Investigating the role of alcohol in road traffic collision fatalities in Western Cape, South Africa

Road traffic collisions (RTCs) are a major contributor to unnatural death worldwide, but especially in low-to-middle-income countries (LMICs) where motorised transport has rapidly expanded. A literature review into RTCs and alcohol indicated that it is well recognised that alcohol intoxication is a key risk factor in RTCs and road traffic fatalities (RTFs). It also indicated that literature concerning the role of alcohol intoxication in RTFs in South Africa is limited. Hence, this study aimed to investigate alcohol in RTF victims in five of the major mortuaries (Salt River, Tygerberg, Paarl, Worcester and George) in the Western Cape Province between 1 January 2016 and 31 December 2017. Cases were extracted from the Western Cape Forensic Pathology Service (FPS) internal database, which included demographic and crash information, together with blood alcohol concentration (BAC) results. In total, 2079 cases over the two years were included in the study, with most cases admitted in the metropole of Cape Town (Salt River: n=838, Tygerberg: n=693). The proportion of unnatural deaths were greater outside the metropole (George, Worcester and Paarl) compared to the metropole (Salt River and Tygerberg) areas. The majority of fatalities were male individuals (male to female ratio of 3.52:1), with the average age of 35.2 ± 17.2 years. Most victims were pedestrians (n = 1106; 53.7%) and dark wet roads, and highways were noted as risk factors in RTFs. Blood was submitted for alcohol analysis in 1432 (68.9%) cases, and results were available for 1314 (91.8%) cases. Of the available results, 709 cases (54%) were positive for alcohol (BAC of ≥ 0.01g/100 mL). Of the positive cases, most had a BAC between 0.15 and 0.29 g/100mL and the overall average BAC was 0.20 g/100 mL. Pedestrians and drivers had the highest median BACs, and almost a third of all the positive BAC results were from pedestrian deaths. The findings of this dissertation can contribute to the growing research on alcohol and injury in South Africa, especially as it relates to RTFs. Insight into vulnerable populations within the province is highlighted, together with key risk factors associated with RTFs, as well as safety measures that may be targeted for improvement, especially with regards to driving and walking on the roads while intoxicated

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

Marijuana Intoxication Detection Using Convolutional Neural Network

Machine learning is a broad study of computer science, widely used for data analysis and algorithms that has the ability to learn and improve by experience through training. Supervised learning, Unsupervised learning, Dimensionality Reduction, Deep Learning, etc are the methods offered by Machine learning. These techniques are applied in fields like medical, automotive finance, and many more. In this thesis, Convolutional neural network (CNN) which is a part of deep learning techniques is applied to identify if a person is under influence of Marijuana or sober, using facial feature changes like redness in eyes, watery eyes, and drowsiness caused after smoking Marijuana. CNN is a state-of-the-art method in tasks like image classification and pattern recognition. CNN’s ability to learn from training the model using image dataset is a suitable method to be used in the problem of identifying a person’s sobriety based on facial features. The proposed methodology is divided into three components. Which are dataset creation, face detection to extract input image from real-time video, and finally, tuning and training CNN model for making a prediction. The purpose of this thesis is to develop a CNN model that may be helpful if implemented in vehicles in the future to reduce impaired driving incidents. Impaired driving is a major criminal cause of vehicle accidents in Canada. Impaired driving is a serious problem that puts the lives of pedestrians on the road and drivers involved in impaired driving themselves in danger. This thesis presents how Machine Learning can be applied to predict driver’s sobriety that may be helpful in reducing impaired driving incidents in the future by implementing in vehicles

