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theses

Railroads play a key role in the transportation infrastructure and economic development of the United States, and safety is of the utmost importance. Railroad safety is mainly affected by infrastructure, rolling stock, and human factors. Over the past decade, relatively less research has been undertaken to mitigate the human-factor-caused railroad incident risk despite considerable efforts and improvements in the infrastructure and rolling stock. The human errors in railroads may result in injuries or fatalities, infrastructure and rolling stock damages, and environmental impacts. This dissertation presents a methodological framework for railroad human-factor-caused safety risk management that encompasses risk assessment and mitigation. First, accident/incident data and information should be collected and used to identify safety risks, undesired human factor-related events, and risk management objectives. Second, risk assessment should be conducted to evaluate safety risks and contributing factors. The third step is to develop and evaluate effective risk mitigation strategies based on the risk analysis results. The proposed safety risk management framework is applied to two human-factor-caused risk scenarios: restricted-speed train accidents and trespassing events, both of which collectively constitute over 95% of all rail-related fatalities.

First, in terms of restricted-speed safety research, the dissertation consists of a collection of historical restricted-speed train accidents, quantitative and qualitative safety risk analysis, PTC-based accident risk mitigation with a proposed Concept of Operations, and a Monte Carlo simulation-based quantitative assessment of mitigation strategies. Second, in terms of trespassing research, the dissertation focuses on Artificial-Intelligence-aided trespassing data detection and location-specific trespassing data analysis, as well as specific trespassing safety strategies and proactive risk management.

ETD_10946

Ph.D.

Includes bibliographical references

doi:10.7282/t3-nq7c-0a57

ETD doctoral

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