Road Network Vulnerability Research Articles

Measuring urban road network vulnerability to extreme events: An application for urban floods

This paper aims to propose a convenient metric to evaluate the vulnerability of road networks to extreme events in small to medium-sized cities. We present the efficiency of alternative metric, which centers on how obstructions caused by a disaster tends to increase path lengths in the system. A case study was conducted as proof of concept to evaluate the impact of floods on individual modes of transportation in São Carlos, a medium-sized city in Brazil. The results show that walking and cycling tend to be more robust modes in the city while motorized individual transport tends to be more vulnerable and show that longer trips tend to be more vulnerable to floods, evidencing that car-oriented policy can worsen the network’s vulnerabilities. In contrast, compact city planning that encourages walking or cycling for relatively short distances may be more resilient to flooding.

Seismic Vulnerability Assessment Methodologies for Roadway Assets and Networks: A State-of-the-Art Review

Road networks are considered as one of the most important transport infrastructure systems, since they attain the economic and social prosperity of modern societies. For this reason, it is vital to improve the resiliency of road networks in order to function normally under daily stressors and recover quickly after natural disasters such as an earthquake event. In the last decades, vulnerability assessment studies for road networks and their assets gained great attention among the research community. This literature review includes a brief introduction about seismic vulnerability assessment, followed by the roadway assets damage and their damage states, and then the main typologies for the vulnerability assessment of roadway assets. Moreover, it focuses on available assessment methods, which were proposed to quantify the vulnerability of road networks and its assets. These methods are divided into two main categories, physical and traffic-based approaches. Methods based on fragility functions and vulnerability indexes were investigated in physical approach for roadways and its assets. On the other hand, accessibility and link importance index were explored in traffic-based approach for road networks. This paper reviews and comments the most common vulnerability assessment methods for road networks and its assets and points out their advantages and disadvantages. The main gaps and needs are identified and recommendations for future studies are provided.

Measuring the dynamic evolution of road network vulnerability to floods: A case study of Wuhan, China

This study provides a new methodology to uncover the topology and dynamic evolution of road network vulnerability, and understand the interacted effects of the traffic conditions and the users/passengers’ traveling behaviors on it. In this paper, by using a data set included the traffic flows during the morning peak period (07:00 a.m. to 09:00 a.m.) in the normal state and the flood-hit state, we simulated and compared the dynamic characterisitics of the normal traffic condition with that of the flood-hit traffic condition. We identified and visualized 51 flood-prone areas to predict the geographical distribution of hot spots in the road network to floods. We built a conceptual framework to define and measure the vulnerability as a function of exposure and importance. We measured and mapped the flood-prone areas’ vulnerability scores in the normal state and the flood-hit state, respectively, and used statistical analysis to compare their dynamic characteristics. We investigated the influence of the traffic conditions and the users/passengers’ traveling behaviors on the dynamic evolution of road network vulnerability. Our findings helped transport planners and decision-makers better derive the dynamic evolution of road network vulnerability affected by the users/passengers’ traveling behaviors, and they can also be used to guide the users/passengers to choose the optimum routes for improving the overall performance of the road network effectively when a flood occurs.

Characterization of Vulnerability of Road Networks to Random and Nonrandom Disruptions Using Network Percolation Approach

AbstractThis paper examines the vulnerability of road networks to two types of disruptions by modeling the percolation dynamics in road networks under different disruption scenarios. The objective ...

Compound generalized extreme value distribution for modeling the effects of monthly and seasonal variation on the extreme travel delays for vulnerability analysis of road network

