{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/46945"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/46945","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimal strategies to improve railroad train safety and reduce hazardous materials transportation risk","abstract":"Railways offer a safe and efficient means to transport hazardous materials in North America. Nonetheless, the potential risk of hazardous materials release incidents to human health, property and the environment must be appropriately managed and minimized to the extent feasible. Railroad train safety is affected by a variety of infrastructure, rolling stock and operational factors. These factors, individually and in combination, should be properly accounted for in hazardous materials transportation risk management. This dissertation presents analytical models to analyze train derailment probability and severity, evaluate hazardous materials release risk and identify optimal solutions to efficiently reduce the risk. This research is organized into three principal parts: derailment analysis, risk analysis and decision analysis. First, statistical models are developed to estimate train derailment rates. The analysis shows the variation of train and car derailment rates under different infrastructure and traffic characteristics. Understanding derailment severity is also important. A quantile regression model is developed to analyze derailment severity distribution at various quantiles. After a hazardous materials train is derailed, the probability distribution of the number of tank cars releasing is estimated based on a series of related events using the Law of Total Probability. Based on train derailment probability and the number of tank cars releasing per derailment, the risk of a hazardous materials release incident is estimated in the second part of this dissertation. The model accounts for FRA track class, method of operation, traffic density, train length, derailment speed, number and placement of tank cars in a train, tank car safety design and population density along rail lines. In the final part, this dissertation evaluates the safety effectiveness of several risk reduction strategies, individually and in combination. An integrated risk reduction framework is developed to determine the optimal portfolio of investments for multiple risk reduction strategies. In summary, this dissertation presents statistical analysis, risk analysis and decision analysis models to assist decision makers to develop, evaluate, prioritize and implement potential strategies for reducing railroad hazardous materials transportation risk. The methodology and results provide insights that have been implemented or could potentially be used to develop better-informed rail safety policies and practices.","abstract_html":"Railways offer a safe and efficient means to transport hazardous materials in North America. Nonetheless, the potential risk of hazardous materials release incidents to human health, property and the environment must be appropriately managed and minimized to the extent feasible. Railroad train safety is affected by a variety of infrastructure, rolling stock and operational factors. These factors, individually and in combination, should be properly accounted for in hazardous materials transportation risk management. This dissertation presents analytical models to analyze train derailment probability and severity, evaluate hazardous materials release risk and identify optimal solutions to efficiently reduce the risk. This research is organized into three principal parts: derailment analysis, risk analysis and decision analysis. First, statistical models are developed to estimate train derailment rates. The analysis shows the variation of train and car derailment rates under different infrastructure and traffic characteristics. Understanding derailment severity is also important. A quantile regression model is developed to analyze derailment severity distribution at various quantiles. After a hazardous materials train is derailed, the probability distribution of the number of tank cars releasing is estimated based on a series of related events using the Law of Total Probability. Based on train derailment probability and the number of tank cars releasing per derailment, the risk of a hazardous materials release incident is estimated in the second part of this dissertation. The model accounts for FRA track class, method of operation, traffic density, train length, derailment speed, number and placement of tank cars in a train, tank car safety design and population density along rail lines. In the final part, this dissertation evaluates the safety effectiveness of several risk reduction strategies, individually and in combination. An integrated risk reduction framework is developed to determine the optimal portfolio of investments for multiple risk reduction strategies. In summary, this dissertation presents statistical analysis, risk analysis and decision analysis models to assist decision makers to develop, evaluate, prioritize and implement potential strategies for reducing railroad hazardous materials transportation risk. The methodology and results provide insights that have been implemented or could potentially be used to develop better-informed rail safety policies and practices.","abstract_has_math":false,"creators":["Liu, Xiang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Barkan, Christopher P.L.","Saat, M. 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Understanding derailment severity is also important. A quantile regression model is developed to analyze derailment severity distribution at various quantiles. After a hazardous materials train is derailed, the probability distribution of the number of tank cars releasing is estimated based on a series of related events using the Law of Total Probability. Based on train derailment probability and the number of tank cars releasing per derailment, the risk of a hazardous materials release incident is estimated in the second part of this dissertation. The model accounts for FRA track class, method of operation, traffic density, train length, derailment speed, number and placement of tank cars in a train, tank car safety design and population density along rail lines. In the final part, this dissertation evaluates the safety effectiveness of several risk reduction strategies, individually and in combination. An integrated risk reduction framework is developed to determine the optimal portfolio of investments for multiple risk reduction strategies. In summary, this dissertation presents statistical analysis, risk analysis and decision analysis models to assist decision makers to develop, evaluate, prioritize and implement potential strategies for reducing railroad hazardous materials transportation risk. The methodology and results provide insights that have been implemented or could potentially be used to develop better-informed rail safety policies and practices.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2013-12-06T16:31:13Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Liu_Xiang.pdf: 1361965 bytes, checksum: 27c8c7c7f2387e81de444184aa8ff8ad (MD5)","Made available in DSpace on 2014-01-16T18:27:14Z (GMT). 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