Abstract:
This research manuscript aims to explore the use of nature-inspired methods to optimise the evacuation of vulnerable communities during rapid-onset hazards, especially considering pandemic situations and additional challenges posed by pandemics. Multi-hazard scenarios can be identified as potential threats to public safety, and economic and infrastructure functionality in cities, especially in densely populated urban areas. The current COVID-19 pandemic has amplified these issues as multi-hazard responses including evacuation of communities were never prepared for such compound incidents, especially for highly dynamic and changing circumstances. This research proposes the use of three nature-inspired algorithms to optimise evacuation planning and ensure the safety and efficiency of response operations during compound hazards. The characteristics in Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and Artificial Bee Colony (ABC) Optimisation algorithms are used to propose a novel approach to minimising the total distance of evacuation routes while considering a dispersed evacuation strategy to ensure social distancing in shelter spaces and between evacuation zones. The optimisation characteristics have incorporated the pheromone information and heuristic information to determine the most suitable evacuation route and direction to shelter based on information such as integrated risk at vulnerable locations, time and distance estimation to a safe location, shelter location and service capacity. The application of nature-inspired optimisation to multi-hazard evacuation planning can be considered a promising approach to improve public safety, especially in the context of compound hazards amidst pandemics. The result of this research could provide transport planners as well as emergency managers with novel insights for planning adaptive and dynamic evacuation plans for changing circumstances.
