import os
from .logger import WranglerLogger
from pyproj import CRS
def get_base_dir(lasso_base_dir=os.getcwd()):
d = lasso_base_dir
for i in range(3):
if "metcouncil_data" in os.listdir(d):
WranglerLogger.info("Lasso base directory set as: {}".format(d))
return d
d = os.path.dirname(d)
msg = "Cannot find Lasso base directory from {}, please input using keyword in parameters: `lasso_base_dir =` ".format(
lasso_base_dir
)
WranglerLogger.error(msg)
raise (ValueError(msg))
[docs]class Parameters:
"""A class representing all the parameters defining the networks
including time of day, categories, etc.
Parameters can be set at runtime by initializing a parameters instance
with a keyword argument setting the attribute. Parameters that are
not explicitly set will use default parameters listed in this class.
.. highlight:: python
Attr:
time_period_to_time (dict): Maps time period abbreviations used in
Cube to time of days used on gtfs and highway network standard
Default:
::
{
"EA": ("3:00", "6:00"),
"AM": ("6:00, "10:00"),
"MD": ("10:00", "15:00"),
"PM": ("15:00", "19:00"),
"EV": ("19:00", "3:00"),
}
cube_time_periods (dict): Maps cube time period numbers used in
transit line files to the time period abbreviations in time_period_to_time
dictionary.
Default:
::
{"1": "EA", "2": "AM", "3": "MD", "4": "PM", "5": "EV"}
categories (dict): Maps demand category abbreviations to a list of
network categories they are allowed to use.
Default:
::
{
# suffix, source (in order of search)
"sov": ["sov", "default"],
"hov2": ["hov2", "default", "sov"],
"hov3": ["hov3", "hov2", "default", "sov"],
"truck": ["trk", "sov", "default"],
}
properties_to_split (dict): Dictionary mapping variables in standard
roadway network to categories and time periods that need to be
split out in final model network to get variables like LANES_AM.
Default:
::
{
"lanes": {
"v": "lanes",
"time_periods": self.time_periods_to_time
},
"ML_lanes": {
"v": "ML_lanes",
"time_periods": self.time_periods_to_time
},
"use": {
"v": "use",
"time_periods": self.time_periods_to_time
},
}
county_shape (str): File location of shapefile defining counties.
Default:
::
r"metcouncil_data/county/cb_2017_us_county_5m.shp"
county_variable_shp (str): Property defining the county n ame in
the county_shape file.
Default:
::
NAME
lanes_lookup_file (str): Lookup table of number of lanes for different data sources.
Default:
::
r"metcouncil_data/lookups/lanes.csv"
centroid_connect_lanes (int): Number of lanes for centroid connectors.
Default:
::
1
mpo_counties (list): list of county names within MPO boundary.
Default:
::
[
"ANOKA",
"DAKOTA",
"HENNEPIN",
"RAMSEY",
"SCOTT",
"WASHINGTON",
"CARVER",
]
taz_shape (str):
Default:
::
r"metcouncil_data/TAZ/TAZOfficialWCurrentForecasts.shp"
taz_data (str):
Default:
::
??
highest_taz_number (int): highest TAZ number in order to define
centroid connectors.
Default:
::
3100
output_variables (list): list of variables to output in final model
network.
Default:
::
[
"model_link_id",
"link_id",
"A",
"B",
"shstGeometryId",
"distance",
"roadway",
"name",
"roadway_class",
"bike_access",
"walk_access",
"drive_access",
"truck_access",
"trn_priority_EA",
"trn_priority_AM",
"trn_priority_MD",
"trn_priority_PM",
"trn_priority_EV",
"ttime_assert_EA",
"ttime_assert_AM",
"ttime_assert_MD",
"ttime_assert_PM",
"ttime_assert_EV",
"lanes_EA",
"lanes_AM",
"lanes_MD",
"lanes_PM",
"lanes_EV",
"price_sov_EA",
"price_hov2_EA",
"price_hov3_EA",
"price_truck_EA",
"price_sov_AM",
"price_hov2_AM",
"price_hov3_AM",
"price_truck_AM",
"price_sov_MD",
"price_hov2_MD",
"price_hov3_MD",
"price_truck_MD",
"price_sov_PM",
"price_hov2_PM",
"price_hov3_PM",
"price_truck_PM",
"price_sov_EV",
"price_hov2_EV",
"price_hov3_EV",
"price_truck_EV",
"roadway_class_idx",
"facility_type",
"county",
"centroidconnect",
"model_node_id",
"N",
"osm_node_id",
"bike_node",
"transit_node",
"walk_node",
"drive_node",
"geometry",
"X",
"Y",
"ML_lanes_EA",
"ML_lanes_AM",
"ML_lanes_MD",
"ML_lanes_PM",
"ML_lanes_EV",
"segment_id",
"managed",
"bus_only",
"rail_only"
]
osm_facility_type_dict (dict): Mapping between OSM Roadway variable
and facility type. Default:
area_type_shape (str): Location of shapefile defining area type.
