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Merged
i-am-sijia merged 24 commits into from
Sep 25, 2024
Merged

Remove assignable #6

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7bc2762
updated filter
lachlan-git Aug 1, 2024
a48942e
taz_network outputs
lachlan-git Aug 1, 2024
00e872d
mid updating emme
lachlan-git Aug 2, 2024
6eea715
all code changes need to be tested on another machine
lachlan-git Aug 2, 2024
f908e0a
updated
lachlan-git Aug 14, 2024
2f8d594
fixed sign
lachlan-git Aug 14, 2024
d2cfeb0
fixed ft comments
lachlan-git Aug 14, 2024
b5f6b92
copy to stop side effects
lachlan-git Aug 14, 2024
ea103f0
refacored function
lachlan-git Aug 14, 2024
586c1a1
updated docstring
lachlan-git Aug 14, 2024
6a1a83c
maz node connectors re-added
lachlan-git Aug 14, 2024
840fa7c
removed duplicated drop connector code
lachlan-git Aug 14, 2024
c437733
fixed typo
lachlan-git Aug 14, 2024
df424f9
fixed docstring
lachlan-git Aug 14, 2024
d017d70
maz treated as regular drive
lachlan-git Aug 14, 2024
eb010eb
Minor Issue with Network regen
lachlan-git Aug 20, 2024
fe08573
updated doc string
lachlan-git Aug 21, 2024
59e7bbd
fixed doc string
lachlan-git Aug 22, 2024
06ebf5b
Updatede Doc Strings
lachlan-git Aug 22, 2024
7de1ac7
raise not implimented error on regenerate connectors
lachlan-git Aug 30, 2024
b8b893e
fixed bus logic
lachlan-git Sep 4, 2024
fb3fabc
updated final comments
lachlan-git Sep 4, 2024
dfcf88e
updated maz connectors to only include drive access==1
lachlan-git Sep 9, 2024
b032853
osm lanes fix inherited from AlaCC
i-am-sijia Sep 20, 2024
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228 changes: 104 additions & 124 deletions lasso/emme.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,10 @@
"""Setup Emme project, database (Emmebank) and import network data."""


import os as _os
from collections import defaultdict as _defaultdict
from copy import deepcopy as _copy
from pathlib import Path

from pandas.core.frame import DataFrame

Expand All @@ -27,6 +29,13 @@
from .roadway import ModelRoadwayNetwork
from .parameters import Parameters
from .logger import WranglerLogger

try:
import ranch
except ImportError:
WranglerLogger.warning("'Ranch' is not installed in this environment, if you wish to rebuild connectors when writing the emme network you will need to install ranch https://github.com/BayAreaMetro/Ranch")
ranch = None

from .mtc import _is_express_bus, _special_vehicle_type

from lasso import StandardTransit
Expand All @@ -46,11 +55,11 @@ def create_emme_network(
include_transit: Optional[bool] =False,
links_df: Optional[GeoDataFrame]=None,
nodes_df: Optional[GeoDataFrame]=None,
shapes_df: Optional[GeoDataFrame]=None,
name: Optional[str]="",
path: Optional[str]="",
write_drive_network: bool = False,
write_taz_drive_network: bool = False,
write_maz_drive_network: bool = False,
gtfs_directory: Optional[str] = None,
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write_maz_active_modes_network: bool = False,
write_tap_transit_network: bool = False,
write_taz_transit_network: bool = False,
Expand Down Expand Up @@ -91,16 +100,23 @@ def create_emme_network(

model_tables = {}


if write_drive_network and (gtfs_directory is None):
raise ValueError("'gtfs_directory' is required when writing a taz network")
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if roadway_network:
links_df = roadway_network.links_mtc_df.copy()
nodes_df = roadway_network.nodes_mtc_df.sort_values("N").reset_index(drop=True).copy()
shapes_df = roadway_network.shapes_df.copy()

elif (len(links_df)>0) & (len(nodes_df)>0):
links_df = links_df.copy()
nodes_df = nodes_df.sort_values("N").reset_index(drop=True).copy()
if shapes_df is not None:
shapes_df = shapes_df.copy()

else:
msg = "Missing roadway network to write to emme, please specify either model_net or links_df and nodes_df."
msg = "Missing roadway network to write to emme, please specify either model_net or links_df and nodes_df"
WranglerLogger.error(msg)
raise ValueError(msg)

