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The Australian main road network showing all main roads, designated state highways and the National Transport Network (NTN) subnetworks
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This paper describes the development of a method for strategic assessment of vulnerability in road networks. Our vulnerability analysis considers the socio-economic impacts of network degradation, on the basis of changes in the levels of accessibility provided by the degraded network. A number of standard indices of accessibility are considered, in...
Contexts in source publication
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... forms the basic skeleton of the national road system of Australia (see Fig. 2). This subset of the national main road network has been designated in AusLink as of prime importance in providing a national road transport system. The full main road network connecting cities and regions is of course much more extensive than the NTN road network (see Fig. ...
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... the NTN, the other subnetworks are the state highways and designated main roads, which provide connectivity at the state level and are the direct responsibility of the state governments, and the other main roads, which provide regional connectivity and for which responsibility may be shared between state and local government. Figure 3 highlights the NTN and state highways and designated roads subnetworks as a skeleton amidst the matrix of the full road network. In the more densely settled regions of the southeast, east coast and south west, there is a substantial main road network. ...
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... coverage away from those regions, in the less settled parts of the nation, is much sparser and here the NTN and state highways really do represent almost the entirety of the navigable road system. This may be seen in Fig. 4, which shows the NTN and the designated state highway networks. A GIS database of the entire strategic road network of Fig. 3 has been set up using the ArcGIS package. This database holds a number of attributes for all of the identified road links, including: In addition, attributes concerning pavement condition and traffic volume (AADT) are being progressively added to the database as they become available, using data supplied by the various state road ...
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... concepts and techniques described in this paper have much wider application. In particular and as a next part of our research and development of the vulnerability method, we will be adapting and applying the methods for use in the much larger and more complex road networks that exist in the real world, such as the full main road network shown in Fig. 3. What we can say at present is that our research has yielded useful concepts and a method for analysis of network vulnerability in terms of the spatial or topological configuration of the network and possible socio-economic impacts assessed in terms of changes in accessibility to markets, service and facilities resulting from ...
Citations
... The role of communication arteries such as streets and passages as the points of connection and escape routes in cities is fundamental in times of a natural disaster. Inappropriate land uses around these streets with old fabric cause the drowning and closing of these connection spots, disrupt the process of relief and close escape routes multiplying problems, deaths and financial damage (Adnan et al., 2023a(Adnan et al., , 2023b(Adnan et al., , 2023cLin et al., 2022;Taylor et al., 2006). ...
Natural hazards are considered one of the greatest challenges today. Preventing transformation processes that lead to risk and then, crisis need a structural-strategic approach. An approach that can identify the issues and challenges ahead in a systematic and comprehensive method by formulating an operational plan can provide resilience and reduce vulnerability of human settlements and urban infrastructures. The road networks as one of the most important urban elements having a crucial role in management of crisis during the occurrence of natural crises (such as earthquakes) aid in the transferring the injured and rescue forces. The main purpose of this study was to determine the vulnerability of urban road networks for earthquake risk with neural network and machines learning algorithms with a comparative and systematic approach. In order to identify the most accurate and efficient model, a comparative comparison between neural network model (ANN) and machine learning algorithms including ADTree and KNN was carried out. The results of the present study in evaluating the structural condition of the urban road network with Fractal Dimension on hazardous and vulnerable zones showed that these zones were of low fractal dimension, and the distribution and differentiation of roads were low, reducing the efficiency of the road network at times of crisis. Other results of the present research on the application of machine learning algorithms indicate that the accuracy of the ADTree algorithm was equal to 1. In addition, at the stage of measuring the efficiency of the model with the Classification metrics algorithm, the ADTree algorithm efficiency was equal to 1. However, the accuracy of the KNN algorithm (K-Nearest Neighbors) and the artificial neural network model in predicting the vulnerability of the internal road network was equal to 0.92% and 0.98%, respectively. Therefore, since the degree of accuracy of the ADTree algorithm was higher, it is the most accurate and efficient algorithm to predict the vulnerability of the road network at times of the occurrence of hazardous events, and it can be useful and effective in decision-making of policy makers and planners in pre-crisis management.
... Other methods used in the literature to investigate critical network components include that of Demšar et al. [10] according to which two measures have to be used to identify critical locations in vulnerability analysis: cut vertices (a vertex is a cut vertex if its removal causes an increase in the number of connected component) and each node betweenness centrality (the higher the value of this measure, the more critical a node is). 6 The second approach, the system-based one, also considers the effect of congestion, and thus travel cost issues [19] and is deeply related to the concept of accessibility: "a network node is vulnerable if loss (or substantial degradation) of a small number of links significantly diminishes the accessibility of the node, as measured by a standard index of accessibility; a network link is critical if loss (or substantial degradation) of the link significantly diminishes the accessibility of the network or of particular nodes, as measured by a standard index of accessibility" [44]. Within this approach, the graph theory is integrated with transport supply and demand analysis with the aim of investigating real responses to shocks by analyzing demand and supply of transport according to different vulnerability scenario. ...