Default:
::
r"metcouncil_data/area_type/ThriveMSP2040CommunityDesignation.shp"
area_type_variable_shp (str): property in area_type_shape with area
type in it.
Default:
::
"COMDES2040"
area_type_code_dict (dict): Mapping of the area_type_variable_shp to
the area type code used in the MetCouncil cube network.
Default:
::
{
23: 4, # urban center
24: 3,
25: 2,
35: 2,
36: 1,
41: 1,
51: 1,
52: 1,
53: 1,
60: 1,
}
downtown_area_type_shape (str): Location of shapefile defining downtown area type.
Default:
::
r"metcouncil_data/area_type/downtownzones_TAZ.shp"
downtown_area_type (int): Area type integer for downtown.
Default:
::
5
mrcc_roadway_class_shape (str): Shapefile of MRCC links with a property
associated with roadway class. Default:
::
r"metcouncil_data/mrcc/trans_mrcc_centerlines.shp"
mrcc_roadway_class_variable_shp (str): The property in mrcc_roadway_class_shp
associated with roadway class. Default:
::
"ROUTE_SYS"
widot_roadway_class_shape (str): Shapefile of Wisconsin links with a property
associated with roadway class. Default:
::
r"metcouncil_data/Wisconsin_Lanes_Counts_Median/WISLR.shp"
widot_roadway_class_variable_shp (str): The property in widot_roadway_class_shape
associated with roadway class.Default:
::
"RDWY_CTGY_"
mndot_count_shape (str): Shapefile of MnDOT links with a property
associated with counts. Default:
::
r"metcouncil_data/count_mn/AADT_2017_Count_Locations.shp"
mndot_count_variable_shp (str): The property in mndot_count_shape
associated with counts. Default:
::
"lookups/osm_highway_facility_type_crosswalk.csv"
legacy_tm2_attributes (str): CSV file of link attributes by
shStReferenceId from Legacy TM2 network. Default:
::
"lookups/legacy_tm2_attributes.csv"
osm_lanes_attributes (str): CSV file of number of lanes by shStReferenceId
from OSM. Default:
::
"lookups/osm_lanes_attributes.csv"
tam_tm2_attributes (str): CSV file of link attributes by
shStReferenceId from TAM TM2 network. Default:
::
"lookups/tam_tm2_attributes.csv"
tom_tom_attributes (str): CSV file of link attributes by
shStReferenceId from TomTom network. Default:
::
"lookups/tomtom_attributes.csv"
sfcta_attributes (str): CSV file of link attributes by
shStReferenceId from SFCTA network. Default:
::
"lookups/sfcta_attributes.csv"
output_epsg (int): EPSG type of geographic projection for output
shapefiles. Default:
::
102646
output_link_shp (str): Output shapefile for roadway links. Default:
::
r"tests/scratch/links.shp"
output_node_shp (str): Output shapefile for roadway nodes. Default:
::
r"tests/scratch/nodes.shp"
output_link_csv (str): Output csv for roadway links. Default:
::
r"tests/scratch/links.csv"
output_node_csv (str): Output csv for roadway nodes. Default:
::
r"tests/scratch/nodes.csv"
output_link_txt (str): Output fixed format txt for roadway links. Default:
::
r"tests/scratch/links.txt"
output_node_txt (str): Output fixed format txt for roadway nodes. Default:
::
r"tests/scratch/nodes.txt"
output_link_header_width_txt (str): Header for txt roadway links. Default:
::
r"tests/scratch/links_header_width.txt"
output_node_header_width_txt (str): Header for txt for roadway Nodes. Default:
::
r"tests/scratch/nodes_header_width.txt"
output_cube_network_script (str): Cube script for importing
fixed-format roadway network. Default:
::
r"tests/scratch/make_complete_network_from_fixed_width_file.s
"""
[docs] def __init__(self, **kwargs):
"""
Time period and category splitting info
"""
