Crash response planning is a vital component of emergency management and highway emergency response planning. Evaluation of coverage of medical facilities is required to determine adequate access from crash sites to medical facilities. This study proposes a proof of concept for a planning tool that evaluates fatal and serious injury crash response coverage from crash sites to medical facilities in the Commonwealth of Virginia. Calculated travel times from fatal crash sites to medical facilities are compared with reported travel times to better estimate travel time modification factors. The modified travel times are used to determine coverage areas and evaluate serious injury crash response coverage of medical facilities in Virginia. A geo grid approach is used to demonstrate the proof of concept for a crash response planning tool. A risk grid is developed based on the aggregate number of fatal and serious injury crashes. This study includes serious injury crash response coverage because the number of serious injuries and serious injury rate are now included as reportable safety performance measures for state highway safety agencies. A mitigation grid is developed based on the travel time to the closest facility. Finally, a planning grid that combines risk and mitigation factors based on a decision matrix is presented. The resulting tool serves as a proof of concept for developing a crash response planning tool which enables planners to identify areas that do not have timely access from crash sites to medical facilities. / Master of Science / An objective of emergency responders is to safely transport crash victims from crash sites to medical facilities. Ensuring adequate access is an important goal of highway safety professionals. This study proposes a proof of concept for a planning tool that evaluates this access in the Commonwealth of Virginia. This study focuses on serious injury crash sites because the number of serious injuries and serious injury rate are now included as reportable safety performance measures for state highway safety agencies. Travel times from serious injury crash sites to medical facilities are used to identify areas that do not have timely access. Risk and mitigation assessments are performed by dividing the study area into equal sized cells. Risk and mitigation assessments are based on number of crashes and response travel times to the closest medical facility, respectively. These assessments are used to generate a proof of concept for a crash response planning tool which enables planners to identify areas that do not have timely access from crash sites to medical facilities.

Disasters can occur without warning and severely test society’s capacity to cope, significantly altering the relationship between society and the built and natural environments. The scale of a disaster is a direct function of the pre-event actions and decisions taken by society. Poor pre-event planning is a major contributor to disaster, while effective pre-event planning can substantially reduce, and perhaps even avoid, the disaster. Developing and undertaking effective planning is therefore a vital component of disaster risk management in order to achieve meaningful societal resilience. Disaster scenarios present arguably the best and most effective basis to plan an effective emergency response to future disasters. For effective emergency response planning, disaster scenarios must be as realistic as possible. Yet for disasters resulting from natural hazards, intricately linked secondary hazards and effects make development of realistic scenarios difficult. This is specially true for large earthquakes in mountainous terrain. The primary aim of this thesis is therefore to establish a detailed and realistic disaster scenario for a Mw8.0 earthquake on the plate boundary Alpine fault in the South Island of New Zealand with specific emphasis on secondary effects. Geologic evidence of re-historic earthquakes on this fault suggest widespread and large-scale landsliding has resulted throughout the Southern Alps, yet, currently, no attempts to quantitatively model this landsliding have been undertaken. This thesis therefore provides a first attempt at quantitative assessments of the likely scale and impacts of landsliding from a future Mw8.0 Alpine fault earthquake. Modelling coseismic landsliding in regions lacking historic inventories and geotechnical data (e.g. New Zealand) is challenging. The regional factors that control the spatial distribution of landsliding however, are shown herein to be similar across different environments. Observations from the 1994 Northridge, 1999 Chi-Chi, and 2008 Wenchuan earthquakes identified MM intensity, slope angle and position, and distance from active faults and streams as factors controlling the spatial distribution of landsliding. Using fuzzy logic in GIS, these factors are able to successfully model the spatial distribution of coseismic landsliding from both the 2003 and 2009 Fiordland earthquakes in New Zealand. This method can therefore be applied to estimate the scale of landsliding from scenario earthquakes such as an Alpine fault event. Applied to an Mw8.0 Alpine fault earthquake, this suggests that coseismic landsliding could affect an area >50,000 km2 with likely between 40,000 and 110,000 landslides occurring. Between 1,400 and 4,000 of these are expected to present a major hazard. The environmental impacts from this landsliding would be severe, particularly in west-draining river catchments, and sediment supply to rivers in some catchments may exceed 50 years of background rates. Up to 2 km3 of total landslide debris is expected, and this will have serious and long-term consequences. Fluvial remobilisation of this material could result in average aggradation depths on active alluvial fans and floodplains of 1 m, with maximum depths substantially larger. This is of particular concern to the agriculture industry, which relies on the fertile soils on many of the active alluvial fans affected. This thesis also investigated the potential impacts from such landsliding on critical infrastructure. The State Highway and electrical transmission networks are shown to be particularly exposed. Up to 2,000 wooden pole and 30 steel pylon supports for the transmission network are highly exposed, resulting in >23,000 people in the West Coast region being exposed to power loss. At least 240 km of road also has high exposure, primarily on SH6 between Hokitika and Haast, and on Arthur’s and Lewis Passes. More than 2,750 local residents in Westland District are exposed to isolation by road as a result. The Grey River valley region is identified as the most critical section of the State Highway network and pre-event mitigation is strongly recommended to ensure the road and bridges here can withstand strong shaking and liquefaction hazards. If this section of the network can remain functional post-earthquake, the emergency response could be based out of Wellington using Nelson as a forward operating base with direct road access to some of the worst-affected locations. However, loss of functionality of this section of road will result in >24,000 people becoming isolated across almost the entire West Coast region. This thesis demonstrates the importance and potential value of pre-event emergency response planning, both for the South Island community for an Alpine fault earthquake, and globally for all such hazards. The case study presented demonstrates that realistic estimates of potential coseismic landsliding and its impacts are possible, and the methods developed herein can be applied to other large mountainous earthquakes. A model for developing disaster scenarios in collaboration with a wide range of societal groups is presented and shown to be an effective method for emergency response planning, and is applicable to any hazard and location globally. This thesis is therefore a significant contribution towards understanding mountainous earthquake hazards and emergency response planning.

