Implementation and optimization of multi-agent cooperative game algorithm in urban rail transit train path planning

Chuanzhen Liu; Pin Zhang; Dali Wu

doi:10.1117/12.3049758

8 November 2024 Implementation and optimization of multi-agent cooperative game algorithm in urban rail transit train path planning

Chuanzhen Liu, Pin Zhang, Dali Wu

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134160I (2024) https://doi.org/10.1117/12.3049758
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

With the acceleration of urbanisation and population densification, urban rail systems face path planning challenges, and traditional methods are difficult to cope with complex traffic. This study focuses on the application of multi-agent cooperative game algorithm in urban railway train path planning. Based on the theory of cooperative game, we design the algorithm framework, clarify the cooperation mechanism, game rules and benefit distribution among agents, and promote the optimisation of individuals and the whole. Experimental validation shows that compared with the traditional algorithm, the new algorithm significantly reduces the number of iterations (79.39%), path nodes (50%), path length (8.26%), and planning time (5.19%), which verifies its high efficiency and practicality.

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

Citation Download Citation

Chuanzhen Liu, Pin Zhang, and Dali Wu "Implementation and optimization of multi-agent cooperative game algorithm in urban rail transit train path planning", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134160I (8 November 2024); https://doi.org/10.1117/12.3049758

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