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    Multi-source multimodal deep learning to improve situation awareness : an application of emergency traffic management : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand
    (Massey University, 2023) Hewa Algiriyage, Rangika Nilani
    Traditionally, disaster management has placed a great emphasis on institutional warning systems, and people have been treated as victims rather than active participants. However, with the evolution of communication technology, today, the general public significantly contributes towards performing disaster management tasks challenging traditional hierarchies in information distribution and acquisition. With mobile phones and Social Media (SM) platforms widely being used, people in disaster scenes act as non-technical sensors that provide contextual information in multiple modalities (e.g., text, image, audio and video) through these content-sharing applications. Research has shown that the general public has extensively used SM applications to report injuries or deaths, damage to infrastructure and utilities, caution, evacuation needs and missing or trapped people during disasters. Disaster responders significantly depend on data for their Situation Awareness (SA) or the dynamic understanding of “the big picture” in space and time for decision-making. However, despite the benefits, processing SM data for disaster response brings multiple challenges. Among them, the most significant challenge is that SM data contain rumours, fake information and false information. Thus, responding agencies have concerns regarding utilising SM for disaster response. Therefore, a high volume of important, real-time data that is very useful for disaster responders’ SA gets wasted. In addition to SM, many other data sources produce information during disasters, including CCTV monitoring, emergency call centres, and online news. The data from these sources come in multiple modalities such as text, images, video, audio and meta-data. To date, researchers have investigated how such data can be automatically processed for disaster response using machine learning and deep learning approaches using a single source/ single modality of data, and only a few have investigated the use of multiple sources and modalities. Furthermore, there is currently no real-time system designed and tested for real-world scenarios to improve responder SA while cross-validating and exploiting SM data. This doctoral project, written within a “PhD-thesis-withpublication” format, addresses this gap by investigating the use of SM data for disaster response while improving reliability through validating data from multiple sources in real-time. This doctoral research was guided by Design Science Research (DSR), which studies the creation of artefacts to solve practical problems of general interest. An artefact: a software prototype that integrates multisource multimodal data for disaster response was developed adopting a 5-stage design science method framework proposed by Johannesson et al. [175] as the roadmap for designing, developing and evaluating. First, the initial research problem was clearly stated, positioned, and root causes were identified. During this stage, the problem area was narrowed down to Emergency traffic management instead of all disaster types. This was done considering the real-time nature and data availability for the artefact’s design, development and evaluation. Second, the requirements for developing the software artefacts were captured using the interviewing technique. Interviews were conducted with stakeholders from a number of disaster and emergency management and transport and traffic agencies in New Zealand. Moreover, domain knowledge and experimental information were captured by analysing academic literature. Third, the artefact was designed and developed. The fourth and final step was focused on the demonstration and evaluation of the artefact. The outcomes of this doctoral research underpin the potential for using validated SM data to enhance the responder’s SA. Furthermore, the research explored appropriate ways to fuse text, visual and voice data in real-time, to provide a comprehensive picture for disaster responders. The achievement of data integration was made through multiple components. First, methodologies and algorithms were developed to estimate traffic flow from CCTV images and CCTV footage by counting vehicle objects. These outcomes extend the previous work by annotating a large New Zealand-based vehicle dataset for object detection and developing an algorithm for vehicle counting by vehicle class and movement direction. Second, a novel deep learning architecture is proposed for making short-term traffic flow predictions using weather data. Previous research has mostly used only traffic data for traffic flow prediction. This research goes beyond previous work by including the correlation between traffic flow and weather conditions. Third, an event extraction system is proposed to extract event templates from online news and SM text data, answering What (semantic), Where (spatial) and When (temporal) questions. Therefore, this doctoral project provides several contributions to the body of knowledge for deep learning and disaster research. In addition, an important practical outcome of this research is an extensible event extraction system for any disaster capable of generating event templates by integrating text and visual formats from online news and SM data that could assist disaster responders’ SA.
