Designing risk-aware mixed-mode evacuation strategies for tsunamis: insights from Istanbul

Tsunamis pose severe and time-critical risks to densely populated coastal cities, where limited warning times and infrastructure constraints demand carefully coordinated evacuation strategies. This study develops an integrated, risk-aware optimization framework that jointly considers vertical and horizontal sheltering options together with mixed pedestrian-vehicular evacuation dynamics. The proposed mixed-integer second-order cone programming (MISOCP) model simultaneously determines vertical shelter location, evacuee assignment, road-use designation for pedestrians and vehicles, and route selection under congestion, capacity, and budget constraints. Vehicle travel times incorporate congestion effects through a convex flow-dependent function, while pedestrian routing ensures convergent and conflict-free evacuation paths. A risk-minimization objective accounts for tsunami hazard levels, inundation conditions, and spatial exposure, prioritizing safer evacuation routes over purely time-based approaches.

The model is applied to Istanbul’s Büyükçekmece district, one of the areas most vulnerable to tsunami impact following a major Marmara Sea earthquake. Using real geographic, demographic, and infrastructure data, we evaluate multiple budget and demand scenarios to examine the model’s sensitivity to shelter investment decisions and varying proportions of pedestrian and vehicular evacuees. Results show that opening strategically located vertical shelters substantially reduces total evacuation risk and alleviates congestion in critical zones. Increasing vertical shelter investment provides diminishing returns beyond a moderate budget level, indicating the existence of an efficient investment threshold. Scenario analysis further reveals that mixed-mode evacuations can generate significant congestion on shared road segments; the model mitigates these effects through modal separation and speed adjustments. Overall, the proposed framework offers robust, context-aware decision support for tsunami-prone urban regions.