Nowadays, NRTS (Nighttime Road Traffic Safety) and energy saving are very hot topics in transportation field. This thesis investigates a general optimal model for minimizing NRTAs (nighttime road traffic accidents) and power consumption of the road lighting. To establish this model, the relationship between N/D RTAs (Night to Day Road Traffic Accidents) ratio and road lighting condition and the relationship between power consumption and road lighting condition have been studied and explained. A media variable “economic cost” has been chosen which is used for making a connection between these two relationships. The evaluations of NRTAs and power consumption from cost point of view are introduced as well. The impacts of each internal factor defined by author are explained carefully. The result of the model based on these relationships and internal influencing factors is presented in the paper. Finally, the recommendations for reducing NRTAs and/or power consumption, as well as other interesting areas for further study are presented.

The overall aim of this study was to examine the work environment and the associations with safety, and see the relations with occupational accidents and undesired events on board industrial and cruise ships. 215 seafarers participated in this quantitative survey study, with a response rate of 35%. When conducting the hierarchical block regression analysis separately on superiors/officers and subordinates/ratings, the work environment emerged as a predictor for safety status (compliance, attitudes and commitment). Several significant differences in the beta value between the two groups were also found. When testing the differences in the safety status on ships with high and low number of undesired events and accidents, separately on the two groups, significant differences emerged only for superiors and officers; Significant differences were found in compliance when testing high and low number of undesired events, and for high and low number of accidents safety status and compliance emerged significant. Without assuming causation, the work environment appears to be a possible alternate and indirect way of improving on the safety status on board ships. However, safety status and the relations with undesired events and accidents require further investigation before a more accurate conclusion can be made.

It has been well established in previous research that driving during rainfall is associated with increased risk of traffic collision involvement. Of particular concern are heavy rain events, which result in elevated risks up to three times higher than those for light rainfalls. As the global climate changes in the coming century, altered precipitation patterns are likely. The primary objective of this thesis is to estimate the potential impacts of climate change on traffic safety in two large Canadian urban regions: the Greater Toronto Area and Greater Vancouver. A secondary objective is to provide a framework or methodology for exploring this question. In accomplishing the primary objective, daily collision and climate records are utilized to establish an empirical estimate of present-day rainfall-related crash risk. This estimate is combined with results of a climate modelling exercise to arrive at a possible traffic safety future for urban Canada over the next 40 years. For the second objective, several important decisions related to data acquisition, compatibility, and completeness are considered, and the tradeoffs are mapped out and discussed, in order to provide guidance for future studies. Results indicate that over the next 40 years, Toronto is likely to see a mean annual increase in rain days of all intensities, resulting in marginally more collisions and casualties each year. Substantially more rainfall days are projected for Vancouver by mid-century, resulting in a small increase in annual incident counts. In both study regions, the greatest adverse safety impact is likely to be associated with moderate to heavy rainfall days (≥ 10 mm); this estimate is consistent with the greater risk increases associated with these conditions today, and suggests that attention should be paid to future changes in the frequency and intensity of extreme rainfall events. Indeed, heavy rain days are likely to account for approximately half of all additional collision and casualty incidents.

The purpose of the current study was to assess the efficacy of mental models as a predictor of driving outcomes. In contrast to more traditional measures of knowledge, mental models capture the configural property of knowledge, that is, an individual's understanding of the interrelationships that exist among critical concepts within a particular knowledge domain. Given that research has consistently shown the usefulness of mental models for the prediction of performance in a number of settings, it was hypothesized that the development of accurate driving mental models would also play an important role in the prediction of driving outcomes, especially in comparison to traditional measures of driving knowledge—such as the multiple-choice type tests typically required to obtain a driver license. Mental models of 130 college students (52 percent females) between 17 and 21 years-old (M = 18.68, SD = 0.80) were analyzed and compared to a subject matter expert (SME) referent structure using Pathfinder. A statistically significant correlation was found for mental model accuracy and moving violations (r = –.18, p <.05), but not for at-fault crashes. Evidence of incremental validity of mental models over commonly used predictors of moving violations (but not for at-fault crashes) was also found. Exploratory analyses revealed that driving knowledge, general mental ability (GMA), and emotional stability were the best predictors of mental model accuracy. Issues related to the measurement of mental models were extensively addressed. First, statistically significant correlations between GMA and several mental model properties (i.e., accuracy scores, within participant similarity, and within participant correlation) suggest that challenges inherent to the task for eliciting mental models may influence mental model scores which, in turn, may lower mental model reliability estimates. Also, the selection of model components (i.e., terms) and the identification of the "best" reference structure for deriving mental model accuracy scores are undoubtedly critical aspects of mental model-related research. Along with illustrating the decisions made in the context of this particular study, some suggestions for conducting mental model-related research are provided.

In the aftermath of the nuclear accidents at Chernobyl in Russia, at the Three Mile Island reactor in the United States and the nuclear criticality accident at the Tokai-mura JCO in Japan, the need for serious attention has been directed toward response planning for nuclear emergencies. Currently Taiwan has one of the highest population densities in the world. To handle energy needs, there are three nuclear facilities. Recently the first serious electrical failure occurred at the third nuclear reactor (3A). This incident in particular brought the need for proper measures to handle accidents to the attention of the public. The main purpose of this thesis is to investigate the effectiveness of the national planning for handling nuclear emergencies under the existing laws for nuclear accident emergency response and the laws for disaster prevention at the local government level. Attention is given to the capability of the design and organizational system for handling nuclear emergencies and whether this plan can be realized in actual practice. Having reached a thorough understanding through the analysis, the paper makes suggestions about the procedures to be followed for the national government¡¦s handling of nuclear plant emergencies.

