Road Network Vulnerability Research Articles

Disaster vulnerability in road networks: a data-driven approach through analyzing network topology and movement activity

The rise in natural disasters and climate-induced events, such as wildfires, hurricanes, and flooding, has significantly affected urban life. These events can disrupt daily activity and flows of individuals and goods on road and transit networks. To enhance urban resilience against disasters, it’s crucial to study and understand road network vulnerability, utilizing data-driven insights to inform planning and preparedness efforts. The aim of this paper is to develop a data-driven exploratory approach to assess vulnerability in road networks in response to a disruption. To accomplish this, we compare the centrality of road segments before, during, and after disaster, considering the network topological structure and movement activity as it is observed through large tracking data of cellphone traces on the network. The novelty of our approach lies in inferring the impact from movement data, instead of manually removing links from the network. The results obtained from this study suggest that incorporating movement data into the assessment of network functionality provides a more realistic estimation of the road network vulnerability in response to a disruption, compared to solely using network topology.

Assessment on vulnerability of road networks considering the dynamic impact of urban waterlogging and the mitigation effect of LID measures

The urban waterlogging stemming from the rainstorm exerts a considerable adverse influence on road networks. Accurately evaluating the vulnerability of road networks during waterlogging is a crucial measure to alleviate flood risk and damage. Nevertheless, previous research merely considered the maximum inundation depth of waterlogging, but neglecting the dynamic variation of waterlogging. In this study, we firstly develop a coupled hydrodynamic model (IFMS-Urban + SWMM) and integrated it with a road traffic model to analyze the impact of waterlogging on road networks; then, a time factor is involved to assess the dynamic variation of flood vulnerability by using a novel index road vulnerability recovery index (RVRI); finally, the mitigatory effect of low impact development infrastructure/practices (LIDs) on the vulnerability of transportation roads under waterlogging was investigated. The results show that, both the coupled hydrodynamic model and road traffic model were verified for logical soundness and show strong adaptability. The urban waterlogging becomes increasingly severe with the increase of return period of rainstorms and leads to a corresponding rise in the vulnerability of the road network. The evaluation system of road network vulnerability becomes more comprehensive and reasonable with the consideration of the time factor. The well-arranged LIDs can effectively reduce the vulnerability of road network. This study serves as a valuable reference for dynamically assessing the vulnerability of road networks to urban waterlogging, offering guidance for urban waterlogging prevention, road traffic management, and road operation and maintenance in urbanized areas.

Quantifying the vulnerability of road networks to flood-induced closures using traffic simulation

Disruptions in road networks can significantly impact traffic flow, necessitating the identification and mitigation of vulnerable components. This study presents a methodology to assess the vulnerability and robustness of road networks, addressing the research gap in comprehensive approaches that effectively evaluate both controlled intersections and uncontrolled/free-flow highway segments. The proposed framework employs traffic simulation tools to model road closures within designated study areas, focusing on networks in Qatar. Performance measures such as average control delay and volume to capacity ratios are utilized to calculate vulnerability indices for network components. The two-pronged approach quantifies the impact of simulated closures at both the component and network levels, revealing critical intersections and road segments that markedly affect network performance during disruptions. The findings contribute to the field by providing transportation engineers with an effective quantitative tool to detect vulnerabilities in their road network, guiding design plans and actions to mitigate the potential harmful impacts of disruptive events on transportation infrastructure. The methodology's scalability ensures its applicability to various operational contexts, offering significant insights for transportation planning and infrastructure resilience. By establishing a comprehensive vulnerability analysis framework, this study enhances the understanding and management of road network vulnerabilities in complex urban environments. Further research on integrating dynamic traffic assignment and real-time data is recommended to further enhance the predictive accuracy and applicability of the proposed framework, ultimately improving transportation infrastructure resilience.

