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README.md

 # mm4b
 
+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.
 
-## To be updated
+## 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
 
-This repository will be updated soon.
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+## 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".
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config_mapmatch.py

+# how many nodes pgrouting can route at once.
+# If you have lots of memory, increase. If you get a memory error, reduce
+maxNodes = 250
+
+# All these parameters assume meters, but must match the units of your projection
+# State Plane meters will normally work best
+weight_1stlast = 1      # for the first and last node, how much distance weight is increased by. This helps to ensure the match is closer to and from the start and endpoint
+gpsError = 50           # max distance that streets can be from GPS point to be considered
+gpsError_fway = 70      # same, but for freeway (kmh>100) - because these roads are much wider
+maxSpeed = 120           # speed threshold for deleting pings (kmh) in traceCleaner
+
+# primary_punish = 1000
+# secondary_punish = 0.5
+# secondary_link_punish = 0.5
+# tertiary_punish = 0.5
+
+
+
+dist_threshold = 20
+ 
+sigma_z = 4#10.0          # std dev parameter for geometric likelihood. Relates to GPS noise. Newson and Kummel 2009 say 4.0
+sigma_t = 4 #0.3           # std dev parameter for temporal likelihood
+sigma_topol = 0.6 #0.6      # std dev parameter for topological likelihood
+temporal_scale = 1 #0.55   # scale parameter for temporal likelihood
+
+temporal_weight = 1   # how more more the temporal likelihood is weighted relative to the distance likelihood score
+topol_weight = 1     # how more more the topological likelihood is weighted relative to the distance likelihood score
+skip_penalty = 5     # penalty for skipping a point is temporalLL(skip_penalty)
+max_skip = 2       # maximum number of points to skip. Reducing this will improve performance
+# uturnCost = 10        # if None, use the default (the average of the median cost and reverse cost in the edges)
+allowFinalUturn = True  # if True, allow a U-turn on the final edge
+
+# column identifiers for the PostGIS table of streets
+# the default values here are compatible with osm2po
+streetIdCol = 'id'          # unique id for street edge (i.e. segment or block)
+streetGeomCol = 'geom_way'  # geometry column (LineString) for street edge
+startNodeCol = 'source'     # id of node at which the street edge starts
+endNodeCol = 'target'       # id of node at which the street edge ends
+travelCostCol = 'cost'      # generalized cost to go from startNode to endNode
+travelCostReverseCol = 'reverse_cost'  # generalized cost to go from endNode to startNode. Can be same as travelCostCol if you have no one-way streets
+streetLengthCol = 'km'      # length of street, in km
+speedLimitCol = 'kmh'       # speed limit on street, in km per hour
+
+# SQL-compliant query that identifies freeways (with the higher gps error tolerance)
+fwayQuery = 'clazz<15 OR kmh>=100'
+# comma-separated list of columns that are needed in fwayQuery, but are not listed above
+fwayCols = 'clazz'
+
+### Input files and save paths
+raw_osm_file = "raw_map/rotterdam_citycenter"
+point_save_path = "graph/raw/" #intermediate output storing point and linestring dictionary
+complete_graph_path = "mapMatch_result/full_roads.shp"
+simplified_graph_path = "mapMatch_result/coarse_roads.csv"
+confirmed_coarse_path = "mapMatch_result/penalized_coarse_roads.csv" #file path storing all roads including confirmed main car roads
+confirmed_roads_txt = "mapMatch_result/penalty_roads.txt"
+confirmed_roads_html = "visualization/map_match/penalty_roads.html"
+
+
+### Define coefficients for cost function
+revser_coeffi = 0.2
+alpha_coefficient = 1
+beta_coefficient = 1
+extra_mapped_penalty =5*alpha_coefficient # penalty for main car roads with low availability
+alpha0, alpha1, alpha2, alpha3, alpha4 = 0, round(1*alpha_coefficient, 1), round(2*alpha_coefficient,1), round(3*alpha_coefficient, 1), round(extra_mapped_penalty, 1)
+beta0, beta1, beta2, beta3, beta4 = 0, round(0.1*beta_coefficient,2), round(0.2*beta_coefficient,2), round(0.3*beta_coefficient,2), round(0.5*beta_coefficient, 2)
+suspicious_penalty_ratio = 0.2
+mapped_suspicious_penality = 5 
+
+### A rough classification of availability based on road type
+class0 = ["cycleway"]
+class1 = ["residential", "tertiary" ]
+class2 = ["service", "path", "living_street"]
+class3 = ["secondary","footway",  "unclassified", "services", "pedestrian", "steps", "bridleway", "busway", "platform", "construction"]
+class4 = ['primary', 'primary_link', 'secondary_link', 'motorway', 'motorway_link']
+
+### Bikeable and unbikeable road types based on disrepancy between OSM and real-world observation
+unconfirmed_roads_type = ["secondary", "tertiary"]
+confirmed_car_roads_type=["primary", "primary_link", "secondary_link", "motorway", "motorway_link"]
+bikable_type = ["cycleway", "footway", "residential"] 
+unbikeable_type = ["primary", "primary_link","tertiary", "secondary", "secondary_link", "motorway", "motorway_link"]
+max_km_coefficient = 2 # searching distance expanding threshold for each road type
+max_depth = 5 # searching depth threshold
+
+### Rules
+case1_theta = 1.2 # detour ratio threshold in case 1
+case2_theta = 2 # detour ratio threshold in case 2
+case3_delta = 0.02 # detour distance threshold in case 2, in km
+frontier_exception_nodes = [19466] #Nodes that are in the frontier of network and do not contain alternative bikeable roads nearby
+
+### Hyperparameters for the special case(a)
+ver_invser_km_dict = {i:0.3 for i in unbikeable_type}
+ver_invser_km_dict["tertiary"]=0.2
+
+### Hyperparameters for the special case(b)
+network_interrupt_threshold = 3
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mapMatch_result/full_roads.cpg

+ISO-8859-1
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mapMatch_result/full_roads.prj

+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
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mapMatch_result/penalty_roads.txt

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