A Hybrid Optimization Model for Shelter Location and Materials Assignment in Earthquakes Based on Evacuation Behavior.

Transportation simulation and stakeholder exercises for urban seismic resilience

Earthquakes in the Central United States are low-frequency but high-consequence events that can increase evacuation demand while reducing transport capacity through bridge damage and precautionary closures. Municipal engineers and transport agencies therefore need planning methods that connect asset screening to corridor accessibility and coordination actions for the initial response period. This paper presents an integrated workflow for the St. Louis metropolitan region combining (i) earthquake-specific evacuation demand profiles derived from a household stated-response survey, benchmarked against a larger companion New Madrid Seismic Zone dataset, and embedded in a regional travel demand model; (ii) a USGS ShakeCast bridge screening for a Mw 6.7 scenario (3862 bridges analysed; 552 flagged for priority inspection); (iii) macroscopic and mesoscopic traffic simulation across 13 evacuation scenarios (earthquake timing, demand level and network status); and (iv) a multi-agency tabletop exercise (34 participants). Results show that a 07:00 event produces the largest demand surge and the deepest speed reductions on priority routes. Operating speed ratios identify MO-100, I-170 and US-67 as recurrent bottlenecks affecting emergency ingress and inspection logistics. The workflow yields transferable outputs: bridge and corridor prioritisation, congestion windows and coordination triggers for inclusion in municipal and state response plans.

Urban Flood Emergency Evacuation Path Optimization Research

Existing methods for optimizing urban flood emergency evacuation routes often fail to account for the dynamic evolution of inundation and the heterogeneity of evacuees' ages. To address this gap, this paper proposes a personalized evacuation route optimization approach that integrates Agent-Based Modeling (ABM) with Deep Q-Network (DQN) reinforcement learning. The proposed method incorporates water-depth-dependent speed reduction models and instability thresholds specific to children, adults, and the elderly into both the state space and reward function, enabling DQN to iteratively learn optimal routes that balance safety and efficiency. Using the "7·20" extreme rainstorm in Zhengzhou as a case study, we constructed a 100-year return-period pluvial flooding scenario and conducted evacuation simulations under three activation timings-0, 1, and 2 hours after rainfall onset-comparing the proposed method against the traditional A* algorithm. Results demonstrate that the experimental group reduced average evacuation time by 17.8%-42.1% relative to the control group, with particularly pronounced improvements for children and the elderly (up to 45.1%). The safety metric F-value in the experimental group exceeded that of the control group by more than 0.3 under deep-water conditions and exhibited a more gradual decline. These findings confirm that the DQN-based approach, which integrates age heterogeneity and dynamic water depth, substantially enhances both the timeliness and safety of emergency evacuation during urban floods, offering more reliable route planning for high-risk populations and providing a scientific basis for optimizing urban flood emergency response protocols.

Dynamic Sweep: A Hierarchical Framework for Emergency Rescue

In the critical domain of emergency response, rapid building evacuation and rescue are often hindered by complex layouts and dynamic hazards such as fire spread, toxic smoke, structural instability, and unpredictable occupant behaviour. To address the challenge of optimizing rescue sweep strategies, this paper establishes a comprehensive mathematical framework that transitions from traditional static planning to dynamic, adaptive decision-making, with the objectives of minimizing rescue completion time and maximizing occupant safety under uncertain conditions. First, a three-level evaluation system based on the AHP–Entropy Weight Method is constructed to quantify 32 critical influence factors, ranging from environmental conditions and building layout to occupant behaviour and technical support. The resulting weighted framework provides a data-driven foundation for subsequent optimization models. Three representative building configurations are then examined. For a 1D linear layout, a Divide-and-Conquer strategy reduces completion time by 42% compared with a single-entry approach. For a 2D branching layout, the rescue problem is formulated as a load-balanced Multi-Agent Traveling Salesman Problem (MA-TSP). For a 3D high-rise structure, a hierarchical Pincer Strategy is developed by nesting the MA-TSP within a multi-objective vehicle-routing framework and strategically leveraging vertical assets such as fire-service elevators. Finally, a Dynamic Adaptive Replanning Model is introduced using time-dependent hazard functions, dynamic edge costs, and occupant re-entry events. A Rolling-Horizon Replanning mechanism continuously updates rescue routes as the emergency evolves.

Evacuation Planning for Disaster Preparedness: An Adaptive Robust Optimization Approach

Evacuation planning for disaster preparedness requires making critical decisions under uncertainty before the number and spatial distribution of evacuees are known, including shelter location, evacuation route assignment, and relief supply prepositioning. Because these decisions are highly interdependent, planners must balance the competing objectives of maximizing relief demand coverage and minimizing evacuation time. We propose, to our knowledge, the first adaptive robust evacuation planning model to jointly optimize shelter locations, evacuation route assignments, relief supply prepositioning, and post-disaster relief item distribution. The model minimizes the worst-case weighted sum of unmet demand for relief items across shelters and the congestion-dependent evacuation time. We characterize theoretical complexity drivers of the resulting problem with mixed-integer recourse and develop a partition-and-bound algorithm that maintains tractability by selectively partitioning only the most critical subpartition of the uncertainty set while producing strong upper and lower bounds. To quantify the value of centralized route planning, we also formulate a user route choice alternative in which evacuees choose among acceptable routes. Computational experiments quantify the value of centralized route planning, which reduces worst-case unmet demand and evacuation time by up to 90.6\% and 79.3\%, respectively, relative to decentralized user route choice. Adaptive post-disaster supply redistribution further improves relief demand coverage. Coordination between evacuation routing and relief distribution creates substantial operational value under uncertainty. Centralized route planning primarily mitigates congestion by coordinating evacuee flows across shelters, whereas adaptive redistribution primarily improves relief demand coverage when relief supplies are scarce or inflexibly prepositioned.

A Hybrid GeoAI Framework Integrating Smile Random Forest for Comprehensive Seismic Vulnerability Assessment: A Case Study of Mersin, Türkiye

Multiscale Wildfire-Evacuation Modeling: Assessing Differential Access to Safe Egress in Marin County, CA.

Wildfire evacuation outcomes vary due to interactions among fire progression, human behavior, and traffic dynamics. Most models treat these factors separately, leaving a gap in integrated approaches. This study develops a tri-coupled framework linking fire spread, multichannel communications, and agent-based traffic modeling. The framework integrates a fire model, a communications model capturing varied cognition times, and a spatial-queue traffic model, applied to three Marin County communities. Using a multi-scalar strategic action fields perspective (macro, meso, and micro), the analysis shows how asynchronous communication, background traffic, and localized behaviors interact to shape outcomes. Evacuation efficiency depends on communication timing and coordination, with cognitive delays producing nonlinear congestion. Peak traffic reduces capacity, underscoring the need for governance mechanisms separating emergency from routine flows. At the community level, demographic, infrastructural, and temporal characteristics shape performance, requiring geographically targeted strategies. Micro-level analyses highlight the disproportionate impact of everyday responsibilities, such as child pickup, on congestion, emphasizing behavioral realism in planning. The study offers implications for embedding adaptive communication, dynamic traffic management, and behavioral insights into institutionalized crisis information governance. The findings reveal that evacuation capacity functions as a form of access to safety, unevenly distributed by communications access, mobility constraints, and network characteristics. Linking these to continuous, data-driven evaluation can enhance wildfire preparedness while creating scalable models for other hazard-prone regions, advancing resilience and equity in evacuation systems.