Thesis (M.S.P.H.)--University of Michigan, 1939.

Thesis (M.S.P.H.)--University of Michigan, 1939.

Pedestrians and bicyclists are the most vulnerable road users and suffer the most severe consequences when crashes take place. An extensive literature is available for crash severity in terms of driver safety, but fewer studies have explored non-motorized users' crash severity. Furthermore, most research efforts have examined pedestrian and bicyclist crash severity in urban areas. This study focuses on state roads (mostly outside major urban areas) and aims to identify contributing risk factors of fatal and severe crashes involving pedestrians and bicyclists in state roads. Two ordinal regression models were developed (one for pedestrian and the other for bicyclist crashes) to examine crash severity risk factors. Additional models were developed to investigate road and traffic characteristics that could increase the likelihood of fatal crashes. In the model for pedestrian crash severity risk factors such as age, vehicle type and movement, light conditions, road classification, traffic control device, posted speed limit, location of the pedestrian and wet road surface during clear weather conditions are statistically significant. The bicyclist crash severity model indicates that age, crash location, vehicle movement and alcohol intoxication during dark conditions are statistically significant. In terms of road characteristics and traffic conditions, the models suggested risk factors such as arterials, light conditions, posted speed limit, roadways, and high heavy vehicle volume, increased the odds of a crash being fatal. The results seem to suggest that besides improvements in roadway characteristics, additional countermeasures to reduce crash severity for vulnerable users should include separation of vulnerable users from traffic, educational campaigns, more strict control of alcohol intoxicated drivers, and protection strategies of senior pedestrians.

Thesis (M.Ing.)--Stellenbosch University, 2001. / ENGLISH ABSTRACT: A road safety remedial programme has as an objective the improvement of road transportation safety by applying road safety engineering remedial measures to hazardous road network elements in a manner that will be economically efficient. Since accident data is the primary manifestation of poor safety levels it must be analysed in manner that will support the overall objective of economic efficiency. Three steps in the process of implementing a road safety remedial programme, that rely on the systematic analysis of accident data, are the identification of hazardous locations, the ranking of hazardous locations and the evaluation of remedial measure effectiveness. The efficiency of a road safety remedial programme can be enhanced by using appropriate methodologies to measure safety, identify and rank hazardous locations and to determine the effectiveness of road safety remedial measures. There are a number of methodologies available to perform these tasks, although some perform much better than other. Methodologies based on the Empirical Bayesian approach generally provide better results than the Conventional methods. Bayesian methodologies are not often used in South Africa. To do so would require the additional training of students and engineering professionals as well as more research by tertiary and other research institutions. The efficiency of a road safety remedial programme can be compromised by using poor quality accident data. In South Africa the quality of accident data is generally poor and should more attention be given to the proper management and control of accident data. This thesis will report on, investigate and evaluate Bayesian and Conventional accident data analysis methodologies. / AFRIKAANSE OPSOMMING: Die doel van 'n padveiligheidsverbeteringsprogram is om op die mees koste effektiewe manier die veiligheid van onveilige padnetwerkelemente te verbeter deur die toepassing van ingenieursmaatreëls. Aangesien padveiligheid direk verband hou met verkeersongelukke vereis die koste effektiewe implementering van 'n padveiligheidsverbeteringsprogram die doelgerigte en korrekte ontleding van ongeluksdata. Om 'n padveiligheidsverbeteringsprogram te implementeer word die ontleding van ongeluksdata verlang vir die identifisering en priortisering van gevaarkolle, sowel as om die effektiwiteit van verbeteringsmaatreëls te bepaal. Die koste effektiwiteit van 'n padveiligheidsverbeteringsprogram kan verbeter word deur die regte metodes te kies om padveiligheid te meet, gevaarkolle te identifiseer en te prioritiseer en om die effektiwiteit van verbeteringsmaatreëls te bepaal. Daar is verskeie metodes om hierdie ontledings te doen, alhoewel sommige van die metodes beter is as ander. Die 'Bayesian' metodes lewer oor die algemeen beter resultate as die gewone konvensionele metodes. 'Bayesian' metodes word nie. in Suid Afrika toegepas nie. Om dit te doen sal addisionele opleiding van studente en ingenieurs vereis, sowel as addisionele navorsing deur universiteite en ander navorsing instansies. Die gebruik van swak kwaliteit ongeluksdata kan die integriteit van 'n padveiligheidsverbeteringsprogram benadeel. Die kwaliteit van ongeluksdata in Suid Afrika is oor die algemeen swak en behoort meer aandag gegee te word aan die bestuur en kontrole van ongeluksdata. Die doel van hierdie tesis is om verslag te doen oor 'Bayesian' en konvensionele metodes wat gebruik kan word om ongeluksdata te ontleed, dit te ondersoek en te evalueer.

