In the last several years, I have investigated the impact of service disruptions in public transport networks. In a series of network topology and dynamic transit assignment studies, I have looked into indicators of link criticality, measures of impacts on system performance, mitigation value of real-time information provision, identifying strategic links for increased capacity and the robustness value of new links and extension plans.
One common limitation to all of these studies was the lack of information on the probabilities associated with disruptions. This prevented a complete risk analysis and assessing the (e.g. annual) costs and benefits associated with disruptions and mitigation measures.
Together with Menno Yap and Niels van Oort, the frequency and duration of various disruption types on each public transport mode (train, metro, tram and bus) were estimated based on a unique dataset. We also identify which is the primary predictor of each variable to allow researchers and professionals in other contexts to estimate disruption probabilities in the lack of local data.
We propose a method for embedding link exposure into the identification and evaluation of critical links and perform a risk analysis for the multi-modal public transport network of the Rotterdam The Hague Metropolitan Area. By comparing the results with the conventional measures, we demonstrate that disregarding exposure risks prioritizing heavily utilized links instead of those which are actually the weakest.