Resilience Of Transportation Networks Research Articles

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 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.

Policies towards the resilience of road-based transport networks to wildfire events. The Iberian case

Policies towards the resilience of road-based transport networks to wildfire events. The Iberian case

Two-Stage Stochastic Programming Model for Improving Transportation Network Resilience of Relief Supplies in Sequential Hazards Scenario

Cascading failure of road transport networks caused by complex sequential natural hazards adversely affects the use of pre-positioning relief supplies. Therefore, it is vital to improve the transportation network resilience of relief supplies by using an optimization model. The purpose of this paper is to pre-position and distribute relief supplies in uncertain scenarios of sequential hazards. A two-stage stochastic programming model to maximize the total resilience is proposed to provide an optimal plan against the uncertain impact of sequential natural hazards. The combined impact of the Jiuzhaigou 7.0 magnitude earthquake and its associated landslide is a prototype disaster scenario for the implementation of the method proposed in this paper. The model is solved by a neighborhood search-based genetic algorithm (NS-based GA), which has both the global search capability of a genetic algorithm and the local search capability of a large-scale neighborhood search algorithm, can improve the solution finding capability. A case study focusing on finding the optimal solution for the pre-position and distribution of relief supplies in the sequential hazard of Jiuzhaigou earthquake is conducted to illustrate the validity of the proposed model.

Resilience modeling and pre-hazard mitigation planning of transportation network to support post-earthquake emergency medical response

Earthquakes can cause traffic network disruption, extensive blacked-out traffic signals, considerable dislocation trips, and numerous injuries. To avoid injury-transporting private vehicles jamming on few routes on a first-come, first-served basis, existence of redundant routes is essential for transporting injured people to hospitals in a timely manner. To save as many lives as possible, a resilient transportation network with redundant paths between injured people and hospitals is crucial following major earthquakes. This study aims to support the emergency medical response through improving system resilience with a focus on redundancy by integrating post-hazard emergency response and pre-hazard mitigation planning holistically. First, a new methodology to quantify the resilience of transportation networks is developed with two main contributions: incorporating infrastructure fragility, seismic uncertainty, search-and-rescue activity, and life vitality decay of injured people to quantify the urgency of hospitalization trips; and introducing equivalent resistance theory to model the redundancy of road networks. Second, the feasibility of developing a resilience-based pre-hazard mitigation strategy of vulnerable infrastructures to proactively support post-earthquake emergency medical response is investigated. A comparison with existing resilience metrics is conducted to reveal the advantage of the proposed methodology. Shelby County, Tennessee, is selected as the prototype community to demonstrate the proposed framework.

Resilience Assessment of Traffic Networks in Coastal Cities under Climate Change: A Case Study of One City with Unique Land Use Characteristics

How to assess the risk of flood disasters and improve the resilience of coastal cities has become a scientific problem that must be solved urgently. This paper aims to construct a resilience assessment model for transport systems in the context of climate change based on an analysis of the spatial characteristics of regional transport networks and complex network theory, using the Pudong New Area in Shanghai, China as the empirical object. Other objectives of the developed model are to establish a system of homogeneity, efficiency, and stability indicators and to assess the impact of flood depth (up to 7 m) on the resilience of transport networks in terms of static network structure and dynamic network performance by designing flood inundation disturbance scenarios. Finally, the characteristics, change trends, and conceptual connotations of the resilience of transport networks in coastal cities are condensed. The results of this study provide a solid scientific basis for future flood disaster risk management in global coastal cities.

Editorial: The crux of bridge and transport network resilience – advancements and future-proof solutions

Editorial: The crux of bridge and transport network resilience – advancements and future-proof solutions

Identifying Topological Credentials of Physical Infrastructure Components to Enhance Transportation Network Resilience: Case of Florida Bridges

Identifying Topological Credentials of Physical Infrastructure Components to Enhance Transportation Network Resilience: Case of Florida Bridges

Freeway Incident Diversionary Behavior as a Measure of Transportation Network Resilience

Traffic flow disruptions caused by road incidents reduce the performance of transportation networks. Under such conditions, drivers often attempt to reduce delay by diverting to alternate paths, redistributing trips to less congested routes. In the absence of route guidance information, the speed and efficiency of network recovery are most significantly influenced by driver familiarity with a network and, in particular, its daily variability. The goal of this study was to propose metrics and a general formula to relate driver diversionary behavior during incidents to assess resulting actions and conditions from the perspective of network resiliency. Another key objective of the work was to assess the applicability of routine traffic volume data collected by the Louisiana Department of Transportation and Development (LA DOTD) for assessing both driver adaptation and local network resiliency during incidents. The findings from the study suggest that resiliency during incident conditions is not a fixed value for a facility but varies depending on the severity of the incident, opportunities for travel diversion, and the actions of network managers.