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

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.