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Louvre_Evacuation

来源:2019年 美赛 D题

问题背景

法国发生的恐怖袭击越来越多,在许多热门目的地,亟需一个应对紧急情况的疏散计划。 你们的ICM团队正在帮助设计在法国巴黎卢浮宫的疏散计划。 总的来说,疏散的目标是让所有的人都撤离,尽快安全离开大楼。 接到疏散通知后,为了尽快清空建筑,每个人尽可能的通过一个最佳出口。

问题要求

建立一个卢浮宫紧急疏散模型,以满足:

  • 当出现突发意外事件时,指导游客人群从卢浮宫疏散 (evacuate visitors from the museum)
  • 探讨安保/急救人员进入策略 (allow emergency personnel to enter the building)
  • 确定人流移动的瓶颈 (identify potential bottlenecks limiting movement towards the exits)
  • 模型考虑各种威胁 (a broad set of considerations and various types of potential threats)

My Work:

不考虑个体行为的情况下,可以将卢浮宫抽象为二维平面图,记录各个关键节点、出口、节点之间的长度、路径信息等,建立图论模型,求解最短路、网络流等,从而得到疏散时间,瓶颈等结果。

从人群疏散的角度看,查阅资料,我们发现目前疏散模型大致有两种处理方式,元胞自动机模型和社会力模型。 本程序主要模拟了人员疏散的撤离情况。

编程目的

  • 研究人群在一般空间的流动情况
  • 出口数量、分布对疏散时间的影响
  • 障碍物对人员流动的影响
  • 定量求解人流密度,确定疏散瓶颈

算法**

利用元胞自动机实现 (社会力模型人员会在地图上重叠)

  • 初始化地图,地图为矩形区域,基本信息包含长Length、宽Width、若干出口Exit、障碍物Barrier等
  • 基于该论文,出口距离在地图上反映为势能的高低,初始化地图的势能
  • 初始化人群,人群随机分布在地图的合法区域内
  • 疏散模拟:
    • 移动方向:每个人优先选择最短路进行撤离,考虑使用Moore型元胞,有8个移动方向
    • 移动速度:一定区域内(周围8个邻居元胞)人流密度决定人员的移动速度 (待改进:统计可视角度内的人流密度)

模拟结果

人员疏散过程模拟

gif1 gif2 png3 png4

热力图

反映瓶颈位置

pic1 pic2

代码说明

请见code目录
https://github.com/izcat/Louvre_Evacuation/blob/master/code/README.md

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