Please wait a minute...
 
Remote Sensing for Land & Resources    2019, Vol. 31 Issue (1) : 277-282     DOI: 10.6046/gtzyyg.2019.01.36
|
Research on emergency evacuation vulnerability of the traffic network model based on GIS
Wen JIANG1,2,3, Qiming QIN1,2,3()
1.Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
2.Beijing Key Lab of Spatial Information Integration and 3S Application, Peking University, Beijing 100871, China
3.Mapping and Geo-information for Geographic Information Basic Software and Applications, Engineering Research Center of National Administration of Surveying, Beijing 100871, China
Download: PDF(4483 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

In order to analyze the influence of traffic network on urban emergency evacuation, the authors studied the vulnerabilities of traffic network and the model of emergency evacuation by GIS spatial analysis. Searching for vulnerable points in road network was conducted by minimal cost maximal flow (MCMF) algorithm. Then the algorithm was evaluated using the road network data in Beijing. Compared with existing software, the proposed method based on GIS platform extracts the road network vulnerabilities more accurately, and it also increases the utilization of the road network in the emergency evacuation and speed up the evacuation.
Keywords GIS      traffic network model      emergency evacuation      vulnerability      MCMF     
:  P208  
Corresponding Authors: Qiming QIN     E-mail: qmqin@pku.edu.cn
Issue Date: 14 March 2019
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Wen JIANG
Qiming QIN
Cite this article:   
Wen JIANG,Qiming QIN. Research on emergency evacuation vulnerability of the traffic network model based on GIS[J]. Remote Sensing for Land & Resources, 2019, 31(1): 277-282.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.36     OR     https://www.gtzyyg.com/EN/Y2019/V31/I1/277
Fig.1  Single source-single terminal network
Fig.2  Multisource-multiterminal network
Fig.3  Transformation of multisource-multiterminal network
Fig.4  Flow chart of SPFA algorithm
Fig.5  Distribution of road network in a region of Beijing
Fig.6  Road network model of emergency evacuation
Fig.7  Design diagram of system interface
Fig.8  Flow distribution and analysis of vulnerability
Fig.9  Flow distribution under minimal cost maximal flow
类别 扩容前 扩容后
最大流/(个/min) 2 637 5 052
最小费用/min 16.361 16.337
Tab.1  Minimal time cost and maximal flow before and after road network expansion
[1] Hans J M, Sell T C . Evacuation Risks-An Evaluation[R]. Washington,D.C.US Environmental Protection Agency, 1974.
[2] Voorhees A M . Evacuation Times Assessment for the Diablo Canyon Nuclear Power Plant[R]. Pacific Gas and Electric Company, 1980.
[3] Sheffi Y . Evacuation studies for nuclear power plant sites:A new challenge for transportation engineers[J]. Institute of Transportation Ergineers, 1981,35(6):2528-2535.
[4] Hobeika A G, Kim C . Comparison of traffic assignments in evacuation modeling[J]. IEEE Transactions on Engineering Management, 1998,45(2):192-198.
doi: 10.1109/17.669768 url: http://ieeexplore.ieee.org/document/669768/
[5] 袁媛, 汪定伟 , 灾害扩散实时影响下的应急疏散路径选择模型[J]. 系统仿真学报, 2008,20(6):1563-1566.
url: http://www.cqvip.com/Main/Detail.aspx?id=26859935
[5] Yuan Y, Wang D W . Route selection model in emergency evacuation under real time effect of disaster extension[J]. Journal of System Simulation, 2008,20(6):1563-1566.
[6] 张培红, 陈宝智, 卢兆明 . 人员应急疏散行动开始前的决策行为[J]. 公路交通科技, 2008,25(9):154-158.
doi: 10.3321/j.issn:1005-3026.2005.02.021 url: http://d.wanfangdata.com.cn/Periodical/dbdxxb200502021
[6] Zhang P H, Chen B Z, Lu Z M . Personal decision behavior before emergency evacuation[J]. Journal of Northeastern University(Natural Science), 2008,25(9):154-158.
[7] 谢素华 . 论我国公路交通应急及运输保障体系的建设[J]. 公路交通科技, 2008,26(2):179-182.
doi: 10.3969/j.issn.1002-0268.2008.09.031 url: http://www.cnki.com.cn/Article/CJFDTotal-GLJK200809030.htm
[7] Xie S H . Study on emergency response and transport organization system between China and developed countries[J]. Journal of Northeastern University(Natural Science), 2008,26(2):179-182.
[8] 刘小明, 胡红 . 应急交通疏散研究现状与展望[J]. 交通运输工程学报, 2008,8(3):14.
doi: 10.3321/j.issn:1671-1637.2008.03.023 url: http://d.wanfangdata.com.cn/Periodical/jtysgcxb200803023
[8] Liu X M, Hu H . Research status and prospect of emergency transportation evacuation[J]. Journal of Traffic and Transportation Engineering, 2008,8(3):14.
[9] 宋永朝, 潘晓东, 杨轸 , 等. 灾害事件下局域路网应急疏散交通分配模型[J]. 公路交通科技, 2010,27(5):88-92.
doi: 10.3969/j.issn.1002-0268.2010.05.018 url: http://www.cqvip.com/Main/Detail.aspx?id=33739854
[9] Song Y C, Pan X D, Yang Z , et al. Traffic assignment model of emergency evacuation in local network against disaster affairs[J]. Journal of Highway and Transportation Research and Development, 2010,27(5):88-92.
