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Indiana University-Purdue University Indianapolis (IUPUI) / Road accident reconstruction and simulation investigates the accident causes, suggests improvements in vehicle design and investigates failures in vehicle control and safety systems such as the anti-lock brake system (ABS) and air-bag deployment. This thesis focuses on analysis of crash data from vehicles not equipped with collision warning systems. Vehicle parameters before and during an accident can be recorded using an Event Data Recorder (EDR) which helps in reconstructing an accident. This tool, installed in the vehicle, records different crash parameters like vehicle speed, lateral and longitudinal acceleration, seat-belt status, and air-bag deployment over a period that spens the accident. This thesis focuses on accident reconstruction with and without EDR data. A simulation software tool called HVE is used to visually recreate the reconstructed accidents. HVE is a platform to execute different accident simulation methods which are used for specific types of simulations. Two such simulation methods, EDSMAC4 and EDHIS, are discussed in this thesis. The former is an important method for vehicle-to-vehicle collisions and the latter is used for analysis of human behavior involved in the accident. Three real-life accidents were chosen for reconstruction and simulation. They were Bus and Car accident, Three Vehicle accident and Intersection accident. These particular accidents were chosen to represent a diverse selection of accidents based on the following parameters: the locations of the accidents, the vehicles involved in each accident, and the data available. A qualitative analysis of vehicle occupant's behavior is also presented for one of the three accidents. The thesis discusses in detail the reconstruction of these three accidents. Throughout these simulations, the thesis illustrates the advantages and limitations of the EDR and HVE simulation software for accident reconstruction and simulation.

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During the last few decades immigration by foreigners seeking work in the construction industry in the United States has increased dramatically. Of those seeking jobs in construction, Hispanics represent the largest and fastest growing population. The proportion of reportable accidents among Hispanic on-site construction workers in the United States is higher than that of non-Hispanics. This trend of on-site construction accidents is evident not only in states that have traditionally high populations of Hispanics, such as New York, Florida, California, Arizona, and Texas, it is also manifested in the State of Utah. This research focused on causes of accidents among Hispanic on-site construction workers along the Wasatch Front of Utah, which includes Brigham City, Utah in the north to Provo, Utah in the south. General contractors, subcontractors, job-site superintendents and on-site workers were interviewed to determine both causes of and solutions to accidents among Hispanic on-site construction workers. Results of this research indicated that some of the major causes of on-site accidents among Hispanics included worker negligence, lack of proper safety training, language barriers, and cultural issues. Suggested solutions include financial bonuses for good safety records, on-site translators, recognition of workers who complete approved training in job-site safety, and worker education on the importance of safety

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features.

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