Identification Of Critical Links Research Articles

Identification of critical links based on the optimal reliable path in stochastic traffic networks.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.

Identification of Critical Links Based on Electrical Betweenness and Neighborhood Similarity in Cyber-Physical Power Systems.

Identifying critical links is of great importance for ensuring the safety of the cyber-physical power system. Traditional electrical betweenness only considers power flow distribution on the link itself, while ignoring the local influence of neighborhood links and the coupled reaction of information flow on energy flow. An identification method based on electrical betweenness centrality and neighborhood similarity is proposed to consider the internal power flow dynamic influence existing in multi-neighborhood nodes and the topological structure interdependence between power nodes and communication nodes. Firstly, for the power network, the electrical topological overlap is proposed to quantify the vulnerability of the links. This approach comprehensively considers the local contribution of neighborhood nodes, power transmission characteristics, generator capacity, and load. Secondly, in communication networks, effective distance closeness centrality is defined to evaluate the importance of communication links, simultaneously taking into account factors such as the information equipment function and spatial relationships. Next, under the influence of coupled factors, a comprehensive model is constructed based on the dependency relationships between information flow and energy flow to more accurately assess the critical links in the power network. Finally, the simulation results show the effectiveness of the proposed method under dynamic and static attacks.

Identification of Critical Links in Urban Road Network Based on GIS

A GIS-based method is proposed to identify critical links in urban road networks. This study utilizes a geographic information system (GIS) to evaluate the distribution of road infrastructure, road density, and network accessibility at the micro, meso, and macro levels. At the micro level, GIS is used to assess the distribution of public facilities along the roads. At the meso level, a city’s road density distribution is evaluated. At the macro level, a spatial barrier model and a transportation network model are constructed to assess the network accessibility. An inverse distance weighting method is employed to interpolate the accessibility. Furthermore, a network topology is established, and the entropy method is utilized to evaluate the sections comprehensively. The sections are ranked based on the evaluation results to identify the critical links in the urban road network. The road-network data and points of interest (POI) data from the Anning District in Lanzhou are selected for a case study, and the results indicate that the top five critical links have scores of 0.641, 0.571, 0.570, 0.519, and 0.508, respectively. Considering the three indicators enhances the accuracy of critical section identification, demonstrating the effectiveness of the proposed method. Visualizing each indicator using GIS 10.7 provides a new approach to identifying critical links in urban road networks and offers essential theoretical support for urban planning.

Critical link identification algorithm for power communication networks in SDN architecture

With the maturity of software-defined network (SDN) technology, its application in power communication networks (PCNs) is being introduced. SDN controllers can assign working and backup routes for arriving serve requests and provide one-to-one (1:1) protection, which is crucial for the transmission of power system data with high reliability and delay requirements. For PCNs in SDN architecture, a critical link identification algorithm based on link-related risk (LRR-CLIA), which considers both working and backup routes between nodes, is proposed in this paper. The algorithm calculates link importance to identify critical links by quantifying the impact of links on the network risk on service layer, transport layer, and topology layer. To verify the effectiveness of the LRR-CLIA, we compare the network loss on service layer, transport layer, topology layer, and comprehensive layer with other algorithms after ranking and removing the identified critical links in descending order. In the simulation results, the LRR-CLIA outperforms the other algorithms by an average of 39.5% and 51.77% in the small PCN and medium-scale PCN respectively, which shows that the LRR-CLIA can identify the critical links more effectively and accurately in PCNs whose services have both working and backup paths.

Optimization of Backbone Optical Network for Power Communication Based on Critical Link

The existing optimization algorithms for power communication backbone optical networks do not comprehensively consider the real-time performance of power communication services and the difference of substation voltage levels, and the optimization results are difficult to meet the actual needs of power communication networks. Firstly, this paper uses the link critical comprehensive index to express the characteristics of critical links, and designs a critical link identification algorithm; then, on the basis of link critical ranking, the network performance is optimized by using link redundancy and link expansion methods; finally, the feasibility and correctness of the method are verified by an example of provincial power backbone optical network. The research results have reference value for the planning, design and operation management of power communication network.

Identification of Critical Links in Urban Road Network considering Cascading Failures

The identification of critical road links is greatly important to the management and control of the transportation system. Existing works fail to fully consider the influence of the distribution of traffic flow and its dynamic characteristics on critical road link identification. In this paper, we propose a criticality calculation method for urban road networks considering the effect of cascading failures which models the distribution change of traffic flow after a specific road link failed. Firstly, a sequence diagram calculation method is proposed to model how the traffic failure on one road link propagates to related links. Secondly, the diagram of the cascade failure sequence is divided into different stages according to the consistency of the objective function. The influence value of each stage is computed for the target road link. Finally, the failure probability model and the importance indicator are proposed to calculate the criticality for each road link. We evaluate our critical road link identification method on both simulated and read scenes. In our simulation, we achieve 90.6% and 91.7% for the accuracy on two key metrics, respectively, i.e., the length of failure road link and the total parking delay, which proves the feasibility of our method. Our method also achieves reasonable conclusions on real data and helps to find the critical road links.

