| Name | fastmm JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | Fast Map Matching - High-performance map matching library |
| upload_time | 2025-10-24 00:38:42 |
| maintainer | None |
| docs_url | None |
| author | kodonnell |
| requires_python | >=3.8 |
| license | None |
| keywords |
map-matching
gps
trajectory
routing
gis
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# fastmm
fastmm is a fast (C++) map-matching library for python with no dependencies, and the ability to interpolate time on the match, not just position.
It's based on a desire to map match a lot of vehicle trace data quickly, without the infrastructure to spin up OSRM / Valhalla. (And this is probably faster as there's no IPC ... ?)
It is based on <https://github.com/cyang-kth/fmm> but updated to:
- Remove GDAL/OGR dependencies - networks are created programmatically from Python
- Include Python helper classes for automatic trajectory splitting and time interpolation
- Be buildable on Windows/Linux/Mac with modern tooling
- Focus on Python packaging with distributable wheels
- Remove STMatch - we'll focus on FMM for now
**Status:**
- [ ] Tested ... = )
- [ ] MapMatcher helper class with auto-splitting and time interpolation
- [x] FASTMM algorithm working
- [x] Python API for network creation and matching
- [x] Windows, linux, and macOS wheel builds
## Installation
```bash
pip install fastmm
```
## TODO
- Bring in extra python code.
- Get test working in python.
- If not found in UBODT, instead of bailing, do a normal djikstra lookup.
- Need to check reverse tolerance - on our edges, they're all directed, so we probably shouldn't allow reversing. This causes errors when we're parsing - if you reverse on the same edge, the geometry gets flipped (I think - line = ALGORITHM::cutoffseg_unique(e0.geom, start_offset, end_offset); goes backward?), which then messes with our python post-processing of associating time as the segment start/stop are now the edge stop/start, not the other way round. We could add a reversed flag to the edge? That would help. For now, just don't have a reverse tolerance.
- Could move the journey splitting (e.g. when unmatched candidate or points too far apart) into the C++ code here. Would be more optimal as a) C++, and b) don't need to repeat candidate lookup etc.
- Improve serialization of UBODT to be cross-platform.
- Specify versions for build libs (e.g. cibuildwheel).
### Custom costs
To implement, just update the NetworkGraph construction, and set g[e].length = edge.cost (where you read edge in from the read_ogr_file etc.). The change transition probability to
```
// double tp = TransitionGraph::calculate_transition_probability(shortest_path_distance, euclidean_distance);
double tp = exp(-0.01 * shortest_path_distance); //
```
Seems to work:
- set cost to 1 and it minimizes the number of edges
- set cost to edge length, and gives similar result (as both the methods minimise distance - only differences is the previous method is using euclidean distance between candidates maybe, not matched points? would only matter with dense points).
- can prevent some edges being used by manually bumping up their cost (tested on one road by cost *= 100 for those edges - worked, it avoided those edges).
- Untested:
- time based: if we have speed on all edges, set cost = distance / speed.
- use road hierachy or similar - prioritise main highways. More useful for addinsight.
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"description": "# fastmm\n\nfastmm is a fast (C++) map-matching library for python with no dependencies, and the ability to interpolate time on the match, not just position.\n\nIt's based on a desire to map match a lot of vehicle trace data quickly, without the infrastructure to spin up OSRM / Valhalla. (And this is probably faster as there's no IPC ... ?)\n\nIt is based on <https://github.com/cyang-kth/fmm> but updated to:\n\n- Remove GDAL/OGR dependencies - networks are created programmatically from Python\n- Include Python helper classes for automatic trajectory splitting and time interpolation\n- Be buildable on Windows/Linux/Mac with modern tooling\n- Focus on Python packaging with distributable wheels\n- Remove STMatch - we'll focus on FMM for now\n\n**Status:**\n\n- [ ] Tested ... = )\n- [ ] MapMatcher helper class with auto-splitting and time interpolation\n- [x] FASTMM algorithm working\n- [x] Python API for network creation and matching\n- [x] Windows, linux, and macOS wheel builds\n\n\n## Installation\n\n```bash\npip install fastmm\n```\n\n## TODO\n\n- Bring in extra python code.\n- Get test working in python.\n- If not found in UBODT, instead of bailing, do a normal djikstra lookup.\n- Need to check reverse tolerance - on our edges, they're all directed, so we probably shouldn't allow reversing. This causes errors when we're parsing - if you reverse on the same edge, the geometry gets flipped (I think - line = ALGORITHM::cutoffseg_unique(e0.geom, start_offset, end_offset); goes backward?), which then messes with our python post-processing of associating time as the segment start/stop are now the edge stop/start, not the other way round. We could add a reversed flag to the edge? That would help. For now, just don't have a reverse tolerance.\n- Could move the journey splitting (e.g. when unmatched candidate or points too far apart) into the C++ code here. Would be more optimal as a) C++, and b) don't need to repeat candidate lookup etc.\n- Improve serialization of UBODT to be cross-platform.\n- Specify versions for build libs (e.g. cibuildwheel).\n\n### Custom costs\n\nTo implement, just update the NetworkGraph construction, and set g[e].length = edge.cost (where you read edge in from the read_ogr_file etc.). The change transition probability to\n\n```\n// double tp = TransitionGraph::calculate_transition_probability(shortest_path_distance, euclidean_distance);\ndouble tp = exp(-0.01 * shortest_path_distance); //\n```\n\n Seems to work:\n\n- set cost to 1 and it minimizes the number of edges\n- set cost to edge length, and gives similar result (as both the methods minimise distance - only differences is the previous method is using euclidean distance between candidates maybe, not matched points? would only matter with dense points).\n- can prevent some edges being used by manually bumping up their cost (tested on one road by cost *= 100 for those edges - worked, it avoided those edges).\n- Untested:\n - time based: if we have speed on all edges, set cost = distance / speed.\n - use road hierachy or similar - prioritise main highways. More useful for addinsight.\n",
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