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This work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in a certain period of days are on a disequilibrium...
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A range of predictable and unpredictable events can cause road perturbations, disrupting traffic flows and more generally the functioning of society. To manage this threat, stakeholders need to understand the potential impact of a multitude of predictable and unpredictable events. The present paper adopts a hazard-independent approach to assess the...
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... Road repair prioritizing based on damage [14], average daily traffic volumes [15], or network robustness [16,17] have been proposed. Many heuristic and meta-heuristic optimization techniques have been employed to minimize the effect of extreme events and develop restoration programs [11,[18][19][20][21][22][23][24][25]. However, these studies are limited to small networks due to the significant computational overhead of the optimization methods employed. ...
Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for planners and emergency managers to enhance the network’s efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.
... Current optimisation approaches mainly aim to enhance resilience during the recovery stage: identifying the optimal recovery actions, the optimal recovery sequences, or the optimal resources allocation (Bhavathrathan and Patil, 2015;Chen and Miller-Hooks, 2012;Liao et al., 2018;Faturechi et al., 2014). Bi-level stochastic programming models are commonly designed to solve the optimisation problem (see Somy et al. (2022), Mao et al. (2021), Li et al. (2019b), Bababeik et al. (2018), Vugrin et al. (2014), andFaturechi et al. (2014)). For instance, Li et al. (2019b) design a bi-level programming model, where the upper level seeks to determine the road segments to be recovered and the repair sequence in order to maximise system performance which is indicated by the recovery rapidity and the cumulative loss. ...
While the trend towards building resilience in infrastructure systems for disaster risk reduction is accelerating, the application of infrastructure resilience is largely conceptual or an embellishment to established modelling techniques. In response, this paper sets out the theoretical foundations of infrastructure resilience, which broadly applies across critical infrastructure networks. This is followed by reviewing system-based and network-based approaches to resilience assessment with a focus on transport infrastructure, where there is an emerging body of studies. It spotlights critical issues in conflating resilience with other concepts of infrastructure safety management or merely tagging resilience on established methods, indicating a phenomenon of "old wine in new bottles". Also, oversimplifying disruption scenarios does not provide a sufficient basis for informed resilience planning. This paper reminds readers to look beyond resilience as a mere buzzword and offers guidance on how to do so by recognising the theoretical and methodological cornerstones of infrastructure resilience.
... In the present literature, various algorithms are used to minimize the consequences of extreme events on transportation infrastructure and develop restoration programs; including stochastic mixedinteger programs (Chen and Miller-Hooks 2012;Zhang et al. 2021), genetic algorithms (GAs) (Chen and Tzeng 1999;Mao et al. 2021;Moghtadernejad et al. 2020), ant colony system (ACS) (Yan and Shih 2012), or simulated annealing (SA) (Hackl et al. 2018;Vugrin et al. 2014). According to the studies, these algorithms have been successful in finding good solutions for transportation network recovery. ...
One of the main challenges in the postdisaster management of large transportation networks involves the determination of the priority and the level of service recovery for each damaged asset in the network. Presently, the application of metaheuristic algorithms in developing restoration programs is receiving increasing attention. These algorithms determine a good solution to minimize the consequences of extreme events on the network of study in a relatively short period of time. This paper investigates the suitability of a discrete particle swarm optimization (DPSO) algorithm in finding a good solution to a restoration model developed for minimizing the overall direct and indirect costs of postdisaster restorative interventions. This model can consider constraints and limitations on the available budget, work groups and equipment, as well as different levels and speeds of service recovery for assets per damage state, and the changes in the traffic flow as the restorative interventions are executed. Moreover, the model has the capacity to process complex networks; hence, it can be implemented in real-world postdisaster decision making related to the development of restoration programs. The results suggest that the DPSO algorithm is a suitable choice of optimization algorithm in situations where the number of damaged objects is medium to large.
... Wang et al. [27] introduced the degree of nodes as a measure of node resilience with the help of the entropy method in physics. Furthermore, some researchers use mathematic models to perform the evaluation and optimization work in transportation systems from the perspective of resilience [28,29]. ...
Malignant traffic accidents are typical devastating events suffered by the urban road network. They cause severe functional loss when loading on the urban road network is high, exerting a significant impact on the operation of the city. The resilience of a road network refers to its ability to maintain a certain level of capacity and service when disturbed by external factors and to recover after a disturbance event, which is a crucial factor in the construction of transportation infrastructure systems. A comprehensive understanding of the adverse effects of malignant traffic accidents on the urban road network is imperative, and resilience is a concept employed to systematically explain this. This study investigates the impact of malignant traffic accidents on the resilience of the urban road network. A simulation is carried out focusing on an ideal urban road network, describing the temporal and spatial distribution of the average speed of road sections in the network. Inspired by the simulation experiment results, the ideal resilience curve is summarized, and the theory of resilience concept portrayal is innovatively developed into “6R” (redundancy, reduction, robustness, recovery, reinforcement, and rapidity). Combining the topological and “6R” resilience attributes of the urban road network, the urban road network resilience evaluation system is constructed, which yields an all-round and full-process evaluation for the urban road network with malignant traffic accidents. Results show that under malignant traffic accidents, the resilience of high-class surface roads, such as primary roads, is the poorest, suggesting that more attention and resources must be devoted to high-class surface roads. This study on the urban road network deepens the understanding and portrayal of its resilience and proposes an evaluation method to analyze its performance under disruption events.
Natural and man-made disasters can disrupt road networks. The post-disaster period is usually divided into short and long-term response phases. In the short-term phase, blocked roads can jeopardize emergency operations such as search-and-rescue, evacuation of victims, and the distribution of emergency supplies. The detritus may pose an environmental challenge in the long-term, as the rubble must be properly collected, disposed of, and recycled. Optimization models designed to schedule repair and restoration activities and determine the repair crews' routes are helpful to give insights to decision-makers on how to improve emergency response plans. This article presents a literature review on short and long-term network models designed for roads repair and restoration. This review identifies the trends in publications, solution approaches, frequency of locations and type of disaster in the case studies, and the size of the networks. In addition, a qualitative analysis of the models’ characteristics, such as the type of network, uncertainties, and interdependencies, is presented. A discussion is provided to indicate future research directions. Among these, future research should focus on the combination of roads repair and restoration with other emergency activities such as relief distribution.
This study proposed a modified metric inspired by the well-applied “resilience-triangle” framework to integrate the resilience concept within the traffic speed. Firstly, for setting the evolving normal functionality, this study added the concepts of robustness loss and rapidity to characterize and compare the recovery processes of local roads and assess the corresponding resilience under different traffic operation conditions. Secondly, these different evolving resilience patterns provide a quantitative benchmark for detecting the links between resilience and traffic operating conditions and exploring its impact on total resilience. Finally, this study simulated and compared the dynamic evolution of the total resilience of local roads, which accurately captured the weak and poorly resilient road locations. Our findings indicated that the proposed metric was quite efficient and accurate in assisting stakeholders to prioritize the transport planning and the retrofit projects of some specific local roads, which could improve the resilience of overall transport system significantly.