Regional traffic congestion coordination control based on critical links

Critical links have the characteristics of carrying large traffic flow and quickly affecting the operational performance of the road network. Existing regional traffic congestion coordination control methods do not consider the impact of critical links. To solve this issue, this paper proposes a regional traffic congestion coordination control method based on critical links. First, the median, gravitational index, and traffic sharing ratio are selected as the identification indexes of critical links in urban networks. Second, the road network vulnerability is assessed, and road link criticality is calculated based on joint entropy theory and multi-scale factor synthesis of multi-metric identification results. Then, the regional signal control range is determined, and a multi-stage signal control optimization model is developed. Finally, the optimal traffic signals for intersections are obtained by minimizing the pressure at the maximum phase of the road link and maximizing the capacity of critical links. To demonstrate the effectiveness of the proposed methods, two benchmarks (i.e., the MAXBAND method and existing traffic signals) are compared. The results show that (1) in terms of boundary control, the average vehicle delay at the boundary intersection of the road network is 34.62 s, an increase of 7.49 s and 5.03 s compared to the benchmark methods, respectively. (2) In terms of internal control, the average delay of the road network based on the proposed method is 87.54 s, which is reduced by 16.38 % and 28.73 % compared with the benchmark methods, and the queue lengths of the critical links are reduced by 21.47 % and 34.15 %, respectively. (3) The control process of the proposed method enables the number of vehicles within the road network to be maintained near the optimal carrying capacity, which gives the road network an efficient operating efficiency.

A Dual-Layer Complex Network-Based Quantitative Flood Vulnerability Assessment Method of Transportation Systems

Evaluating the vulnerability of urban transportation systems to flood disasters can provide scientific support for urban disaster prevention and mitigation. Current methods for assessing the flood vulnerability of urban roads often overlook the internal relationships within the complex spatial composition of road networks and surface structures. In this study, based on the theory of complex networks, a dual-layer network assessment model is established for evaluating the flood vulnerability of urban transportation systems by coupling basic geographic data with road network vector data. Unlike traditional methods, this model considers the complex relationship between road network structures and ground surfaces, uncovering a correlation between road network structure and road flood vulnerability. By utilizing this model, the flood vulnerability of road networks in Shenzhen, as well as the city’s spatial flood vulnerability, are quantitatively assessed. Based on the quantitative results, we create maps illustrating the distribution of road and spatial flood vulnerability in Shenzhen. The study results reflect that roads highly vulnerable to flooding are mainly located in the central urban area of the southwest, with the flood vulnerability spatially concentrated primarily in the northern and western regions. Using data from government reports, news stories, and other sources over the past five years, we compile recorded instances of urban waterlogging. The quantitative results of the model are consistent with the distribution trend in recorded waterlogging points, indicating that the model’s outcomes are authentic and reliable.

An analysis of the vulnerability of road networks in response to disruption events through accessibility indicators specification

ABSTRACT The objective of this paper is to propose an analysis of the vulnerability of road transportation networks in response to external shocks. Through the estimation of the accessibility and Link Importance Indicator (LII), a vulnerability road transportation analysis has been carried out and applied to a real case study. The case study comprises the 112 districts of the metropolitan area of the city of Naples, in the south of Italy. The main findings of this study are represented by the accessibility indicator introduced, which can be considered a measure to capture the vulnerability due to disruptions in the road network, and the LII, which can be used to identify critical roads. The proposed methodology represents a useful tool for local administrations, transportation agencies, and civil protection that need to deal with emergency situations, which are becoming more common.

An Assessment of Flood Risk in the Gambia: A Data-Driven Approach to Flood Risk Management and Prevention

Coastal African countries, including Gambia, are increasingly vulnerable to a range of natural disasters, notably flooding, which poses significant challenges to sustainable development and safety. This study set out to comprehensively assess the flood risk in Gambia using a data-driven approach, aiming to enhance flood risk management and prevention strategies. Utilizing geospatial data and Geographic Information System (GIS) techniques, the research involved mapping and categorizing flood risk zones across various regions. The analysis focused on the identification and categorization of flood risk zones, assessment of the vulnerability of key infrastructures, health facilities, road networks, and settlements to flooding. The findings reveal that a total of 2,721 square kilometers of land is at flood risk categorized as either high or very high. This constitutes 26.89 percent of the country’s of terrestrial area. A total of 25 critical health infrastructure were in areas of this high flood risk categorization – ranging from general hospitals to clinics. Up to 24.5% of the nation's road network was found to be in this flood risk zone. Of the country’s five administrative regions, the West Coast Region which is home to most of its urban centers was most at risk of flooding. Here, of the 353 settlements in this region, 172 of them fell under the high to very high-risk flood zone categorization, constituting 48.7% of the settlements at a significant level of flood vulnerability. This study enhances the understanding of flood hazards in Gambia, providing essential perspectives for decision makers, city planners, and emergency response teams. This research highlights the need for using data-driven methods in identifying and managing environmental risks. It also advocates for the adoption of long-term strategic planning, infrastructure enhancements, and community-based activities to reduce the effects of floods.