This paper proposes a new class of extreme value distribution called compound generalized extreme value (CGEV) distribution for investigating the effects of monthly and seasonal variation on extreme travel delays in road networks. Since the frequency and severity of extreme events are highly correlated to the variation in weather conditions as an extrinsic cause of incidents and long delays, monthly and seasonal changes in weather contributes to extreme travel time variability. The change in driving behavior, which itself varies according to road/weather conditions, also contributes to the monthly and seasonal variation in observed extreme travel times. Therefore, it is critical to model the effect of monthly and seasonal changes on observed extreme travel delays on road networks. Based on the empirically revealed linear relationship between mean and standard deviation (SD) of extreme travel delays for both monthly and seasonal levels, two multiplicative error models are formulated. The CGEV distribution is then obtained by linking the two multiplicative error models and forming a compound distribution that characterizes the overall variation in extreme travel delay. The CGEV distribution parameters are calibrated and the underlying assumptions that are used to derive the CGEV distribution are validated using multi-year observed travel time data from the City of Calgary road network. The results indicate that accounting for the seasonality by identifying seasonal specific parameters provides a flexible and not too complex CGEV distribution that is shown to outperform the traditional GEV distribution. Finally, the application of the proposed CGEV distribution is evaluated in the context of road network vulnerability taking into account the observed probability of extreme event occurrences and the link importance. This derived data-driven vulnerability index incorporates a wealth of information related to both network topography in terms of connectivity and the dynamic interaction between travel demand and supply. This new data-driven vulnerability measure can thus be used as a decision support tool to inform decision-makers in prioritizing improvements to critical links to enhance overall network vulnerability, reliability, and resilience.

Analysing vulnerability of road network and guiding evacuees to sheltered areas: Case study of Mt Merapi, Central Java, Indonesia

Historically people traded the risk of living in dangerous places such as volcanic slopes for the benefit of farming in rich soils. Road network around risk prone area plays a key role in saving lives when evacuation is required in an emergency, and thus needs to be in full preparedness to face the eventuality. This will need analysing vulnerability to disruption and identifying critical network links of the evacuation routes. It is also crucial to ensure that the evacuees are aware of recommended routes to sheltered areas. Traffic models to assess road network performance due to natural disasters have been developed in the past. But few researchers investigated whether the evacuees are aware of the recommended routes to sheltered areas and whether they are willing to use them indeed. This paper adopts a vulnerability index and identifies network links to improve, by mapping them to a simple ‘traffic light style’ congestion scale. A special mobile phone application software was developed to guide the residents to reach sheltered areas which takes account of the fact that a third of the residents living around Mt Merapi are not aware of evacuation routes to sheltered areas.

An Assessment Method for Traffic State Vulnerability Based on a Cloud Model for Urban Road Network Traffic Systems

Directed against the shortcoming of the vulnerability assessment based on complex network theory for urban road network traffic systems, a state vulnerability assessment method, considering the influence of congestion, is constructed by using a cloud model to describe the randomness and uncertainty characteristics of risks. First, based on complex network theory, the primary index assessment system of road network vulnerability is introduced. Second, to describe the congestion states of roads, the cloud model theory is introduced to characterize the congestion features of road sections. After that, a state vulnerability identification method based on congestion cloud charts is constructed. Finally, on the basis of topological mapping of road network in Nan’an District in Chongqing, experiments and analyses are carried out in light of the actual congestion delay index dataset provided by AutoNavi Maps to verify the effectiveness and rationality of our proposed scheme. The experimental results show that the assessment method of state vulnerability can better describe the overall operational state and vulnerable road sections for road network.

Assessing road network vulnerability: A user equilibrium interdiction model

Road networks are vulnerable to natural and man-made disruptions. The loss of one or many critical links of the network often leads to increased traffic congestion. Therefore, quantitative models are necessary to identify these critical assets so that actions can be taken by decision makers to mitigate the impact of disruptions. This paper proposes an optimisation model to identify the set of arcs that, when lost, results in the worst congestion under user equilibrium traffic. The model is formulated as a bi-level non-linear problem. The challenging formulation is solved via a customised version of Greedy Randomised Adaptive Search Procedure (GRASP) meta-heuristic. Computational experiments are run on a dataset of artificial grids and managerial insights are provided based on popular Sioux and Berlin network case-studies.

