Resilience Of Transportation Networks Research Articles

Structural resilience disparities in regional city networks under the influence of Typhoon Fitow and COVID-19

The integration of regional city networks plays a pivotal role in the regional development. Assessing and analyzing the structural resilience of these networks holds significance in enhancing regional comprehensive disaster prevention. This study focuses on the Yangtze River Delta, examining its regional city networks encompassing information, economic, transportation, and innovation domains. Utilizing the social network analysis, projection pursuit model based on the genetic algorithm, coupling coordination degree model, coefficient of variation and spatial Gini coefficient, we scrutinized the structural resilience and evolution difference of these networks under different disasters. We found that the structural resilience of economic and transportation networks that depend on infrastructure construction are more limited by geographical distance, and information and innovation networks with weaker physical dependence have stronger structural resilience. The impact of Typhoon Fitow on the structural resilience of individual networks was mainly concentrated in coastal affected cities, while the impact after COVID-19 was mainly in marginal cities. The spatial pattern of the structural resilience of multiple networks shifted from low level equilibrium to agglomeration development. Finally, nodes were identified into three types according to the resilience performance. This research offers a scientific foundation for quantifying regional resilience and formulating network optimization strategies.

Predictive resilience assessment of road networks based on dynamic multi-granularity graph neural network

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: (1) Simulation methods for modeling rainfall impacts lack realism; (2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial–temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios.

A fairness-based multi-objective distribution and restoration model for enhanced resilience of supply chain transportation networks

Disruptive events play a significant role in the performance of supply chain networks (SCNs) due to their high dependency on transportation networks. Therefore, it is critical to develop coordinated strategies that consider (i) efficient restoration plans for the transportation network (TN) and (ii) distribution plans for downstream SCN, which consider critical aspects such as road congestion, travel time, and required service levels. Thus, we propose a novel multi-objective non-linear mixed-integer programming (NMIP) model to deal with disruptions in transportation networks while maximizing SCN performance. We developed a model for optimal restoration that reduces the effects of disruptions that disrupt the performance of the road TN and SCNs while considering a fair distribution of commodities. The model provides optimal restoration strategies for the TN after unpredicted events, not only based on a cost-effectiveness approach but also on the fair distribution of commodities among demand nodes on a complex network structure with a multi-supplier, multi-demand, and multi-commodity framework. To illustrate the model's capabilities, we study the transportation of commodities in Colombia and how disruptions impact their SCN performance. The results show that a fairness-based distribution helps to obtain satisfaction rates faster when compared to restoration plans based on cost-effectiveness only.

Flow-based Seismic-resilience Estimation of Urban-bridge Transportation Networks

The Gyeongju earthquake (magnitude 5.8) and Pohang earthquake (magnitude 5.4), which occurred recently in South Korea, induced profound social confusion. In particular, severe damage to critical infrastructures, such as bridges, water, and power-supply facilities, not only directly damages the structures but also indirectly affects the local residents and community. In this study, a comprehensive methodology was proposed to evaluate the seismic resilience of urban-bridge transportation networks based on flow analysis. To demonstrate the proposed methodology, an actual bridge transportation network in Gyeongju, South Korea was adopted, and a network map was reconstructed based on a geographic information system. Numerical analysis show that the area of the seismic-resilience curve of the bridge transportation network increases with the earthquake magnitude. Moreover, the required travel time decreases as the bridge structure is restored.

A Max–Min Fairness-Inspired Approach to Enhance the Performance of Multimodal Transportation Networks

Disruptions in multimodal transportation networks can lead to significant damage and loss, affecting not only the networks’ efficiency but also their sustainability. Given the size, dynamics, and complex nature of these networks, it is essential to understand and enhance their resilience against disruptions. This not only ensures their functionality and performance but also supports sustainable development by maintaining equitable service across various communities and economic sectors. Therefore, developing efficient techniques to increase the robustness and resilience of transportation networks is crucial for both operational success and sustainability. This research introduces a multicriteria mixed integer linear programming (MCMILP) model aimed at enhancing the resilience and performance of multimodal–multi-commodity transportation networks. By ensuring effective distribution of commodities, alongside a cost-efficient distribution strategy in the wake of disruptive events, our model contributes significantly to sustainable transportation practices. The proposed MCMILP model demonstrates that integrating equality considerations while seeking a cost-efficient distribution strategy significantly mitigates the impact of disruptions, thereby bolstering the resilience of multimodal transportation networks. To illustrate the capabilities of the proposed modeling approach, we present a case study based on the multimodal transportation network in Colombia. The results show a significant improvement in the number of nodes that satisfy their demand requirements with respect to other approaches based on reducing total unsatisfied demand and transportation costs.