Master of Science / Department of Industrial and Manufacturing Systems Engineering / Jessica L. Heier Stamm / During public health emergencies, organizations in charge require an immediate and e ffcient method of distributing supplies over a large scale area. Due to the uncertainty of where individuals will choose to receive supplies, these distribution strategies have to account for the unknown demand at each facility. Current techniques rely on population ratios or requests by health care providers. This can lead to an increased disparity in individuals' access to the medical supplies. This research proposes a mathematical programming model, along with a solution methodology to inform distribution system planning for public health emergency response. The problem is motivated by distribution planning for pandemic influenza vaccines or countermeasures. The model uses an individual choice constraint to determine what facility the individual will choose to receive their supplies. This model also determines where to allocate supplies in order to meet the demand of each facility. The model was solved using a decomposition method. This method allows large problems to be solved quickly without losing equity in the solution. In the absence of publicly-available data on actual distribution plans from previous pandemic response e fforts, the method is applied to another representative data set. A computational study of the equity and number of people served depict how the model performed compared to the actual data. The results show that implementing an individual choice constraint will improve the effectiveness of a public health emergency response campaign without losing equity. The thesis provides several contributions to prior research. The first contribution is an optimization model that implements individual choice in a constraint. This determines where individuals will choose to receive their supplies so improved decisions can be made about where to allocate the resources. Another contribution provided is a solution methodology to solve large problems using a decomposition method. This provides a faster response to the public health emergency by splitting the problem into smaller subproblems. This research also provides a computational study using a large data set and the impact of using a model that accounts for individual choice in a distribution campaign.

