Traffic safety involves accident-free movement of physical entities. It includes all means of transportation and is a function of mobility, an important variable affecting directly the mortality rate and increase of accidents. All transportation groups have coincidentally with or without increase of mobility been attempting to reduce accidents. Those notably succeeding are the shipping, railroad, and airline interests. The operating motor vehicle and pedestrian groups have failed conspicuously. The early means of attaining a better degree of traffic safety in the motor vehicle and pedestrian groups were based on opinions, and though rational, were incomplete because of the dynamic and static factors involved were not considered as a whole. The real solution lay in coordination and more effective legislation, motor vehicle administration, enforcement, engineering, personnel training, education, and research. The extensive scope of the field of traffic safety engineering could not be completely covered in this tehsis; wherefore, and attempt has been made to present the traffic safety situation in Utah and to venture a limited account of traffic safety engineering procedures for solution of a few accident situations

This thesis presents a collection of network optimization strategies aimed at aiding the local practitioner in selecting, implementing, and evaluating appropriate strategies to achieve community goals and objectives in the urban environment. The urban environment is often challenging due to the plethora of activity and variety in mode choice. Growing interest in sustainable transportation practices along with encouragement at the Federal, State, and Local levels to is leading to the growing use of non-motorized modes of transportation such as walking and bicycling. The combination of high population density and mixed land use in the urban environment creates unique safety and operational challenges. This research presents a synthesis of strategies designed to improve local transportation safety and efficiency by targeting speeding and cut-through volumes as improving pedestrian and bicycle facilities in urban areas such as those found in Western Massachusetts. Additionally, this research evaluates two local network optimization strategies; speed cushions and reverse angle parking. The effectiveness of the speed cushions in achieving the community’s goal of reducing speeds was evaluated and determined to be a recommended strategy for future implementation, especially when couple with enforcement. Reverse angle parking, however, was not determined to be an effective strategy due to the high occurrence of events as well as lower parking volume exhibited during implementation.

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.

The goal of this thesis is to determine the contributing factors affecting severe traffic crashes (severe: incapacitating and fatal - non-severe: no injury, possible injury, and non-incapacitating), and in particular those factors influencing crashes involving large vehicles (heavy trucks, truck tractors, RVs, and buses). Florida Department of Highway Safety and Motor Vehicles (DHSMV) crash reports of 2008 have been used. The data included 352 fatalities and 9,838 injuries due to large vehicle crashes. Using the crashes involving large vehicles, a model comparison between binary logit model and a Chi-squared Automatic Interaction Detection (CHAID) decision tree model is provided. There were 13 significant factors (i.e. crash type with respect to vehicle types, residency of driver, DUI, rural-urban, etc.) found significant in the logistic procedure while 7 factors found (i.e. posted speed limit, intersection, etc.) in the CHAID model. The model comparison results indicate that the logit analysis procedure is better in terms of prediction power. The following analysis is a modeling structure involving three binary logit models. The first model was conducted to estimate the crash severity of crashes that involved only personal vehicles (PV). Second model uses the crashes that involved large vehicles (LV) and passenger vehicles (PV). The final model estimated the severity level of crashes involving only large vehicles (LV). Significant differences with respect to various risk factors including driver, iii vehicle, environmental, road geometry and traffic characteristics were found to exist between those crash types and models. For example, driving under the influence of Alcohol (DUI) has positive effect on the severity of PV vs. PV and LV vs. PV while it has no effect on LV vs. LV. As a result, 4 of the variables found to be significant were similar in all three models (although often with quite different impact) and there were 11 variables that significantly influenced crash injury severity in PV vs. PV crashes, and 9 variables that significantly influenced crash injury severity in LV vs. PV crashes. Based on the significant variables, maximum posted speed, number of vehicles involved, and intersections are among the factors that have major impact on injury severity. These results could be used to identify potential countermeasures to reduce crash severity in general, and for LVs in particular. For example, restricting the speed limits and enforcing it for large vehicles could be a suggested countermeasure based on this study.

In order to increase the safety of all air travel, technologies that continueto augment the pilot's ability to avoid collisions and stay clear of danger areneeded. But, before these can be certified and deployed, their performance andpotential failure cases have to be understood. This requires evaluating a modelof the system on simulated encounters, consisting of different trajectoriesthat should replicate the real world. This is commonly done using a statistical encounter model, which produces largeamounts of data but relies on the accuracy of the statistical model, thuslimited in its ability to produce realistic data. The goal with this project isto create an encounter dataset of real trajectories that would provide analternative to encounter models. This is done using an ADS-B dataset from The OpenSky Network (provided byDaedalean AI), consisting of 226 billion air traffic data points from 2019.First, a solution to efficiently query and reconstruct trajectories from thedataset is designed and implemented. Using it, a NMAC (Near Mid-Air Collision)dataset is created to demonstrate the viability of ADS-B as a source forcreating an encounter dataset, and to prove the capabilities of the designedsolution.

