Importance evaluation of edges in transportation network under dynamic and randomly disruptive event

A dynamic Bayesian importance measure method was proposed for edge importance evaluation of transpartation network. The random process theory was applied to characterize the generation process of external disruptive events, and a transportation network reliability model was established. Probabilistic techniques were utilized to obtain a formula of dynamic Bayesian importance measure of each network edge, and a maximum value for the importance measure and corresponding maximum edges were determined. Based on the formula, an numerical algorithm was developed to evaluate the Bayesian importance measure of each edge at different times. An actual case of a transportation network was introduced, and the dynamic random disruptive shock process incurred by the edges was a saturated non-time homogeneous Poisson counting process with given scale parameters and shape parameters. The calculation method of dynamic Bayesian importance measure was demonstrated, and the sensitivity analysis of the scale parameter and shape parameter of the importance ranking of the connected edges was made. Research results show that, regardless of the changes in random disruptive events from the external environment, the one-edge cut in the network remains the most important edge, which verifies the correctness of the theoretical analysis. Considering external random disruptive events and the network structure, the Bayesian importance measure can timely and accurately identify the importance of all edges, which fills the gap left by the traditional static measure for edge importance that only considers the position of an edge. As the values of the scale parameters and shape parameters become larger, the importance ranking of the two edges changes faster.