Urban communities often lack the ability to recover after disaster plans have been implemented because of a lack of coordinated resources among federal, state, and local agencies. As a result, economically marginalized citizens find themselves in risky conditions, particularly concerning finding and securing post-disaster housing. Using social conflict theory as a guide, the purpose of this exploratory case study of an urban area in a southern state was to better understand the specific vulnerabilities of urban communities and develop solutions for challenges related to emergency or temporary shelters to victims. Data were primarily collected through interviews with 10 residents who experienced a series of tornadoes in 2011. These data were inductively coded and then subjected to a thematic analysis. Findings indicate that participants tended to consider themselves as displaced, but not homeless, even though temporary housing needs ranged between 45 days and 18 months. Participants also reported that coordination efforts to distribute funding to displaced residence failed, as did private insurance in most cases. As a result, competition for scarce resources was significant and most people tended to rely upon financial help from friends and family members. The positive social change implications stemming from this study include recommendations to city planners and emergency managers to strengthen relationships with community leaders to assess needs prior to a disaster and establish a 'bottom-up' planning policy rather than wait for a disaster to assess the availability of federal or state funding that may not come in order to proactively protect vulnerable community members from post-disaster housing deficiencies.

This dissertation explores methods for finding irreducible infeasible subsystems (IISs) of systems of inequalities with binary decision variables and for solving probabilistically constrained stochastic integer programs (SIP-C). Finding IISs for binary systems is useful in decomposition methods for SIP-C. SIP-C has many important applications including modeling of strategic decision-making problems in wildfire initial response planning. New theoretical results and two new algorithms to find IISs for systems of inequalities with binary variables are developed. The first algorithm uses the new theory and the method of the alternative polyhedron within a branch-and-bound (BAB) approach. The second algorithm applies the new theory and the method of the alternative polyhedron to a system in which zero/one box constraints are appended. Decomposition schemes using IISs for binary systems can be used to solve SIP-C. SIP-C is challenging to solve due to the generally non-convex feasible region. In addition, very weak lower (upper) bounds on the objective function are obtained from the linear programming (LP) relaxation of the deterministic equivalent problem (DEP) to SIP-C. This work develops a branch-and-cut (BAC) method based on IIS inequalities to solve SIP-C with random technology matrix and random righthand- side vector. Computational results show that the LP relaxation of the DEP to SIP-C can be strengthened by the IIS inequalities. SIP-C modeling can be applied to wildfire initial response planning. A new methodology for wildfire initial response that includes a fire behavior simulation model, a wildfire risk model, and SIP-C is developed and tested. The new method- ology assumes a known standard response needed to contain a fire of given size. Likewise, this methodology is used to evaluate deployment decisions in terms of the number of firefighting resources positioned at each base, the expected number of escaped and contained fires, as well as the wildfire risk associated with fires not receiving a standard response. A study based on the Texas district 12 (TX12) that is one of the Texas A&M Forest Service (TFS) fire planning units in east Texas demonstrates the effectiveness of the new methodology towards making strategic deployment decisions for wildfire initial response planning.

Railroad transportation of hazardous materials (hazmat) has grown significantly in recent years in Canada. Although rail is one of the safest modes for hazmat transport, the risk of catastrophic events such as the Lac Mégantic train disaster, does exist. In this thesis, we study a number of measures to manage and mitigate the risk associated with rail hazmat shipments. First, we propose a methodology that makes use of analytics to dis-aggregate national freight data to estimate hazmat traffic on rail-links and at rail-yards in Canada. Further, a focused analysis is conducted on crude oil rail shipments to develop long-term forecasts and evaluate the impact of proposed pipeline projects. Second, we present an emergency response planning problem, aimed at the effective and efficient response to rail hazmat incidents. A two-stage stochastic programming problem is solved over part of the Canadian railroad network, which provides recommendations on where to locate response facilities, and which equipment packages to stockpile at each facility. Finally, we study infrastructure investment as a strategy to mitigate the risk associated with rail hazmat shipments. This strategy is based on building new railway tracks to provide alternative routes to the riskiest parts of the network. Given the hierarchical relationship between the decisions made by regulatory agencies and railroad companies, a bilevel programming approach is used to identify the optimal set of infrastructure investment options given an allocated budget. Our computational experiments show that significant network-wide risk reduction is possible if hazardous shipments are routed using some of the proposed alternative rail tracks. / Thesis / Doctor of Philosophy (PhD)

