Improving wildfire resilience of road networks through generative models

Wildfires pose a significant threat to road networks by causing blockages, structural degradation, and impeding vehicular movement, which complicates emergency response and evacuation efforts. It is crucial to comprehensively evaluate wildfire risks and strategically enhance the improvement measures for road networks, such as capacity expansion for evacuation purposes. This paper introduces a comprehensive optimization framework aimed at enhancing the resilience of road networks in wildfire-prone regions. The proposed framework integrates wildfire simulation, vulnerability assessment, and decision-making strategies for widening critical road segments to improve network resilience. Using a Generative Adversarial Network (GAN)-based model, the framework simulates potential wildfire ignition and propagation scenarios, combining synthetic data with historical weather patterns to assess wildfire risks. Critical network performance metrics—safety, connectivity, reliability, and efficiency—are synthesized into a multi-dimensional network performance tensor (NPT), allowing for systematic analysis and optimal improvement decisions. The framework is implemented on a large road network in the hillside region of Los Angeles, an area exposed to wildfire hazards. The results demonstrate that this framework can effectively prioritize capital improvements for enhancing road network resilience, offering valuable insights and strategic guidance for mitigating wildfire risks. This capital improvement framework has the potential to be adapted and generalized for addressing other natural hazards as well as for other infrastructure networks from a risk-optimal perspective.