Option 1: The system can update the cost of that specific shortcut in the base graph and quickly re-run the Dijkstra search (Step 2) on the abstract graph to find an alternative high-level path.
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!
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based on the GPT-3 model and can generate code in multiple programming