Determining Critical Links in a Road Network: Vulnerability and Congestion Indicators

Oliveira, Eduardo; Portugal, Licínio da Silva; Porto, Walter

doi:10.1016/j.sbspro.2014.12.196

Cited by 48 publications

(30 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vulnerability refers to the degree of inability of a system to function due to disruption [33]. Essentially, the concept can be considered as the reciprocal of robustness [34]. Poorzahedy & Bushehri [19] define the network reliability for the (k, s) origin-destination (OD) pair user group as the probability that the network may hold a suitable condition for those users after an incident.…”

Section: Literature Review 21 Post-disaster Transportation Network mentioning

confidence: 99%

Robust component: a robustness measure that incorporates access to critical facilities under disruptions

Dong

Wang

Mostafavi

et al. 2019

J. R. Soc. Interface.

View full text Add to dashboard Cite

The objective of this paper is to integrate the post-disaster network access to critical facilities into the network robustness assessment, considering the geographical exposure of infrastructure to natural hazards. Conventional percolation modelling that uses generating function to measure network robustness fails to characterize spatial networks due to the degree correlation. In addition, the giant component alone is not sufficient to represent the performance of transportation networks in the post-disaster setting, especially in terms of the access to critical facilities (i.e. emergency services). Furthermore, the failure probability of various links in the face of different hazards needs to be encapsulated in simulation. To bridge this gap, this paper proposed the metric robust component and a probabilistic link-removal strategy to assess network robustness through a percolation-based simulation framework. A case study has been conducted on the Portland Metro road network during an M9.0 earthquake scenario. The results revealed how the number of critical facilities severely impacts network robustness. Besides, earthquake-induced failures led to a two-phase percolation transition in robustness performance. The proposed robust component metric and simulation scheme can be generalized into a wide range of scenarios, thus enabling engineers to pinpoint the impact of disastrous disruption on network robustness. This research can also be generalized to identify critical facilities and sites for future development.

show abstract

Section: Literature Review 21 Post-disaster Transportation Network mentioning

confidence: 99%

Robust component: a robustness measure that incorporates access to critical facilities under disruptions

Dong

Wang

Mostafavi

et al. 2019

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…Mokhtarian [8] proposed that congestion happens when the input volume (demand) exceeds the output capacity (supply). Oliveira et al [9] considered that a segment of a road system is congested when the traffic demand approaches or exceeds the stable flow capacity of the road. In China, it is generally considered to be congested when the value of standing density exceeds 5 passengers/m 2 [10], which is significantly larger than 1.4 passengers/m 2 in the United States (i.e., LOS "D") [7].…”

Section: Passenger Flow Congestion Propagation and Control Strategymentioning

confidence: 99%

“…From Figure 6, we can infer that the number of eventually congested stations increases with the infection rate. The infection rate is determined by the number of passengers waiting on platform and the operating speed of trains to some extent according to Equation (9). The trains in URT are usually run at a fixed speed.…”

Section: Sensitivity To Infection Rate and Recovery Ratementioning

confidence: 99%

Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model

2019

View full text Add to dashboard Cite

With the construction of the urban rail transit (URT) network, the explosion of passenger volume is more rapid than the increased capacity of the newly built infrastructure, which results in serious passenger flow congestion (PLC). Understanding the propagation process of PLC is the key to formulate sustainable policies for reducing congestion and optimizing management. This study proposes a susceptible-infected-recovered (SIR) model based on the theories of epidemiological dynamics and complex network to analyze the PLC propagation. We simulate the PLC propagation under various situations, and analyze the sensitivity of PLC propagation to model parameters. Finally, the control strategies of restricting PLC propagation are introduced from two aspects, namely, supply control and demand control. The results indicate that both of the two control strategies contribute to relieving congestion pressure. The propagating scope of PLC is more sensitive when taking mild supply control, whereas, the demand control strategy shows some advantages in flexibly implementing and dealing with serious congestion. These results are of important guidance for URT agencies to understand the mechanism of PLC propagation and formulate appropriate congestion control strategies.

show abstract

“…Conventional centrality metrics such as degree centrality and betweenness centrality are often used to quantify the importance of individual nodes. However, since these metrics are based only on network topology, other factors that are more realistic, such as probabilistic information diffusion model and geodesic distance have also been used to assess a certain network performance metric [11,12,14,[17][18][19]21]. The objective of these studies is identifying the most critical nodes/links in maintaining or maximizing a desired network performance, that is, identifying those nodes/links that degrade the performance substantially if they fail to function.…”

Section: Introductionmentioning

confidence: 99%

Critical Node Identification based on Articulation Point Detection for Uncertain Network

Ohara

Saito

Kimura

et al. 2019

IJNC

View full text Add to dashboard Cite

The problem of efficiently identifying critical nodes that substantially degrade network performance if they do not function is crucial and essential in analyzing a large complex network such as social networks on the Web and road network in the real world, and it is still challenging. In this paper, we tackle this problem under a realistic situation where each link is probabilistically disconnected as assumed in studies in uncertain graphs. This reflects that in case of a social network an information path between two persons is not always open and may not pass on any information from one to the other and in case of a road network a road between two intersections is not always travelable and may be blocked by a traffic accident, a road repair, a nearby construction, etc. To solve this problem, we focus on the articulation point and utilize the bridge detection technique in graph theory to efficiently identify critical nodes when the node reachability is taken as the performance measure. In case of a social network disfunction of a node causes loss of the total number of people receiving information and in case of a road network it causes loss of the total number of people movable to other places. Using two real-world social networks and one road network, we empirically show that the proposed method has a good scalability with respect to the network size and the nodes our method identified possesses unique properties and they are difficult to be identified by using conventional centrality measures.

show abstract

Determining Critical Links in a Road Network: Vulnerability and Congestion Indicators

Cited by 48 publications

References 6 publications

Robust component: a robustness measure that incorporates access to critical facilities under disruptions

Robust component: a robustness measure that incorporates access to critical facilities under disruptions

Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model

Critical Node Identification based on Articulation Point Detection for Uncertain Network

Contact Info

Product

Resources

About