+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))
+
+# should be a dataclass
+[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",
+
}
+
+
self.taz_net_max_ft = 6
+
self.maz_net_max_ft = 7
+
# Potentially make a named tuple
+
self.taz_node_join_tolerance = (100, "US survey foot")
+
self.max_length_centroid_connector_when_none_in_taz = 999999999999999999999999999999999999
+
+
#TODO make this relative
+
self.taz_shape_file = r"C:\Users\USLP095001\code\MTC\travel-model-two\maz_taz\shapefiles\tazs_TM2_v2_2.shp"
+
self.maz_shape_file = r"C:\Users\USLP095001\code\MTC\travel-model-two\maz_taz\shapefiles\mazs_TM2_v2_2.shp"
+
+
"""
+
#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.emme_drive_filter_criteria = [
+
""
+
]
+
+
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
+
self.fare_2015_to_2000_deflator = 180.20/258.27
+
####
+
#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",
+
]
+
+
# paramters added for PNR simulation
+
self.pnr_node_location = os.path.join(
+
self.data_file_location, "lookups", "pnr_stations.csv"
+
)
+
self.pnr_buffer = 20
+
self.knr_buffer = 2.5
+
self.walk_buffer = 0.75
+
self.transfer_buffer = 1
+
self.taz_list = os.path.join(
+
self.data_file_location, "lookups", "taz_lists.csv"
+
)
+
self.sf_county = os.path.join(
+
self.data_file_location, "lookups", "SFcounty.shp"
+
)
+
+
self.__dict__.update(kwargs)
+
![[source]](../../_modules/shapely/geometry/point/#Point.svg){kind=link}
![[source]](../../_modules/shapely/geometry/polygon/#Polygon.svg){kind=link}
![[docs]](../../../../_generated/lasso.util/#lasso.util.Point.svg){kind=link}
![[docs]](../../../../_generated/lasso.util/#lasso.util.Polygon.svg){kind=link}