Title from PDF of title page. / Document formatted into pages; contains 119 pages. / Thesis (M.S.P.H.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Occupational slip, trip, and fall, (STF), incidents are a significant cause of traumatic occupational injuries and has been identified as NORA priority area. Objective: The National Institute for Occupational Safety and Health in collaboration with Liberty Mutual Research Center for Safety and Health, the Finnish Institute for Occupational Safety and Health, and BJC Health System is conducting a 3 year study titled "Slip and Falls Prevention in Health Care Workers". A key component of the overall study is the descriptive analysis of 72 months (1996-2001) of STF incidents. Setting: This analysis encompasses data from the James A. Haley Veteran's Administration Medical Center, (JAH). Results: Forty- five months of historical STF data from the ASISTS database was analyzed. / ABSTRACT: Of 279 STF incidents, 71.22%, (240) were female, the median age was 49 years, RN's were the most common occupational category (70 =21.74%), trips were the most common type of incident, (105 = 33.44%), the parking lot was the most common location, (75 = 23.70%), Non- specified slick surfaces (56 = 17.83%) and non- patient related objects were the most common cause (56 =17.83%), 70.85%, (192) returned to full duty, and 83.67%, (246) had no lost work time. Wilcoxon Ranked Sum test comparing those with affected work time found no significant difference in age (p= 0.4133). Analysis could not be performed using exact number of lost work days and days on light duty because of discrepancies between the ASISTS and Safety Office alternative databases. Conclusion: Efforts and resources to decrease the number of STF incidents at the JAH would be best concentrated in the following areas: Occupations, locations, and causes associated with the highest frequencies of STF incidents. / ABSTRACT: Proposed improvements in the method of data collection include: Identify what STF questions want to be answered. Decide what data is required to answer the question. Design a data collection system around this. Strive for a more integrated approach; encourage employee reporting; altering VA form 2162. Given the downward trend in the three year analysis of STF incidents, caution should be used in analyzing the results of a pre and post intervention study. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

Thesis (M. S.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2009. / Committee Member: Hunter, Michael; Committee Member: Meyer, Michael; Committee Member: Rodgers, Michael. Part of the SMARTech Electronic Thesis and Dissertation Collection.

