Please wait a minute...
 
引用检索 快速检索 DOI 高级检索
地理学报    2017, Vol. 72 Issue (8): 1458-1475     DOI: 10.11821/dlxb201708010
  城市研究 本期目录 | 过刊浏览 | 高级检索 |
基于应急疏散智能体模型模拟的城市避难所空间配置——以上海市静安区为例
於家1(),温家洪1,陈芸2,廖邦固1,杜士强1
1. 上海师范大学地理系,上海 200234
2. 澳大利亚联邦科学与工业研究组织(CSIRO)水土研究所(Land & Water),堪培拉
Spatial configuration of urban shelters based on simulation using emergency evacuation agent-based model:A case study in Jing'an District, Shanghai
YU Jia1(),WEN Jiahong1,CHEN Yun2,LIAO Banggu1,DU Shiqiang1
1. Department of Geography, Shanghai Normal University, Shanghai 200234, China
2. CSIRO Land & Water, Canberra, ACT 2601, Australia
全文: PDF (7429 KB)   HTML
输出: BibTeX | EndNote (RIS)     
摘要 

城市应急避难所的空间配置一直是灾害防治和城市安全研究领域的热点问题。本文以城市居民尽快地,尽少拥挤地到达满足容纳需求的应急避难所为目标,整合遥感影像数据、高精度人口分布数据、交通路网数据和专家知识等数据,综合运用智能体模型和多准则决策方法,对城市避难所空间配置开展研究。本文设计了三类与应急疏散相关的智能体:政府智能体、避难所智能体和居民智能体,来实现应急疏散的模拟,并根据模拟结果支持应急避难所的空间选址和配置。选址方法上运用了多准则决策方法和权重敏感性分析,在选址高适宜区域内选定避难所的新建方案。以新的避难所空间布局和配置为条件,执行新一轮的应急疏散模拟过程,实现选址的循环优化,从而获得最终的避难所空间配置方案。本文以上海市静安区的应急避难所空间配置分析为案例,生成了该区域应急避难所的详细空间配置方案,该方案能帮助居民在尽少拥挤风险下尽快疏散到附近的避难所。本文提出的方法充实了中国城市避难所选址的相关理论与可操作性的技术基础,为其他地区开展避难所的配置工作提供借鉴与参考。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
於家
温家洪
陈芸
廖邦固
杜士强
关键词 应急疏散智能体避难所空间选址地理信息系统上海静安区 
Abstract

Spatial configuration of urban emergency shelters is one of the hotspot issues in disaster prevention and urban emergency management. The purpose of this study is to conduct spatial configuration of urban shelters which can help urban residents have access to the emergency shelters as soon as possible with less congestion. The spatial configuration model is built on the basis of the agent-based model and multi-criteria decision-making method. Remote sensing image data, high-precision population data, road network data and expert knowledge are integrated in the model. Three types of agents involved in emergency evacuation are designed, which include the government agent, shelter agents and resident agents, to conduct evacuation simulation. A government agent can delimitate the service areas of shelters in accordance with the administrative boundaries and road distances between the positions of residents and the locations of the shelters. Shelter agents can select specified land uses as potentially available shelters for different disasters, generate the service areas of shelters, record the information of the residents in their service areas and do relative statistical work of evacuation processes. Resident agents have a series of attributes, such as ages, positions, and walking speeds. They also have several behaviors, such as reducing speed when walking in the crowd, and helping old people and children. Integrating these three types of agents which are correlated with each other, we can simulate evacuation procedures. The simulation results are utilized to support location-allocation and configuration of emergency shelters. The location-allocation method of this study is based on multi-criteria decision making and weight sensitivity analysis, so that the locations of new shelters can be selected in highly-suitable regions for location-allocation. When the new shelters are allocated, a new round of emergency evacuation simulation will be executed to realize loop optimization of location-allocation based on the new spatial distribution of shelters to generate the final spatial configuration plan. A case study in Jing'an District, Shanghai, China, was conducted to demonstrate the feasibility of the model. The simulation results convinced that the new model can provide detailed planning for spatial configuration of urban shelters, which can help the residents evacuate to nearby shelters as quickly as possible with less congestion risks. The model provides a new methodology to conduct high-quality location-allocation of urban emergency shelters. It can also be extended to conduct similar spatial configuration work in other urban regions for different kinds of emergency shelters.

Key wordsemergency evacuation    agent    shelter    spatial location-allocation    GIS    Jing'an District    Shanghai
收稿日期: 2016-07-18      出版日期: 2017-08-23
基金资助:国家自然科学基金项目(41201548, 41401603, 71603168)
引用本文:   
於家, 温家洪, 陈芸等 . 基于应急疏散智能体模型模拟的城市避难所空间配置——以上海市静安区为例[J]. 地理学报, 2017, 72(8): 1458-1475.
