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Filter to transform GPS coordinates of vehicles into reconstructed trajectories

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README.rst

Path inference filter quick documentation

The path inference filter (PIF in short) maps GPS waypoints into trajectories on a road network. It is used to map this sort of data: http://youtu.be/OxCPL4KsDfI into this sort of output: http://youtu.be/tj53gGCCNgs

This code is the academic code that goes along the paper: ...

This code is designed to be readable and correct, not fast. As such, you will probably want to rewrite some core sections in your favourite programming language. It is also extensively covered by a test suite that you can use as a reference. If you want a (much faster and more complete) implementation in scala and java, please contact the author.

Quick start guide

The python PIF uses the following libraries:

  • numpy >= 1.3
  • nose >= 1.5 (for testing only)

All you should need is:

git clone git://github.com/tjhunter/Path-Inference-Filter
cd Path-Inference-Filter
nosetest

If all the diagnostic tests return correctly, you should be all set!

Basic filtering

A tutorial has been written to explain how to filter trajectories, which you can find in mm/path_inference/example.py. The python PIF code does not include mapping or path discovery due to licensing issues. You have to write your own code to interface the PIF with your favorite data source and road network.

Learning

No tutorial has been written for learning yet. However, the learning functions should have enough documentation. You can take a look at the tests of the optimizer as a starting point: mm/path_inference/learning_traj_optimizer.py.

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