Risk analysis of hurricane disruptions on workforce and interdependent regional sectors
This paper assesses risks related to hurricane and pandemic scenarios and evaluates risk management alternatives to enhance preparedness, response, and recovery capabilities of the Commonwealth of Virginia. Such disasters could potentially disrupt the operations of critical transportation infrastructure systems. Virginia Department of Transportation (VDOT) manages Virginia's highway infrastructure through its five high-technology communications hubs called smart traffic centers (STCs). STCs are staffed by transportation engineers, who monitor traffic flow, provide traveler information, and manage incidents. In the event of incidents, STC operators dispatch roadside assistance and post warnings to motorists via variable messaging signs and public radio advisories. Transportation system failures further compound a disaster's devastating economic, physical, and social consequences. A hurricane makes roads inaccessible, resulting in the inability of the general workforce to commute. Since STCs enable workforce mobility, the hurricane's effects are magnified should the STC itself be impacted. The STC's physical structure is vulnerable to high winds and flooding, which may disconnect its power supply, disable communications, or damage essential equipment. A pandemic, on the other hand, can impact the health and availability of STC employees, who require specialized training and experience with intelligent transportation systems. It is critical for the STCs to develop continuity of operations (COOP) plans. Such plans identify and detail specific procedures for maintaining essential functions in the event of a disaster. The Virginia information technology agency (VITA) and Virginia Department of Emergency Management (VDEM) require STCs to comply with COOP standards and other security policies. The project team employed risk-based methodologies to assist the STCs in achieving compliance. In particular, this paper discusses four case studies featuring the development and application of: (i) probabilistic risk analysis tools (e.g., data mining, regression analysis, and Monte Carlo simulation using incident databases), (ii) a dynamic recovery analysis model aided by geographic information systems (GIS) to predict the ripple effects of disasters across multiple sectors, and (iii) multi-criteria tradeoff analysis to evaluate the efficacy of risk management solutions. The results and findings from this research will contribute to Virginia's preparedness planning for managing disasters.