Real-Time Work Zone Traffic Management via Unmanned Air Vehicles

Highway work zones are prone to traffic accidents when congestion and queues develop. Vehicle queues expand at a rate of 1 mile every 2 minutes. Back-of-queue, rear-end crashes are the most common work zone crash, endangering the safety of motorists, passengers, and construction workers. The dynamic nature of queuing in the proximity of highway work zones necessitates traffic management solutions that can monitor and intervene in real time. Fortunately, recent progress in sensor technology, embedded systems, and wireless communication coupled to lower costs are now enabling the development of real-time, automated, “intelligent” traffic management systems that address this problem. The goal of this project was to perform preliminary research and proof of concept development work for the use of UAS in realtime traffic monitoring of highway construction zones in order to create real-time alerts for motorists, construction workers, and first responders. The main tasks of the proposed system was to collect traffic data via the UAV camera, analyze that a UAV based highway construction zone monitoring systems would be capable of detecting congestion and back-of-queue information, and alerting motorists of stopped traffic conditions, delay times, and alternate route options. Experiments were conducted using UAS to monitor traffic and collect traffic videos for processing. Prototype software was created to analyze this data. The software was successful in detecting vehicle speed from zero mph to highway speeds. Review of available mobile traffic apps were conducted for future integration with advanced iterations of the UAV and software system that has been created by this research. This project has proven that UAS monitoring of highway construction zones and real-time alerts to motorists, construction crews, and first responders is possible in the near term and future research is needed to further development and implement the innovative UAS traffic monitoring system developed by this research

Designing and Operating Safe and Secure Transit Systems: Assessing Current Practices in the United States and Abroad, MTI Report 04-05

Public transit systems around the world have for decades served as a principal venue for terrorist acts. Today, transit security is widely viewed as an important public policy issue and is a high priority at most large transit systems and at smaller systems operating in large metropolitan areas. Research on transit security in the United States has mushroomed since 9/11; this study is part of that new wave of research. This study contributes to our understanding of transit security by (1) reviewing and synthesizing nearly all previously published research on transit terrorism; (2) conducting detailed case studies of transit systems in London, Madrid, New York, Paris, Tokyo, and Washington, D.C.; (3) interviewing federal officials here in the United States responsible for overseeing transit security and transit industry representatives both here and abroad to learn about efforts to coordinate and finance transit security planning; and (4) surveying 113 of the largest transit operators in the United States. Our major findings include: (1) the threat of transit terrorism is probably not universal—most major attacks in the developed world have been on the largest systems in the largest cities; (2) this asymmetry of risk does not square with fiscal politics that seek to spread security funding among many jurisdictions; (3) transit managers are struggling to balance the costs and (uncertain) benefits of increased security against the costs and (certain) benefits of attracting passengers; (4) coordination and cooperation between security and transit agencies is improving, but far from complete; (5) enlisting passengers in surveillance has benefits, but fearful passengers may stop using public transit; (6) the role of crime prevention through environmental design in security planning is waxing; and (7) given the uncertain effectiveness of antitransit terrorism efforts, the most tangible benefits of increased attention to and spending on transit security may be a reduction in transit-related person and property crimes

Retrospective analysis of blunt force trauma associated with fatal road traffic accidents in Cape Town (South Africa) over a two-year period

Road transportation systems are a global developmental achievement. However, with them comes increased morbidity and mortality rates in the form of road traffic accidents. In South Africa, there is a need to characterize road traffic accidents and the injuries associated with them, to determine the preventative mechanisms required to reduce their morbidity and mortality rates. A brief review of fatal road traffic accidents from a global perspective is presented, highlighting the current literature surrounding the prevalence, demographics and blunt force trauma injuries associated with road traffic accidents in South Africa. There is limited research regarding the prevalence and characteristics of road traffic accidents. The objective of this study was to determine the prevalence of fatal road traffic accidents, necessitating the need for research, particularly at the regional level. A retrospective analysis was therefore conducted of all fatal road traffic accident related deaths autopsied at Salt River Mortuary (which services the West Metropole region of Cape Town, South Africa) from January 1st , 2013 to December 31st , 2014. The mean prevalence of road traffic accidents for the reviewed period was 15.9 / 100 000 population. The majority of road traffic accident victims were males who fell in the age group of 30 – 49 years. Over the two-year period, the majority of road traffic accident victims were pedestrians with elevated blood alcohol concentration levels. The head and facial regions of victims commonly exhibited external injuries, while the majority of fractures and organ injury were seen in the head and chest regions. There are limited studies which have investigated the blunt force trauma injuries associated with road traffic accidents in South Africa, and there is a need for further research. Interventions are of paramount importance to decrease fatal road traffic accidents, particularly amongst pedestrians as a road user. This study presents recent data on road traffic accidents for the West Metropole region of Cape Town (South Africa)