Crucially, this distribution of border points is agnostic of routing speed profiles. It’s based only on whether a road is passable or not. This means the same set of clusters and border points can be used for all car routing profiles (default, shortest, fuel-efficient) and all bicycle profiles (default, prefer flat terrain, etc.). Only the travel time/cost values of the shortcuts between these points change based on the profile. This is a massive factor in keeping storage down – map data only increased by about 0.5% per profile to store this HH-Routing structure!
neat, right? and this is just the tip of the iceberg. there are so many more use cases where these operators shine, and i encourage you to play around with them in the web app and see what you can come up with. you can also see the resulting automaton for each regex, and how the different components contribute to the state machine (msagl helped me a lot here).
，这一点在搜狗输入法2026中也有详细论述
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