Characterization of vulnerability of road networks to fluvial flooding using SIS network diffusion model

This study aims to characterize the vulnerability of road networks to fluvial flooding using a network diffusion-based method. Various network diffusion models have been applied widely for modeling the spreading of contagious diseases or capturing opinion dynamics in social networks. By comparison, their application in the context of physical infrastructure networks has just started to gain some momentum, although physical infrastructure networks also exhibit diffusion-like phenomena under certain stressors. This study applies a susceptible-impacted-susceptible (SIS) diffusion model to capture the impact of flooding on the road network connectivity. To that end, this paper undertook the following four steps. First, the road network was modeled as primal graphs and nodes that were flood-prone (or the origins of the fluvial flood) were identified. Second, temporal changes in the flood depth within the road network during a flooding event were obtained using a data-driven geospatial model. Third, based on the relationship between vehicle speed and flood depth on road networks, at each time step, the nodes in the road network were divided into two discrete categories, namely functional and closed, standing for Susceptible and Impacted in the SIS diffusion model, respectively. Then, two parameters of the SIS model, average transition probabilities between states, were estimated using the results of the hydraulic simulation. Fourth, the robustness of the road network under various SIS diffusion scenarios was estimated, which was used to test the statistical significance of the difference between the robustness of the road network against diffusions started from the randomly chosen nodes and nodes with different high centrality measures. The methodology was demonstrated using the road network in the Memorial super neighborhood in Houston. The results show that diffusive disruptions that start from nodes with high centrality values do not necessarily cause a more significant loss to the connectivity of the road network. The proposed method has important implications for applying link predictions on road networks, and it casts significant insights into the mechanism by which cascading disruptions spread from flood control infrastructure to road networks, as well as the diffusion process in the road networks.

Research on urban transport network topology vulnerability identification under rainfall conditions

Under the impact of global climate change, meteorological disasters, such as rainstorms, have occurred frequently during recent years and have led to several severe disruptions of transport network in urban areas. In order to minimise the risk of potential losses of life and properties, this paper presents a method to identify topology vulnerability of road networks for a range of rainfall scenarios. Meanwhile, a review is presented regarding the theories and methods of vulnerability analysis. The paper defines the concept of vulnerability of road network considering the intensity of rainfall. It establishes a comprehensive assessment index of 'importance of nodes and edges' by combining the 'betweenness and traffic flow' to find out the source of road network fragility considering the intensity of rainfall. Afterwards, the assessment index 'mincuts frequency vector' is introduced into the assessment of topology vulnerability. This paper provided the scientific basis for disaster control and reduction of the urban road network.

A MODELING FRAMEWORK OF HIERARCHICAL EARTHQUAKE RELIEF CENTER LOCATIONS UNDER DEMAND UNCERTAINTY

This paper presents a modeling framework for locating earthquake disaster relief centers, developed through multidimensional and integrated perspectives of transportation engineering and humanitarian logistics planning. The proposed framework consists of three sequential steps: 1) Seismic risk analysis, to evaluate the vulnerability of both road network and area-covering disruptions, using the spaghetti and meatballs method, which is a geographic information system (GIS) based analytical approach enriched with historical earthquake statistics and earthquake fault data; 2) Travel demand analysis, to forecast travel demand, travel behavior, travel pattern, and traffic volume, using the four-step transportation model developed based on field traffic survey data and seismic risk analysis data; and 3) Facility location problem analysis, to locate optimal earthquake disaster relief centers, using the hierarchical location problem model formulated based on a meta-heuristic genetic algorithm (GA) optimization technique. To evaluate model mechanism and performance, a preliminary model was then applied to the simulated geographical area with simulated road network of Chiang Rai Province, Thailand. As a result, two-level hierarchical disaster relief centers in response to earthquakes, taking into account accessibility and functional ability of transportation networks, risk covered/uncovered demand and supply distribution, can be viably determined. In the upperlevel, a central disaster relief center is functioned to collect rescue equipment and survival bags from both inside and outside the area and to distribute those to local disaster relief centers in the area. In the lowerlevel, three local disaster relief centers were optimally located, functioned to receive supplies from the central disaster relief center and then distribute to demand points affected by earthquakes. The resulting model can provide government and related agencies profound information in planning and developing either pre-disaster or post-disaster operations.