if "time_periods_to_time" in kwargs:
self.time_periods_to_time = kwargs.get("time_periods_to_time")
else:
self.time_period_to_time = {
"EA": ("3:00", "6:00"),
"AM": ("6:00", "10:00"),
"MD": ("10:00", "15:00"),
"PM": ("15:00", "19:00"),
"EV": ("19:00", "3:00"),
}
#MTC
self.cube_time_periods = {
"1": "EA",
"2": "AM",
"3": "MD",
"4": "PM",
"5": "EV",
}
"""
#MC
self.route_type_bus_mode_dict = {"Urb Loc": 5, "Sub Loc": 6, "Express": 7}
self.route_type_mode_dict = {0: 8, 2: 9}
self.cube_time_periods = {"1": "AM", "2": "MD"}
self.cube_time_periods_name = {"AM": "pk", "MD": "op"}
"""
if "categories" in kwargs:
self.categories = kwargs.get("categories")
else:
self.categories = {
# suffix, source (in order of search)
"sov": ["sov", "default"],
"hov2": ["hov2", "default", "sov"],
"hov3": ["hov3", "hov2", "default", "sov"],
"truck": ["trk", "sov", "default"],
}
# prefix, source variable, categories
self.properties_to_split = {
"lanes": {
"v": "lanes",
"time_periods": self.time_period_to_time,
},
"ML_lanes": {
"v": "ML_lanes",
"time_periods": self.time_period_to_time,
},
"useclass": {
"v": "useclass",
"time_periods": self.time_period_to_time,
},
}
"""
Details for calculating the county based on the centroid of the link.
The NAME varible should be the name of a field in shapefile.
"""
#MTC
if 'lasso_base_dir' in kwargs:
self.base_dir = get_base_dir(lasso_base_dir = kwargs.get("lasso_base_dir"))
else:
self.base_dir = get_base_dir()
if 'data_file_location' in kwargs:
self.data_file_location = kwargs.get("data_file_location")
else:
self.data_file_location = os.path.join(self.base_dir, "mtc_data")
#MC
if "lasso_base_dir" in kwargs:
self.base_dir = get_base_dir(lasso_base_dir=kwargs.get("lasso_base_dir"))
else:
self.base_dir = get_base_dir()
"""
if "data_file_location" in kwargs:
self.data_file_location = kwargs.get("data_file_location")
else:
self.data_file_location = os.path.join(self.base_dir, "metcouncil_data")
"""
#--------
if "settings_location" in kwargs:
self.settings_location = kwargs.get("settings_location")
else:
self.settings_location = os.path.join(self.base_dir, "examples", "settings")
if "scratch_location" in kwargs:
self.scratch_location = kwargs.get("scratch_location")
else:
self.scratch_location = os.path.join(self.base_dir, "tests", "scratch")
### COUNTIES
self.county_shape = os.path.join(
self.data_file_location, "county", "county.shp"
)
self.county_variable_shp = "NAME"
#MTC
self.county_code_dict = {
'San Francisco':1,
'San Mateo':2,
'Santa Clara':3,
'Alameda':4,
'Contra Costa':5,
'Solano':6,
'Napa':7,
'Sonoma':8,
'Marin':9,
'External':10,
}
self.county_centroid_range_dict = {
'San Francisco':range(1,100000),
'San Mateo':range(100001,200000),
'Santa Clara':range(200001,300000),
'Alameda':range(300001,400000),
'Contra Costa':range(400001,500000),
'Solano':range(500001,600000),
'Napa':range(600001,700000),
'Sonoma':range(700001,800000),
'Marin':range(800001,900000),
'External':range(900001,1000000)
}
self.county_node_range_dict = {
'San Francisco':range(1000000,1500000),
'San Mateo':range(1500000,2000000),
'Santa Clara':range(2000000,2500000),
'Alameda':range(2500000,3000000),
'Contra Costa':range(3000000,3500000),
'Solano':range(3500000,4000000),
'Napa':range(4000000,4500000),
'Sonoma':range(4500000,5000000),
'Marin':range(5000000,5500000),
}
self.county_hov_node_range_dict = {
'San Francisco':range(5500000,6000000),
'San Mateo':range(6000000,6500000),
'Santa Clara':range(6500000,7000000),
