Research on improving ant colony algorithm for evacuation path planning in congested scenarios

Junxin Yan; Zhibing Shu

doi:10.1117/12.3036719

21 July 2024 Research on improving ant colony algorithm for evacuation path planning in congested scenarios

Junxin Yan, Zhibing Shu

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132192F (2024) https://doi.org/10.1117/12.3036719
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

Targeting unexpected scenarios such as crowd congestion and road congestion, this paper presents an emergency evacuation path planning model based on an enhanced ant colony algorithm. The heuristic function and pheromone concentration update strategy were enhanced, leading to the optimization of the evacuation path. A grid map model was constructed to simulate emergency evacuations under both congested and non-congested conditions in a real-world scenario. It is demonstrated that the enhanced ant colony algorithm exhibits superior convergence speed and global search capability, enabling the generation of safer evacuation routes considering both crowd congestion and road congestion.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junxin Yan and Zhibing Shu "Research on improving ant colony algorithm for evacuation path planning in congested scenarios", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132192F (21 July 2024); https://doi.org/10.1117/12.3036719

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