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Journal ArticleDOI

Defense resource allocation in road dangerous goods transportation network: A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach

TLDR
In this article, a Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach is proposed to allocate the defense resource in road dangerous goods transportation network.
About
This article is published in Reliability Engineering & System Safety.The article was published on 2021-11-01. It has received 7 citations till now. The article focuses on the topics: Dangerous goods & Resource allocation.

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Citations
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Journal ArticleDOI

Resilience model and recovery strategy of transportation network based on travel OD-grid analysis

TL;DR: Wang et al. as mentioned in this paper proposed a resilience model and investigated the optimization of the recovery strategy according to this model, which constructs a modeling framework based on the OD-grid network and provides a brand-new performance metric for transportation network resilience analysis based on grid capacity.
Journal ArticleDOI

Minimisation of the probability of serious road accidents in the transport of dangerous goods

TL;DR: In this article, the authors presented the problem of determining the routes of vehicles in the transport of dangerous materials in the context of minimizing the probability of a road accident with a serious degree of damage to the car, i.e. serious accidents.
Journal ArticleDOI

Minimisation of the probability of serious road accidents in the transport of dangerous goods

TL;DR: In this paper , the authors presented the problem of determining the routes of vehicles in the transport of dangerous materials in the context of minimizing the probability of a road accident with a serious degree of damage to the car, i.e. serious accidents.
Journal ArticleDOI

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

TL;DR: A layout optimization model for multiple rescue points is developed, an improved particle swarm algorithm is suggested by including a variation that can reduce optimization time and avoid the disadvantage of precocity, and a MATLAB program is designed using an adaptive variation algorithm.
Journal ArticleDOI

Risk-Informed Based Comprehensive Path-Planning Method for Radioactive Materials Road Transportation

TL;DR: In this paper, the authors proposed a risk-informed based comprehensive path-planning method (RICPP) for RMRT route optimization by considering radiological risk cost, time cost, and economical cost.
References
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Journal ArticleDOI

Community structure in social and biological networks

TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Book

Social Network Analysis: A Handbook

TL;DR: Networks and Relations The Development of Social Network Analysis Handling Relational Data Lines, Direction and Density Centrality and Centralization Components, Cores, and Cliques Positions, Roles and Clusters Dimensions and Displays Appendix Social Network Packages
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Defining and identifying communities in networks.

TL;DR: This article proposes a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability and applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods.
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Finding communities in linear time: a physics approach

TL;DR: This method is based on notions of voltage drops across networks that are both intuitive and easy to solve regardless of the complexity of the graph involved and allows for the swift discovery of the community surrounding a given node.
Journal ArticleDOI

Identification of critical locations across multiple infrastructures for terrorist actions

TL;DR: This paper presents a possible approach to ranking geographic regions that can influence multiple infrastructures, and identifies these locations by calculating a value for a geographic region that represents the combined values to the decision makers of all the infrastructure crossing through that region.
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