'Alameda':range(7000000,7500000),
'Contra Costa':range(7500000,8000000),
'Solano':range(8000000,8500000),
'Napa':range(8500000,9000000),
'Sonoma':range(9000000,9500000),
'Marin':range(9500000,10000000),
}
self.county_link_range_dict = {
'San Francisco':range(1,1000000),
'San Mateo':range(1000000,2000000),
'Santa Clara':range(2000000,3000000),
'Alameda':range(3000000,4000000),
'Contra Costa':range(4000000,5000000),
'Solano':range(5000000,6000000),
'Napa':range(6000000,7000000),
'Sonoma':range(7000000,8000000),
'Marin':range(8000000,9000000)
}
#MC
"""
self.county_code_dict = {
"Anoka": 1,
"Carver": 2,
"Dakota": 3,
"Hennepin": 4,
"Ramsey": 5,
"Scott": 6,
"Washington": 7,
"external": 10,
"Chisago": 11,
"Goodhue": 12,
"Isanti": 13,
"Le Sueur": 14,
"McLeod": 15,
"Pierce": 16,
"Polk": 17,
"Rice": 18,
"Sherburne": 19,
"Sibley": 20,
"St. Croix": 21,
"Wright": 22,
}
"""
self.mpo_counties = [
1,
3,
4,
5,
6,
7,
8,
9
]
self.taz_N_list = list(range(1, 10000)) + list(range(100001, 110000)) + list(range(200001, 210000)) + list(range(300001, 310000))\
+ list(range(400001, 410000)) + list(range(500001, 510000)) + list(range(600001, 610000)) + list(range(700001, 710000))\
+ list(range(800001, 810000)) + list(range(900001, 1000000))
self.maz_N_list = list(range(10001, 90000)) + list(range(110001, 190000)) + list(range(210001, 290000)) + list(range(310001, 390000))\
+ list(range(410001, 490000)) + list(range(510001, 590000)) + list(range(610001, 690000)) + list(range(710001, 790000))\
+ list(range(810001, 890000))
self.tap_N_list = list(range(90001, 99999)) + list(range(190001, 199999)) + list(range(290001, 299999)) + list(range(390001, 399999))\
+ list(range(490001, 499999)) + list(range(590001, 599999)) + list(range(690001, 699999)) + list(range(790001, 799999))\
+ list(range(890001, 899999))
self.tap_N_start = {
"San Francisco" : 90001,
"San Mateo" : 190001,
"Santa Clara" : 290001,
"Alameda" : 390001,
"Contra Costa" : 490001,
"Solano" : 590001,
"Napa" : 690001,
"Sonoma" : 790001,
"Marin" : 890001
}
#MTC
self.osm_facility_type_dict = os.path.join(
self.data_file_location, "lookups", "osm_highway_facility_type_crosswalk.csv"
)
#MC
### Lanes
self.lanes_lookup_file = os.path.join(
self.data_file_location, "lookups", "lanes.csv"
)
### TAZS
self.taz_shape = os.path.join(
self.data_file_location, "TAZ", "TAZOfficialWCurrentForecasts.shp"
)
######
#MTC
self.osm_lanes_attributes = os.path.join(
self.data_file_location, "lookups", "osm_lanes_attributes.csv"
)
self.legacy_tm2_attributes = os.path.join(
self.data_file_location, "lookups", "legacy_tm2_attributes.csv"
)
self.assignable_analysis = os.path.join(
self.data_file_location, "lookups", "assignable_analysis_links.csv"
)
###
### AREA TYPE - MC
self.area_type_shape = os.path.join(
self.data_file_location,
"area_type",
"ThriveMSP2040CommunityDesignation.shp",
)
self.area_type_variable_shp = "COMDES2040"
# area type map from raw data to model category
# source https://metrocouncil.org/Planning/Publications-And-Resources/Thrive-MSP-2040-Plan-(1)/7_ThriveMSP2040_LandUsePoliciesbyCD.aspx
# urban center
# urban
# suburban
# suburban edge
# emerging suburban edge
# rural center
# diversified rural
# rural residential
# agricultural
self.area_type_code_dict = {
23: 4, # urban center
24: 3,
25: 2,
35: 2,
36: 1,
41: 1,
51: 1,
52: 1,
53: 1,
60: 1,
}
self.downtown_area_type_shape = os.path.join(
self.data_file_location,
"area_type",
"downtownzones_TAZ.shp",
)