Infrastructure networks have become increasingly complex, whose progressively higher levels of interdependence make them even more vulnerable. This empirical analysis based on the Morandi bridge collapse examines the robustness and vulnerability of the motorway and railway networks in north-western Italy. By following a network topology-based approach, motorways and railways are studied as one single interconnected multi-layer transport infrastructure. Based on the concepts of geographic and functional interdependence the study provides insight into which nodes (and links) should be restored as quickly as possible when an emergency and destructive event renders them inaccessible. Moreover, it highlights the greater fragility of the intermodal network which opens up the debate on regulation and coordination of restoring measures carried out by the relevant authorities.
... In 1995, scholars gradually recognized the vulnerability of transportation systems. For instance, Taylor proposed many ways to measure road network vulnerability [12] . AVCI presented many procedures to evaluate the vulnerability of metro stations [1] . ...
... Compared to those located in lowlands, infrastructure elements in mountains are exposed to additional natural hazards, such as avalanches, rockfalls, and mudflows, which are likely to become more frequent and more intense due to climate change. Consequently, striving for increased resilience of physical infrastructure means improved accessibility, robustness, and fast response by current infrastructure networks and systems to maintain the desired network performance (D'Este and Taylor 2003;Taylor et al 2006;Yin et al 2016). ...
Mountains are home to a considerable share of the human population. Around a billion people live in mountainous areas, which harbor rich natural and sociocultural diversity. Today, many people living in mountainous areas worldwide face fundamental changes to their cultural and economic living conditions. At the same time, mountain communities have defied harsh environments in the past by adapting to changing natural conditions and showing remarkable levels of resilience. In this review paper, we provide a comprehensive overview of English-language scientific literature on resilience-related topics in mountain areas based on a systematic review of the Scopus® literature database. We propose a structured starting point for science–practice interactions and concrete action-based activities to support livelihoods and strengthen resilience in mountain areas. We suggest that existing knowledge gaps can be addressed by relying on local knowledge and cocreating solutions with communities. In this way, we can build innovative capacity and actively buffer against the impact of crises while supporting deliberate transformation toward sustainability and regeneration to further enhance resilience.
... Later, in [22], the vulnerability of a link in a road transportation system was understood as 'a susceptibility to incidents that can results in considerable reductions in road serviceability', and in [23] vulnerability was defined as 'sensitivity to threats and hazards'. In [24], this concept is analysed in terms of accessibility. In this work, the authors assume that networks are created to give access to different locations, and for that reason, the vulnerability of the elements is measured depending on the capacity of these to provide access in different scenarios. ...
In traffic networks, some elements are more prone to suffer or to create disruptive situations, and the identification of these elements becomes a challenge due to the large number of possible threats. The following paper presents two new methodologies to identify and rank vulnerable and critical links of traffic networks. These methodologies use the Fisher Information Matrix, and the analysis of eigenvalues and eigenvectors, to systematically rank the links of a network. The identification is done by using traffic variables, such as the demand, the travel time, and the network’s flow. For the ranking of the links, disruptions are considered in all the possible locations of the network, and the effects are systematically evaluated. In addition, the evaluation of traffic resilience is included in the process to validate the results. Finally, both methodologies are tested in a real network to infer on the validity of the results.
... Compared to centralities, efficiency has the ability to capture and quantify changes made in a network, similar to the changes that may be occurred during a disturbance. Finally, vulnerability, a generic concept based on efficiency, can be defined as the sensitivity of the network to incidents which can result in reducing its operational performance Jenelius and Mattsson, 2015;Jenelius and Mattsson, 2021;Jenelius et al., 2006;Croope, 2012;Wang et al., 2016;Mou et al., 2020;Chen and Lu, 2020;Santos et al., 2020;Vivek and Conner, 2022;Knoop et al., 2012;Morelli and Cunha, 2021;Mitsakis et al., 2015;Taylor et al., 2006;García-Palomares et al., 2018;Ortega et al., 2020;Borghetti et al., 2021). Concerning the demand-related metrics, criticality introduced by the research of Nagurney and Qiang (Nagurney and Qiang, 2008) can be defined as the metric which aims at classifying the links of a network based on the impact their removal may have on the operational performance of the road network Taylor et al., 2006;García-Palomares et al., 2018;Ortega et al., 2020;Borghetti et al., 2021;Nagurney and Qiang, 2008;Martín et al., 2021;Mitsakis et al., 2014;Stamos et al., 2015;Almotahari and Yazici, 2020;Yang et al., 2018;Li and Ozbay, 2012). ...