This thesis presents an analysis of the characteristics of malfunction flash incidents based on the Georgia Department of Transportation crash reporting database. Malfunction flash is an unintentional state of flash mode in intersection signal hardware. The flash mode is a signal indication of yellow/red or red/red flash. The flash mode can be due to many issues, such as hardware failure, damage, or storms. Crash reports are completed by police officers at the scene. After processing by the local jurisdiction reports are sent to GDOT for archiving and analysis. GDOT archives the reports in a PDF image format without editable text. This research will develop a procedure to convert the archived PDF reports to text files using optical character recognition (OCR) software. The developed procedure will extract the description paragraph of the incident from the PDF. The extracted descriptions may then be searched for useful information about the incident. The text files will be run through a filter for keywords, such as; "malfunction flash," "red/red flash," "yellow/red flash," and others. Incidents flagged by the keywords will be reexamined to determine if they are malfunction flash incidents. The 2006 GDOT incident data base will be used for this effort. From an original possible candidate list of 70,000 signalized intersection incidents malfunction incidents will be identified using this method. A statistical analysis will be completed seeking trends and important characteristics of malfunction incidents.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Statistics from mining accidents expose that the potential for injury or death to employees from occupational accidents is relatively high. This study attempts to contribute to the on-going efforts to improve occupational safety in the mining industry by creating a model capable of predicting the continuous risk of occupational accidents occurring. Model inputs include the time of day, time into shift, temperatures, humidity, rainfall and production rate. The approach includes using an Artificial Neural Network (ANN) to identify patterns between the input attributes and to predict the continuous risk of accidents occurring. As a predecessor to the development of the model, a comprehensive literature study was conducted. The objectives of the study were to understand occupational safety, explore various forecasting techniques and identify contributing factors that influence the occurrence of accidents and in so doing recognise any gaps in the current knowledge. Another objective was to quantify the contributing factors identified, as well as detect the sensitivity amongst these factors and in so doing deliver a groundwork for the present model. After the literature was studied, the model design and construction was performed as well as the model training and validation. The training and validation took the form of a case study with data from a platinum mine near Rustenburg in South Africa. The data was split into three sections, namely, underground, engineering and other. Then the model was trained and validated separately for the three sections on a yearly basis. This resulted in meaningful correlation between the predicted continuous risk and actual accidents as well as the majority of the actual accidents only occurring while the continuous risk was estimated to be above 80%. However, the underground section has so many accidents, that the risk is permanently very high. Yet, the engineering and other sections produced results useful for managerial decisions. / AFRIKAANSE OPSOMMING: Mynbou ongeluk statistieke dui aan dat die potensiaal vir besering of dood as gevolg van beroepsongelukke relatief hoog is. Die studie poog om by te dra tot die voortdurende verbetering van beroepsveiligheid in die mynbedryf deur middel van ’n model wat die risiko van beroepsongelukke voorspel. Die model vereis die tyd, tyd verstreke in die skof, temperatuur, humiditeit, reënval en produksie tydens die ongeluk as inset. Die benadering tot hierdie model maak gebruik van ’n Kunsmatige Neurale Netwerk (KNN) om patrone tussen die insette te erken en om die risiko van ’n voorval te beraam. As ’n voorloper tot die model ontwikkeling, is ’n omvattende literatuurstudie onderneem. Die doelwitte van die literatuur studie was om beroepsveiligheid beter te verstaan, verskeie voorspellings tegnieke te ondersoek en kennis van bydraende faktore wat lei tot voorvalle te ondersoek. Nog ’n doelwit sluit die kwantifisering in van geidentifiseerde bydraende faktore, asook die opsporing van die sensitiwiteit tussen hierdie faktore en hierdeur ’n fondasie vir die voorgestelde model te skep. Na afloop van die literatuurstudie is die model ontwikkel, opgelei en gevalideer. Die opleiding en validasie is deur middel van ’n gevallestudie in ’n platinummyn naby Rustenburg in Suid Afrika gedoen. Die data is verdeel in drie afdelings, d.i. ondergronds, ingenieurswese en ander. Die model is vir elke afdeling apart opgelei en gevalideer op ’n jaarlikse basis. Hierdie het gelei tot ’n betekenisvolle korrelasie tussen die voorspelde risiko en die werklike ongelukke met die meerderheid van die werklike ongevalle wat voorgekom het terwyl die risiko 80% oorskry het. In die ondergrondse afdeling is so baie voorvalle waarneem dat die risiko permanent hoog is. Die ander afdelings het wel resultate verskaf wat sinvol gebruik kan word in bestuursbesluite.

Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2020 / Background: Death and economic losses due to road traffic accidents (RTA) are huge global public health and developmental problems and need urgent attention. Each year nearly 1.24 million people die and millions suffer various forms of disability as a result of road accidents. This puts road traffic injuries (RTIs) as the eighth leading cause of death globally and RTIs are set to become the fifth leading cause of death worldwide by the year 2030 unless urgent actions are taken. Aim: In this paper, we investigate factors that contribute to road traffic deaths (RTDs) in the Limpopo province of South Africa using models such as the generalized linear models (GLM) and zero inflated models. Methods: The study was based on retrospective data that comprised of reports of 18,029 road traffic accidents and 4,944 road traffic deaths over the years 2009 – 2015. Generalized linear modelling and zero-inflated models were used to identify factors and determine their relationships to RTDs. Results: The data was split into two categories: deaths that occurred during holidays and those that occurred during non-holiday periods. It was found that the following variables, namely, Monday, human actions, vehicle conditions and vehicle makes, were significant predictors of RTDs during holidays. On the other hand, during non-holiday periods, weekend, Tuesday, Wednesday, national road, provincial road, sedan, LDV, combi and bus were found to be significant predictors of road traffic deaths. Conclusion: GLM techniques, such as the standard Poisson regression model and the negative binomial (NB) model, did little to explain the zero excess, therefore, zero-inflated models, such as zero-inflated negative binomial (ZINB), were found to be useful in explaining excess zeros. Recommendation: The study recommends that the government should make more human power available during the festive seasons, such as the December holidays, and over weekends.