Disaster events cause detrimental impacts for communities across the globe, ranging from large numbers of fatalities and injuries, to the loss of homes and devastating financial impacts. Emergency professionals are facedwith the challenge of providing sustainable solutions to mitigate these consequences and require tools to aid the assessment of potential impacts. Current modelling tools have either focused on modelling either the microscale (e.g. individual confined spaces such as buildings or stadiums) or the macroscale (e.g. city scale). The aim of thisresearch is to create microscale agent-based modelling (ABM) tools, incorporating a realistic representation of human behaviours, which will help management professionals assess and improve their contingency plans for emergency scenarios. The focus has been on creating a microscale agent-based model of a pedestrian pavement and crossroads, to include overtaking and giving way, alongside the inclusion of varied population characteristics. This research has found that by improving pedestrian interactions (e.g. overtaking and giving way interactions) on pavements and at crossroads more robust travel time estimates can be achieved. To produce more realistic behaviour traits, microscale models should consider: (1) varied walking speed (2) population density, (3) patience level and (4) an exit split percentage for crossroads. Comparisons to 1.34 m/s (3mph) models without additional variables show the travel times may be misrepresentative by up to 78% in pavements and 305% in crossroads for some population types. This has the potential to cause cascading effects such as a significant increase in fatalities or injuries as communities cannot reach safety in the anticipated time.
Barnes, B., Dunn, S., & Wilkinson, S. (2022). Replicating capacity and congestion in microscale agent-based simulations. Travel Behaviour and Society, 29, https://doi.org/10.1016/j.tbs.2022.07.006