Improving human behaviour in macroscale city evacuation agent-based simulation

Disasters affect millions of people annually, causing large social impacts, including high numbers fatalities, the displacement communities, and detrimental economic impacts. Emergency professionals recurrently tackle these impacts therefore need assessment methods to understand potential consequences deliver sustainable resolutions, regularly with incomplete information. Current models simulating human behaviours movement exist but are bespoke in nature non-transferable (solving one problem only), meaning it is not possible keep software “current” or future proofed. The aim this research create an agent-based modelling (ABM) framework tool, incorporating robust behaviour, help management develop test their contingency plans for emergency scenarios. focus has been on creating a macroscale evacuation ABM case study area, assess whether inclusion varied population characteristics group time. This found that by enhancing representation behaviour more accurate predictions time can be produced. To produce must include: (1) (such as age, sex, mobility), (2) grouping agents, (3) walking speed ratio. Without adequate characteristics, e.g. using agents only 1.34 m/s (3mph) speed, model produced misleading times, potentially increasing 109% some types. may result knock-on effects, such significant increases injuries populations cannot leave homes safety expected