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    Language switching in aviation : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Aviation at Massey University, Manawatū, New Zealand
    (Massey University, 2018) Daskova, Martina
    Clear and precise communication between pilots and air traffic controllers is a precondition for safe operations. Communication has long been identified as a major element of the cockpit–controller interface, explaining one third of general aviation incidents (Etem & Patten, 1998). Yet, despite multilingualism with English as the lingua franca being a characteristic of aviation communication, little research appears to have investigated the efficiency of operation of bilinguals alternating between their dominant, usually native, language and English in a bilingual air traffic environment. The studies undertaken for this research sought to rectify this situation by examining the cognitive aspects of situation awareness during language switching in aviation. Quantitatively and qualitatively analysed responses to an online-distributed survey aimed at investigating the current bilingual situation in aviation revealed that while situation awareness for the majority (76%) of native-English speakers was adversely affected by bilingualism, almost 30% of bilinguals also reported their situation awareness being affected. Subsequent experimental analyses using a language switching paradigm investigated how participants recognize a target call sign, identify an error and predict in bilingual compared with monolingual English conditions. The effect of the language condition participants’ native Chinese only, English only, or a mix of both, varied across the three tasks. Call sign recognition performance was found to be faster in the English condition than in the bilingual condition, but accuracy did not differ, a finding that was attributed to the effect of call sign similarity. However, when the task was more complicated, the difference between the conditions diminished. No effect on performance was found for simultaneously listening to two speech sources, which is potentially analogous to cockpit communication and radio calls. The error analyses served to test for response bias by calculating sensitivity, d', and decision criterion C in accordance with Stanislaw and Todorov’s (1999) Signal Detection Theory calculations. Several cognitive implications for practice were proposed, for example, in Crew Resource Management (CRM) training and personal airmanship development, exploration of own behavioural biases might be used to adjust the placement of the criterion. The cognitive implications largely focused on affecting attitudes to increase awareness. Attention was focused on performance of bilinguals to identify which language condition facilitated faster and more accurate responses. The findings were unable to support any of the conditions, leaving the question: Would a universal language for communication on radio frequencies be worth considering, to allow everyone to understand what is said? Disentangling the effects of language switching on the performance of bilingual pilots and air traffic controllers remains a task for future studies.
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    Design and evaluation of mass evacuation support systems using ontologies for improved situation awareness : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Sciences at Massey University, Albany, New Zealand
    (Massey University, 2012) Javed, Yasir
    Large-scale emergencies, such as tsunamis, are managed by several teams, e.g. emergency managers, military, police, fire services, health care professionals, etc. Close co-ordination within and between teams is essential since the failure of a single link can risk the whole operation, for example, the mass evacuation of a city or region. Decision-making in such emergencies is necessarily complex as the situations are dynamic, unfolding rapidly, and invariably stressful. Computerised decision support systems can facilitate and improve co-ordination and decision making by presenting, structuring, processing, and interpreting huge amounts of information in a short span of time. However, the power of such systems is enhanced even further if they are designed to improve the situation awareness (SA) of individual managers, their shared situation awareness (SSA), and team situation awareness (TSA). The goal is to ensure that team members have a comprehensive understanding of the situation, not just for their individual roles but also for the roles of their colleagues. The aim of the thesis is to design a computer based information system to support SA, SSA, and TSA of emergency managers for effective decision making and collaborative task performance. The thesis describes elicitation of the information requirements for various emergency management roles during a mass evacuation using a cognitive task analysis technique. Based on the requirements, it explains the design and development of a computer based system dubbed Situation Aware Vigilant Emergency Reasoner (SAVER) using ontologies for situation assessment and reasoning. It is demonstrated that ontologies can be used to classify the SA information since they can model the situations in detail and allow the inference on rules and axioms. Ontology based reasoning successfully provided the automatic situation assessment according to the SA levels. The thesis also details the evaluation of SAVER by measuring SA, SSA and TSA of emergency managers using Situation Awareness Global Assessment Technique (SAGAT) in simulated mass evacuation scenarios. The evaluation demonstrated the superior performance of the computer based system for improving SA, SSA and TSA of emergency managers. Moreover, the user interfaces of SAVER were also evaluated positive for the human computer interaction (HCI) parameters such as usability, ease of use, understandability, learnability, functionality, etc.