Expand Down Expand Up @@ -192,31 +208,9 @@ def create_emme_network(
model_tables = prepare_table_for_drive_network(
nodes_df=nodes_df,
links_df=links_df,
parameters=parameters
)

setup = SetupEmme(model_tables, out_dir, _NAME, include_transit, parameters)
setup.run()

if write_taz_drive_network:
_NAME = "emme_taz_drive_network"
include_transit = False
model_tables = prepare_table_for_taz_drive_network(
nodes_df=nodes_df,
links_df=links_df,
parameters=parameters
)

setup = SetupEmme(model_tables, out_dir, _NAME, include_transit, parameters)
setup.run()

if write_maz_drive_network:
_NAME = "emme_maz_drive_network"
include_transit = False
model_tables = prepare_table_for_maz_drive_network(
nodes_df=nodes_df,
links_df=links_df,
parameters=parameters
shapes_df=shapes_df,
parameters=parameters,
gtfs_directory=gtfs_directory,
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)

setup = SetupEmme(model_tables, out_dir, _NAME, include_transit, parameters)
Expand Down Expand Up @@ -268,61 +262,31 @@ def create_emme_network(
setup = SetupEmme(model_tables, out_dir, _NAME, include_transit, parameters)
setup.run()

def prepare_table_for_taz_drive_network(
nodes_df,
links_df,
parameters,
):

"""
prepare model table for taz-scale drive network, in which taz nodes are centroids
keep links that are drive_access == 1 and assignable

Arguments:
nodes_df -- node database
links_df -- link database

Return:
dictionary of model network settings
"""

model_tables = dict()

# use taz as centroids, drop maz nodes and connectors
model_tables["centroid_table"] = nodes_df[
nodes_df.N.isin(parameters.taz_N_list)
].to_dict('records')
def extract_gtfs_from_dir(path: str):
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path = Path(path)
shapes = pd.read_csv(path / "shapes.txt")
trips = pd.read_csv(path / "trips.txt")
routes = pd.read_csv(path / "routes.txt")

model_tables["connector_table"] = links_df[
(links_df.A.isin(parameters.taz_N_list)) | (links_df.B.isin(parameters.taz_N_list))
].to_dict('records')

drive_links_df = links_df[
~(links_df.A.isin(parameters.taz_N_list + parameters.maz_N_list)) &
~(links_df.B.isin(parameters.taz_N_list + parameters.maz_N_list)) &
((links_df.drive_access == 1) & (links_df.assignable == 1))
].copy()

model_tables["link_table"] = drive_links_df.to_dict('records')

drive_nodes_df = nodes_df[
(nodes_df.N.isin(drive_links_df.A.tolist()) + nodes_df.N.isin(drive_links_df.B.tolist()))
].copy()

model_tables["node_table"] = drive_nodes_df.to_dict('records')

return model_tables
bus_routes = routes.loc[routes["route_type"].isin([3]), "route_id"]
bus_trips = trips.loc[trips["route_id"].isin(bus_routes), "shape_id"]
bus_shapes = shapes[shapes["shape_id"].isin(bus_trips)]
return bus_shapes

def prepare_table_for_drive_network(
nodes_df,
links_df,
parameters,
nodes_df: pd.DataFrame,
links_df: pd.DataFrame,
shapes_df: Optional[GeoDataFrame],
parameters: Parameters,
gtfs_directory: Union[Path, str],
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maximum_ft: int=7,
regenerate_connectors: bool=False,
):

"""
prepare model table for drive network, in which taz nodes are centroids
maz and tap nodes are included, but not as centroids
keep links that are drive_access == 1 and assignable == 1
prepare model table for taz-scale drive network, in which taz nodes are centroids
keep links that are drive_access == 1 and assignable
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Arguments:
nodes_df -- node database
Expand All @@ -332,81 +296,97 @@ def prepare_table_for_drive_network(
dictionary of model network settings
"""


model_tables = dict()

# use taz as centroids
# use taz as centroids, drop maz nodes and connectors
model_tables["centroid_table"] = nodes_df[
nodes_df.N.isin(parameters.taz_N_list)
].to_dict('records')

# taz connectors as centroid connectors
model_tables["connector_table"] = links_df[
(links_df.A.isin(parameters.taz_N_list)) | (links_df.B.isin(parameters.taz_N_list))
].to_dict('records')

