In order for public transportation to remain an attractive travel option, its reliability must be improved. To enable this, extensive knowledge on the passenger delay phenomenon is necessary. This study explores the possibility of using empirical data of passenger delay to determine the relationships between stations through a Bayesian Network approach. This approach is able to uncover any dependencies, regardless of known or unknown causes. The results from the Bayesian Network can be used to calculate a set of newly defined informativity indicators. These indicators give information on a station’s capacity of providing information on the delay state of the rest of the network. Among the possible applications of these indicators are: providing accurate information to passengers and the operator on delays, and aiding in determining the most effective areas for delay mitigation measures. To increase the usefulness of these indicators, they are compared to centrality indicators, so that even for networks with little available data, the informativity indicators can still be approximated. It was found that the centrality and informativity indicators correlate to some extent, meaning the informativity indicators could be very meaningful and relevant in further gaining knowledge on and improving public transport reliability.