Validation of Evacuation Assessment Algorithm in Finding the Best Indoor Evacuation Model

This paper proposed an indoor evacuation assessment algorithm. Indoor evacuation wayfinding to the nearest exit becomes more difficult due to the intricacy of the inside layout and the involvement of numerous people. Thus, evacuation models were developed by researchers to assist evacuees in safely exiting a building. Unfortunately, building owners are unsure which evacuation model is best for their high-rise buildings. Therefore, we proposed an assessment algorithm to help the owners assess the best evacuation model. This research uses floor plan levels 13 and 14 of Yayasan Melaka’s, an office building, to simulate the evacuation. Ten simulation studies for each level are created. The proposed assessment algorithm focuses on three Microscopic evacuation models; agent-based, cellular automata, and social force. Hence, three simulation software were used to represent the mentioned evacuation model: Pathfinder, PedGo, and AnyLogic. K-Mean is then used to cluster the simulation time results. Elbow, Silhouette and V-measure techniques were applied to produce accurate results of the K-Mean. We compiled and analyzed the results from ten simulation studies for each level. The validation was done by comparing the final results. It shows that 70% of the lowest time taken is from Pathfinder, 30% from PedGo, and 0% from AnyLogic. Based on the result, it is proven that the proposed assessment algorithm can provide the best indoor evacuation model followed the attributes set for the building.

@article{Halim2022,
  title     = {Validation of Evacuation Assessment Algorithm in Finding the Best Indoor Evacuation Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Amir Haikal Abdul Halim and Khyrina Airin Fariza Abu Samah},
  doi       = {10.14569/IJACSA.2022.0130540},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130540}
}