Dynamic assessment of postdisaster road network vulnerability using crowdsourced traffic data

Accurate identification of the spatial and temporal dynamic impacts of disasters on road networks is crucial for making informed decisions regarding postdisaster emergency response and recovery. In this paper, we propose a multidimensional vulnerability assessment framework that utilizes time series crowdsourced data. The framework enables a spatiotemporal analysis of traffic flows at the link level, allowing us to identify the primary factors that contribute to vulnerability after a disaster. The results show that there is a significant variation in link vulnerability over time, with the overall network gradually returning to a normal state. We also observe a global correlation in link vulnerability across the spatial aspect, although the autocorrelation effect is only present among directly contiguous links. Furthermore, metrics affecting link vulnerability varied during different periods of response. Graph-based metrics of road network structure, population density, and road type are key factors influencing postearthquake vulnerability.

Risk analysis of road networks under the influence of landslides by considering landslide susceptibility and road vulnerability: A case study

Landslides are the most common natural hazards and can cause casualties and damage forests, roads, water conservancy, hydropower, and other infrastructure. Road networks are essential components of critical infrastructure networks. Damage from natural disasters significantly impact the production and life in the region. It is important to evaluate the risk of road networks under landslides. In this paper, we investigate the risk of road networks under the influence of landslides in six counties adjacent to Sichuan Province and Yunnan Province by considering landslide susceptibility and road vulnerability. First, according to the characteristics of the study area, six evaluation indicators were selected for landslide susceptibility and road network vulnerability. Second, the combination weighting method is used to assign the weight of each evaluation index. Finally, the risk analysis results combining landslide susceptibility and road network vulnerability are obtained using the landslide risk formula. The research work presented in this paper can help reduce the property loss caused by landslides and realize the timely identification and protection of high-risk road sections.

Real-time Vulnerability Analysis of Urban Expressway

ABSTRACT This study proposes a new method for evaluating the road network vulnerability per unit time: the real-time vulnerability index (RVI). The practical application of the proposed measure in real traffic scenarios is investigated by using empirical data from Shanghai Expressway.To prevent traffic congestion, a critical threshold of RVI (RVI*) is suggested for traffic control. Furthermore, the hysteresis on macroscopic fundamental diagram (MFD) and RVI is further analyzed in the phase transition of traffic states. The results show that RVI can help derive some representative traffic characteristics in real time and dynamically reflect road network vulnerability: when traffic congestion or accidents occur on the road network, the results observed for the traffic hysteresis on MFD and RVI were consistent, and RVI is easier to observe and more suitable for traffic management. The findings prove that RVI is a reliable indicator to help predict the dynamic operation of the road network.

Quantitative analysis of the importance and correlation of urban bridges and roads in the study of road network vulnerability

The city development is closely related to the performance of the transportation network system. Bridges and roads are important parts of the transportation system, and are also inseparable components of the transportation network. However, the effect of the correlation between bridges and roads on the network system has not been studies thoroughly in the literature. Therefore, it is necessary to analyse the vulnerability of the road network when both bridges and roads are involved. In this paper, the urban road network is modeled into the form of network connection and node, based on the analysis of the related research results of road network vulnerability in the literature. Taking the urban roads at all levels as the connection and the transportation hubs (including bridges) as the nodes, the paper puts forward the corresponding measurement indexes and calculation methods, and establishes the importance and correlation analysis model of roads and bridges in the urban road network. At last, the model is applied to the road network which is 5 × 3 km2 besides Yangpu Bridge of Shanghai for verification, the importance and correlation of specific roads and bridges in the analyzed urban road network are calculated, which provides a certain basis for dealing with various emergencies leading to the decline of urban road network vulnerability. In this paper, the importance analysis of urban road network is extended to the bridge correlation analysis, so that the proposed model of the vulnerability assessment of the urban road network system is more suitable for the increasingly demand of road and bridge construction in China, and provides a certain basis for dealing with the decline of road network vulnerability caused by various emergencies.

A global assessment of national road network vulnerability

Every country relies on a well-functioning road system. However, we do not have a clear understanding yet of the vulnerability of each of these road networks to different forms of disruption. In this study, we aim to better understand how road networks are affected by different disruptive events, to identify hotspots of road network vulnerabilities, and to better target where and what type of future investments can be made to develop more resilient networks. To do so, we developed a fully open-source modelling framework to expose over 200 country road systems across the world to random, local, and targeted disruption schemes. For each country, we assessed the impact of such disruptions on intra-country travel activities and regional accessibility. The results highlight the vulnerability of road systems in mountainous and small-island countries owing to the limited availability of alternative routes. Additionally, we find that, on average, low-income countries experience a collapse of road-system services with much fewer disruptions, relative to high-income countries, due to the lack of redundancy in their systems. While the value of goods and services disrupted may be higher in wealthier countries, the results highlight that from an equity perspective, transport infrastructure investments are more desired in low-income country networks.