YU Jia, WEN Jiahong, CHEN Yun et al . Spatial configuration of urban shelters based on simulation using emergency evacuation agent-based model:A case study in Jing'an District, Shanghai[J]. Acta Geographica Sinica, 2017, 72(8): 1458-1475.
链接本文:  
http://www.geog.com.cn/CN/10.11821/dlxb201708010      或      http://www.geog.com.cn/CN/Y2017/V72/I8/1458
Fig. 1  研究方法框架
Fig. 2  上海市静安区南部5个街道的地理位置
Fig. 3  上海市静安区的空间数据
Fig. 4  上海市静安区4个时间步对应的模拟图层
Fig. 5  到达避难所的居民数、道路上(正在疏散)居民数和仍在住宅建筑中的居民数
Fig. 6  基于路网距离和基于智能体方法的上海市静安区各避难所总疏散时间
Fig. 7  上海市静安区避难所人口容量和服务范围内需接纳居民数
Fig. 8  上海市静安区标识道路中的拥挤路段:整个疏散过程中各个路段的拥挤时间
Fig. 9  多准则决策(AHP)中的评价因子图层及其权重
避难所
编号
所在
街道
服务范围内是否
新建避难所
是否拟
扩容
服务范围内是否进行道路拓宽
1 曹家渡
2 曹家渡
3 曹家渡
4 曹家渡
5 曹家渡
6 江宁路
7 江宁路
8 江宁路
30 静安寺
Tab. 1  避难所相关规划决策方案示例
Fig. 10  上海市静安区新建避难所的选址适宜性和选址结果
Fig. 11  第二轮疏散模拟中,到达避难所的居民数、道路上(正在疏散)居民数和仍在住宅建筑中的居民数
Fig. 12  基于智能体方法的上海市静安区第二轮各避难所总疏散时间
Fig. 13  上海市静安区第二轮标识道路中的拥挤路段:整个疏散过程中各个路段的拥挤时间
[1] Kılcı F, Kara B Y, Bozkaya B.Locating temporary shelter areas after an earthquake: A case for Turkey. European Journal of Operational Research, 2015, 243(1): 323-332.http://www.sciencedirect.com/science/article/pii/S0377221714009588
[2] Wei L, Li W, Li K, Liu H, et al.Decision support for urban shelter locations based on covering model. Procedia Engineering, 2012, 43: 59-64.http://www.sciencedirect.com/science/article/pii/S1877705812030226
[3] Bashawri A, Garrity S, Moodley K.An overview of the design of disaster relief shelters. Procedia Economics and Finance, 2014, 18: 924-931.http://dx.doi.org/10.1016/S2212-5671(14)01019-3
[4] Hu F, Xu W, Li X.A modified particle swarm optimization algorithm for optimal allocation of earthquake emergency shelters. International Journal of Geographical Information Science, 2012, 26(9): 1643-1666.http://www.tandfonline.com/doi/full/10.1080/13658816.2011.643802
[5] Roth R.Computer solutions to minimum cover problems. Operations Research, 1969, 17(3): 455-465.http://www.jstor.org/stable/168379
[6] Dagoberto R Q, Roger Z R.A new heuristic for the capacitated vertex P-center problem. Advances in Artificial Intelligence, 2013, 8109: 279-288.http://link.springer.com/10.1007/978-3-642-40643-0_29
[7] Xiao N.A parallel cooperative hybridization approach to the P-median problem. Environment and Planning B, 2012, 39(4): 755-774.http://www.researchgate.net/publication/286174777_A_Parallel_Cooperative_Hybridization_Approach_to_the_p-Median_Problem
DOI: 10.1068/b38004     
[8] Teixeira J C, Antunes A P.A hierarchical location model for public facility planning. European Journal of Operational Research, 2008, 185(1): 92-104.http://www.sciencedirect.com/science/article/pii/S0377221707000240
[9] Fernandez J, Pelegrin B, Plastria F, et al.Planar location and design of a new facility with inner and outer competition: an interval lexicographical-like solution procedure. Networks and Spatial Economics, 2007, 7(1): 19-44.http://link.springer.com/article/10.1007/s11067-006-9005-4
[10] Ebrahimi Z A, Hosseini N H, Zare M Y, et al.Multi-period hub set covering problems with flexible radius: A modified genetic solution. Applied Mathematical Modelling, 2016, 40(4): 2968-2982.http://www.sciencedirect.com/science/article/pii/S0307904X15005983
[11] Liu S, Chan F T S, Chung S H. A study of distribution center location based on the rough sets and interactive multi-objective fuzzy decision theory. Robotics & Computer-Integrated Manufacturing, 2011, 27(2): 426-433.
