The September 11, 2001 terrorist attacks exposed considerable breakdowns in communications interoperability and information sharing among first responders. Multijurisdictional responses to the active-shooter incidents at the University of Texas in 2010; Sandy Hook Elementary of Newtown, Connecticut in 2012, and the Reynolds High School shooting of Multnomah County, Oregon in 2014 were replete with interoperability failures as well. Recent multijurisdictional response events continue to illuminate difficulties with first-responder interoperability and minimal research exists to promote understanding of the interoperability challenges of university police departments. The purpose of this study was to explore the barriers that impede communications of campus based law enforcement agencies during multiagency or multijurisdictional response. General systems theory and the unified theory of acceptance and use of technology model provided the conceptual framework for this qualitative case study. Face-to-face interviews were conducted with 10 leaders of university public safety agencies in California. Data were collected, inductively coded, and thematically analyzed. Key findings indicate that participants perceived barriers of funding, policy, inclusiveness, and training that affect communications interoperability performance. The positive social change implications from this study include recommendations of policy change for improved interoperability during multiagency or multijurisdictional response which can contribute to increased first-responder safety, more efficient multijurisdictional response, and improved safety of students and society at large.

Biological emergency response planning plays a critical role in protecting the public from possible devastating results of sudden disease outbreaks. These plans describe the distribution of medical countermeasures across a region using limited resources within a restricted time window. Thus, the ability to determine that such a plan will be feasible, i.e. successfully provide service to affected populations within the time limit, is crucial. Many of the current efforts to validate plans are in the form of live drills and training, but those may not test plan activation at the appropriate scale or with sufficient numbers of participants. Thus, this necessitates the use of computational resources to aid emergency managers and planners in developing and evaluating plans before they must be used. Current emergency response plan generation software packages such as RE-PLAN or RealOpt, provide rate-based validation analyses. However, these types of analysis may neglect details of real-world traffic dynamics. Therefore, this dissertation presents Validating Emergency Response Plan Execution Through Simulation (VERPETS), a novel, computational system for the agent-based simulation of biological emergency response plan activation. This system converts raw road network, population distribution, and emergency response plan data into a format suitable for simulation, and then performs these simulations using SUMO, or Simulations of Urban Mobility, to simulate realistic traffic dynamics. Additionally, high performance computing methodologies were utilized to decrease agent load on simulations and improve performance. Further strategies, such as use of agent scaling and a time limit on simulation execution, were also examined. Experimental results indicate that the time to plan completion, i.e. the time when all individuals of the population have received medication, determined by VERPETS aligned well with current alternate methodologies. It was determined that the dynamic of traffic congestion at the POD itself was one of the major factors affecting the completion time of the plan, and thus allowed for more rapid calculations of plan completion time. Thus, this system provides not only a novel methodology to validate emergency response plans, but also a validation of other current strategies of emergency response plan validation.

<p> </p> <p>A search and rescue (SAR) operation requires a rapid, accurate, and effective response to provide the missing person the best chance of being rescued. Personnel from the local area are likely to be closest to the location of the missing person, be familiar with the area, but they may not be adequately trained, experienced, or equipped with the best tools to effectively locate, identify, and retrieve the missing person. Thus, most SAR operations rely on a mix of trained personnel and volunteers. Among the trained personnel, there is a wide variance in proficiency, experience, and access to technology, leading to some emergency response agencies being better prepared than others. Volunteers, on the other hand, could be very helpful, but are largely untrained and inexperienced, reducing their inherent likelihood of success. The primary challenge to successful SAR operations is the lack of consistently trained, adequately equipped, and diversely experienced personnel. Despite the lack of desired resources, SAR operations must be completed rapidly and emergency responders often turn to volunteers. In response to this challenge, the use of unmanned aerial systems, UAS, in small volunteer teams was proposed. Available, off-the-shelf UAS technology can be used to simplify training with the help of affordable advanced technology, and thereby enable rapid, accurate, and effective SAR operations.  </p> <p>The following research was executed in the form of three independent, but related, studies. The first study focused on the efficiency of a UAS-equipped SAR operation; the second study focused on the accuracy of a UAS and image analysis software-enabled SAR operation; and the third study tested the ability of novice volunteers to learn and apply the new technologies (UAS plus image analysis) efficiently and effectively. The goal of these studies was to determine whether affordable commercial, off-the-shelf technologies could be used to enhance the efficiency and effectivity of SAR operations. The experimental methodology used specifically designed simulations of SAR operational scenarios. Two operational tactics were tested: (a) Equip the SAR team with UAS and (b) equip the SAR team with UAS and image analysis software. The specific scenarios selected were similar in complexity, but different enough to minimize the transfer of learning from the first study to the second study.  Finally, the reference times for manual SAR operations were compared against UAS and computerized image analysis software-assisted methods. The results of the proposed studies determined whether off-the-shelf UAS and image analysis technologies could be used to enable rapid, accurate, and effective SAR operations. </p>

