Purpose–The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem(DCNDP)and dynamic critical links detection problem(DCLDP)in stochastic networks...Purpose–The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem(DCNDP)and dynamic critical links detection problem(DCLDP)in stochastic networks.DCNDP and DCLDP consist of identifying the subset of nodes and links,respectively,whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs,in the graph modeling the transport network.The identification of such nodes(or links)helps to better control the road traffic and predict the necessary measures to avoid congestion.Design/methodology/approach–A Markovian decision process is used to model the shortest path problem underdynamic trafficconditions.Effectivealgorithmstodeterminethe criticalnodes(links)whileconsideringthe dynamicity of the traffic network are provided.Also,sensitivity analysis toward capacity reduction for critical links is studied.Moreover,the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.Findings–The numerical results demonstrate that the use of dynamic shortest path(time dependency)as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.Research limitations/implications–The research in this paper also revealed several challenges,which call for future investigations.First,the authors have restricted our experimentation to a small network where the only focus is on the model behavior,in the absence of historical data.The authors intend to extend this study to very large network using real data.Second,the authors have considered only congestion to assess network’s criticality;future research on this topic may include other factors,mainly vulnerability.Practical implications–Taking into consideration the dynamic and 展开更多
基金acknowledgment to Dr Ali Benssam for his invaluable support during all the steps of the project and in the writing of the paper.
文摘Purpose–The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem(DCNDP)and dynamic critical links detection problem(DCLDP)in stochastic networks.DCNDP and DCLDP consist of identifying the subset of nodes and links,respectively,whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs,in the graph modeling the transport network.The identification of such nodes(or links)helps to better control the road traffic and predict the necessary measures to avoid congestion.Design/methodology/approach–A Markovian decision process is used to model the shortest path problem underdynamic trafficconditions.Effectivealgorithmstodeterminethe criticalnodes(links)whileconsideringthe dynamicity of the traffic network are provided.Also,sensitivity analysis toward capacity reduction for critical links is studied.Moreover,the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.Findings–The numerical results demonstrate that the use of dynamic shortest path(time dependency)as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.Research limitations/implications–The research in this paper also revealed several challenges,which call for future investigations.First,the authors have restricted our experimentation to a small network where the only focus is on the model behavior,in the absence of historical data.The authors intend to extend this study to very large network using real data.Second,the authors have considered only congestion to assess network’s criticality;future research on this topic may include other factors,mainly vulnerability.Practical implications–Taking into consideration the dynamic and
