基于舆情传播理论的应急疏散需求演化特性研究
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摘要
为更准确的刻画应急疏散需求演化规律、揭示演化特性,进一步准确预测应急疏散交通需求、为制定应急疏散策略提供量化支撑。分析影响疏散需求演化的因素,构建了基于舆情传播理论的应急疏散需求演化微分方程模型;从微观个体的角度,定义基于舆情传播的个体交互规则并建立多智能体模型,并运用仿真方法刻画疏散舆情在社会网络结构上的传播过程以及社会联系紧密程度、个体敏感性、初始疏散者数量及分布等因素变换条件下疏散需求演化的差异性。基于舆情传播理论的应急疏散需求演化微分方程模型刻画了应急疏散需求曲线的宏观特性,该曲线斜率呈现"小-大-小"的"S型"曲线特征;随着初始疏散者数量从100增加至500、个体平均敏感性由25%增加至100%以及平均节点度由3增加至12,疏散需求达到90%的时间分别减少了62%,57%,90%,应急疏散需求曲线的整体速率增大,此3项因素主要影响应急疏散需求曲线的整体速率,对曲线的线形影响较小,而初始疏散者的分布类型对应急疏散需求曲线的线形及疏散需求增长速率的影响均较大;个体平均敏感性和社会联系紧密程度根据取值的不同,对疏散需求演化影响的敏感性呈现明显差异。
In order to accurately describe evolution rules of emergency evacuation demands and evolutionary characteristics,and to provide quantitative support for strategies of emergency evacuation based on predicted traffic demands,this study analyzes influencing factors of evolution of evacuation demand,then a differential equation model is established based on public opinion propagation theory.Rules of individual interaction are defined from microcosmic point of view,then a multi-agent model is developed.Meanwhile,simulation is used to describe the propagation process of evacuation in social network structure.Differences of evolution are analyzed considering various social connection degree,individual sensitivity,number and distribution of initial evacuees.The results show macro-characteristics of the curve of emergency evacuation demands,and the slope of the curve presents as"S"curve characteristics with a trend of small-large-small.With the number of initial evacuees increases from 100 to 500,average sensitivity of individual increases from 25% to100%,and average node degree increases from 3 to 12,time of evacuation demand reaching 90% decreases by 62%,57%,and 90%,respectively.The speed of the emergency evacuation demand increases.These three factors mainly affect overall speed of emergency evacuation demand,but have little influence on shape of the curve,while distribution type of the initial evacuees has great impacts on both shape of the curve and growth rate of the evacuation demand.Average sensitivity of individuals and degree of social connection show significant differences in sensitivity to the evolution of evacuation demands,depending on different values.
In order to accurately describe evolution rules of emergency evacuation demands and evolutionary characteristics,and to provide quantitative support for strategies of emergency evacuation based on predicted traffic demands,this study analyzes influencing factors of evolution of evacuation demand,then a differential equation model is established based on public opinion propagation theory.Rules of individual interaction are defined from microcosmic point of view,then a multi-agent model is developed.Meanwhile,simulation is used to describe the propagation process of evacuation in social network structure.Differences of evolution are analyzed considering various social connection degree,individual sensitivity,number and distribution of initial evacuees.The results show macro-characteristics of the curve of emergency evacuation demands,and the slope of the curve presents as"S"curve characteristics with a trend of small-large-small.With the number of initial evacuees increases from 100 to 500,average sensitivity of individual increases from 25% to100%,and average node degree increases from 3 to 12,time of evacuation demand reaching 90% decreases by 62%,57%,and 90%,respectively.The speed of the emergency evacuation demand increases.These three factors mainly affect overall speed of emergency evacuation demand,but have little influence on shape of the curve,while distribution type of the initial evacuees has great impacts on both shape of the curve and growth rate of the evacuation demand.Average sensitivity of individuals and degree of social connection show significant differences in sensitivity to the evolution of evacuation demands,depending on different values.
引文
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[19]SONG Y L,YAN X D.A method for formulizing disaster evacuation demand curves based on SImodel[J].International Journal of Environmental Research&Public Health,2016,13(10):986.
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[2]WANG X L,GUO W,CHENG Y.Understanding the centripetal effect and evacuation efficiency of evacuation assistants:Using the extended dynamic communication field model[J].Safety Science,2015(74):150-159.