A computationally efficient metric for identification of critical links in large transportation networks

Maintaining safety and resilience in transportation system requires identification of critical components. The existing transportation link criticality metrics typically require multiple traffic assignments to perform a full scan of the network. Link removals create computational burden and can cause network disconnectivity which makes it problematic to run traffic assignment. The authors previously introduced Link Criticality Index (LCI) that identifies the criticality ranking within a single User Equilibrium (UE) traffic assignment using Frank-Wolfe (FW) algorithm. While LCI is shown to provide balanced rankings with respect to connectivity and flow conditions, its computational efficiency diminishes for larger networks due to the need for path enumeration. This paper formulates an adjusted LCI to make it computationally efficient for larger networks. Adjusted LCI utilizes 1) viable routes instead of the complete list of enumerated paths; and, 2) a path-based traffic assignment algorithm (Gradient Projection) to replace FW. The consistency of rankings between original/adjusted LCIs, and other measures from the literature are analyzed and compared for three networks, and through an experimental setup. The proposed method is also applied on a large-scale transportation network, i.e., Chicago network. The results show that both modifications are effective and yield faster results without compromising LCI's advantages.

Practice Friendly Metric for Identification of Critical Links in Road Networks

Despite the important planning value of transportation link criticality, the existing methodologies are mostly in the academic domain, and require in-depth technical skills and extensive data. The most common approach to identify critical links in transportation networks is to remove each link iteratively, conduct traffic assignment, and assess the criticality of each link based on the consequences of its removal. Since conducting multiple traffic assignment is costly for large networks, the authors of this paper recently introduced the link criticality index (LCI). The LCI utilizes the iterations in the Frank–Wolfe solution of the user equilibrium (UE) problem to provide link criticality ranking within a single traffic assignment. The LCI was shown to provide balanced rankings with respect to alternative routes as well as the link flows. However, the LCI is not practice-friendly because of the technical knowledge and data needed to run traffic assignments. Accordingly, this paper introduces a practice friendly link criticality index (PF-LCI). PF-LCI relaxes some of the technical requirements and uses some expert knowledge input data to provide “top” link criticality rankings that are consistent with the LCI. PF-LCI utilizes the network flow instances at different times of day instead of iterations of UE assignment solution. Expert knowledge input is sought for the major origin–destination pairs (ODs) and the viable routes between the selected ODs. The method is implemented on a small sample network and the Sioux Falls network to test PF-LCI’s capabilities. Results show that PF-LCI produces accurate rankings for the top critical links that are most relevant to practitioners’ concerns.

Identification of critical links in a large-scale road network considering the traffic flow betweenness index.

The traditional full-scan method is commonly used for identifying critical links in road networks. This method simulates each link to be closed iteratively and measures its impact on the efficiency of the whole network. It can accurately identify critical links. However, in this method, traffic assignments are conducted under all scenarios of link disruption, making this process prohibitively time-consuming for large-scale road networks. This paper proposes an approach considering the traffic flow betweenness index (TFBI) to identify critical links, which can significantly reduce the computational burden compared with the traditional full-scan method. The TFBI consists of two parts: traffic flow betweenness and endpoint origin–destination (OD) demand (rerouted travel demand). There is a weight coefficient between these two parts. Traffic flow betweenness is established by considering the shortest travel-time path betweenness, link traffic flow and total OD demand. The proposed approach consists of the following main steps. First, a sample road network is selected to calibrate the weight coefficient between traffic flow betweenness and endpoint OD demand in the TFBI using the network robustness index. This index calculates changes in the whole-system travel time due to each link’s closure under the traditional full-scan method. Then, candidate critical links are pre-selected according to the TFBI value of each link. Finally, a given number of real critical links are identified from the candidate critical links using the traditional full-scan method. The applicability and computational efficiency of the TFBI-based approach are demonstrated for the road network in Changchun, China.

