Multi-Agent Path Finding (MAPF) seeks collision-free paths for teams of agents and has a wide range of practical applications. LaCAM*, an anytime configuration-based solver, currently represents the state-of-the-art. Recent work has explored using guidance paths to steer LaCAM* toward configurations that avoid traffic congestion, thereby improving solution quality. However, existing approaches rely on Frank–Wolfe–style optimisation to repeatedly invoke single-agent search before executing LaCAM*, which creates a large computational overhead in large-scale problems. The guide path is also static, which is only helpful for finding the first solution in LaCAM*. To overcome this problem, we propose a new approach that exploits LaCAM*’s ability to construct a dynamic, lightweight traffic map during LaCAM*'s search. Experiments show that our method achieves higher solution quality than state-of-the-art guidance-path approaches in two variants of MAPF problems.