Internet of Things Based Solutions for Transport Network Vulnerability Assessment in Intelligent Transportation Systems

Intelligent Transportation System (ITS) is essential for effective identification of vulnerable units in the transport network and its stable operation. Also, it is necessary to establish an urban transport network vulnerability assessment model with solutions based on Internet of Things (IoT). Previous research on vulnerability has no congestion effect on the peak time of urban road network. The cascading failure of links or nodes is presented by IoT monitoring system, which can collect data from a wireless sensor network in the transport environment. The IoT monitoring system collects wireless data via Vehicle-to-Infrastructure (V2I) channels to simulate key segments and their failure probability. Finally, the topological structure vulnerability index and the traffic function vulnerability index of road network are extracted from the vulnerability factors. The two indices are standardized by calculating the relative change rate, and the comprehensive index of the consequence after road network unit is in a failure state. Therefore, by calculating the failure probability of road network unit and comprehensive index of road network unit in failure state, the comprehensive vulnerability of road network can be evaluated by a risk calculation formula. In short, the IoT-based solutions to the new vulnerability assessment can help road network planning and traffic management departments to achieve the ITS goals.

Humanitarian Logistic Relief Team Challenges during Flood

This paper demonstrates the challenges faced by humanitarian logistics relief team in rescuing the flood victims during floods. Flood is a disaster that commonly happened in Malaysia and this disaster can become chaotic and panic if handled improperly. The objective of this paper is to assess the challenges in humanitarian logistics relief mission during flood. To achieve the objective, semi-structure interview with six humanitarian logistics relief team has been conducted; two are from police, two from fire brigade and two from civil defense. The result from interview reveal that the factors that made the humanitarian logistics relief mission during flood inefficient are bound to three factors which are lack of emergency response transport and equipment, vulnerability of road network and attitude of flood victims. This study thus recommend that the government can add more budget to buy more equipment and emergency transport vehicle and educate by giving more awareness to public on the dangerous of flood to people and what should be done if they think the flood is going to be happened, without only hoping for the humanitarian logistics relief team to come and save them.

Identifying road network vulnerability during disaster, a case study of road network evacuation in Mount Merapi eruption

Identifying road network vulnerability during disaster, a case study of road network evacuation in Mount Merapi eruption

Measuring road network topology vulnerability by Ricci curvature

Describing the basic properties of road network systems, such as their robustness, vulnerability, and reliability, has been a very important research topic in the field of urban transportation. Current research mainly uses several statistical indicators of complex networks to analyze the road network systems. However, these methods are essentially node-based. These node-based methods pay more attention to the number of connections between nodes, and lack of consideration for interactions, leading to the well-known node paradox problem, and their ability of characterizing the local and intrinsic properties of a network is weak. From the perspective of network intrinsic geometry, we propose a method for measuring road network vulnerability using a discrete Ricci curvature, which can identify the key sections of a road network and indicate its fragile elements. The results show that our method performs better than complex network statistics on measuring the vulnerability of a road network. Additionally, it can characterize the evolution of the road network vulnerability among different periods of time in the same city through our method. Finally, we compare our method with the previous method of centrality and show the different between them. This research provides a new perspective on a geometry to analyze the vulnerability of a road network and describes the inherent nature of the vulnerability of a road system from a new perspective. It also contributes to enriching the analytical methods of complex road networks.

An Integrative Vulnerability Evaluation Model to Urban Road Complex Network

In order to analyze topological properties and vulnerable elements of urban road complex network, 98 lines and 128 nodes are selected by taking Shiyan city in China as an example. Firstly, the definitions of the urban road network’s running state vulnerability and structural vulnerability are proposed in this paper, and the assessment indexes are given respectively. From the perspective of the urban road network topology’s structural characteristics and the traffic demand characteristics, the internal relationship of the running state vulnerability and the structural vulnerability is introduced in order to show the necessity and reasonableness of the urban road network vulnerability assessment. Secondly, the concepts of the urban road network element’s structural-running state parameters are proposed to identify the source of network vulnerability. The Original Method is used to acquire the topology of the urban road network. Finally, with static vulnerability assessment principle, the structural vulnerability index is calculated on the premise that the cascading failure is not considered. With dynamic vulnerability assessment principle, the running state vulnerability index is calculated on the premise that the propagation effects of traffic congestion are considered. The results show that the vulnerability of road network is influenced by two factors: network topology structure and network running state.

