MapMatchingHMM

This project has been abandoned.The functionality is replaced by tmmpy and hmmpy.

Map Matching using Hidden Markov Models.

The data used to represent road networks is obtained from OpenStreetMap.

A route is simulated together with related observations. The observations are intended to resemble GPS - measurements and signals received from objects such as WiFi Access Points and cellphone towers.

The conditional probabilties of the state sequences given observations are estimated using the Forward-Backward algorithm. The MAP sequence of segments is found using Viterbi.

The method can be tested by calling

python run_single_remotely.py

Requirements are listed in requirements.txt. Installation instructions (using conda) can be seen at the top of the file. Use pip install utm and pip install osmapi to get remaining dependencies.

Please note that this is a work in progresss.

Update, 5th of September 2019: This project has been abandoned in favour of two other projects, tmmpy and hmmpy. hmmpy aims to implement to common Hidden Markov Model functionality for arbitrary state spaces, observations, transition probabilities and emission probabilties. tmmpy leverages the functionality of hmmpy in order to do map matching. Both projects are already in a better state than this one, and will be worked on actively at least for a couple of months. I expect both projects to eventually end up on PyPI.