[3]YIN W,TUITE P M,UKKUSURI S V,et al.An agent-based modeling system for travel demand simulation for hurricane evacuation[J].Transportation Research Part C,2014(42):44-59.
[4]史建港,陈艳艳,任福田.修正四阶段法在城市交通抗灾规划中的应用[J].公路交通科技,2006,23(5):79-83.SHI Jiangang,CHEN Yanyan,REN Futian.Application of revised four-stage method in urban transport plan for emergency response[J],Journal of Highway and Transportation Research and Development,2006,23(5):79-83.(in Chinese)
[5]LUJAK M,GIORDANI S.Centrality measures for evacuation:Finding agile evacuation routes[J].Future Generation Computer Systems,2018(83):401-412.
[6]BAYRAM V,TANSEL B C,YAMAN H.Compromising system and user interests in shelter location and evacuation planning[J].Transportation Research Part B,2015(72):146-163.
[7]BAKER E J.Predicting response to hurricane warnings-reanalysis of data from four studies[J].Mass Emerg,1979,4(1):9-24.
[8]RADWAN A,HOBEIKA A,SIVASAILAM D.Acomputer simulation model for rural network evacuation under natural disasters[J].ITEJ.1985(55):25-30.
[9]US ARMY CORPS OF ENGINEERS.Alabama hurricane evacuation study technical data report:behavioral analysis[R].Washington,D.C.:Department of the Army,2000.
[10]林建新,韦献兰.基于S曲线的时变交通应急疏散需求预测[J].交通信息与安全,2009,27(3):92-96.LIN Jianxin,WEI Xianlan.Time variation demand forecast of emergent traffic evacuation based on s-curve[J].Journal of Transport Information and Safety,2009,27(3):92-96.(in Chinese)
[11]耿彦斌,韦献兰.多集结点的应急交通时变疏散需求预测模型[J].交通运输系统工程与息,2010,4(10):131-136.GENG Yanbin,WEI Xianlan.Time varying evacuation demand forecasting model for emergency traffic with multiple assembly locations[J].Transportation Systems Engineering and Information Technology,2010,4(10):131-136.(in Chinese)
[12]XU K,DAVIDSON R A,NOZICK L K,et al.Hurricane evacuation demand models with a focus on use for prediction in future events[J].Transportation Research Part A,2016(87):90-101.
[13]NEKOVEE M,MORENO Y,BIANCONI G,et al.Theory of rumor spreading in complex social networks[J].Physica A:Statistical Mechanics and Its Applications,2007,374(1):457-470.
[14]熊国强,赵昕.耦合情绪因素的群体性突发事件模型与演化仿真[J].系统工程,2016,34(5):112-120.XIONG Guoqiang,ZHAO Xin.The system dynamic model and evolution simulation on coupling-emotional grope events[J].Systems Engineering,2016,34(5):112-120.(in Chinese)
[15]张耀峰,肖人彬.群体性突发事件的舆情演化模型与仿真[J].计算机应用研究,2015,32(2):351-355.ZHANG Yaofeng,XIAO Renbin.Model and simulation of consensus evolution in mass emergency[J].Application Research of Computers,2015,32(2):351-355.(in Chinese)
[16]WEINISCH K,BRUECKNER P.The impact of shadow evacuation on evacuation time estimates for nuclear power plants[J].Journal of Emergency Management,2015,13(2):145.
[17]HASAN S.UKKUSURI S V.A threshold model of social contagion proccess for evacuation decision Making[J].Transportation Research Part B.2011,45(10):1590-1605.
[18]MADIREDDY M,KUMARA S,MEDEIROS DJ,et al.Leveraging social networks for efficient hurricane evacuation[J].Transportation Research Part B.2015(77):199-212.
[19]SONG Y L,YAN X D.A method for formulizing disaster evacuation demand curves based on SImodel[J].International Journal of Environmental Research&Public Health,2016,13(10):986.
[20]宋玉垒.突发事件下城市道路交通疏散模型研究[D].北京:北京交通大学,2017.SONG Yulei.Research on urba road traffic evacuation models in emergency[D].Beijing:Beijing Jiaotong University,2017.(in Chinese)