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Welcome to the map-matching package tailored for bicycles! The paper can be found here: https://doi.org/10.1049/itr2.12567.
## Preparatory work
1. Create a folder graph/raw/ to store raw road network information
2. Create mapMatch_result/ to store the processed road network
3. Create mapMatch_result/ptsDf_/ to store the selected road candidates (within 50m)
4. Create mapMatch_result/rlt_/viterbi/ to store the map-matched result
5. Put the OpenStreetMap shapefile in the raw_map folder
6. Put the raw GPS data as data/stepII.h5, csv form as data/stepII.csv. Columns:tripID;timestamp;lat;lon.
## Run steps
1. In local computer, run step1_simplify_graph.py and step2_main_road_evaluation.py
2. Sbatch step3_supercomputer_run.sh on supercomputer or run parallel_run.py with function_name=getsPts in server
3. Sbatch step4_supercomputer_run.sh on supercomputer or run parallel_run.py with function_name=matchTrace in server
## Output format
Output results are in mapMatch_result/rlt_/viterbi/, format:
tripID: identity of each trip trajectory to be mapped
nid: recorded order of GPS point
edge: mapped road segment ID in the compact graph (section 3.2)
full_edge: mapped road segment ID in the raw graph
dist: distance (m) between GPS point and the mapped road
frcalong: travelled ratio over this edge
secs: time difference (s) from 2020-10-01
seglength: travelled distance (km) over this edge
## Acknowledgement
Part of code in this repository is derived from "https://github.com/amillb/pgMapMatch".