Extreme Weather Events: The Impact of Flooding on Transportation Network: Case Study of Buyukcekmece Basin

Transportation is a critical sector which is vulnerable to hazardous events like urban floods. Thedisruption in transportation infrastructure affects the whole community in terms of loss of life and property.A resilient transport system is fundamental for resilient communities, and considering the adverse impactsof climate change, it becomes even more important. The Büyükçekmece Basin is a densely populated andeconomically significant area that also includes regions of natural importance requiring preservation. Thus,a flood hazard can affect more people, cause greater monetary losses and have negative impact on theenvironment and nature.The purpose of this research is to present the disruption of road network in Büyükçekmece Basin due toflooding and create an analytical framework. Vulnerability of road systems to rainfall-induced floods isquantified by integrating different models. An integrated framework linking hydrological model andinundation model is established and the relation between flood depth and road network vulnerability isdetermined. Flood hazard maps are created for two different rainfall events with 1 in 50 year and 1 in 100-year return period and flooded road lengths are determined. The flood levels have shown that there are roadsegments that will face moderate and major flooding. In Büyükçekmece Basin where serious floods haveoccurred in the past, measures need to be taken regarding the road network for possible flood risks.

Simulation optimization for stochastic casualty collection point location and resource allocation problem in a mass casualty incident

After a severe disaster strikes, a large number of casualties in need of urgent treatment, known as a mass casualty incident (MCI) event, arise from multiple disaster areas in a very short period of time and overwhelm available resources of the local healthcare system. This paper focuses on the effective design of casualty collection point (CCP) locations and the efficient allocation of limited emergency medical service (EMS) resources to transport casualties quickly to appropriate hospitals and increase the survival rate of casualties. A hybrid simulation–optimization approach to optimize the CCP location and EMS resource allocation problem over mixed binary and integer feasible domains for the minimization of expected complete delivery time of all casualties from disaster region to hospitals is presented in this work due to the stochastic and dynamic nature of the MCI logistics environment. A high-resolution stochastic discrete event simulation model considering time-varying stochastic casualty arrivals, random triage service times, and stochastic travel times caused by road network vulnerability is first constructed to describe a more detailed modeling of comprehensive MCI humanitarian logistics from the disaster regions to the hospitals. Then, a novel two-stage sequential algorithm, namely a combination of optimal computing budget allocation-based rapid-screening algorithm and adaptive particle global and hyperbox local search (ORSA-APGHLS), is developed to speed up convergence to near-optimal solutions compared to three common existing algorithms (Genetic Algorithm, Tabu Search and Stochastic Nelder-Mead Algorithm) under a limited simulation budget. We collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in Taiwan to conduct computational experiments to demonstrate the efficiency and efficacy of the proposed two-stage ORSA-APGHLS algorithm according to a potential earthquake scenario occurring in Tainan City, Taiwan. Through sensitivity analysis, the influence of different levels of scarce emergency medical resources and degrees of road damage on the expected complete delivery time of all casualties and the location-allocation decisions are investigated.

Spatial prediction of the geological hazard vulnerability of mountain road network using machine learning algorithms

The current assessment index of the geological hazard vulnerability assessment for mountain road network is relatively simple, and the assessment methods used are subjective, complex, and inefficient. This study proposes a prediction model for geological hazard vulnerability assessment of mountain road network incorporating machine learning algorithms. First, based on the quantification of the characteristics of the mountain road network and the local rescue forces, an objective and reasonable index-based system of vulnerability assessment of the mountain road network was constructed by combining the population, economic, and material factors. Second, the FAHP and AHP-TOPSIS were applied for the development of the vulnerability assessment models to carry out the preliminary vulnerability assessment for different road types. Third, the results of the preliminary vulnerability assessment were used as the sample set to build a road vulnerability prediction model using SVM, RF, and BPNN algorithms. Finally, the five-fold cross-validation and statistical parameter accuracy analysis were conducted to determine the most reasonable model with the highest prediction accuracy for geological hazard vulnerability mapping of the mountain road network. The results indicated that the vulnerability prediction model based on the FAHP sample set using the RF algorithm demonstrated the highest accuracy and robustness.

