Vulnerability Identification of Urban Road Network under Unexpected Congestion

Road network connectivity is an important indicator for measuring the operational efficiency and reliability of urban road networks, and it plays an important role in supporting traffic planning and management decisions. The implementation of traffic management measures, such as traffic bans and temporary traffic flow changes, will restrict access to some sections and lanes, reduce the passable paths in the road network, and thus affect the overall connectivity performance of the road network. Existing road network research results mostly evaluate the topological connectivity of the network at the physical level, and it is difficult to accurately portray the actual road network connectivity under traffic management conditions. To quantitatively evaluate the road network connectivity performance after the implementation of traffic management tools, this paper proposes a road network connectivity evaluation method based on strongly connected effective paths. Firstly, the node steering coefficients are used to describe the no‐traffic constraints of turning lanes, and the connectivity evaluation indexes are constructed based on the number of strongly connected effective paths and the shortest paths of strongly connected paths. Secondly, combining the Floyd‐Warshall algorithm and the depth‐first search algorithm, we constructed a strong connectivity effective path search algorithm to adapt to the refined traffic management situation, and identified the key road sections that have the greatest impact on the connectivity of the road network by considering the maximum acceptable level of the path and the road access constraints. Finally, Sioux‐Falls network and nine urban road networks with different layout patterns are selected for the case study and compared with traditional road network connectivity indicators. The case studies show that: (1) the connectivity of the square grid road network structure is superior, while the connectivity of the free‐form road network is the lowest; (2) road access management measures reduce the overall road network connectivity, and the banning of traffic in critical sections has the most significant effect on connectivity. Accurately assessing the changes in road network connectivity performance under different traffic management measures provides a scientific basis for the development of road control strategies, which can effectively improve urban traffic fluency and residents’ travel efficiency.

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.

A Flow-based Vulnerability Measure for the Resilience of Urban Road Network

Mass movements are linked to increasing amounts of damage and disruptions to transportation infrastructures. A valid risk assessment in order to reduce future costs is not always appropriate, as adequate information on landslide data is missing. The presented study estimates the rockfall susceptibility on a rural road network in the Harz mountains using a bivariate statistical method (information value method). The model is validated using a receiver operating characteristic (ROC) analysis. In addition, the vulnerability of the road network is estimated using vulnerability indicators. The susceptibility model assigns a high or very high susceptibility to 23% of the area in the road network corridor. The relevant road sections are linked to high slope values, NE orientations of road sections, and low-to-moderate vulnerability values. The highest vulnerability values can be found on marginal road sections with high average daily traffic volumes. The combination of the presented methods proposes an easily applicable estimate of vulnerability where conventional methods (i.e., vulnerability curves, matrices) cannot be implemented.

The road network maintaining stability is critical for guaranteeing urban traffic function. Therefore, the vulnerable links need to be identified accurately. Previous vulnerability research under static condition compared the operating states of the old equilibrium before the event and the new equilibrium after the event to assess vulnerability ignoring the dynamic variation process. Does road network vulnerability change over time? This paper combines the vulnerability assessment with the traffic flow evolution process, exploring the road network vulnerability evaluation from the perspective of time dimension. More accurate identification and evaluation of vulnerable nodes and links can help to strengthen the ability of road network resisting disturbances. A modified dynamic traffic assignment (DTA) model is established for dynamic path selection (reselect the shortest path at the end of each link) based on the dynamic user optimal (DUO) principle. A modified cell transmission model is established to simulate the traffic flow evolution processes. The cumulative and time-varying index of vulnerability assessment is established from the viewpoint of traveler’s time loss. Then the road network vulnerability assessment combined the traffic flow model with the vulnerability index. The road network vulnerability assessment of Bao’an Central District of Shenzhen, China, reveals that road network vulnerability does contain a dynamic process, and vulnerable links in each phase can be exactly identified by the model. Results showed that the road network would have a large vulnerability during the disordered phase when the main road fails. Therefore, prioritizing the smooth flow of main roads can weaken the impact of road network vulnerability exposure.

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.

The vulnerability of an urban road network is affected by many factors, such as internal road network layout, network structure strength, and external destructive events, which have great uncertainty and complexity. Thus, there is still no unified and definite vulnerability analysis scheme available to cities. This paper proposes an integrative vulnerability identification method for urban road networks, which mainly relates to the vulnerability connotation and characteristics analysis of urban road networks during emergency, and vulnerability comprehensive evaluation indices design based on urban road network connectivity, traffic efficiency and performance, and an empirical study on a vulnerability identification method of an urban road network. In the empirical case, a real road network and traffic operation data were used from Science and Technology Park of Shenzhen City, China. In the context of one certain emergency scenario, the stated preference survey method and maximum likelihood method are used to solve the road users’ random travel choice behavior parameters; subsequently, based on the traffic equilibrium distribution prediction, the traffic vulnerability identification methods of the road network in this region were verified before and after the emergency. The method presented here not only considers the impact of network topology changes on road network traffic function during emergency but also considers the impact of dynamic changes in road network traffic demand on vulnerability; therefore, it is closer to the actual distribution of urban road network traffic vulnerability.

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.

Construction of road network vulnerability evaluation index based on general travel cost

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.

BackgroundFractal theory, developed by Benoit B. Mandelbrot in 1983, serves to describe intricate patterns in nature that traditional geometry finds challenging to measure. This theory employs the fractal dimension as...

Probabilistic connectivity assessment of road networks exposed to spatially correlated rainfall-triggered landslides

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

Transportation Cyber-Physical Systems (TCPS) are facing an emerging cyber threat from disinformation attacks that can manipulate individual cognition and behaviours, potentially making urban transportation vulnerable. Traditional studies on road network vulnerability have primarily focussed on physical attacks like node or link removal, with little attention to disinformation attacks. To fill this gap, this study introduces a novel disinformation attack mode for road TCPS. It hacks navigation applications by strategically modifying the link cost information, thereby affecting drivers' routing decisions. Crucially, this attack mode leaves the physical topology of TCPS-based road networks unchanged, impacting only the cyber layer's information. Additionally, intriguing link metrics, like the partial derivative of total travel time to link flow, are introduced to identify attack targets. On this basis, we design multi-strategy disinformation attacks to assess road network vulnerability. The proposed research framework, validated by San Francisco's large-scale urban road network, reveals that disinformation attacks on just 0.12% of links could cause an annual city-wide economic loss of $79.26 million in the worst-case scenario. This study offers a unique viewpoint on road network vulnerability, emphasising the vital need for TCPS cybersecurity to ensure urban transportation's reliability and resilience.

Connectivity of road network is an index to evaluate the rationality of urban road network planning. In the past, it was defined as the ratio of the number of edges to the number of nodes from static perspective. In practice, the network is unreliable; some roads may be blocked at certain times. So dynamic connectivity of road network is put forward from two points of view in this paper: (1) based on the scanty two forms—blockage and non-blockage of each edge, dynamic connectivity of binomial distribution obeyed by each blocked edge is presented; (2) based on the number of edges, dynamic connectivity of random distribution obeyed by the number of unblocked edges is introduced.Keywordsroad networkbinomial distributionrandom distributiondynamic connectivity