Risk analysis and safety policy: example of transporting people

During an accident, transport of passengers or industrial plants can have harmful impacts on the environment or on the population. As this kind of activities is exposed to various exogenous threats, the management of the associated risk requires using a global approach

The example below illustrates the methodology that can be used with BayesiaLab in the risk management problematic. Modeling a prevention policy implies modeling the random variables of the global system security and the "barriers" that have been defined during the Defence in Depth analysis. (see: « Et si les risques m’étaient comptés » - J. Valancogne, J.L. Nicolet, G. Planchette). The following Bayesian network describes the barriers used to prevent trains collisions.

When two barriers are added to the light system (automatic braking system and sound signal), the accident probability is divided by 10 with a utility gain of 77%.
The Dynamic Bayesian Networks of BayesiaLab allow simulating the temporal evolution of the security state of the system and to evaluate maintenance policies. The graph below illustrates the evolution of the accident probabilities and the probability evolution of the accidents with physical injuries. The maintenance policy that is evaluated (the system is repaired every 5 time steps) allows obtaining a probability of physical injuries lower than 0.43%

The maintenance policy can also be learned directly thanks to the BayesiaLab's Decision nodes. BayesiaLab then offers a complete and original risk management toolbox that is both powerful and understandable to anyone. As it can be seen on the graph below, the automatically learned maintenance policy reduces the accident and physical injuries probabilities.