This change takes the existing typedefs for weight, duration and
distance, and makes them proper types, using the existing Alias
functionality.
Primarily this is to prevent bugs where the metrics are switched,
but it also adds additional documentation. For example, it now
makes it clear (despite the naming of variables) that most of the
trip algorithm is running on the duration metric.
I've not made any changes to the casts performed between metrics
and numeric types, they now just more explicit.
The data facade interface contains numerous methods for looking up
datapoints by identifiers.
Many of the parameters use the NodeID or EdgeID types. However, these two
identifier types are used for representing three different contexts:
1. Node-based graph edges and nodes
2. Edge-based graph edges and nodes
3. Packed geometries
Consider the use of identifier parameters in these examples:
---
GetWeightPenaltyForEdgeID(const EdgeID id) <- edge-based edge
GetUncompressedForwardWeights(const EdgeID id) <- packed geometry
IsLeftHandDriving(const NodeID id) <- edge-based node
GetBearingClass(const NodeID node) <- node-based node
---
This mixing of contexts within the same interface makes it
difficult to understand the relationships and dependencies between
the OSRM datasets.
For 1. and 2. we continue to use the NodeID and EdgeID types, but
change the interface parameter names to identify them as
edge-based or node-based graph properties.
For 3. we define a new type definition, PackedGeometryID.
These changes are to aid with readability. A next step would be
to strongly type these definitions, leveraging the Alias template
already used for OSM identifiers.
For the MLD algorithm we can partition the NodeID range into boundary
and non-boundary nodes. Since there are only we boundary nodes we can
use the ArrayStorage for those yielding much faster query times.
Rename module partition to partitioner.
This cultivates naming used in existing modules like extractor,
customizer, etc. - noun vs verb (word partition is both though).
This graph enables efficient boundary edge scans at each level.
Currenly this needs about |V|*|L| bytes of storage.
We can optimize this when the highest boundary nodes ID is << |V|.
