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Merge pull request #233 from SANDAG/ttr_taz_active_skims
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Include TAZ skims for travel times by active modes in Travel Time Reporter
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@bhargavasana
bhargavasana authored Nov 5, 2024
2 parents 75196dd + 6e8e466 commit d7807ae
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Showing 4 changed files with 106 additions and 59 deletions.
8 changes: 4 additions & 4 deletions src/main/python/AMTravelTimeReporterConfigs.yaml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
time_period: AM
time_threshold: 45
time_threshold: 30
infinity: 999
outfile: report\walkMgrasWithin45Min_AM.csv
outfile: report\walkMgrasWithin30Min_AM.csv

transit_skim_matrices: # If set to True, replace values of zero with the number set as infinity
WALK_LOC_XFERWALK__{}: False
Expand All @@ -20,5 +20,5 @@ traffic_skim_matrices: # If set to True, replace values of zero with the number
SOV_NT_L_TIME__{}: False

active_skim_files:
walk_time: maz_maz_walk.csv
bike_time: maz_maz_bike.csv
maz_walk_time: maz_maz_walk.csv
maz_bike_time: maz_maz_bike.csv
8 changes: 4 additions & 4 deletions src/main/python/MDTravelTimeReporterConfigs.yaml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
time_period: MD
time_threshold: 45
time_threshold: 30
infinity: 999
outfile: report\walkMgrasWithin45Min_MD.csv
outfile: report\walkMgrasWithin30Min_MD.csv

transit_skim_matrices: # If set to True, replace values of zero with the number set as infinity
WALK_LOC_XFERWALK__{}: False
Expand All @@ -20,5 +20,5 @@ traffic_skim_matrices: # If set to True, replace values of zero with the number
SOV_NT_L_TIME__{}: False

active_skim_files:
walk_time: maz_maz_walk.csv
bike_time: maz_maz_bike.csv
maz_walk_time: maz_maz_walk.csv
maz_bike_time: maz_maz_bike.csv
145 changes: 96 additions & 49 deletions src/main/python/TravelTimeReporter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,6 +106,7 @@ def read_active_skims(self):
"""
Reads active skims into memory as data frames
"""
# Read MAZ-level skims
for skim_name in self.settings["active_skim_files"]:
active_skims = pd.read_csv(
os.path.join(
Expand All @@ -126,6 +127,52 @@ def read_active_skims(self):
["i", "j"]
)

# Read bike and walk times from AM traffic skim
am_traffic_skim_file = os.path.join(
self.model_run,
"output",
"skims",
"traffic_skims_AM.omx"
)
skims = omx.open_file(am_traffic_skim_file, "r")
zones = skims.mapping("zone_number").keys()
active_taz_skims = {
"BIKE_TIME": "taz_bike_time",
"walkTime": "taz_walk_time"
}
for core in active_taz_skims:
skim_values = np.array(skims[core])
skim_values = np.where(
skim_values == 0,
self.settings["infinity"],
skim_values
)
self.skims[active_taz_skims[core]] = self.expand_skim(
pd.DataFrame(
skim_values,
zones,
zones
)
)

# Replace TAZ-skim level values with MGRA-skim values if they are present
self.skims["taz_bike_time"] = self.unpivot_skim("taz_bike_time", False)
self.skims["taz_walk_time"] = self.unpivot_skim("taz_walk_time", False)

self.skims["bike_time"] = self.skims["taz_bike_time"].copy()
self.skims["walk_time"] = self.skims["taz_walk_time"].copy()
self.skims["bike_time"].loc[self.skims["maz_bike_time"].index] = self.skims["maz_bike_time"]["BIKE_TIME"]
self.skims["walk_time"].loc[self.skims["maz_walk_time"].index] = self.skims["maz_walk_time"]["walkTime"]