... Finally, vulnerability, a generic concept based on efficiency, can be defined as the sensitivity of the network to incidents which can result in reducing its operational performance Jenelius and Mattsson, 2015;Jenelius and Mattsson, 2021;Jenelius et al., 2006;Croope, 2012;Wang et al., 2016;Mou et al., 2020;Chen and Lu, 2020;Santos et al., 2020;Vivek and Conner, 2022;Knoop et al., 2012;Morelli and Cunha, 2021;Mitsakis et al., 2015;Taylor et al., 2006;García-Palomares et al., 2018;Ortega et al., 2020;Borghetti et al., 2021). Concerning the demand-related metrics, criticality introduced by the research of Nagurney and Qiang (Nagurney and Qiang, 2008) can be defined as the metric which aims at classifying the links of a network based on the impact their removal may have on the operational performance of the road network Taylor et al., 2006;García-Palomares et al., 2018;Ortega et al., 2020;Borghetti et al., 2021;Nagurney and Qiang, 2008;Martín et al., 2021;Mitsakis et al., 2014;Stamos et al., 2015;Almotahari and Yazici, 2020;Yang et al., 2018;Li and Ozbay, 2012). This is achieved by considering the impact that the removal of links has in the generalized cost, as occurred by a static traffic assignment. ...
... As an example, in the research of Bhatia et al. (2015) and Zhang et al. (2015), disturbances in the network are simulated by removing links and nodes from the network, and resilience is quantified by monitoring the performance of chosen network measures. In the same sense, the research of Taylor et al. (2006), García-Palomares et al. (2018), and Ortega et al. (2020) examine criticality and vulnerability by considering accessibility, through the elimination of all the links of the network sequentially. ...
Resilience is a complex term, bearing multiple definitions and resilience-related metrics. The most commonly utilized metrics are network efficiency and criticality that examine the impact that the removal of a structural element of a graph/ network has on its operation. Efficiency examines this term from a pure topological aspect and takes into account the shortest paths between the nodes whereas, on the other hand, criticality takes into account transportation-related variables like travel demand. In the present research, the interrelation between efficiency and criticality is further examined through a series of simulation experiments on the Athens city-center urban road network. For the quantification of efficiency, vulnerability, and criticality an iterative approach is used where, per iteration, one link of the network was removed. The outputs of the experiments are, then, statistically analyzed, using unsupervised and supervised techniques. Findings reveal that a polynomial relationship between the ratio of criticality and efficiency, and traffic flow exists. Finally, the implications of these findings to traffic and network management are discussed.
... Researchers are focusing on developing tools and methodology to identify critical roads within network, where the failures of such roads result in the deterioration of the network and the transportation functionalities ( Jin et al., 2022 ). Typical road criticality evaluation approaches use topological ( Akbarzadeh et al., 2019 ;Zhang and Khani, 2020 ) and traffic ( Jenelius and Mattsson, 2015 ;Cantillo et al., 2019 ) indicators, and rely on simulations with expected reduction in travel time ( Taylor et al., 2006 ) to identify critical road combination. These engineering approaches for criticality evaluation are well-suited for generic assessments of road networks. ...
Transportation is a critical sector for communities. It is, however, particularly vulnerable to climate change, and a disruption in its infrastructure impacts the whole community. Enhancing the resilience of transportation infrastructure is vital for reliable and sustainable functionalities, and subsequently, to more resilient communities. There are indices and frameworks that assess and evaluate transportation resilience and performance, ranging from component-based, to network and system metrics. The existing approaches, however, usually overlook location and capacity of critical transportation components. These two characteristics possess significant effects on the resilience and performance of the transportation sector. Moreover, the stress type and its magnitude should be incorporated in the resilience assessment. In this work, we leverage a recent resilience quantification framework to compute the Exposure and Capacity-based Transportation Resilience Index (EC-TRI), describing the resilience of the transportation infrastructure, focusing on the location and capacity of certain assets. We argue for the adoption of this index as a complement to existing frameworks, and we develop a web-based GIS framework to evaluate EC-TRI for communities within New Jersey (NJ). The novelty of EC-TRI lies in its consideration for the stress level, the exposure and capacity characteristics of transportation assets, as well as in its practicality and scalability. Users can leverage EC-TRI to locate the weak and vulnerable components within the transportation network, and to provide a community-based assessment of the resilience level of the transportation infrastructure. In addition, we provide EC-TRI as a highly scalable GIS framework, providing users with the ability to adjust the quantification components as per local needs and priorities.
