Two-stage stochastic optimization for emergency management of metro systems under uncertain storm floods

Climate change and urbanization have increasingly exacerbated the threat of storm floods to urban metro systems. To enhance the flood emergency management of metro systems, this study proposes a two-stage stochastic optimization model. In this model, the closure decisions for risky stations and the allocation of flood control resources are implemented before and during the rainstorm, respectively, to maximize the average utility of passengers in the metro network. A case study on the Shanghai metro system is conducted to demonstrate the applicability and effectiveness of the proposed model. The results indicate that the two-stage stochastic optimization model can generate refined closure schemes and dynamically adaptive protection schemes for risky metro stations. Compared to one-stage strategies that do not consider the uncertainty of rainstorms, the two-stage model achieves higher passenger utility. Furthermore, the mechanisms behind the closure decisions made by the two-stage model are interpreted using an explainable artificial intelligence (XAI) technique, SHAP (SHapley Additive explanation). It is revealed that a metro station with low passenger volume in a high-rainfall sub-catchment has a greater probability of being closed before floods. Future works can be conducted to further explore feedback mechanisms between the two optimization stages or optimize the location and inventory of resource warehouses for metro systems.