Abstract
A Bayesian network model for probabilistic safety analysis of roads and highways is introduced. After indicating how the list of variables and the conditional probability tables of the Bayesian network model are built, based on a video of the road, a short discussion about how maximum likelihood and Bayesian network methods can be applied to estimate the model parameters using standard methods. Next, a partitioning technique is suggested to convert the non-linear problem of computing marginal and conditional probabilities after evidence into a problem whose complexity becomes linear in the number of variables. Finally an example of application is used to illustrate the proposed methodology and some conclusions are drawn.
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Notes
- 1.
ENSI refers to the expected number of equivalent severe incidents, where 6.4 medium incidents and 230 light incidents are considered equivalent as one severe incident. The relative mean costs of these incidents have been used to determine these factors.
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Castillo, E., Grande, Z., Mora, E. (2018). A Bayesian Network Model for the Probabilistic Safety Assessment of Roads. In: Gil, E., Gil, E., Gil, J., Gil, M. (eds) The Mathematics of the Uncertain. Studies in Systems, Decision and Control, vol 142. Springer, Cham. https://doi.org/10.1007/978-3-319-73848-2_8
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