Assessing vessel transportation delays affected by tropical cyclones using AIS data and a bayesian network: A case study of veronica in northwestern Australia

Tropical cyclones frequently disrupt maritime transportation systems, impacting the normal operation of vessels and causing transportation delays. Analyzing the occurrence and degree of vessel transportation delays under disaster conditions is one of the critical aspects of maritime transportation management. This article, based on Automatic Identification System (AIS) data, examined the real behavioral trajectories of vessels and delay characteristics during tropical cyclones. Taking the example of Tropical Cyclone Veronica, which occurred in the waters off northwestern Australia in 2019, we conducted a comprehensive analysis. This involved numerical simulations of the entire disaster process and the cleaning and matching of trajectory data for all affected vessels in the area. We delved deeply into the relationship between vessel behavior characteristics and tropical cyclones, identified factors contributing to delays, and, based on our findings, constructed a Bayesian network inference model for vessel transportation delays under disaster conditions. This model uses information such as tropical cyclone intensity, vessel basic attributes, behavior choices, and port disaster avoidance measures as its primary nodes. The research results indicate that vessel rerouting, vessel loss of control, and waiting for port entry are the three major sources of vessel delays during disasters. Key influencing factors contributing to these consequences include port closure measures, the duration of vessels being adversely affected by strong winds, vessel tendencies toward loss of control, and risk preferences. To control the extent of vessel delays more effectively, it is crucial to prioritize these sensitive factors and strengthen monitoring and management efforts. This model is driven by real data, providing an objective reflection of real-world scenarios, and its effectiveness has been validated through sensitivity analysis. The research perspective and algorithmic framework presented in this paper offer a novel paradigm and fresh perspective for the study of maritime transportation disasters. The research findings have significant implications for bolstering the resilience of maritime transportation networks, enhancing our comprehension and anticipation of delays, and ensuring the continuity, security, and efficiency of the global supply chain for goods.

Assessing transport network resilience: empirical insights from real-world data studies

ABSTRACT Determining the factors that positively and negatively affect the resilience of transport networks provides valuable information that leads to a deeper understanding of the preparedness and response of networks to external disruptions. Over the past few decades, several review papers have explored various interpretations of transport network resilience and its calculation metrics. Nevertheless, only a limited number of these papers have paid attention on the utilisation of empirical data in resilience studies. This paper, through a systematic literature review, contributes to filling this gap. To this end, from a pool of 127 relevant articles, a subset of 53 articles using real-world data was selected. The paper analyses and classifies empirical findings in transport network resilience studies. In particular, it highlights and thoroughly discusses spatial patterns of resilience and relevant influencing factors that positively or negatively affect the resilience attributes of a transport network. Although it is possible to place the empirical results within the theoretical framework proposed by the literature, two main issues on target reference levels arise from the graphical representation of transport network resilience as suggested by the theory. Based on these findings, research gaps are identified and future directions for transport researchers are proposed.

Topology-based Resilience Assessment of Port Multimodal Transport Networks: A Case Study of Shanghai Port

As transit nodes in the field of international trade, ports are of critical importance for establishing and maintaining the effectiveness of the trade supply chain. However, the system performance of ports is vulnerable to disruptions caused by unexpected events such as natural disasters, accidental catastrophes, and public health emergency. These disruptive events will have serious negative impacts on the normal operation performance of ports and international trade among countries. This paper is to propose a comprehensive assessment framework of the system resilience of the port multimodal transport networks before and after the occurrence of disruption events, which can effectively improve its ability to deal with emergencies, minimize losses, and enhance the system performance levels. This paper also establishes several simulation experiments focusing on the full lifecycle of resilience based on topology of the transport networks, and simulates a variety of scenarios regarding different damage and recovery modes, such as deliberate attack, random attack, planned recovery, and random recovery, in the assessment process. Then the actual transport situation of Shanghai port is taken as a case study, and three multimodal transport networks including the sea-rail network, the sea-road network, and the sea-road-rail network under different disruption scenarios are compared，and evaluate the evolutions of the network resilience of the multimodal transport networks of Shanghai port stage by stage. In addition, managerial suggestions are also proposed based on the resilience assessments of different disruption scenarios to maximize the resilience of multimodal transport networks of Shanghai port.

