A Review and Implementation Guide for Basic Cellular Automata Models in Pedestrian Evacuation Simulation

Year 2024, Volume: 12 Issue: 3, 517 - 530, 30.09.2024

Abdurrahman Yağmur Topraklı

Abstract

Efficient and safe evacuation of individuals during emergencies is critical to building design and public safety planning. Cellular Automata (CA) models have emerged as a valuable tool for simulating and analyzing pedestrian evacuation dynamics, offering a balance of computational efficiency and the ability to capture complex emergent behavior. This paper comprehensively reviews basic CA models for pedestrian evacuation simulation, exploring their fundamental principles, strengths, limitations, and implementation considerations. We delve into commonly used models like Floor Field Cellular Automata (FFCA) and Social Force Cellular Automata (SFCA), highlighting their unique characteristics, advantages, and potential drawbacks. A practical implementation guide outlines step-by-step procedures for developing and executing CA-based simulations, from defining the virtual environment and establishing behavioral rules to analyzing the simulation output. We emphasize the importance of data-driven approaches for model calibration and validation, ensuring the accuracy and reliability of simulation results. Finally, we discuss future directions for CA-based evacuation modeling, including integrating more sophisticated behavioral rules, developing hybrid models, and exploring three-dimensional simulations to enhance realism and predictive capabilities. This comprehensive review aims to equip researchers, practitioners, and students with the knowledge and tools to effectively utilize CA models for enhancing safety and preparedness in various evacuation scenarios.

Keywords

Cellular Automata, Evacuation Simulation, Pedestrian Dynamics, Crowd Modeling, Safety Planning, Emergency Preparedness

Ethical Statement

This article is a review of existing literature on Cellular Automata models for pedestrian evacuation simulation. As such, it does not involve the collection, analysis, or interpretation of any original human participant data.

Supporting Institution

Innoarc Arge Ltd. Şti. Gazi Teknopark/Ankara/Türkiye

Thanks

The authors would like to acknowledge the support provided by Innoarc Arge Ltd. Şti. (Gazi Teknopark, Ankara, Türkiye) during the preparation of this manuscript.

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