scholarly journals Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3172
Author(s):  
Suchat Tachaudomdach ◽  
Auttawit Upayokin ◽  
Nopadon Kronprasert ◽  
Kriangkrai Arunotayanun
Keyword(s):  
Road Network ◽  
Critical Infrastructure ◽  
Policy Issue ◽  
Analytical Framework ◽  
Transportation Systems ◽  
Network Robustness ◽  
System Response ◽  
Chiang Mai ◽  
Protection Measures ◽  
Response And Recovery

Amidst sudden and unprecedented increases in the severity and frequency of climate-change-induced natural disasters, building critical infrastructure resilience has become a prominent policy issue globally for reducing disaster risks. Sustainable measures and procedures to strengthen preparedness, response, and recovery of infrastructures are urgently needed, but the standard for measuring such resilient elements has yet to be consensually developed. This study was undertaken with an aim to quantitatively measure transportation infrastructure robustness, a proactive dimension of resilience capacities and capabilities to withstand disasters; in this case, floods. A four-stage analytical framework was empirically implemented: 1) specifying the system and disturbance (i.e., road network and flood risks in Chiang Mai, Thailand), 2) illustrating the system response using the damaged area as a function of floodwater levels and protection measures, 3) determining recovery thresholds based on land use and system functionality, and 4) quantifying robustness through the application of edge- and node-betweenness centrality models. Various quantifiable indicators of transportation robustness can be revealed; not only flood-damaged areas commonly considered in flood-risk management and spatial planning, but also the numbers of affected traffic links, nodes, and cars are highly valuable for transportation planning in achieving sustainable flood-resilient transportation systems.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

Road Network Robustness for Avoiding Functional Isolation in Disasters

2004 ◽  
Vol 130 (5) ◽  
pp. 560-567 ◽  
Author(s):  
Hiroyuki Sakakibara ◽  
Yoshio Kajitani ◽  
Norio Okada
Keyword(s):  
Road Network ◽  
Network Robustness

scholarly journals Verifying Forecast Precipitation Type with mPING*

2015 ◽  
Vol 30 (3) ◽  
pp. 656-667 ◽  
Author(s):  
Kimberly L. Elmore ◽  
Heather M. Grams ◽  
Deanna Apps ◽  
Heather D. Reeves
Keyword(s):  
Critical Infrastructure ◽  
Current Model ◽  
Model Performance ◽  
Transportation Systems ◽  
Forecast System ◽  
Freezing Rain ◽  
The North ◽  
Skill Scores ◽  
Precipitation Type ◽  
Surface Observations

Abstract In winter weather, precipitation type is a pivotal characteristic because it determines the nature of most preparations that need to be made. Decisions about how to protect critical infrastructure, such as power lines and transportation systems, and optimize how best to get aid to people are all fundamentally precipitation-type dependent. However, current understanding of the microphysical processes that govern precipitation type and how they interplay with physics-based numerical forecast models is incomplete, degrading precipitation-type forecasts, but by how much? This work demonstrates the utility of crowd-sourced surface observations of precipitation type from the Meteorological Phenomena Identification Near the Ground (mPING) project in estimating the skill of numerical model precipitation-type forecasts and, as an extension, assessing the current model performance regarding precipitation type in areas that are otherwise without surface observations. In general, forecast precipitation type is biased high for snow and rain and biased low for freezing rain and ice pellets. For both the North American Mesoscale Forecast System and Global Forecast System models, Gilbert skill scores are between 0.4 and 0.5 and from 0.35 to 0.45 for the Rapid Refresh model, depending on lead time. Peirce skill scores for individual precipitation types are 0.7–0.8 for both rain and snow, 0.2–0.4 for freezing rain and freezing rain, and 0.25 or less for ice pellets. The Rapid Refresh model displays somewhat lower scores except for ice pellets, which are severely underforecast, compared to the other models.

scholarly journals Measures to Improve the Cybersecurity of Critical Infrastructure in Brazil

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Evandro Leonel Pereira ◽  
Aristides Sebastião Lopes Carneiro ◽  
Eder Ruschel ◽  
Marcio De Lima Corcovado ◽  
Jordan Da Silva Paiva ◽  
...  
Keyword(s):  
Critical Infrastructure ◽  
Field Research ◽  
Well Being ◽  
Critical Infrastructures ◽  
Technological Evolution ◽  
Dynamic Nature ◽  
Protection Measures ◽  
Strategic Actions ◽  
Private And Public ◽  
Current Context