self.downtown_area_type = int(5)
self.centroid_connect_lanes = int(1)
self.osm_assgngrp_dict = os.path.join(
self.data_file_location, "lookups", "osm_highway_asgngrp_crosswalk.csv"
)
self.mrcc_roadway_class_shape = os.path.join(
self.data_file_location, "mrcc", "trans_mrcc_centerlines.shp"
)
####
###MTC
self.tam_tm2_attributes = os.path.join(
self.data_file_location, "lookups", "tam_tm2_attributes.csv"
)
self.sfcta_attributes = os.path.join(
self.data_file_location, "lookups", "sfcta_attributes.csv"
)
self.tomtom_attributes = os.path.join(
self.data_file_location, "lookups", "tomtom_attributes.csv"
)
self.pems_attributes = os.path.join(
self.data_file_location, "lookups", "pems_attributes.csv"
)
self.centroid_file = os.path.join(
self.data_file_location, "centroid", "centroid_node.pickle"
)
####
###MC
self.widot_shst_data = os.path.join(
self.data_file_location,
"Wisconsin_Lanes_Counts_Median",
"widot.out.matched.geojson",
)
####
self.centroid_connector_link_file = os.path.join(
self.data_file_location, "centroid", "cc_link.pickle"
)
self.centroid_connector_shape_file = os.path.join(
self.data_file_location, "centroid", "cc_shape.pickle"
)
self.tap_file = os.path.join(
self.data_file_location, "tap", "tap_node.pickle"
)
self.tap_connector_link_file = os.path.join(
self.data_file_location, "tap", "tap_link.pickle"
)
self.tap_connector_shape_file = os.path.join(
self.data_file_location, "tap", "tap_shape.pickle"
)
self.net_to_dbf_crosswalk = os.path.join(
self.settings_location, "net_to_dbf.csv"
)
###MTC
self.log_to_net_crosswalk = os.path.join(self.settings_location, "log_to_net.csv")
self.emme_name_crosswalk_file = os.path.join(
self.data_file_location, "lookups", "emme_attribute_names.csv"
)
####
#MC
self.mndot_count_variable_shp = "AADT_mn"
self.widot_county_shape = os.path.join(
self.data_file_location,
"Wisconsin_Lanes_Counts_Median",
"TRADAS_(counts).shp",
)
###
###MTC
self.mode_crosswalk_file = os.path.join(
self.data_file_location, "lookups", "gtfs_to_tm2_mode_crosswalk.csv"
)
self.veh_cap_crosswalk_file = os.path.join(
self.data_file_location, "lookups", "transitSeatCap.csv"
)
self.faresystem_crosswalk_file = os.path.join(
self.data_file_location, "lookups", "faresystem_crosswalk.txt"
)
# https://app.asana.com/0/12291104512575/1200287255197808/f
self.fare_2015_to_2010_deflator = 0.927
####
#MC
self.widot_count_variable_shp = "AADT_wi"
self.net_to_dbf_crosswalk = os.path.join(
self.settings_location, "net_to_dbf.csv"
)
self.log_to_net_crosswalk = os.path.join(
self.settings_location, "log_to_net.csv"
)
self.subregion_boundary_file = os.path.join(
self.data_file_location, 'emme', 'subregion_boundary_for_active_modes.shp'
)
self.subregion_boundary_id_variable = 'subregion'
####
self.output_variables = [
"model_link_id",
"link_id",
"A",
"B",
"shstGeometryId",
#MTC
'name',
"distance",
#"roadway",
#"name",
#MC
#"shape_id",
#"distance",
#"roadway",
#"name",
#"roadway_class",
####
"bike_access",
"walk_access",
"drive_access",
"truck_access",
"lanes_EA",
"lanes_AM",
"lanes_MD",
"lanes_PM",
"lanes_EV",
"county",
"model_node_id",
"N",
"osm_node_id",
"geometry",
"X",
"Y",
"segment_id",
"managed",
"bus_only",
"rail_only",
#MTC
"assignable",
"cntype",
"useclass_AM",
"useclass_MD",
"useclass_PM",
"useclass_EV",
"useclass_EA",
"transit",
"tollbooth",
"tollseg",
"ft",
"tap_drive",
"tollbooth",
"tollseg",
"farezone",
"tap_id",
####
#MC
"bike_facility",
"mrcc_id",
"ROUTE_SYS", # mrcc functional class
]