Resilience of transportation infrastructure networks to road failures.

Anthropogenic climate change drives extreme weather events, leading to significant consequences for both society and the environment. This includes damage to road infrastructure, causing disruptions in transportation, obstructing access to emergency services, and hindering humanitarian organizations after natural disasters. In this study, we develop a novel method for analyzing the impacts of natural hazards on transportation networks rooted in the gravity model of travel, offering a fresh perspective to assess the repercussions of natural hazards on transportation network stability. Applying this approach to the Ahr valley flood of 2021, we discovered that the destruction of bridges and roads caused major bottlenecks, affecting areas considerably distant from the flood's epicenter. Furthermore, the flood-induced damage to the infrastructure also increased the response time of emergency vehicles, severely impeding the accessibility of emergency services. Our findings highlight the need for targeted road repair and reinforcement, with a focus on maintaining traffic flow for emergency responses. This research provides a new perspective that can aid in prioritizing transportation network resilience measures to reduce the economic and social costs of future extreme weather events.

Resilience Assessment Framework for an Urban Road Network Subjected to Disruptions

This research paper introduces a novel framework for assessing the resilience of urban transport networks. Traditional approaches to quantify resilience primarily focus on evaluating resilience based on the accessibility and vulnerability of network links. However, this study proposes a different approach by considering the dynamic role of links within the overall network, including their ability to accommodate additional traffic demand from other links. To accomplish this, a comprehensive scanning network approach is implemented using an iterative traffic assignment performed. The framework is applied to the urban transport network of Karachi, Pakistan. The model incorporates parameters for calibrations derived from volume delay functions, specifically alpha and beta, which are obtained from actual traffic travel times during free-flow and peak hours. The accuracy of the model is validated by comparing observed traffic counts at various locations with the simulated traffic volumes, resulting in an average error of 9% and a standard deviation of 7.4%. The study's findings reveal significant losses within the network and identify critical links that are particularly susceptible to disruptions. The analysis provides valuable insights into delays experienced by travellers and the additional distances travelled when specific links are closed. Furthermore, the framework enables the evaluation of the overall impacts of disruptions at the network level. The proposed methodology will allow traffic engineers and decision-makers to effectively plan rerouted through alternative links during disruptions.

Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network

With the continuous development of public transportation, the impact of unexpected events on the operation of bus networks has become increasingly severe due to the growing demand for public transportation and passenger volume. To accurately assess the impact of unexpected events on the operation of bus networks and scientifically evaluate their resilience, this paper proposes a framework for analyzing the resilience of bus networks. With the aim of providing scientific evidence to enhance the reliability of public transportation networks, this framework can be used to determine the resilience of bus networks to unexpected events. The main contributions of this framework include three aspects: 1. Construction of the CRITIC–entropy weighting model for screening and calculating key indicators of the resilience of the bus network; 2. Use of resilience cycle theory to construct a model for analyzing the resilience of bus routes, and design a set of resilience quantification factors to calculate the resilience of bus routes; 3. Use of complex network theory to construct a model for analyzing the resilience of the bus network, by taking the bus route resilience obtained in the second step as the edge weight to calculate the resilience of the bus network. This paper takes the Beijing public transit system as an example and uses real data to verify the accuracy, scientificity, and feasibility of the proposed framework for analyzing the resilience of public transit networks to sudden events. The resilience analysis framework constructed in this paper has improved the existing research on transportation network resilience in theoretical aspects. Furthermore, the results outputted by this framework can provide a decision-making basis for network adjustment and disaster recovery for the management departments of public transportation networks in practical applications.

