model Based on Risk Avoidance Behavior during Road Blockage and Evacuation Simulation of Regional Urban Disaster
来源期刊 土木工程与建筑:英文版 学科 工学
关键词 Wide-area EVACUATION simulation MULTI-AGENT model risk AVOIDANCE BEHAVIOR regional DISASTER prevention plan
年,卷(期) 2019,(4) 所属期刊栏目
研究方向 页码范围 219-237
页数 19页 分类号 TU
字数 语种
DOI
五维指标
传播情况
(/次)
(/年)
引文网络
引文网络
二级参考文献  (0)
共引文献  (0)
参考文献  (0)
节点文献
引证文献  (0)
同被引文献  (0)
二级引证文献  (0)
2019(0)
  • 参考文献(0)
  • 二级参考文献(0)
  • 引证文献(0)
  • 二级引证文献(0)
研究主题发展历程
节点文献
Wide-area
EVACUATION
simulation
MULTI-AGENT
model
risk
AVOIDANCE
BEHAVIOR
regional
DISASTER
prevention
plan
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
土木工程与建筑:英文版
月刊
1934-7359
武汉洪山区卓刀泉北路金桥花园C座4楼
出版文献量(篇)
1394
总下载数(次)
3
总被引数(次)
0
论文1v1指导