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Hurricane Irma caused widespread evacuation activity across Florida and some of its neighboring states in September of 2017. The researchers gathered estimated travel times from the Google Distance Matrix API over about a month to identify and analyze evacuation periods on roads in Florida, Georgia, and South Carolina during this time. Travel time data were mathematically adjusted to show more realistic estimations. Both sets of travel times were then graphed, with the assumption that elevated travel times prior to and during hurricane landfall were indicative of evacuation activity. The study generally corroborated the well-established daytime evacuation preference. However, not all evacuation periods followed the daytime travel preference, and at least one nighttime evacuation may have been caused by flooding. In another case, later elevated travel coincided with significant power loss. Finally, the Florida data suggest that most of the evacuation traffic departed before local jurisdictions’ recommended evacuation start times.
相似文献2.
Royce A. Francis Stefanie M. Falconi Roshanak Nateghi Seth D. Guikema 《Climatic change》2011,106(1):31-55
One effect of climate change may be increased hurricane frequency or intensity due to changes in atmospheric and geoclimatic
factors. It has been hypothesized that wetland restoration and infrastructure hardening measures may improve infrastructure
resilience to increased hurricane frequency and intensity. This paper describes a parametric decision model used to assess
the tradeoffs between wetland restoration and infrastructure hardening for electric power networks. We employ a hybrid economic
input–output life-cycle analysis (EIO-LCA) model to capture: construction costs and life-cycle emissions for transitioning
from the current electric power network configuration to a hardened network configuration; construction costs and life-cycle
emissions associated with wetland restoration; and the intrinsic value of wetland restoration. Uncertainty is accounted for
probabilistically through a Monte Carlo hurricane simulation model and parametric sensitivity analysis for the number of hurricanes
expected to impact the project area during the project cycle and the rate of wetland storm surge attenuation. Our analysis
robustly indicates that wetland restoration and undergrounding of electric power network infrastructure is not preferred to
the “do-nothing” option of keeping all power lines overhead without wetland protection. However, we suggest a few items for
future investigation. For example, our results suggest that, for the small case study developed, synergistic benefits of simultaneously
hardening infrastructure and restoring wetlands may be limited, although research using a larger test bed while integrating
additional costs may find an enhanced value of wetland restoration for disaster loss mitigation. 相似文献
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Hurricanes can severely damage the electric power system, and therefore, predicting the potential impact of an approaching
hurricane is of importance for facilitating planning and storm-response activities. A data mining approach, classification
and regression trees (CART), was employed to evaluate whether the inclusion of soil and topographic variables improved the
predictive accuracy of the power outage models. A total of 37 soil variables and 20 topographic variables were evaluated in
addition to hurricane, power system, and environmental variables. Hurricane variables, specifically the maximum wind gust
and duration of strong winds, were the most important variables for predicting power outages in all models. Although the inclusion
of soil and topographic variables did not significantly improve the overall accuracy of outage predictions, soil type and
soil texture are useful predictors of hurricane-related power outages. Both of these variables provide information about the
soil stability which, in turn, influences the likelihood of poles remaining upright and trees being uprooted. CART was also
used to evaluate whether environmental variables can be used instead of power system variables. Our results demonstrated that
certain land cover variables (e.g., LC21, LC22, and LC23) are reasonable proxies for the power system and can be used in a
CART model, with only a minor decrease in predictive accuracy, when detailed information about the power system is not available.
Therefore, CART-based power outage models can be developed in regions where detailed information on the power system is not
available. 相似文献
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Accurate estimates of the duration of power outages caused by hurricanes prior to landfall are valuable for utility companies and government agencies that wish to plan and optimize their restoration efforts. Accurate pre-storm estimates are also important information for customers and operators of other infrastructures systems, who rely heavily on electricity. Traditionally, utilities make restoration plans based on managerial judgment and experience. However, skillful outage forecast models are conducive to improved decision-making practices by utilities and can greatly enhance storm preparation and restoration management procedures of power companies and emergency managers. This paper presents a novel statistical approach for estimating power outage durations that is 87 % more accurate than existing models in the literature. The power outage duration models are developed and carefully validated for outages caused by Hurricanes Dennis, Katrina, and Ivan in a central Gulf Coast state. This paper identifies the key variables in predicting hurricane-induced outage durations and their degree of influence on predicting outage restoration for the utility company service area used as our case study. 相似文献
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