The Effectiveness of Improvement Measures in Road Transport Network Resilience: A Systematic Review and Meta-Analysis

Achieving improvement in the resilience of road transport networks by ensuring their smooth functioning and prompt recovery in the event of damage is crucial. This study focused on optimal measures and compared the effect of improvement measures on the resilience of road transport networks. A meta-analysis was performed to assess whether and to what degree the resilience of road transport networks was improved with different categories of measures. The articles were divided based on improvement measures, such as infrastructure investment, structure and planning, traffic signal management, and recovery schedule. The methodology of how to define and measure the resilience of road transport networks is considerably diverse, and most definitions are based on basic infrastructure structures. The efficiency of four types of improvement methods was grouped: structure and planning, infrastructure investment, recovery schedule, and traffic signal management. This study supports the use of structure and planning as a promising way for improving the resilience of road transport networks. Increasing comparability in studies and finally developing effective improvement measures in transport planning and decision making require more precise conceptual and methodological standardization in road transport network resilience.

An integrated resilience assessment model of urban transportation network: A case study of 40 cities in China

The transportation network is essential in providing daily accessibility and essential service to societies. An acceptable level of service needs to be maintained in critical infrastructures, even in the case of disruptions. We develop an integrated model to assess the resilience of urban transportation networks when exposed to different disruptions. Resilience is evaluated from perspectives of both traffic network topology and traffic flow. This model contains three parts: (1) a traffic flow simulation model to get flow distributed throughout the whole network, (2) different real-world disruptions are abstracted as random, localized, and flood disturbances, (3) functional and topological resilience are accessed by analyzing variations in travel time and connected components before and after a disruption occurs. 40 major cities are chosen to conduct resilience assessments. As the intensity of the disruption increases, common trends in the changes of functional and topological resilience values are observed and analyzed across all studied cities. In most cases, flood disruption is the least disruptive one of all three types of disruptions. Regression models are developed to estimate functional and topological resilience after random, localized, and flood disturbances. Possible resilience enhancement strategies are discussed based on analyses of regression models. This study could aid stakeholders in gaining a clear understanding of resilience not only topologically but also from the perspective of traffic flow and offer them practical strategies to enhance resilience in face of disruptions.

Quantification of coastal transportation network resilience to compounding threats from flooding and anthropogenic disturbances: A New York City case study

Technological advancements and management adaptations have improved the function of engineered systems in response to threats from coastal flooding. Other natural and anthropogenic disturbances such as pandemics, utility hijacking, infrastructure destruction, and biochemical releases can stress a system beyond acceptable limits or in ways not previously conceived. Such threats can be direct or indirect and often result in large-scale disruption to the critical functions of the system. Traditional risk management approaches, while effective for known and predictable threats, are not adequate preparation for compound disturbances that are often unpredictable and not well defined. Rather, a resilience-based approach is required. Resilience-based approaches acknowledge that multiple disruptions to the system will occur, such as the interaction of impending hurricanes and evacuation complications due to a pandemic, while focusing on the recovery and maintenance of critical functions. The system can be stressed with multiple disturbances to determine its capacity to resist and recover. Analysis of these capacities or subsequent failures can then be used to determine the resilience of the system and provide insight into remedial actions or improvements. A framework combining hydrologic modeling and network science can be used to determine critical weaknesses in transportation infrastructure due to compounding threats. This determination could be used to address pre-disaster staging by identifying areas that are likely to be isolated and to identify the characteristics of a resilient network to incorporate into future designs. To analyze resilience under compounded disturbances, coastal flood modeling is combined with hypothetical vehicle bridge failure in New York City, USA, and connectivity is analyzed through the use of ego networks. Our results show network analysis can be effectively used to identify areas of need to improve whole network resilience and is a valuable tool to quantify the compounding effects of multiple threats.

Comparative Study of the Seismic Resilience of Transportation Networks according to Bridge Recovery Priority

Recently, frequent earthquakes have damaged bridge structures, causing great inconvenience owing to the disruption of transportation networks and increasing travel time. In this study, the restoration priority of bridges under seismic conditions was proposed and the seismic resilience curves were compared based on an artificial neural network model. To determine the restoration priority of bridges, 1) performance-based and 2) benefit-cost-based restoration priorities were introduced. For demonstration purposes, the actual Pohang road network was reconstructed and the seismic resilience curves of the transportation network corresponding to the proposed restoration priorities were compared. Numerical analysis showed that the performance-based restoration priority had a more rapid seismic resilience curve than benefit-cost-based restoration priority.

