The algorithms do divide the map up into chunks that are themselves divided up and so on, but not on the strict geographical basis a quadtree uses. You might not want to divide Manhattan in two for routing purposes, even if the 74th longitude line runs straight through it.
[1] https://turing.iem.thm.de/routeplanning/hwy/esaHwyHierarchie... [2] https://publikationen.bibliothek.kit.edu/1000028701/14297392...
FWIW, Valhalla already does a version of this: it partitions the routing graph into hierarchical tiles and runs with multiple hierarchy levels (highway / arterial / local) specifically to keep search + in-memory working set smaller on long routes: https://valhalla.github.io/valhalla/tiles/
The "quadtree tile unlock" mental model is a nice way to think about it though - if you have a favorite paper / implementation that leans harder into the tiling aspect, I’d love a pointer. I’m currently focused on packaging + offline data consistency, but routing performance on constrained edge boxes is definitely a core constraint I care about.