The wind power development in Greece has been rising the last decade, aiming to exploit the country's excellent wind resources. Though the project developers' experience in the wind energy industry has been expanded, wind power project risks are always present, having the potential to have a major impact on the project outcome. Thereafter, the question generated is “which are those risks, and how can they effectively be addressed?”. This Thesis is aiming to identify those risks, specifically for the Greek wind energy sector, present the results of a quantitative risk analysis, and propose risk response strategies for each of the identified risks. For that purpose, a case-study in S. Euboea region was applied; a typical 8 MW onshore wind farm was designed inside a Wind Priority Area. The risk data were collected by a panel of experts, chosen to identify and quantify the potential risks through the implementation of the Delphi and the Project Risk FMEA (RFMEA) methods. The results showed 21 risks that should be considered when developing a wind power project in Greece, and also presented their response priority ranking, based on each risk's ratings inprobability, impact and detection factors. Afterwards, the risk response strategies, for each risk, were proposed, as those were collected by the experts panel group-interviewing. Through the analysis of the results, it is clarified that the country's economic crisis and the current changes in the political scene have created new and affected the already existing risks of the Greek wind energy sector, bringing the regulatory risk to the top of the critical risks.

Mitigation response plans must be created to protect affected populations during biological emergencies resulting from the release of harmful biochemical substances. Medical countermeasures have been stockpiled by the federal government for such emergencies. However, it is the responsibility of local governments to maintain solid, functional plans to apply these countermeasures to the entire target population within short, mandated time frames. Further, vulnerabilities in the population may serve as barriers preventing certain individuals from participating in mitigation activities. Therefore, functional response plans must be capable of reaching vulnerable populations.Transportation vulnerability results from lack of access to transportation. Transportation vulnerable populations located too far from mitigation resources are at-risk of not being able to participate in mitigation activities. Quantification of these populations requires the development of computational methods to integrate spatial demographic data and transportation resource data from disparate sources into the context of planned mitigation efforts. Research described in this dissertation focuses on quantifying transportation vulnerable populations and maximizing participation in response efforts. Algorithms developed as part of this research are integrated into a computational framework to promote a transition from research and development to deployment and use by biological emergency planners.

No description available.

Optimization of relief networks in humanitarian logistics often exemplifies the need for solutions that are feasible given a hard constraint on time. For instance, the distribution of medical countermeasures immediately following a biological disaster event must be completed within a short time-frame. When these supplies are not distributed within the maximum time allowed, the severity of the disaster is quickly exacerbated. Therefore emergency response plans that fail to facilitate the transportation of these supplies in the time allowed are simply not acceptable. As a result, all optimization solutions that fail to satisfy this criterion would be deemed infeasible. This creates a conflict with the priority optimization objective in most variants of the generic vehicle routing problem (VRP). Instead of efficiently maximizing usage of vehicle resources available to construct a feasible solution, these variants ordinarily prioritize the construction of a minimum cost set of vehicle routes. Research presented in this dissertation focuses on the design and analysis of efficient computational methods for optimizing high-consequence variants of the VRP for relief networks. The conflict between prioritizing the minimization of the number of vehicles required or the minimization of total travel time is demonstrated. The optimization of the time and capacity constraints in the context of minimizing the required vehicles are independently examined. An efficient meta-heuristic algorithm based on a continuous spatial partitioning scheme is presented for constructing a minimized set of vehicle routes in practical instances of the VRP that include critically high-cost penalties. Multiple optimization priority strategies that extend this algorithm are examined and compared in a large-scale bio-emergency case study. The algorithms designed from this research are implemented and integrated into an existing computational framework that is currently used by public health officials. These computational tools enhance an emergency response planner's ability to derive a set of vehicle routes specifically optimized for the delivery of resources to dispensing facilities in the event of a bio-emergency.