Critical link identification and prioritization using Bayesian theorem for dynamic channel assignment in wireless mesh networks

Wireless Mesh Networks (WMN) is a key backhaul technology used in 802.11 networks to provide ubiquitous coverage to isolated areas that require high-speed connectivity. The multi-radio feature of WMN has enabled the mesh routers to derive the full benefits of multiple channels for providing parallel transmissions in a single collision domain. However, co-channel interfering links badly affect the channel capacity and force the mesh routers to switch the radio interface to other less interfering channel. In dynamic channel assignment, if the channel switches occur frequently, the traffic disruptions lead to excessive packet delays and drops. These problems are mostly observed in specific dense areas, where traffic saturation occurs. The existing schemes lack in properly identifying the bandwidth starved links. Therefore, the focus of this paper is to enhance the throughput and minimize the packet drops by critically identifying the bottleneck links and prioritize them for better channel assignments. The proposed metric exploits the statistical inference on dropped packets to determine the effect of interference on the achievable capacity of the links. The traffic load and the effective capacity are collectively used to identify the saturated links. The proposed metric has been evaluated through extensive simulations. The results demonstrate the validation of proposed metric with a considerable increase in performance.

Recognition of critical links for gating using queue detector data

This paper introduces the concept of rolling time occupancy, and presents a method for recognizing critical links for gating, where spillovers will occur or have done already. The key idea of this new method is that, when the queue length becomes sufficient for the end of the queue to approach the queue detector, the speed of vehicles arriving from upstream will be slower than the free flow speed, which implies that a series of rolling time occupancies will all be greater than a particular threshold. Given that the value of a single rolling time occupancy is strongly influenced by buses and other confounders during a minor time interval, the new criteria for critical link identification are divided into two parts: the occupancy threshold for possible congestion, and the number of rolling time occupancies for inevitable congestion. Next, this paper presents the relationship between queue length and rolling time occupancy, using traffic data procured using VISSIM simulations. On this basis, the queue length threshold at which a spillover can be judged to be going to occur or to have already occurred is determined. Finally, using the misjudgment ratio and the trigger postponement amount as evaluation indices, the method proposed in this paper is evaluated under ten different simulation conditions. The results show that the maximum and average misjudgment ratios are 32.32% and 10.11%, respectively; meanwhile, the average postponement amount is less than 50 seconds for all ten simulation conditions.

A Cooperative Peer Clustering Scheme for Unstructured Peer-to-Peer Systems

This paper proposes a peer clustering scheme for unstructured Peer-to-Peer (P2P) systems. The proposed scheme consists of an identification of critical links, local reconfiguration of incident links, and a retaliation rule. The simulation result indicates that the proposed scheme improves the performance of previous schemes and that a peer taking a cooperative action will receive a higher profit than selfish peers.

Seismic Risk Assessment and Mitigation of Water Supply Systems

This paper describes a process for seismic risk assessment and identification of critical links of water supply systems subjected to earthquakes. Probabilistic performance of water supply systems is reflected by the system serviceability index (SSI), damage consequence index (DCI), and upgrade benefit index (UBI). The process is illustrated using a hypothetical water supply system subjected to a seismic damage scenario, where direct Monte Carlo simulation is used for estimating the performance indices. It is shown that probabilistic characteristics of SSI can be attributed to system characteristics (e.g., demand distribution pattern) of the water supply system. UBI is shown to be the primary index in seismic mitigation, and critical links are pipes with relatively large UBI values. It is recognized that the values of UBI corresponding to different upgrade scenarios of pipes can be estimated using conditional samples from a single run of direct Monte Carlo simulation instead of repeated runs. The concept of efficient frontier is employed to identify the system critical links. It is found that, a group of links that have the largest UBI individually do not necessarily have the largest group UBI, nor are they necessarily the group of critical links.

Spatial distribution of water supply reliability and critical links of water supply to crucial water consumers under an earthquake

This paper describes a process to characterize spatial distribution of water supply reliability among various consumers in a water system and proposes methods to identify critical links of water supply to crucial water consumers under an earthquake. Probabilistic performance of water supply is reflected by the probability of satisfying consumers’ water demand, Damage Consequence Index (DCI) and Upgrade Benefit Index (UBI). The process is illustrated using a hypothetical water supply system, where direct Monte Carlo simulation is used for estimating the performance indices. The reliability of water supply to consumers varies spatially, depending on their respective locations in the system and system configuration. The UBI is adopted as a primary index in the identification of critical links for crucial water consumers. A pipe with a relatively large damage probability is likely to have a relatively large UBI, and hence, to be a critical link. The concept of efficient frontier is employed to identify critical links of water supply to crucial water consumers. It is found that a group of links that have the largest UBI individually do not necessarily have the largest group UBI, or be the group of critical links.

A minimum interference routing algorithm with reduced computational complexity

Minimum interference routing algorithms are designed to reduce rejections of future requests for the establishment of Label Switched Paths (LSPs) but make no assumption about specific patterns of arrival request. This paper introduces a novel minimum interference routing algorithm, Light Minimum Interference Routing (LMIR), which is based on a new approach to the identification of critical links. This approach reduces the computational complexity involved in finding a path for the establishment of an LSP. The LMIR is shown to have the same precision as existing algorithms but with less computational complexity.