交通事故影响下的城市局部路网脆弱性研究

交通事故影响下的城市局部路网脆弱性研究

Vulnerability-based regionalization for disaster management considering storms and earthquakes

Existing approaches to risk assessment in natural disasters usually suffer from information specificity and data density. For example, road vulnerability assessment requires disaster information on the neighbors of all road segments, but weather stations may be far from such locations. The widely used intensity–duration–frequency analysis is too rough to overcome the tradeoff between estimation accuracy and reliability for assessing road vulnerability. Therefore, this study proposes a machine learning method to replace the frequency-style vulnerability with an intelligent measure, which will enhance the assessment by predicting the missing weather information at all road segments. Compared with conventional interpolation methods, our Markov chain random field kriging can reliably estimate the ungauged information when available stations are distributed asymmetrically. Therefore, our model contributes to the assessment methods by providing accurate and reliable vulnerability information for the transportation network under contemporary changing weather conditions. Through elaborate proofs to demonstrate the theoretical validity of our mathematical results, empirical results are also presented for application to practical governance. The empirical accuracy of 0.3 normalized root mean square error for disasters of storms and earthquakes is sufficient to convince local authorities to change their existing methods. The resulting regionalization in road network vulnerability is also tailor-made for practical applicability.

Vulnerability Identification and Cascading Failure Spatiotemporal Patterns on Road Network under the Rainstorm Disaster

Road vulnerability is crucial for enhancing the robustness of urban road networks and urban resilience. In medium or large cities, road failures in the face of unexpected events, such as heavy rainfall, can affect regional traffic efficiency and operational stability, which can cause high economic losses in severe cases. Conventional studies of road cascading failures under unexpected events focus on dynamic traffic flow, but the significant drop in traffic flow caused by urban flooding does not accurately reflect road load changes. Meanwhile, limited studies analyze the spatiotemporal pattern of cascading failure of urban road networks under real rainstorms and the correlation of this pattern with road vulnerability. In this study, road vulnerability is calculated using a network’s global efficiency measures to identify locations of high and low road vulnerability. Using the between centrality as a measure of road load, the spatiotemporal patterns of road network cascading failure during a real rainstorm are analyzed. The spatial association between road network vulnerability and cascading failure is then investigated. It has been determined that 90.09% of the roads in Zhengzhou city have a vulnerability of less than one, indicating a substantial degree of spatial heterogeneity. The vulnerability of roads adjacent to the city ring roads and city center is often lower, which has a significant impact on the global network’s efficiency. In contrast, road vulnerability is greater in areas located on the urban periphery, which has little effect on the global network’s efficiency. Five hot spots and three cold spots of road vulnerability are identified by using spatial autocorrelation analysis. The cascading failure of a road network exhibits varied associational characteristics in distinct clusters of road vulnerability. Road cascading failure has a very minor influence on the network in hot spots but is more likely to cause widespread traffic congestion or disruption in cold spots. These findings can help stakeholders adopt more targeted policies and strategies in urban planning and disaster emergency management to build more resilient cities and promote sustainable urban development.

Flood Hazard Mapping and Road Network Vulnerability Analysis in Kamrup and Kamrup Metropolitan Areas: A Case Study of Assam Floods in 2019

Flood Hazard Mapping and Road Network Vulnerability Analysis in Kamrup and Kamrup Metropolitan Areas: A Case Study of Assam Floods in 2019

Bridge Network Seismic Risk Assessment Using ShakeMap/HAZUS with Dynamic Traffic Modeling

Bridge infrastructures are critical nodes in a transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is essential to identify, retrofit, reconstruct, or, if necessary, demolish the infrastructure systems based on optimal decision-making processes. As one of the crucial components of the transportation network, any bridge failure would impede the post-earthquake rescue operation. Not only the failure of such high-risk critical components during an extreme event can lead to significant direct damages, but it also affects the transportation road network. The consequences of these secondary effects can easily lead to congestion and long queues if the performance of the transportation system before or after an event was not analyzed. These indirect losses can be more prominent compared to the actual damage to bridges. This paper brings about seismic performance assessment for the Cyprus transportation network from which the decision-making platform can be modeled and implemented. This study employs a seismic hazard analysis based on generated USGS ShakeMap scenarios for the risk assessment of the transportation network. Furthermore, identification of the resiliency and vulnerability of the transportation road network is carried out by utilizing the graph theory concept at the network level. Moreover, link performance measures, i.e., traffic modeling of the study region is simulated in a dynamic traffic assignment (DTA) simulation environment. Finally, for earthquake loss analysis of the bridges, the HAZUS loss estimation tool is used. The results of our investigations for three different earthquake scenarios have shown that seismic retrofitting of bridges is a cost-effective measure to reduce the structural and operational losses in the region.