Data and code for Robust Rescue-Bus Deployment Under Uncertain Passenger Behavior

This archive contains the inputs, code, numerical results, and figure sources for the manuscript “Robust Rescue-Bus Deployment Under Uncertain Passenger Behavior” (TRIP-D-26-00924). It supports comparisons of nominal, robust, minimax-regret, and Bayes rescue-bus allocations; the value of behavioral information; contingency menus; and limited bus reallocation. The Zhengzhou case is a constructed corridor-level scenario using reduced network geometry and assumed demand. The New York case uses a selected extract of the Metropolitan Transportation Authority’s 2024 subway origin-destination ridership estimates with stated disruption and access assumptions. A scheduled-service extension represents shared boarding capacity, two passenger arrival cohorts, and repeated vehicle cycles. These scenarios illustrate decision mechanisms; they do not reconstruct an observed disruption or establish field-validated benefits. The ZIP archive contains Python scripts, model inputs, saved cost tensors, derived tables, figures, and a README with the execution order. Python 3.11 or later, NumPy, SciPy, and Matplotlib are required. Original code is released under the MIT License and author-created data and results under CC BY 4.0. The included MTA extract remains subject to the source’s Open NY terms and is not relicensed by the authors; source and rights details are in LICENSE_AND_RIGHTS.md and THIRD_PARTY_ATTRIBUTION.md.