摘要翻译：
勒阿弗尔集聚(CODAH)包括16个被分类为Seveso的高门槛机构。在文献中，我们构建脆弱性地图来帮助决策者评估风险。这种办法仍然是静态的，在估计脆弱性时确实考虑到人口流离失所的情况。我们提出了一个基于动态漏洞图的决策工具来评估CODAH不同部门的疏散难度。本文利用地理信息系统(GIS)对地图进行可视化处理，通过大图中的社区检测算法，使地图随道路交通状态的变化而变化。
---
英文标题：
《A Dynamic Vulnerability Map to Assess the Risk of Road Network Traffic
  Utilization》
---
作者：
Michel Nabaa (LITIS), Cyrille Bertelle (LITIS), Antoine Dutot (LITIS),
  Damien Olivier (LITIS), Pascal Mallet
---
最新提交年份：
2009
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
--

---
英文摘要：
  Le Havre agglomeration (CODAH) includes 16 establishments classified Seveso with high threshold. In the literature, we construct vulnerability maps to help decision makers assess the risk. Such approaches remain static and do take into account the population displacement in the estimation of the vulnerability. We propose a decision making tool based on a dynamic vulnerability map to evaluate the difficulty of evacuation in the different sectors of CODAH. We use a Geographic Information system (GIS) to visualize the map which evolves with the road traffic state through a detection of communities in large graphs algorithm.
---
PDF链接：
https://arxiv.org/pdf/0911.1707