... However, the concept of vulnerability still lacks a consensus definition (Berdica 2002;El-Rashidy and Muller 2014). Taylor et al. (2006) considered vulnerability as the socioeconomic impact of network degradation based on changes in the levels of accessibility provided by the degraded network. Jenelius and Mattsson (2015) defined road network vulnerability as the potential degradation of the road transport system and its impacts on society. ...
... • Consider only single-link failures (Omer et al., 2013;Sullivan et al., 2010;Taylor et al., 2006). However, the consequences of multiple-link failures are not simply the combination of those resulting from single-link failures . ...
... To identify critical links in a network, Taylor et al. (2006) proposed an approach based on single-link failures (SLFs) where each link is removed from the network model and the corresponding effect on the network performance is estimated. The levels of impact are then ranked and the links demonstrating the most significant impacts are considered the most critical. ...
... • Disruptions are limited to single-link failures (Omer et al., 2013;Sullivan et al., 2010;Taylor et al., 2006). However, multiple-link failures are not the simple combination of the most critical links with single-link failure . ...
Road networks are critical to society as they support people's daily mobility, the freight industry, and emergency services. However, a range of predictable and unpredictable events can affect road networks, disrupting traffic flows, connectivity, and, more generally, the functioning of society. With the increased interconnectivity and interdependency of the economic sectors, the need to manage this threat is more critical than ever. To this end, stakeholders need to understand the potential impacts of a multitude of predictable and unpredictable events. The present thesis aims at developing a framework to evaluate and understand the resilience (ability to sustain, resist and recover from perturbations) of road networks under a multitude of potentially unpredictable disruptions, and at assessing the role of different network design (e.g. network topology) and operation (e.g. travel-demand distribution) characteristics in road network resilience. To this end, this thesis adopts a hazard-independent approach that considers all possible scenarios disrupting multiple links (more specifically up to a certain number of links). Novel indicators—including a robustness, unsatisfied-demand and resilience indicator measuring the demand-weighted-average increase in travel time in the disrupted network, the proportion of stranded travellers, and the speed of network-performance recovery, respectively—are developed and tested as part of this thesis. A link-criticality-assessment method based on multiple-link failures is also developed to identify the links that should be given priority for pre-event reinforcement and post-event restoration. To assess the effects of network size, topology, and demand distribution on network resilience, the thesis considers a variety of case studies, including artificial networks generated by a random road network model (developed as part of this thesis) and real network models derived from real-world maps. To assess the influence of demand variations, capacity constraints and congestion on network resilience, this thesis performs a resilience analysis of a network under several demand conditions. Finally, to assess the effects of recovery strategies on network resilience and characterize the optimal recovery strategy, this thesis performs a resilience analysis of a network considering all possible link-repair sequences. This research should ultimately contribute to the incorporation of resilience considerations into transport planning and management standards, which currently give priority to transport efficiency—the efficient movement of vehicles through a transport network under normal conditions—rather than the movement of vehicles under disrupted conditions.
... Similarly, Lordan et al. [18] compared the reduction of the size of the giant component with the consideration of node degree and betweenness. Other than the prevailing topology-based measures, more measures have been introduced, such as accessibility index [13,19] and capacity-based index [20,21]. e topology-based measures are convenient in calculation; however they do not refer to the significant features of a system in the aftermath of an extreme event, such as the information of link types and the distribution of traffic demands. ...
Transportation system has a close bearing on the prosperity of society. However, transportation infrastructures are highly vulnerable to extreme events. Therefore, identifying the most critical component of the transportation system is among the first priorities of transportation network planners and managers. This paper proposes a novel framework to identify the most vulnerable component for a road transportation system. A key characteristic of vulnerability assessment is the travelers’ response to the changes in the transportation network topology and capacity after an extreme event. Hence, the problem is formulated as a nonlinear programme with equilibrium constraints, considering travelers’ route choice behavior. In the methodology, two types of vulnerability measures are developed to assess the vulnerability of road transportation system, namely, system travel time-based vulnerability (STTV) and system emissions-based vulnerability (SEV). The former is developed on the basis of short-term planning, while the latter is put forward on the basis of long-term planning. With these vulnerability measures, the proposed framework is then demonstrated using an extended Nguyen-Dupuis network under different demand levels and different capacity degradation levels, taking into account two modes: bus and car. The results indicate that different vulnerability measures can identify similar vulnerable components. Moreover, it is shown that the SEV can find more critical components than the STTV regardless of capacity degradation or demand growth. Our research helps to create a recovery plan by assigning priority to the critical transportation infrastructures.