self.output_link_shp = os.path.join(self.scratch_location, "links.shp")
self.output_node_shp = os.path.join(self.scratch_location, "nodes.shp")
self.output_link_csv = os.path.join(self.scratch_location, "links.csv")
self.output_node_csv = os.path.join(self.scratch_location, "nodes.csv")
self.output_link_txt = os.path.join(self.scratch_location, "links.txt")
self.output_node_txt = os.path.join(self.scratch_location, "nodes.txt")
self.output_link_header_width_txt = os.path.join(
self.scratch_location, "links_header_width.txt"
)
self.output_node_header_width_txt = os.path.join(
self.scratch_location, "nodes_header_width.txt"
)
self.output_cube_network_script = os.path.join(
self.scratch_location, "make_complete_network_from_fixed_width_file.s"
)
self.output_dir = os.path.join(self.scratch_location)
self.output_proj = CRS("epsg:2875")
self.output_proj4 = '+proj=lcc +lat_0=32.1666666666667 +lon_0=-116.25 +lat_1=33.8833333333333 +lat_2=32.7833333333333 +x_0=2000000.0001016 +y_0=500000.0001016 +ellps=GRS80 +towgs84=-0.991,1.9072,0.5129,-1.25033e-07,-4.6785e-08,-5.6529e-08,0 +units=us-ft +no_defs +type=crs'
self.prj_file = os.path.join(self.data_file_location, 'projection', '2875.prj')
self.wkt_projection = 'PROJCS["NAD83(HARN) / California zone 6 (ftUS)",GEOGCS["NAD83(HARN)",DATUM["NAD83_High_Accuracy_Reference_Network",SPHEROID["GRS 1980",6378137,298.257222101],TOWGS84[-0.991,1.9072,0.5129,-1.25033E-07,-4.6785E-08,-5.6529E-08,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4152"]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["latitude_of_origin",32.1666666666667],PARAMETER["central_meridian",-116.25],PARAMETER["standard_parallel_1",33.8833333333333],PARAMETER["standard_parallel_2",32.7833333333333],PARAMETER["false_easting",6561666.667],PARAMETER["false_northing",1640416.667],UNIT["US survey foot",0.304800609601219],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","2875"]]'
self.fare_matrix_output_variables = ["faresystem", "origin_farezone", "destination_farezone", "price"]
self.zones = 4756
"""
Create all the possible headway variable combinations based on the cube time periods setting
"""
self.time_period_properties_list = [
p + "[" + str(t) + "]"
for p in ["HEADWAY", "FREQ"]
for t in self.cube_time_periods.keys()
]
self.int_col = [
"model_link_id",
"model_node_id",
"A",
"B",
#MTC
#"county",
###
#MC
# "lanes",
"lanes_AM",
"lanes_MD",
"lanes_PM",
"lanes_NT",
"roadway_class",
"assign_group",
#"county",
"area_type",
"trn_priority",
"AADT",
"count_AM",
"count_MD",
"count_PM",
"count_NT",
"count_daily",
"centroidconnect",
"bike_facility",
####
"drive_access",
"walk_access",
"bike_access",
"truck_access",
#MTC
"ML_lanes_AM",
"ML_lanes_MD",
"ML_lanes_PM",
"ML_lanes_EV",
"ML_lanes_EA",
###
#MC
"drive_node",
"walk_node",
"bike_node",
"transit_node",
# "ML_lanes",
"ML_lanes_AM",
"ML_lanes_MD",
"ML_lanes_PM",
"ML_lanes_NT",
####
"segment_id",
"managed",
"bus_only",
"rail_only",
"transit",
##MTC
"ft",
"assignable",
"lanes_AM",
"lanes_MD",
"lanes_PM",
"lanes_EA",
"lanes_EV",
"useclass_AM",
"useclass_EA",
"useclass_MD",
"useclass_PM",
"useclass_EV",
"tollseg",
"tollbooth",
"farezone",
"tap_id",
]
self.float_col = [
"distance",
"price",
"X",
"Y"
"mrcc_id",
]
self.float_col = ["distance", "ttime_assert", "price", "X", "Y"]
self.string_col = [
"osm_node_id",
"name",
"roadway",
"shstGeometryId",
"access_AM",
"access_MD",
"access_PM",
"access_NT",
"ROUTE_SYS",
]
self.__dict__.update(kwargs)