Citation

Lanzilotta EJ. Transp. Res. Rec. 1995; 1485: 140-147.

Copyright

(Copyright © 1995, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

unavailable

PMID

unavailable

Abstract

A significant component in the pursuit of safety is estimation of risk probability. In transportation systems virtually all safety-related events and outcomes involve an intermediate event known as an accident. The safety state model is a probabilistic model that is used to estimate the probability of an accident as a function of the human-machine system state. By using a discrete Markov network, the safety state model forms a framework for capturing the human-machine and human-human interactions in a transportation system. The observed data are used to calibrate the model, which is subsequently used to estimate the risk probability performance of other human operators. The theoretical development of this model is reviewed. In addition, motivation and background, as well as advantages and disadvantages with respect to existing quantitative methods of risk probability estimation, are discussed. Finally, the applicability to driver performance analysis is discussed.

Language: en

Keywords

Accident prevention; Automobile drivers; Highway accidents; Mathematical models; Risk assessment; Motor transportation; Probability; Man machine systems