Environmental factors in tsunami evacuation simulation: topography, traffic jam, human behaviour

Abstract

AbstractThe risk a tsunami, a high-rise wave, poses to coastal cities has been highlighted in recent years. Emergency management agencies have become more prepared, and new policies and strategies are in place to strengthen the city's resiliency to such events. Evacuation is a highly effective response to tsunamis, and recent models and simulations have provided valuable insights into mass evacuation scenarios. However, the accuracy of these simulations can be improved by accounting for additional environmental factors that affect the impact of a tsunami event. To this end, this study has been conducted to enhance an evacuation simulation model by considering topography that impacts traffic mobility and speed, traffic congestion, and human behaviour. The updated model was employed to evaluate the effectiveness of Napier City's current evacuation plan, as it can realistically simulate both pedestrian and vehicular traffic movements simultaneously. The simulation demonstrated in this paper was based on a scenario involving an 8.4 Mw earthquake from the Hikurangi subduction interface, which would trigger a tsunami risk in the area. Based on this event, the final evacuation time (time between after the shake is felt and the arrival of the tsunami wave at the shoreline of Napier City) is considered to be 50 min. The results of the MSEM model are presented within two categories, (1) survival rate and (2) safe zone capacity. The evacuation simulation model used to examine the environmental factors in this study is the Micro-Simulation Evacuation Model (MSEM), an agent-based model capable of considering both pedestrian and vehicular interactions. The results showed that the steep pathway to the safe zone would markedly decrease the moving speed and reduce the survival rate, highlighting the need to have supporting vertical evacuation to reduce the number of evacuees heading to steep routes. Additionally, the modelling and assessment of mass evacuation by vehicles has highlighted regions of severe congestion due to insufficient network capacity. Through highlighting such regions, the model aid policy makers with a more targeted approach to infrastructure investment to improve flows of traffic in mass evacuation scenarios and increase survival rates.