危险品运输网络渗流鲁棒性智能评估方法
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摘要
为高效评估危险品运输网络(HMTN)鲁棒性,科学管理危险品运输,开发一种智能评估网络渗流鲁棒性方法。从实际出发,综合考虑网络拓扑结构、节点/边渗流度、流量压力配比、负载阈值等参数,建立仿真识别模型,通过动态识别巨簇节点数量占总节点数量的比例度量网络渗流鲁棒性,并提出优化算法;以张家港HMTN为背景作仿真分析。结果表明:危险品运输网络渗流鲁棒性与突发事件规模成反比,阈值系数与渗流鲁棒性成正比;相同参数条件下,节点突发事件影响大于边突发事件;投入更多应急救援资源有助于预防边突发事件。
For the sake of effectively evaluating the robustness of HMTN and scientifically managing the transportation of hazmat,an intelligent method was developed for evaluating the percolation robustness of HMTN. Based on the reality,a simulating identification model was built according to considering the topology structure of network,the node/edge percolation degree,the transferring ration of traffic flow,and the loading limitation. The network percolation robustness was measured by dynamic identifying the proportion of the number of giant cluster nodes to the total of nodes,and an optimization algorithm was worked out. The method was applied to Zhangjiagang HMTN. The results show that there is an inversely proportional relationship between the scale of emergency accident and network percolation robustness,and there is a directly proportional relationship between loading limitation and percolation robustness,and that under the same parameters condition,the effect of an node emergency incident is greater than that of an edge emergency incident,and that providing greater rescue resources helps to prevent edge emergency incidents.
For the sake of effectively evaluating the robustness of HMTN and scientifically managing the transportation of hazmat,an intelligent method was developed for evaluating the percolation robustness of HMTN. Based on the reality,a simulating identification model was built according to considering the topology structure of network,the node/edge percolation degree,the transferring ration of traffic flow,and the loading limitation. The network percolation robustness was measured by dynamic identifying the proportion of the number of giant cluster nodes to the total of nodes,and an optimization algorithm was worked out. The method was applied to Zhangjiagang HMTN. The results show that there is an inversely proportional relationship between the scale of emergency accident and network percolation robustness,and there is a directly proportional relationship between loading limitation and percolation robustness,and that under the same parameters condition,the effect of an node emergency incident is greater than that of an edge emergency incident,and that providing greater rescue resources helps to prevent edge emergency incidents.
引文
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[2]HOSSAIN M,ALAM S,REES T,et al.Australian airport network robustness analysis:a complex network approach[C].Australasian Transport Research Forum,2013:1-21.
[3]YANG Yuhao,LIU Yongxue,ZHOU Minxi,et al.Robustness assessment of urban rail transit based on complex network theory:a case study of the Beijing subway[J].Safety Science,2015,79:149-162.
[4]ZOU Zhiyun,XIAO Yao,GAO Jianzhi.Robustness analysis of urban transit network based on complex networks theory[J].Kybernetes,2013,42(3):383-399.
[5]王国华,赵春燕.基于复杂网络的长沙市道路交通网络鲁棒性研究[J].计算机仿真,2014,31(6):178-182.WANG Guohua,ZHAO Chunyan.Study on robustness of Changsha road traffic network based on complex network[J].Computer Simulation,2014,31(6):178-182.
[6]张姝玮.城市路网鲁棒性对比研究[D].西安:长安大学,2016.ZHANG Shuwei.Comparative study on the urban road network robustness[D].Xi'an:Chang'an University,2016.
[7]轩俊伟.基于复杂网络的城市道路网鲁棒性分析[D].乌鲁木齐:新疆大学,2015.XUAN Junwei.Analyzing robustness of urban road network based on complex networks[D].Urumqi:Xinjiang University,2015.
[8]宗刚,陈先婷.突发事件对城市轨道交通网络的影响研究:以北京市为例[J].中国安全科学学报,2015,25(8):106-110.ZONG Gang,CHEN Xianting.Effect of emergency on urban railway transit network:a case study of Beijing[J].China Safety Science Journal,2015,25(8):106-110.
[9]张雅琴.突发事件下铁路网性能及传播影响分析的研究[D].北京:北京交通大学,2010.ZHANG Yaqin.Research on capability of railway network and propagation influence analysis in emergency[D].Beijing:Beijing Jiaotong University,2010.
[10]马晓丽,刘禹杰,陆键.城市道路危险货物运输风险评估[J].中国安全科学学报,2018,28(5):179-184.MA Xiaoli,LIU Yujie,LU Jian.Research on assessment of risk in hazardous materials transportation on urban road[J].China Safety Science Journal,2018,28(5):179-184.
[11]辛春林,孟佳,张建文,等.危险品运输路径优化问题研究综述[J].中国安全科学学报,2018,28(3):102-107.XIN Chunlin,MENG Jia,ZHANG Jianwen,et al.A review of researches on optimization of hazardous materials transportation route[J].China Safety Science Journal,2018,28(3):102-107.
[12]马剑,李文勇,梁满朝.基于卡尔曼滤波的交通突发事件影响范围预测研究[J].长沙大学学报,2012,26(5):86-89.MA Jian,LI Wenyong,LIANG Manchao.Research on the impact scale of transportation emergency events based on Kalman filtering[J].Journal of Changsha University,2012,26(5):86-89.
[13]汪送,战仁军.熵权集结多维数据的事故网络节点重要度评估[J].中国安全科学学报,2014,24(5):26-31.WANG Song,ZHAN Renjun.Evaluation of accident network node importance based on entropy weights integrated multidimensional data[J].China Safety Science Journal,2014,24(5):26-31.
[14]CHRISTENSEN K,MOLONEY N R.Complexity and criticality[M].London:Imperial College Press,2005:115-247.
[15]ALSTR?M P,BOHR T,CHRISTENSEN K,et al.Complexity and criticality[J].Physica A Statistical Mechanics&Its Applications,1996,340(4):4-6.
[16]DIJKSTRA E W.A note on two problems in connexion with graphs[J].Numerische Mathematik,1959,1(1):269-271.
[17]夏秋.危险品道路运输优化选线研究:以张家港市为例[D].南京:南京大学,2014.XIA Qiu.Research on route optimization for hazardous materials road transportation:a case of Zhangjiagang[D].Nanjing:Nanjing University,2014.