Assessing seismic vulnerability of urban road networks by a Bayesian network approach

Evaluating the seismic vulnerability of urban road network is important for proactive reinforcement of the weakest link of the road network and post-disaster response and recovery of infrastructure systems. The paper proposes a Bayesian network (BN) method for seismic vulnerability assessment of an urban road network considering spatial seismic hazard with different levels of ground motion intensities, vulnerability of the components and effect of structural damage of components within the road network to the network functionality.Seismic vulnerability of the components in the road network is quantified based on physical structural damage under the earthquake demand. The vulnerability of the network is evaluated in terms of its dis-connectivity due to structural damage and induced transportation functional loss. BN model is built with vulnerability of the components as prior probability. The BN structure and CPT (Conditional Probability Table) is established based on transportation network analysis. The dis-connectivity of the road network is updated in light of information obtained from the observation of system or component states. Thus, the posterior probability could be calculated to identify the critical links and assess the seismic vulnerability of road network respectively. A case study is presented to show the application of the method. Results may contribute to the decision making on the reinforcement of the road network and the management of available resources to improve the disaster resilience and functionality of transportation systems in terms of seismic vulnerability.

Transportation hazard spatial analysis using crowd-sourced social network data

The safety hazard and the additional costs on transportation due to road accidents invite the necessity to minimize their impact. In this paper, we study the spatial-clustering behavior and hazard vulnerability of car accidents that occurred in Lebanon between 2015 and 2018. Assessment of spatial clustering of accidents and hot spots densities were examined using the Global G method of spatial autocorrelation and Getis–Ord Gi∗ statistics. A novel Road Hazard Index (Hi) was proposed to assess hazard vulnerability of road networks and to develop a road hazard prediction model.

Coupling Natural Hazard Estimates with Road Network Analysis to Assess Vulnerability and Risk: Case Study of Freetown (Sierra Leone)

Many of the world’s most disaster-prone cities are also the most difficult to model and plan. Their high vulnerability to natural hazards is often defined by low levels of economic resources, data scarcity, and limited professional expertise. As the frequency and severity of natural disasters threaten to increase with climate change, and as cities sprawl and densify in hazardous areas, better decision-making tools are needed to mitigate the effects of near- and long-term extreme events. We use mostly public data from landslide and flooding events in 2017 in Freetown, Sierra Leone to simulate the events’ impact on transportation infrastructure and continue to simulate alternative high-risk disasters. From this, we propose a replicable framework that combines natural hazard estimates with road network vulnerability analysis for data-scarce environments. Freetown’s most central road intersections and transects are identified, particularly those that are both prone to serviceability loss due to natural hazard and whose disruption would cause the most severe immediate consequences on the entire road supply in terms of connectivity. Variations in possible road use are also tested in areas with potential road improvements, pointing to opportunities to harden infrastructure or reinforce redundancy in strategic transects of the road network. This method furthers network science’s contributions to transportation resilience under hydrometeorological hazard and climate change threats with the goal of informing investments and improving decision-making on transportation infrastructure in data-scarce environments.

An improved hierarchical fuzzy TOPSIS approach to identify endangered earthquake-induced buildings

As societies increase their preparedness level for facing earthquakes, many unfortunate consequences of the events may significantly decrease. An example of this can be seen in cases where pre-earthquake mitigation activities taken like identifying and renovating vulnerable buildings, assessing road network vulnerability, locating the emergency centers and identifying hazardous materials warehousing. For mitigation decision making, the seismic risk for an individual building consists of the actual dangers of the building and the risk of damage to the building from surrounding environment in time of earthquake occurrence which this concept is considered as a building exposure rate to seismic hazards. Thus, the exploration of an index by using expert knowledge for quantifying the multi-dimensional concept of building exposure rate to seismic hazards before the incidence of earthquakes is vital. According to existence of imprecision and uncertainty in experts’ opinions, this paper adopts the improved hierarchical fuzzy TOPSIS approach as a fuzzy multi criteria decision making technique (FMCDM) for integrating factors affecting building exposure rate in two scenarios (daytime and nighttime). This approach effectively considers the experts’ expressions and the layered hierarchy of criteria. The obtained map was categorized into 4 classes including low, medium, high, and very high risk in one of the most vulnerable regions of Tehran. Then, the robustness of the approach is verified with a sensitivity analysis; 16 experiments are conducted for two scenarios which indicate partial changes in building exposure rate.