# Remove OD-pairs above time threshold
time_threshold = self.settings["time_threshold"]
self.skims["bike_time"] = self.skims["bike_time"].loc[
self.skims["bike_time"] <= time_threshold
]
self.skims["walk_time"] = self.skims["walk_time"].loc[
self.skims["walk_time"] <= time_threshold
]

def init_land_use(self):
"""
Adds fields to land use table to be used in calculations
Expand All @@ -139,6 +186,8 @@ def init_land_use(self):
self.land_use["nev_egress_available"] = (self.land_use["nev"] > 0) & (self.land_use["micro_accegr_dist"] <= self.constants["nevMaxDist"]) & (self.land_use["micro_accegr_dist"] >= self.constants["maxWalkIfMTAccessAvailable"])
self.land_use["nev_accegr_time"] = 60 * self.land_use["micro_accegr_dist"] / self.constants["nevSpeed"]

self.expansion_matrix = pd.get_dummies(self.land_use["TAZ"]) # Indicates which MGRAs belong to which TAZ

# # # # # # # # # # # # UTILITY FUNCTIONS # # # # # # # # # # # #
#===============================================================#
def field2matrix(self, field, origin = True):
Expand Down Expand Up @@ -182,21 +231,22 @@ def expand_skim(self, skim):
expanded_skim (pandas.DataFrame):
Skim matrix where the index and columns are the origins and destinations MGRAs, respectively, and the values are the impedance from each origin MGRA to the destination MGRA
"""
expansion_matrix = pd.get_dummies(self.land_use["TAZ"]) # Indicates which MGRAs belong to which TAZ
return pd.DataFrame(
expansion_matrix.values.dot(skim.values).dot(expansion_matrix.T.values),
self.expansion_matrix.values.dot(skim.values).dot(self.expansion_matrix.T.values),
self.land_use.index,
self.land_use.index
)

def unpivot_skim(self, skim_name):
def unpivot_skim(self, skim_name, filter_for_time = True):
"""
Unpivots a skim into a series with the origin and destination as the index
Parameters
----------
skim_name (str):
Name of skim to unpivot
filter_for_time (bool):
If true, values will above the time threshold will be removed
Returns
-------
Expand All @@ -206,18 +256,31 @@ def unpivot_skim(self, skim_name):
time_threshold = self.settings["time_threshold"]
self.skims[skim_name].index.name = "i"

return pd.melt(
self.skims[skim_name].reset_index(),
id_vars = ["i"],
var_name = "j",
value_name = "time"
).query(
"time <= @time_threshold"
if filter_for_time:
return pd.melt(
self.skims[skim_name].reset_index(),
id_vars = ["i"],
var_name = "j",
value_name = "time"
).query(
"time <= @time_threshold"
).sort_values(
["i", "j"]
).set_index(
["i", "j"]
)["time"]

else:
return pd.melt(
self.skims[skim_name].reset_index(),
id_vars = ["i"],
var_name = "j",
value_name = "time"
).sort_values(
["i", "j"]
).set_index(
["i", "j"]
)["time"]
).set_index(
["i", "j"]
)["time"]

# # # # # # # # # # # # CALCULATOR FUNCTIONS # # # # # # # # # # # #
#==================================================================#
Expand Down Expand Up @@ -274,47 +337,31 @@ def get_accegr_times(self):
"""
Returns transit access and egress times from every MGRA to every other MGRA
"""
print("Calculating direct flexible fleet access and egress times")
microtransit_direct_access_time = self.get_microtransit_direct_accegr_time(access = True, nev = False)
microtransit_direct_egress_time = self.get_microtransit_direct_accegr_time(access = False, nev = False)
nev_direct_access_time = self.get_microtransit_direct_accegr_time(access = True, nev = True)
nev_direct_egress_time = self.get_microtransit_direct_accegr_time(access = False, nev = True)
# print("Calculating direct flexible fleet access and egress times")
# microtransit_direct_access_time = self.get_microtransit_direct_accegr_time(access = True, nev = False)
# microtransit_direct_egress_time = self.get_microtransit_direct_accegr_time(access = False, nev = False)
# nev_direct_access_time = self.get_microtransit_direct_accegr_time(access = True, nev = True)
# nev_direct_egress_time = self.get_microtransit_direct_accegr_time(access = False, nev = True)