# links: not taz connectors, has to be drive, assignable, or maz links
# maz drive connectors are assignable
# tap connectors are non-drive, not assignable
# get the links where buses drive on the network
gtfs_shape_bus_routes = extract_gtfs_from_dir(gtfs_directory)
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# assuming shape_model_node_id are in order for this step
gtfs_shape_bus_routes["next_node_id"] = gtfs_shape_bus_routes["shape_model_node_id"].shift(1)
gtfs_shape_bus_routes = gtfs_shape_bus_routes.sort_values(by=["shape_id", "shape_pt_sequence"])
gtfs_shape_bus_routes = gtfs_shape_bus_routes[(gtfs_shape_bus_routes["shape_pt_sequence"] != 1)]
gtfs_shape_bus_routes = gtfs_shape_bus_routes.drop_duplicates(subset=["shape_model_node_id", "next_node_id"])
gtfs_shape_bus_routes["has_bus_on_link"] = True
links_df = pd.merge(links_df, gtfs_shape_bus_routes[["shape_model_node_id", "next_node_id", "has_bus_on_link"]],
left_on=["A", "B"],
right_on=["shape_model_node_id", "next_node_id"],
how="left"
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).drop(columns=["shape_model_node_id", "next_node_id"])
links_df["has_bus_on_link"] = links_df["has_bus_on_link"].fillna(False)

# links to keep:
# ft >= 7
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# links containing bus routes
# toll booths >= 1
# toll sag >= 1
# make sure connectors gone
# bus links need to be on
# if centroid is empty -> build new connectors
# rebuild connectors for all TAZ

# we want to include managed lanes connectors as well
managed_nodes = list(set(links_df[links_df["managed"] == 1]["A"]) | set(links_df[links_df["managed"] == 1]["B"]))
links_df["managed_lane_connector"] = (links_df["ft"] == 8) & (links_df["A"].isin(managed_nodes) | links_df["B"].isin(managed_nodes))

drive_links_df = links_df[
~(links_df.A.isin(parameters.taz_N_list)) &
~(links_df.B.isin(parameters.taz_N_list)) &
(
(links_df.drive_access == 1) & (links_df.assignable == 1)
)
].copy()

drive_nodes_df = nodes_df[
(nodes_df.N.isin(drive_links_df.A.tolist()) + nodes_df.N.isin(drive_links_df.B.tolist()))
~(links_df.A.isin(parameters.taz_N_list + parameters.maz_N_list)) &
~(links_df.B.isin(parameters.taz_N_list + parameters.maz_N_list)) &
( # ft > 7 should be kept in the nework
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(
(links_df.drive_access == 1) &
(links_df.ft <= maximum_ft)
) |
( # is a tollsegment, should be kept within the network
(links_df.tollseg != 0) |
(links_df.tollbooth != 0)
)
)
) | links_df["has_bus_on_link"] | links_df["managed_lane_connector"] # special cases we would like tp keep
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].copy()
if not regenerate_connectors:
model_tables["connector_table"] = links_df[
(links_df.A.isin(parameters.taz_N_list)) | (links_df.B.isin(parameters.taz_N_list))
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].to_dict('records')
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else:
# check ranch is installed
if ranch is None:
raise ImportError("package 'ranch' is not installed, please go to https://github.com/BayAreaMetro/Ranch for install instructions")

if shapes_df is None:
raise ValueError("shapes_df must be provided to regenerate connectors")

# regenerate connectors
ranch_roadway = ranch.Roadway(nodes_df, links_df, shapes_df, parameters)
ranch_roadway.build_centroid_connectors(build_taz_active_modes=True, build_maz_drive=True)
model_tables["connector_table"] = ranch_roadway.links_df[
(ranch_roadway.links_df.A.isin(parameters.taz_N_list)) | (ranch_roadway.links_df.B.isin(parameters.taz_N_list))
].to_dict('records')


model_tables["link_table"] = drive_links_df.to_dict('records')
model_tables["node_table"] = drive_nodes_df.to_dict('records')

return model_tables

def prepare_table_for_maz_drive_network(
nodes_df,
links_df,
parameters,
):

"""
prepare model table for maz-scale drive network, in which there are no centroids, drop taz nodes and connectors
keep links that are drive_access == 1 and assignable == 1

Arguments:
nodes_df -- node database
links_df -- link database

Return:
dictionary of model network settings
"""

model_tables = dict()

# no centroids, drop taz nodes and connectors

model_tables["centroid_table"] = []

model_tables["connector_table"] = []

drive_links_df = links_df[
~(links_df.A.isin(parameters.taz_N_list)) &
~(links_df.B.isin(parameters.taz_N_list)) &
((links_df.drive_access == 1) & (links_df.assignable == 1))
].copy()

model_tables["link_table"] = drive_links_df.to_dict('records')

drive_nodes_df = nodes_df[
~(nodes_df.N.isin(parameters.taz_N_list)) &
(nodes_df.N.isin(drive_links_df.A.tolist()) + nodes_df.N.isin(drive_links_df.B.tolist()))
].copy()

model_tables["node_table"] = drive_nodes_df.to_dict('records')

return model_tables


def prepare_table_for_maz_active_modes_network(
nodes_df,
links_df,
Expand Down