Diagnosis of causes for high railway traffic based on Bayesian network.

To intuitively analyze and diagnose why high railway traffic often occurs, this paper builds a Bayesian network on a rail transit system, for instance, the Shanghai Metro L1, to explore it from different angles such as holidays, mega-events, bad weather and sudden accidents. The fuzzy set theory is also integrated to quantify the conditional probability of some events using the fuzzy languages. It turns out that the Bayesian network constructed can well infer the probability of massive passenger traffic, and diagnose the dominant factors that may cause it. Hereby, the rail transit management authorities can take proper measures against it to reduce the risks the high rail traffic may impose. [ABSTRACT FROM AUTHOR]