An Assessment Algorithm for Indoor Evacuation Model

Khyrina Airin Fariza Abu Samah - Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Melaka Kampus Jasin, Melaka, Malaysia
Amir Haikal Abdul Halim - Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Melaka Kampus Jasin, Melaka, Malaysia
Zaidah Ibrahim - Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Selangor, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.1-2.933

Abstract


The public buildings increased significantly with the economy's growth and the population's advancement. The complexity of the indoor layout and the involvement of many people cause the indoor evacuation wayfinding to the nearest exit to be more challenging during emergencies such as fire. In order to overcome the problem, each building is compulsory to follow the standard evacuation preparedness required by Uniform Building By-Law (UBBL). Researchers have also developed evacuation models to help evacuees evacuate safely during the evacuation from a building. However, building owners do not know which evacuation model is suitable for implementing the chosen high-rise building. Two problems were identified in choosing a suitable evacuation model during the decision-making process. First, many developed evacuation models focus on studying different features of evacuation behavior and evacuation time. Second, the validation and comparison of the evacuation model is the missing process before applying the suitable evacuation model. Both validation and comparison procedures were made independently without any standard assessment that encapsulates the critical incident features during the indoor evacuation and virtual spatial elements. Therefore, this research proposed an indoor evacuation assessment algorithm to solve the problem. The assessment algorithm refers to the elements developed in our previous study. We determined attributes, executed simulations, and evaluated the cluster performance using the developed framework. The outcome can help the building owners assess which suitable existing evacuation model is the best to implement at the chosen building.


Keywords


Assessment algorithm; evacuation model; indoor evacuation; integrated assessment model; microscopic.

Full Text:

PDF

References


G. M. Ventura, "Patient Evacuation Resource Classification System ( PERCS ) for residential healthcare facilities : Patient classification system translatable to healthcare evacuation protocols , system modeling , and transportation resources," The George Washington University, 2017.

H. Gao, B. Medjdoub, H. Luo, H. Zhong, B. Zhong, and D. Sheng, "Building evacuation time optimization using constraint-based design approach," Sustainable Cities and Society, vol. 52, no. 4, 2020,

Y. Chen, C. Wang, H. Li, J. B. H. Yap, R. Tang, and B. Xu, "Cellular automaton model for social forces interaction in building evacuation for sustainable society," Sustainable Cities and Society, vol. 53, no. September 2019, 2020,

Y. Jiang, B. Chen, X. Li, and Z. Ding, "Dynamic navigation field in the social force model for pedestrian evacuation," Applied Mathematical Modelling, vol. 80, pp. 815–826, 2020,

P. Kontou, I. G. Georgoulas, G. A. Trunfio, and G. C. Sirakoulis, "Cellular automata modelling of the movement of people with disabilities during building evacuation," 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 550–557, 2018,

N. A. A. Bakar, K. Adam, M. A. Majid, and M. Allegra, "A simulation model for crowd evacuation of fire emergency scenario," ICIT 2017 - 8th International Conference on Information Technology, Proceedings, pp. 361–368, 2017,

F. Martinez-Gil, M. Lozano, I. García-Fernández, and F. Fernández, “Modeling, evaluation, and scale on artificial pedestrians: A literature review,†ACM Computing Surveys, vol. 50, no. 5, 2017,

R. Ming and X. Peng, "Study on the social force model of personnel evacuation in large stadiums," 14th International Conference on Services Systems and Services Management, ICSSSM 2017 - Proceedings, pp. 1–5, 2017,

M. Shi, E. W. M. Lee, and Y. Ma, "A novel grid-based mesoscopic model for evacuation dynamics," Physica A: Statistical Mechanics and its Applications, vol. 497, pp. 198–210, 2018,

I. Sakour and H. Hu, "Robot-assisted crowd evacuation under emergency situations: A survey," Robotics, vol. 6, no. 2, 2017,

Y. Li, M. Chen, X. Zheng, Z. Dou, and Y. Cheng, "Relationship between behavior aggressiveness and pedestrian dynamics using behavior-based cellular automata model," Applied Mathematics and Computation, vol. 371, 2020,

L. Fayez, "Modeling family behaviours in crowd simulation," Qatar University, 2017.

K. Fisher-Vanden and J. Weyant, "The evolution of integrated assessment: Developing the next generation of use-inspired integrated assessment tools," Annual Review of Resource Economics, vol. 12, pp. 471–487, 2020,

G. E. Metcalf and J. H. Stock, "Integrated assessment models and the social cost of carbon: A review and assessment of U.S. experience," Review of Environmental Economics and Policy, vol. 11, no. 1, pp. 80–99, 2017,

