Technical Reports

Human Factors Phase IV: Dynamic Risk Estimation Using the Safety State Model

  • 01
  • Mar
  • 1999
AUTHOR: Edward J. Lanzilotta, Thomas B. Sherridan
SUBJECT: Human Factors
KEYWORDS: Automation, High-Speed Trains, Markov Modeling, Risk Estimation, Supervisory Controls, Safety
ABSTRACT: This report covers the theoretical development of the safety state model for railroad operations. Using data from a control automation experiment, experimental applications of the model is demonstrated. A stochastic model of system behavior is developed which is used to estimate the dynamic risk probability in a human-machine system. This model is based on a discrete Markov process model. Based on observer behavior of an existing system, the model is used to determine a instantaneous risk probability function, which is dependent on the system state.