Traffic accidents which damage the safety of human beings are one of the most important problems due to their material losses and effects to human health. Although continuous improvements are made by the governments / losses of traffic accidents are still a significant issue all over the world. The usual studies realized so far are generally related with the accident prevention models. However, there has not been much research done concerning the situation after the traffic accidents happen. After occurrence of traffic accidents, determination of fault rates for each party involved in the accident is urgently important. The aim of this study is to develop an expert system that uses the knowledge of experts for determination of fault rates in traffic accidents. For this purpose, a detailed literature survey was performed to define the determinants influencing the fault rates of each party. In addition, required data, that is, expert-witness reports were taken from academicians. Classification of these data was done and critical factors affecting fault rates were determined. In light of the defined factors, flowcharts were developed for each type of traffic accident. Moreover questionnaire submitted to experts, was prepared to acquire knowledge of experts. The critical factors affecting fault rates were assessed with a quantitative way in questionnaire. The proposed Traffic Accident Expert System (TAES) is on the basis of the knowledge of experts. Quantification of fault rates can change from one expert to another. An expert system such as the one this thesis will propose will prevent these contradictions. In addition, the expert system quantifies fault rates faster and more consistent as well.

The purpose of this thesis is to quantify the economic costs associated with traffic crashes for 83 of the largest metropolitan areas in the United States and compare those costs to that of congestion. This was done by collecting injury and fatality data for each area and multiplying those by economic cost estimates for each developed by the FHWA. The findings of this analysis show that the economic cost of traffic crashes exceeds the economic costs of congestion in every metropolitan area studied. These results indicate that transportation safety deserves similar consideration to that of traffic congestion when allocation transportation funds.

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<p>The subject of this licentiate thesis is experience feedback from accidents and incidents. The thesis aims to contribute to an understanding of how the learning processes within organizations, companies and authorities could be improved.</p><p><i>Essay I </i>(written together with Sven Ove Hansson) reports on an evaluation carried out in 2004 by the Swedish Work Environment Authority’s Accident Investigation Board, called HAKO (Haverikommissionen). An important outcome of this evaluation shows that HAKO have not been able to manage the dissemination of their written reports, which is unfortunate, since the reports are thoroughly written.</p><p><i>Essay II</i> (written with Sven Ove Hansson and Carl Rollenhagen) is an overview of the literature on learning from accidents and incidents. The focus in this essay is on literature that evaluates the effectiveness and usefulness of different methods in accident investigations. The conclusions drawn from this literature review are that the dissemination of results and knowledge from accident investigations must be improved, and experience feedback systems should be integrated into overall systems of risk management.</p><p>The starting point for<i> Essay III </i>was an empirical study conducted in 2005/2006. Twenty-eight supervision cases from eleven local Environment and Health Administrations in Sweden were examined. The overall goal of the study was to find out how, and to what extent, experience feedback occurs between different municipal authorities. Two major problems affecting experience feedback have been found; namely, that the inspectors do not have enough guidance on how to interpret the law, and that they would like more information on what happens to legal cases they have reported to public prosecutors and police.</p>

Includes vita. / Title from PDF of title page. / Document formatted into pages; contains 139 pages. / Thesis (Ph.D.)--University of South Florida, 1998. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Non-recurring congestion caused by incidents is a major source of traffic delay in freeway systems. With the objective of reducing these traffic delays, traffic operation managers are focusing on detecting incident conditions and dispatching emergency management teams to the scene quickly. During the past few decades, a few number of conventional algorithms and artificial neural network models were proposed to automate the process of detecting incident conditions on freeways. These algorithms and models, known as automatic incident detection methods (AIDM), have experienced a varying degree of detection capability. Of these AIDMs, artificial neural network-based approaches have illustrated better detection performance than the conventional approaches such as filtering techniques, decision tree method, and catastrophe theory. So far, a few neural network model structures have been tested to detect freeway incidents. / ABSTRACT: Since the freeway incidents directly affect the freeway traffic flow, majority of these models have used only traffic flow variables as model inputs. However, changes in traffic flow may also be stimulated by the other features (e.g., freeway geometry) to a greater extent. Many AIDMs have also used a conventional detection rate as a performance measure to assess the detection capability. Yet the principle function of incident detection model, which is to identify whether an incident condition exists for a given traffic pattern, is not measured in its entirety by this conventional measure. In this study, new input feature sets, including freeway geometry information, were proposed for freeway incident detection. Sixteen different artificial neural network (ANN) models based on feed forward and recurrent architectures with a variety of input feature sets were developed. ANN models with single and double hidden layers were investigated for incident detection performance. / ABSTRACT: A modified form of a conventional detection rate was introduced to capture full capability of AIDMs in detecting incident patterns in the freeway traffic flow. Results of this study suggest that double hidden layer networks are better than single hidden layer networks. The study has demonstrated the potential of ANNs to improve the reliability using double layer networks when freeway geometric information is included in the model. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.