Downstream vehicles detect an emergency response vehicle (ERV) through sirens and/or strobe lights. These traditional warning systems do not give any recommendation about how to react, leaving the drivers confused and often adopting unsafe behavior while trying to open a passage for the ERV. In this research, an advanced intra-link emergency assistance system, that leverages the emerging technologies of the connected vehicle environment, is proposed. The proposed system assumes the presence of a centralized system that gathers/disseminates information from/to connected vehicles via vehicle-to-infrastructure (V2I) communications. The major contribution of this dissertation is the intra-link level support provided to ERV as well as non-ERVs. The proposed system provides network-wide assistance as it also considers the routing of ERVs. The core of the system is a mathematical program - a set of equations and inequalities - that generates, based on location and speed data from connected vehicles that are downstream of the ERV, the fastest intra-link ERV movement. It specifies for each connected non-ERV a final assigned position that the vehicle can reach comfortably along the link. The system accommodates partial market penetration levels and is applicable on large transportation link segments with signalized intersections. The system consists of three modules (1) an ERV route generation module, (2) a criticality analysis module and (2) the sequential optimization module. The first module determines the ERV's route (set of links) from the ERV's origin to the desired destination in the network. Based on this selected route, the criticality analysis module scans/filters the connected vehicles of interest and determines whether any of them should be provided with a warning/instruction message. As the ERV is moving towards its destination, new non-ERVs should be notified. When a group of non-ERVs is identified by the criticality analysis module, a sequential optimization module is activated. The proposed system is evaluated using simulation under different combinations of market penetration and congestion levels. Benefits in terms of ERV travel time with an average reduction of 9.09% and in terms of vehicular interactions with an average reduction of 35.46% and 81.38% for ERV/non-ERV and non-ERV/non-ERV interactions respectively are observed at 100% market penetration, when compared to the current practice where vehicles moving to the nearest edge. / Doctor of Philosophy / Downstream vehicles detect an emergency response vehicle (ERV) through sirens and/or strobe lights. These traditional warning systems do not give any recommendations about how to react, leaving the drivers confused and often adopting unsafe behavior while trying to open a passage for the ERV. In this research, an advanced intra-link emergency assistance system, that leverages the emerging technologies of the connected vehicle environment, is proposed. The proposed system assumes the presence of a centralized system that gathers/disseminates information from/to connected vehicles via vehicle-to-infrastructure (V2I) communications. The major contribution of this dissertation is the intra-link level support provided to ERV as well as non-ERVs. The proposed system provides network-wide assistance as it also considers the routing of ERVs. The core of the system is a mathematical program - a set of equations and inequalities - that generates, based on location and speed data from connected vehicles that are downstream of the ERV, the fastest intra-link ERV movement. It specifies for each connected non-ERV a final assigned position that the vehicle can reach comfortably along the link. The system accommodates partial market penetration levels and is applicable on large transportation link segments with signalized intersections. The system consists of three modules (1) an ERV route generation module, (2) a criticality analysis module and (2) the sequential optimization module. The first module determines the ERV’s route (set of links) from the ERV’s origin to the desired destination in the network. Based on this selected route, the criticality analysis module scans/filters the connected vehicles of interest and determines whether any of them should be provided with a warning/instruction message. As the ERV is moving towards its destination, new non-ERVs should be notified. When a group of non-ERVs is identified by the criticality analysis module, a sequential optimization module is activated. The proposed system is evaluated using simulation under different combinations of market penetration and congestion levels. Benefits in terms of ERV travel time with an average reduction of 9.09% and in terms of vehicular interactions with an average reduction of 35.46% and 81.38% for ERV/non-ERV and non-ERV/non-ERV interactions respectively are observed at 100% market penetration, when compared to the current practice where vehicles moving to the nearest edge.

This study critically explores the pervasive issue of corruption in humanitarian assistance, focusing on the Liberia Red Cross Society (LRCS) during the Ebola outbreak in Liberia from 2014-2016. For a country grappling with broken infrastructure and fragile healthcare systems due to a 14-year-long civil war, the Ebola crisis in Liberia was an unprecedented disaster as evidenced by 10,672 recorded cases and 4,808 reported fatalities between 2014 and 2016. The LRCS was a prominent local humanitarian actor in the Ebola response and recovery efforts, but its work was marred by corruption stemming from organizational, contextual, and motivational factors. This study sheds light on the nature and extent of corruption within humanitarian action from the perspective of the LRCS and the Ebola epidemic in Liberia, contributing to humanitarianism as a discipline and a profession. The study utilized the principal-agent theory and the organizational culture theory of corruption in humanitarian assistance, which had been employed in earlier studies of a similar nature. In terms of methodology, a qualitative approach with retrospective review was employed to address two research questions regarding the drivers and impact of corruption in the Ebola response and recovery operations conducted by the LRCS. Data for the study were collected from a mix of 14 pre-existing sources, including documents originating from the LRCS and its consortium of donors and partners, as well as published news content from notable local and global media outlets. The results were generated through document analysis facilitated by ATLAS.ti, a qualitative data analysis software, which considered patterns, trends, and insights within the sources gathered for the study. Multiple rounds of analyses on the data were conducted to validate the results of the study.  The findings of this study reveal a complex web of corruption within humanitarian aid delivery during crisis and disaster. Corrupt practices within the LRCS included fraud and misuse of Ebola relief funds and resources, driven by individual motivations coupled with a number of organizational and contextual factors. The corruption had adverse effects on the efficiency, effectiveness, and accountability of relief efforts, potentially leading to reduced donor confidence in the LRCS and funding reduction. The study also stresses the importance of leadership, decision-making processes as well as resource management in preventing or enabling corruption within humanitarian aid organizations. These findings underscore the need for robust internal oversight, accountability mechanisms, and ethical leadership in humanitarian organizations to prevent and address corruption effectively.

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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)