In the current context of global interconnectivity, the cybersecurity of critical infrastructures (CI) is of utmost importance to the private and public sectors. In this regard, based on the analysis of elaborated guidelines and norms, gaps were identified that may hinder the implementation of CI protection measures, facing threats of all kinds, affecting population well-being, economic power and contributing to weakening the reputation of a country in the concert of nations. Considering the dynamic nature and the speed of technological evolution, this study aims to raise subsidies for the improvement of the cybersecurity of CI in Brazil, pointing out norms to be elaborated or adopted, good practices and strategic actions to be followed. The methodology used in the development of this work begins with bibliographic and document research, and through comparative analysis, points out the most relevant, existing standards and initiatives. A diagnosis of the Brazilian situation is provided including field research, a solution proposal and finally an analytical discussion of proposed actions.

scholarly journals Electronic Warfare in NAVWAR: Impact of Electronic Attacks on GNSS / GBAS Approach Service Types C and D Landing systems and their proposed Electronic Protection Measures (EPM)

Hadmérnök ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. 238-255
Author(s):  
Ahmad Alhosban
Keyword(s):  
Critical Infrastructure ◽  
The Other ◽  
Navigation Systems ◽  
Protection Measures ◽  
Electronic Warfare ◽  
Other Hand ◽  
Protection Devices ◽  
Satellite Navigation Systems ◽  
The Military ◽  
The Impact

Global Satellite Navigation Systems (GNSS) applications -using different satellite signals in space- are currently and hugely subjected to Electronic Attacks (EAs) such as Jamming, Spoofing, and/or Meaconing. Many accidents were observed in the past decade, while huge dependency on GNSS applications in governmental and private critical infrastructure, in both civil and military aspects. The EAs could be expensive and high-power such as the military-grade jammers, which are an integral pillar of navigation warfare (NAVWAR) strategies. On the other hand, EAs could be cheap and low-power such as the so-called Personal Protection Devices (PPD), which they are widely available. Electronic Attacks, most critically observed by ICAO and FAA, are in Ground Based Augmentation System -(GNSS/GBAS) Landing systems, in which is riskier and more critical than other applications due to the sensitivity of the final landing phase of all flights. The objective of this study is to evaluate the impact of the three different types of EA on the performance GNSS/GBAS landing system. On the other hand, to address and examine their latest proposed Electronic Protection Measures (EPM).

Community Lifeline Framework for Leveraging Stabilization in Response and Recovery to Major Hurricanes

2019 ◽  
Vol 14 (1) ◽  
pp. 7-9
Author(s):  
Judy Kruger
Keyword(s):  
Transmission Lines ◽  
Critical Infrastructure ◽  
Electrical Power ◽  
The United States ◽  
Public Officials ◽  
Policy Makers ◽  
Emergency Managers ◽  
The Us ◽  
Response And Recovery ◽  
The Impact

The United States (US) and Caribbean regions remain vulnerable to the impact of severe tropical storms, hurricanes, and typhoons. In 2017, a series of hurricanes posed threats to residents living in inland and coastal communities as well as on islands isolated from the US mainland. Harvey, Irma, Jose, and Maria caused catastrophic infrastructure damage, resulting in a loss of electrical power and communications due to damaged or downed utility poles, cell towers, and transmission lines. Critical services were inoperable for many months. Emergency managers are public officials who are accountable to both political leaders and the citizens. During disaster events, emergency managers must prioritize areas of effort, manage personnel, and communicate with stakeholders to address critical infrastructure interdependences. Essential lifeline services (eg, energy and communications) were inoperable for many months, which led to increased attention from policy-makers, media, and the public.

scholarly journals Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

2019 ◽  
Vol 11 (21) ◽  
pp. 2499 ◽  
Author(s):  
Jiang Xin ◽  
Xinchang Zhang ◽  
Zhiqiang Zhang ◽  
Wu Fang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Intelligent Transportation Systems ◽  
Road Network ◽  
Semantic Segmentation ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Road Network Extraction

Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.

GIS-Analysis of Central Asian Transportation Systems’ Configuration

2019 ◽  
Vol 950 (8) ◽  
pp. 45-51
Author(s):  
P.M. Sapanov
Keyword(s):  
Spatial Data ◽  
Road Network ◽  
Road Transport ◽  
Transportation Systems ◽  
Transport Infrastructure ◽  
Gis Analysis ◽  
Road Transportation ◽  
The Road ◽  
Central Asian ◽  
High Degree

The author describes the performed GIS-analysis of the Central Asian transportation systems. The road transportation infrastructure of the whole region and its individual countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) has been studied. The overview of the factors, influencing the formation of regional transportation systems, including historical, political and natural, has been done. The road network of Central Asian countries has been modeled using GIS network analysis toolset, with spatial data provided by OpenStreetMap. The so-called topological tiers of the network have been identified, showing the uneven provision of the studied area with road transport infrastructure. The proposed research method makes it possible to note a high degree of the road network integration between the countries. The areas with low transport accessibility, as well as autonomous parts of road network have been visualized. The research categorizes the countries’ transport networks configurations types formed under the influence of economic, social, agricultural, climatic and topographical factors.

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