Characterizing resilience of flood-disrupted dynamic transportation network through the lens of link reliability and stability

Characterizing resilience of flood-disrupted dynamic transportation network through the lens of link reliability and stability

Modeling the Vulnerability and Resilience of Interdependent Transportation Networks under Multiple Disruptions

Because infrastructure systems are highly interconnected, it is crucial to analyze their vulnerability and resilience with the consideration of interdependencies. This paper constructed a bus–metro interdependent network model based on the passenger transfer relationship and used deep learning to identify the network topology attributes. The vulnerability process of the interdependent network to different disruptions under structural and functional perspective was studied. On this basis, this paper adopted a resilience assessment framework and mainly focused on modeling and resilience analysis of interdependent networks’ recovery processes. The optimal and instructive recovery strategy was determined, and it is shown that the increase of the coupling distance cannot alleviate the vulnerability of the interdependent network effectively; after the tolerance coefficient reaches the threshold, the effect on the vulnerability of the dependent network is weakened; and a betweenness-based strategy (BBS) works best in the preferential recovery of key nodes.

Investigation of different resilience-based optimisation strategies for retrofitting curved bridges

Recent natural or manmade hazards highlighted the pressing need for allocating enough financial resources for the enhancement of the resilience of infrastructure in our society. In this regard, bridges play a pivotal role in transportation networks, where any reduction in their performance can influence the functionality of the whole network. Bridge retrofitting is a common strategy for enhancing transportation network resilience. Composite reinforcement with different thicknesses and concrete seat extenders with different widths have been used to retrofit an existing curved bridge in Iran. A novel hybrid algorithm called NSGAII-SA was proposed and compared against a simple optimisation strategy, a traditional genetic algorithm solver (NSGA-II), and pure simulated annealing (SA). These optimisation strategies identify the optimal retrofit scenarios set in terms of maximum resilience index, minimum recovery time, and minimum retrofitting cost. For this problem, NSGAII-SA outperforms the traditional NSGA-II. The comparison shows that for this particular application, SA has the best overall performance. However, the proposed NSGAII-SA algorithm has satisfactory performance too, it is a clear improvement over NSGAII and it has the potential to be very useful and better suited than SA for problems with large design spaces and many local minima.

Machine Learning-based Seismic Fragility Curves for RC Bridge Piers

A significant part of ongoing studies in the field of earthquake engineering is directed toward the seismic risk assessment of buildings and infrastructures at a territorial scale. This task is usually accomplished by grouping the structures into homogenous classes in terms of typology, for which seismic fragility curves are then obtained for different limit states via numerical simulations or from the statistical analysis of observational data when available. Particularly, the development of typological fragility curves for bridges under earthquake is useful for assessing the reliability and resilience of transportation networks in seismic areas and can be also effective decision-making support. Within this framework, the proposed study establishes a machine learning-based paradigm for the closed-form prediction of the main statistical parameters required to obtain relevant seismic fragility curves for reinforced concrete bridge piers. Initially, a huge training dataset has been obtained by Monte Carlo simulations and displacement-based bridge pier assessments by assuming data representative of the Italian highway transportation network. Next, symbolic nonlinear regression formulae for estimating the main statistical parameters of seismic fragility curves have been generated. With the aid of those formulae, the effort of calculating the seismic fragility curves is greatly reduced since the corresponding main statistical parameters can be directly calculated from a set of commonly available attributes. Therefore, the proposed study provides a helpful tool for the rapid preliminary assessment of damage and risk level of existing highway transportation networks exposed to seismic hazards.

Evaluating the Influence of CAVs on Transport Resilience – Dublin City Case Study

Transport systems are increasingly suffering from perturbations that can cause significant damage by not only disrupting the affected areas but also creating a domino effect. This domino effect can create disruptions in multiple parts and reduce the resilience of the network. Research on transport resilience can increase understanding and lead to the construction of transportation networks with better adaptation and recovery strategies. In recent years, innovative transportation technologies such as Connected and Autonomous Vehicles (CAV) have brought significant changes to the operation of transportation networks. However, the impact of these innovative technologies on the resilience of transportation networks is unclear. To this end, this research will focus on studying how an increment of CAV can affect the resilience of transportation networks in the presence of disruption.