[12] Rogerson P A, Delmelle E, Batta R, et al.Optimal sampling design for variables with varying spatial importance. Geographical Analysis, 2004, 36(2): 177-194.http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.2004.tb01131.x/full
[13] Wang J F, Stein A, Gao B B, et al.A review of spatial sampling. Spatial Statistics, 2012, 2: 1-14.http://www.sciencedirect.com/science/article/pii/S2211675312000255
[14] Huang Dianjian, Wu Zongzhi, Cai Sijing, et al.Emergency adaption of urban emergency shelter: Analytic hierarchy process-based assessment method. Journal of Natural Disasters, 2006, 15(1): 52-58.
[黄典剑, 吴宗之, 蔡嗣经, . 城市应急避难所的应急适应能力: 基于层次分析法的评价方法. 自然灾害学报, 2006, 15(1): 52-58.]年度引用
[15] Nappi M, Souza J.Disaster management: hierarchical structuring criteria for selection and location of temporary shelters. Natural Hazards, 2015, 75(3): 2421-2436.http://link.springer.com/article/10.1007/s11069-014-1437-4
[16] Kar B, Hodgson M E.A GIS-based model to determine site suitability of emergency evacuation shelters. Transactions in GIS, 2008, 12(2): 227-248.http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9671.2008.01097.x/abstract
[17] Lee Y L, Ishii H, Tai C A.Earthquake shelter location evaluation considering road structure. IEEE The 8th International Conference on Intelligent Systems Design and Applications. Kaohsiung: IEEE, 2008, 1: 495-497.
[18] Chu J, Su Y.The application of TOPSIS method in selecting fixed seismic shelter for evacuation in cities. Systems Engineering Procedia, 2012, 3: 391-397.http://www.sciencedirect.com/science/article/pii/S2211381911001500
[19] Ma D X, Chu J Y, Liu X N, et al.Study on evaluation of earthquake evacuation capacity in village based on multi-level grey evaluation. Systems Engineering Procedia, 2011, 1: 85-92.年度引用
[20] Cha Y J, Agrawal A K, Kim Y, et al.Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures. Expert Systems with Applications, 2012, 39(9): 7822-7833.http://www.sciencedirect.com/science/article/pii/S0957417412000838
[21] Yu J, Wen J, Jiang Y. Agent-based evacuation simulation for spatial allocation assessment of urban shelters. International Conference on Intelligent Earth Observing and Applications, Proc. of SPIE, 2015, 9808: 98081N1-10.http://proceedings.spiedigitallibrary.org/article.aspx?articleid=2475959
DOI: 10.1117/12.2209277     
[22] Gwynne S, Galea E R, Lawrence P J, et al.A review of the methodologies used in the computer simulation of evacuation from the built environment. Building and Environment, 1999, 34(6): 741-749.http://www.sciencedirect.com/science/article/pii/S0360132398000572
[23] Lindell M K.EMBLEM2: An empirically based large scale evacuation time estimate model. Transportation Research Part A: Policy and Practice, 2008, 42(1): 140-154.http://www.sciencedirect.com/science/article/pii/S0965856407000560
[24] Hashemi M, Alesheikh A A.GIS: Agent-based modeling and evaluation of an earthquake-stricken area with a case study in Tehran, Iran. Natural Hazards, 2013, 69(3): 1895-1917.http://link.springer.com/article/10.1007/s11069-013-0784-x
[25] Chen P, Zhang J, Zhang L, et al.Evaluation of resident evacuations in urban rainstorm waterlogging disasters based on scenario simulation: Daoli District (Harbin, China) as an example. International Journal of Environmental Research and Public Health, 2014, 11: 9964-9980.http://pubmedcentralcanada.ca/pmcc/articles/PMC4210961/
DOI: 10.3390/ijerph111009964      PMID: 25264676     
[26] Helbing D, Farkas I, Vicsek T.Simulating dynamical features of escape panic. Nature, 2000, 47(5): 487-490.http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM11028994
DOI: 10.1038/35035023      PMID: 11028994     
[27] Murakami Y, Minami K, Kawasoe T, et al.Multi-agent simulation for crisis management. IEEE Workshop on Knowledge Media Networking, 2002, 2: 135-139.http://doi.ieeecomputersociety.org/10.1109/KMN.2002.1115175
[28] Keßel A, Klüpfel H, Wahle J, et al.Microscopic simulation of pedestrian crowd motion. Pedestrian and Evacuation Dynamics, Berlin, 2002, 5(2): 193-200.