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Road accidents pose a significant threat, leading to fatalities and injuries with far-reaching consequences. This study addresses two crucial challenges in road safety: analyzing traffic intersections to enhance safety by predicting potentially risky situations, and monitoring driver activity to prevent distracted driving accidents. Focusing on Virginia's intersections, we thoroughly examine traffic participant interactions to identify and mitigate conflicts, employing graph-based modeling of traffic scenarios to evaluate contributing parameters. Additionally, we leverage graph neural networks to detect and track potential crash situations from intersection videos, offering practical recommendations to enhance intersection safety. To understand the causes of risky behavior, we specifically investigate accidents resulting from distracted driving, which has become more prevalent due to advanced driver assistance systems in semi-autonomous vehicles. For monitoring driver activity inside vehicles, we propose the use of Video Transformers on challenging secondary driver activity datasets, incorporating grayscale and low-quality data to overcome limitations in capturing overall image context. Finally, we validate our predictions by studying attention modules and introducing explainability into the computer vision model. This research contributes to improving road safety by providing comprehensive analysis and recommendations for intersection safety enhancement and prevention of distracted driving accidents. / Master of Science / Road accidents are a serious problem causing numerous deaths and injuries each year. By studying driver behavior, we can uncover common causes of accidents like distracted driving, impaired driving, speeding, and not following traffic rules. New vehicle technologies aim to assist drivers, raising concerns about driver attentiveness. It is crucial for car manufacturers to develop systems that can detect and prevent accidents, especially in semi-autonomous vehicles. This study focuses on intersections in Virginia and examines driver behavior within vehicles to identify and prevent dangerous situations. We create models of different traffic scenarios using graphs/networks and utilize machine learning to identify potential accidents. Our objective is to provide practical recommendations for improving intersection safety. Existing datasets and algorithms for recognizing driver activities often fail to capture common distractions like eating, drinking, and phone use. To address this, we introduce two challenging datasets specifically designed to capture distracted driving activities. Finally, we try to understand the predictions bade by the chosen deep learning model by visualizing the inner workings.

This study investigates community involvement in promoting and improving road safety in Blaauwbosch, Newcastle, KwaZulu-Natal. The aim is to evaluate communitydriven bottom-up approaches like the Participatory Rural Appraisal (PRA). The central premise is that road crashes can be reduced if community involvement is exercised. Road crashes are affecting all the communities globally, and they continue to escalate at an alarming rate. The 2013 and 2015 World Health Organization (WHO) Global Status Reports form the basis of this study by providing the facts and figures about global road crash statistics. Communities are motivated by the outcomes and impacts of road safety interventions in improving their well-being and development. The objectives of the research study were to document the community’s perceptions of road safety, to check how community members can be involved in reducing road crashes, and to specify the role that local authorities can play. These objectives were met with the use of participatory rural appraisal (PRA) as a research tool for data collection. The study found that the community members of Blaauwbosch perceive road safety as an important factor that affects their lives. There was also a belief that, through community involvement, road crashes can be reduced. The authorities also had an important role to play in reducing road crashes, provided there is political will and the required resources. The findings and conclusions drawn affirms that road safety is a collective responsibility and requires joint efforts from all the stakeholders. / Geography / M. Sc. (Geography)

The purpose of this study was to assess the effectiveness of defensive driving as a preventative strategy for road traffic violations and collisions in Zimbabwe. A sample of one hundred defensive driving graduates was used in the study. The descriptive survey method was used and data were collected using a questionnaire schedule. Literature review revealed that the majority of similar s udies by other researchers indicate that defensive driving is effective in preventing traffic violations and traffic collisions. The major findings of the present study showed that: (a) The defensive driving course is effective in preventing traffic violations and collisions; and (b) The defensive driving course should be compulsory in Zimbabwe. In view of these findings, this researcher urges the Traffic Safety Council of Zimbabwe to request the government to make legislation that compels every motorist to attend the defensive driving course. The same organisation should start conducting research on road traffic accidents. / Criminology / MA (Criminology)

The purpose of this study was to assess the effectiveness of defensive driving as a preventative strategy for road traffic violations and collisions in Zimbabwe. A sample of one hundred defensive driving graduates was used in the study. The descriptive survey method was used and data were collected using a questionnaire schedule. Literature review revealed that the majority of similar s udies by other researchers indicate that defensive driving is effective in preventing traffic violations and traffic collisions. The major findings of the present study showed that: (a) The defensive driving course is effective in preventing traffic violations and collisions; and (b) The defensive driving course should be compulsory in Zimbabwe. In view of these findings, this researcher urges the Traffic Safety Council of Zimbabwe to request the government to make legislation that compels every motorist to attend the defensive driving course. The same organisation should start conducting research on road traffic accidents. / Criminology and Security Science / MA (Criminology)