A. H. A. Halim, K. A. F. A. Samah, Z. Ibrahim, and R. Hamzah, "Conceptual framework for intelligent indoor evacuation model assessment algorithm using integrated assessment model," International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1.4, pp. 289–294, 2020,

S. Kasereka, N. Kasoro, K. Kyamakya, E. F. Doungmo Goufo, A. P. Chokki, and M. V. Yengo, "Agent-based modelling and simulation for evacuation of people from a building in case of fire," Procedia Computer Science, vol. 130, pp. 10–17, 2018,

Y. Li, H. Liu, G. peng Liu, L. Li, P. Moore, and B. Hu, "A grouping method based on grid density and relationship for crowd evacuation simulation," Physica A: Statistical Mechanics and its Applications, vol. 473, no. 88, pp. 319–336, 2017,

W. Liu and D. Parhizgar, "Evaluating classroom evacuation with crowd simulation," KTH Royal Institute of Technology, 2018.

E. Ronchi and D. Nilsson, "Fire evacuation in high-rise buildings: a review of human behaviour and modelling research," Fire Science Reviews, vol. 2, no. 1, p. 7, 2013,

A. Garcimartin, D. Maza, J. M. Pastor, D. R. Parisi, C. Martin-Gomez, and I. Zuriguel, "Redefining the role of obstacles in pedestrian evacuation," J. Phys. Energy, vol. 2, no. 1, pp. 0–31, 2020.

K. Zhu, Y. Yang, and Q. Shi, "Study on evacuation of pedestrians from a room with multi-obstacles considering the effect of aisles," Simulation Modelling Practice and Theory, vol. 69, pp. 31–42, 2016,

J. Chen, D. Liu, S. Namilae, S. A. Lee, J. E. Thropp, and Y. Seong, "Effects of exit doors and number of passengers on airport evacuation effeciency using agent based simulation," International Journal of Aviation, Aeronautics, and Aerospace, vol. 6, no. 5, 2019,

A. Vysala and D. J. Gomes, "Evaluating and validating cluster results," Computer Science & Information Technology (CS & IT), pp. 37–47, 2020,

Nurhayati, N. S. Sinatrya, L. K. Wardhani, and Busman, "Analysis of k-means and k-medoids's performance using big data technology," 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, pp. 1–5, 2019,

A. B. S. Serapião, G. S. Corrêa, F. B. Gonçalves, and V. O. Carvalho, "Combining k-means and k-harmonic with fish school search algorithm for data clustering task on graphics processing units," Applied Soft Computing Journal, vol. 41, pp. 290–304, 2016,

A. Pugazhenthi and L. S. Kumar, "Selection of optimal number of clusters and centroids for k-means and fuzzy c-means clustering: A review," Proceedings of the 2020 International Conference on Computing, Communication and Security, ICCCS 2020, pp. 5–8, 2020,

V. B. B. Anguiano, "Integration and visualization of sparse-grid based clustering methods in the SG++ datamining pipeline," Technical University of Munich, 2019.

X. Li, W. Liang, X. Zhang, S. Qing, and P. C. Chang, "A cluster validity evaluation method for dynamically determining the near-optimal number of clusters," Soft Computing, vol. 24, no. 12, pp. 9227–9241, 2020,

J. Guo, "Developing a visualization tool for unsupervised machine learning techniques on *Omics data," 2018.

D. M. Saputra, D. Saputra, and L. D. Oswari, "Effect of distance metrics in determining k-value in kmeans clustering using elbow and silhouette method," vol. 172, no. Siconian 2019, pp. 341–346, 2020,

P. Mengoni, A. Milani, and Y. Li, Clustering students interactions in eLearning systems for group elicitation, Springer International Publishing, 2018.

C. Tomasini, E. N. Borges, K. Machado, and L. Emmendorfer, "A study on the relationship between internal and external validity indices applied to partitioning and density-based clustering algorithms," 2017 19th International Conference on Enterprise Information Systems (ICEIS), vol. 1, pp. 89–98, 2017,

S. Sengupta, S. Basak, and R. A. Peters, "Data clustering using a hybrid of fuzzy c-means and quantum-behaved particle swarm optimization," 2018 IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC 2018, vol. 2018-Janua, pp. 137–142, 2018.

S. Panda, S. Sahu, P. Jena, and S. Chattopadhyay, "Comparing fuzzy-c means and k-means clustering techniques: A comprehensive study," Advances in Intelligent and Soft Computing, vol. 166 AISC, no. VOL. 1, pp. 451–460, 2012.