print("Calculating flexible fleet diverted access and egress times")
microtransit_access_time = self.get_total_microtransit_accegr_times(microtransit_direct_access_time, nev = False)
microtransit_egress_time = self.get_total_microtransit_accegr_times(microtransit_direct_egress_time, nev = False)
nev_access_time = self.get_total_microtransit_accegr_times(nev_direct_access_time, nev = True)
nev_egress_time = self.get_total_microtransit_accegr_times(nev_direct_egress_time, nev = True)
# print("Calculating flexible fleet diverted access and egress times")
# microtransit_access_time = self.get_total_microtransit_accegr_times(microtransit_direct_access_time, nev = False)
# microtransit_egress_time = self.get_total_microtransit_accegr_times(microtransit_direct_egress_time, nev = False)
# nev_access_time = self.get_total_microtransit_accegr_times(nev_direct_access_time, nev = True)
# nev_egress_time = self.get_total_microtransit_accegr_times(nev_direct_egress_time, nev = True)

print("Getting flexible fleet availability")
microtransit_access_available = self.field2matrix("microtransit_access_available", origin = True)
microtransit_egress_available = self.field2matrix("microtransit_egress_available", origin = False)
nev_access_available = self.field2matrix("nev_access_available", origin = True)
nev_egress_available = self.field2matrix("nev_egress_available", origin = False)
# print("Getting flexible fleet availability")
# microtransit_access_available = self.field2matrix("microtransit_access_available", origin = True)
# microtransit_egress_available = self.field2matrix("microtransit_egress_available", origin = False)
# nev_access_available = self.field2matrix("nev_access_available", origin = True)
# nev_egress_available = self.field2matrix("nev_egress_available", origin = False)

print("Getting walk access and egress times")
walk_access_time = self.field2matrix("walk_accegr_time", origin = True)
walk_egress_time = self.field2matrix("walk_accegr_time", origin = False)

print("Calculating access and egress times")
self.skims["access_time"] = np.where(
nev_access_available,
nev_access_time,
np.where(
microtransit_access_available,
microtransit_access_time,
walk_access_time
)
)
self.skims["egress_time"] = np.where(
nev_egress_available,
nev_egress_time,
np.where(
microtransit_egress_available,
microtransit_egress_time,
walk_egress_time
)
)
self.skims["access_time"] = walk_access_time
self.skims["egress_time"] = walk_egress_time

def get_transit_time(self):
"""
Expand Down Expand Up @@ -424,15 +471,15 @@ def coalesce_results(self):
self.results = pd.DataFrame(
{
"transit": self.unpivot_skim("total_transit_time"),
"walk": self.skims["walk_time"].query("walkTime <= @time_threshold")["walkTime"],
"bike": self.skims["bike_time"].query("BIKE_TIME <= @time_threshold")["BIKE_TIME"],
"walk": self.skims["walk_time"],
"bike": self.skims["bike_time"],
"microtransit": self.unpivot_skim("microtransit_time"),
"nev": self.unpivot_skim("nev_time"),
# "drive_alone": self.unpivot_skim("drive_alone_time")
}
).reset_index().fillna(self.settings["infinity"]).sort_values(
["i", "j"]
)
)

_ebikeMaxTime = self.constants["ebikeMaxDist"] / self.constants["ebikeSpeed"] * 60
_escooterMaxTime = self.constants["escooterMaxDist"] / self.constants["escooterSpeed"] * 60
Expand Down
4 changes: 2 additions & 2 deletions src/main/python/datalake_exporter.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -235,8 +235,8 @@ def write_to_datalake(output_path, models, exclude, env):
os.path.abspath(os.path.join(output_path, '..', 'input', 'zone_term.csv')),
os.path.abspath(os.path.join(output_path, '..', 'input', 'trlink.csv')),
os.path.join(output_path, 'bikeMgraLogsum.csv'),
os.path.join(report_path, 'walkMgrasWithin45Min_AM.csv'),
os.path.join(report_path, 'walkMgrasWithin45Min_MD.csv')
os.path.join(report_path, 'walkMgrasWithin30Min_AM.csv'),
os.path.join(report_path, 'walkMgrasWithin30Min_MD.csv')
]
for file in other_files:
try:
Expand Down

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