[29] Uno K, Kashiyama K.Development of simulation system for the disaster evacuation based on multi-agent model using GIS. Tsinghua Science Technology, 2008, 13(Suppl.1): 348-353.http://www.sciencedirect.com/science/article/pii/S1007021408701731
[30] Wu Jianhong, Weng Wenguo, Ni Shunjiang.Urban emergency evacuation plans based on GIS and multi-agent systems. Journal of Tsinghua University, 2010, 50(8): 1168-1172.
[吴建宏, 翁文国, 倪顺江. 基于GIS和Multi-Agent的城市应急疏散. 清华大学学报, 2010, 50(8): 1168-1172.]http://www.cnki.com.cn/Article/CJFDTotal-QHXB201008009.htm
[31] Dong P, Ramesh S, Nepali A.Evaluation of small-area population estimation using LiDAR, Landsat TM and parcel data. International Journal of Remote Sensing, 2010, 31(21): 5571-5586.
[32] Silva´n-ca´rdenas J L, Wang L, Rogerson P, et al. Assessing fine-spatial-resolution remote sensing for small-area population estimation. International Journal of Remote Sensing, 2010, 31(21): 5605-5634.http://www.tandfonline.com/doi/abs/10.1080/01431161.2010.496800
[33] Chen Y, Yu J, Khan S.The spatial framework for weight sensitivity analysis in AHP-based multi-criteria decision making. Environmental Modelling and Software, 2013, 48: 129-140.http://www.sciencedirect.com/science/article/pii/S1364815213001461
[34] Roytman M Y.Principles of Fire Safety Standards for Building Constructions. New Delhi: Amerind Publish Co, 1975: 1-429.http://agris.fao.org/openagris/search.do?recordID=US201300517649
[1] 张绍云,周忠发,熊康宁,田衷珲,陈全,闫利会,谢雅婷. 贵州洞穴空间格局及影响因素分析[J]. 地理学报, 2016, 71(11): 1998-2009.
[2] 汤青,徐勇,董晓辉,李扬,刘艳华,孙晓一. 芦山地震灾后重建地区土地资源安全评价[J]. 地理学报, 2015, 70(4): 650-663.
[3] 查良松, 邓国徽, 谷家川. 1992-2013年巢湖流域土壤侵蚀动态变化[J]. 地理学报, 2015, 70(11): 1708-1719.
[4] 李少英, 黎夏, 刘小平, 吴志峰, 艾彬, 陈明辉, 黎海波, 刘萌伟. 基于多智能体的就业与居住空间演化多情景模拟——快速工业化区域研究[J]. 地理学报, 2013, 68(10): 1389-1400.
[5] 龙瀛, 毛其智, 杨东峰, 王静文. 城市形态、交通能耗和环境影响集成的多智能体模型[J]. 地理学报, 2011, 66(8): 1033-1044.
[6] 刘斌涛, 刘邵权, 陶和平, 史展, 郭仕利, 曹伟超. 基于GIS的山区土地资源安全定量评价模型 ——以四川省凉山州为例[J]. 地理学报, 2011, 66(8): 1131-1140.
[7] 张鸿辉, 曾永年, 谭荣, 刘慧敏. 多智能体区域土地利用优化配置模型及其应用[J]. 地理学报, 2011, 66(7): 972-984.
[8] 潘峰, 田长彦, 邵峰, 周伟, 陈飞. 新疆克拉玛依市生态敏感性研究[J]. 地理学报, 2011, 66(11): 1497-1507.
[9] 张国坤, 邓伟, 张洪岩, 宋开山, 李恒达. 新开河流域土地利用格局变化图谱分析[J]. 地理学报, 2010, 65(9): 1111-1120.
[10] 刘小平, 黎夏, 陈逸敏, 刘涛, 李少英. 基于多智能体的居住区位空间选择模型[J]. 地理学报, 2010, 65(6): 695-707.
[11] 黎夏, 刘小平, 何晋强, 李丹, 陈逸敏, 庞瑶, 李少英. 基于耦合的地理模拟优化系统[J]. 地理学报, 2009, 64(8): 1009-1018.
[12] 陈翔1, 李强1, 王运静1, 陈晋2, 唐巧3. 临界簇模型及其在地面公交线网可达性评价中的应用[J]. 地理学报, 2009, 64(6): 693-700.
[13] 陶海燕, 黎夏, 陈晓翔. 基于多智能体的居住空间格局演变的真实场景模拟[J]. 地理学报, 2009, 64(6): 665-676.
[14] 张鸿辉, 曾永年, 金晓斌, 尹长林, 邹滨. 多智能体城市土地扩张模型及其应用[J]. 地理学报, 2008, 63(8): 869-881.
[15] 陶海燕, 黎夏, 陈晓翔, 刘小平. 基于多智能体的地理空间分异现象模拟 ———以城市居住空间演变为例[J]. 地理学报, 2007, 62(6): 579-588.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2013 《地理学报》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发