[10] 尹洪英, 徐丽群 . 道路交通网络脆弱性评估研究现状与展望[J]. 交通运输系统工程与信息, 2010,10(3):7-13.
doi: 10.3969/j.issn.1009-6744.2010.03.002 url: http://d.wanfangdata.com.cn/Periodical/jtysxtgcyxx201003002
[10] Yin H Y, Xu L Q . Vulnerability assessment of transportation road networks[J]. Journal of Transportation Systems Engineering and Information Technology, 2010,10(3):7-13.
[11] 陈岳明, 萧德云 . 路网应急疏散问题建模及其应用的进展[J]. 科学技术与工程, 2008,20(6):1563-4950.
doi: 10.3969/j.issn.1671-1815.2008.17.037 url: http://d.wanfangdata.com.cn/Periodical/kxjsygc200817037
[11] Chen Y M, Xiao D Y . Review of evacuation modeling in emergency and its application.[J]. Scinece Technology and Engineering, 2008,20(6):1563-4950.
[12] 袁建平, 方正, 卢兆明 . 城市灾时大范围人员应急疏散探讨[J]. 自然灾害学报, 2005,14(6):116-119.
doi: 10.3969/j.issn.1004-4574.2005.06.021 url: http://www.cnki.com.cn/Article/CJFDTotal-ZRZH200506020.htm
[12] Yuan J P, Fang Z, Lu Z M . Large-scale evacuation of people from urban disaster.[J]. Journal of Natural Disasters, 2005,14(6):116-119.
[13] 韩伯棠 . 管理运筹学[M]. 北京: 高等教育出版社, 2010.
[13] Han B T.Management Operational Research.[M]. Beijing: Higher Education Press, 2010.
[1] WU Yijie, KONG Xuesong. Simulation and development mode suggestions of the spatial pattern of “ecology-agriculture-construction” land in Jiangsu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 238-248.
[2] LI Dong, TANG Cheng, ZOU Tao, HOU Xiyong. Detection and assessment of the physical state of offshore artificial reefs[J]. Remote Sensing for Natural Resources, 2022, 34(1): 27-33.
[3] ZANG Liri, YANG Shuwen, SHEN Shunfa, XUE Qing, QIN Xiaowei. A registration algorithm of images with special textures coupling a watershed with mathematical morphology[J]. Remote Sensing for Natural Resources, 2022, 34(1): 76-84.
[4] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[5] ZHANG Jinghua, OUYANG Yuan, LIU Hong, HUANG Hanxiao, ZHANG Tengjiao, LI Fu, LI Tong. Eco-geological vulnerability assessment based on major controlling factors: A case study of Xichang City, Sichuan Province[J]. Remote Sensing for Natural Resources, 2021, 33(4): 181-191.
[6] WANG Shuang, ZHANG Lei, ZHANG Junyong, WANG Yile. Characteristics of GIS applications in national fitness[J]. Remote Sensing for Natural Resources, 2021, 33(4): 265-271.
[7] YANG Wenna, ZHOU Liang, SUN Dongqi. Ecological vulnerability assessment of the Yellow River basin based on partition-integration concept[J]. Remote Sensing for Natural Resources, 2021, 33(3): 211-218.
[8] ZHAO Longxian, DAI Jingjing, ZHAO Yuanyi, JIANG Qi, LIU Tingyue, FU Minghai. A study of mine site selection of the Duolong ore concentration area in Tibet based on RS and GIS technology[J]. Remote Sensing for Land & Resources, 2021, 33(2): 182-191.
[9] MIAO Miao, XIE Xiaoping. Spatial-temporal evolution analysis of Rizhao coastal zone during 1988—2018 based on GIS and RS[J]. Remote Sensing for Land & Resources, 2021, 33(2): 237-247.
[10] ZHANG Mengsheng, YANG Shuwen, JIA Xin, ZANG Liri. An automatic registration algorithm for remote sensing images based on grid index[J]. Remote Sensing for Land & Resources, 2021, 33(1): 123-128.
[11] YAO Kun, ZHANG Cunjie, HE Lei, LI Yuxia, LI Xiaoju. Dynamic evaluation and prediction of ecological environment vulnerability in the middle-upper reaches of the Yalong River[J]. Remote Sensing for Land & Resources, 2020, 32(4): 199-208.
[12] Yongquan WANG, Qingquan LI, Chisheng WANG, Jiasong ZHU, Xinyu WANG. Tethered UAVs-based applications in emergency surveying and mapping[J]. Remote Sensing for Land & Resources, 2020, 32(1): 1-6.
[13] Renbo SONG, Yuxin ZHU, Shangshan DING, Qiaoning HE, Xiyuan WANG, Yuexiang WANG. An automatic method for extracting skeleton lines from arbitrary polygons based on GIS spatial analysis[J]. Remote Sensing for Land & Resources, 2020, 32(1): 51-59.
[14] Yiqiang SHI, Qiuqin DENG, Jun WU, Jian WANG. Regression analysis of MODIS aerosol optical thickness and air quality index in Xiamen City[J]. Remote Sensing for Land & Resources, 2020, 32(1): 106-114.
[15] Xuanchi CHEN, Rong CHEN, Yufeng WU, Yueyue WANG. Research on the geological background of tea planting in Duyun City based on RS and GIS[J]. Remote Sensing for Land & Resources, 2020, 32(1): 224-231.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech