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.