Speaker: Gayamini Shanmuganathan
Evacuation and personal safety are major concerns from wide areas or indoors under emergency. Every disaster, whether man-made or natural, we face many unexpected problems, for instance, fatal accidents, and property damage. In some moments like this, panic among pedestrians beings is frequent. Everyone wants to save their lives. As a result of panic, some people lose the energy of thinking. It can also trigger conflicts among pedestrians. In emergencies there is less time to react; This can lead to a large loss of life, since many people who are caught may unaware of the exit to get out from there and are more likely to run in the wrong direction. To prove this, several investigations indicate that a substantial number of deaths occur due to wrong decisions residents make within the available evacuation time. Besides, the conclusion from the past analysis relieves guiding residents during evacuation proves to be more effective because it decreases the average escape time thereby increasing the chance of survival in a fire emergency. However, widespread fire disaster has the highest occurrence of frequency in several concentrated short periods among disasters. To provide a better solution to the issue, systems have been developed to alert or warn pedestrians in the presence of a fire emergency, so they can act promptly. Several approaches are done by past researchers to simulate the crowd evacuation, Nevertheless, the concept of autonomous agents has been bringing into play successfully to investigate collective human behavior during an emergency evacuation over the past decades. Similarly, artificial intelligence is strongly making its footprint in all disciplines. Our first and foremost objective is to evacuate residents safely in the shortest possible time in the event of a fire. With the support of reinforcement learning, which is a subfield of AI, we have done the pedestrian evacuation simulation system in the fire emergency environment. Several steps were taken to develop our system to achieve efficiency in an evacuation, in other words, safely evacuate residents within a short time. When considering a large-scale environment, it has many features, and only a few selected features are designed by ArcMap to enforce as the foundation of the system environment. We adopt the multi-agent concept to train the agents in the NetLogo environment. IIOne important point is that the moment we start training, any resident can only attend the training if they become pedestrians, and they will then share their experience with
others. Also, the deep reinforcement learning algorithm leads them to achieve the optimal policy. Besides, the A* algorithm directed the pedestrians to designated shelters. As well as fire dynamics are created by fire agencies. These results demonstrate empirically that the proposed simulation system is effective with time efficiency and the system has a strong capability to describe, represent, and explain the reality of evacuation.