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access.py
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access.py
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#do this before importing r5py
import sys
sys.argv.append(["--max-memory", "90%"])
import r5py
from r5py import TransportNetwork, TravelTimeMatrixComputer
from r5py import TransportMode
from tqdm import tqdm
import datetime
import numpy as np
import os
import pandas as pd
import prep_pop_ghsl
import prep_bike_osm
#for use of r5py in journey gap calculations
def prepare_mode_settings(**kwargs):
mode_settings = {}
general_settings = {
'departure': datetime.datetime(2023,3,15,8,30),
#'departure_time_window': datetime.timedelta(hours=1), #this is the default
#'percentiles': [50], #this is the default
'max_time':datetime.timedelta(hours=2),
'max_time_walking':datetime.timedelta(hours=2),
'max_time_cycling':datetime.timedelta(hours=2),
'max_time_driving':datetime.timedelta(hours=2),
'speed_walking':4.8,
'speed_cycling':12.0,
'max_public_transport_rides':4,
}
general_settings.update(kwargs)
walk_settings = general_settings.copy()
walk_settings.update({
'transport_modes':[TransportMode.WALK],
'access_modes':[TransportMode.WALK],
})
mode_settings['WALK'] = walk_settings
transit_settings = general_settings.copy()
transit_settings.update({
'transport_modes':[TransportMode.TRANSIT],
'access_modes':[TransportMode.WALK],
})
mode_settings['TRANSIT'] = transit_settings
bike_lts1_settings = general_settings.copy()
bike_lts1_settings.update({
'transport_modes':[TransportMode.WALK, TransportMode.BICYCLE],
'access_modes':[TransportMode.WALK, TransportMode.BICYCLE],
'max_time_walking':datetime.timedelta(minutes=10),
'speed_walking':4,
'max_bicycle_traffic_stress':1
})
mode_settings['BIKE_LTS1'] = bike_lts1_settings
bike_lts2_settings = general_settings.copy()
bike_lts2_settings.update({
'transport_modes':[TransportMode.WALK, TransportMode.BICYCLE],
'access_modes':[TransportMode.WALK, TransportMode.BICYCLE],
'max_time_walking':datetime.timedelta(minutes=10),
'speed_walking':4,
'max_bicycle_traffic_stress':2
})
mode_settings['BIKE_LTS2'] = bike_lts2_settings
bike_lts4_settings = general_settings.copy()
bike_lts4_settings.update({
'transport_modes':[TransportMode.WALK, TransportMode.BICYCLE],
'access_modes':[TransportMode.WALK, TransportMode.BICYCLE],
'max_time_walking':datetime.timedelta(minutes=10),
'speed_walking':4,
'max_bicycle_traffic_stress':4
})
mode_settings['BIKE_LTS4'] = bike_lts4_settings
car_settings = general_settings.copy()
car_settings.update({
'transport_modes':[TransportMode.CAR],
'access_modes':[TransportMode.CAR],
})
mode_settings['CAR'] = car_settings
return mode_settings
def value_of_cxn(from_pop, to_dests, t_min):
#see SSTI's Measuring Accessibility, appendix (p.68)
#rough average of work and non-work
baseval = from_pop * to_dests
return baseval * np.e ** (-0.05 * t_min)
def journey_gap_calculations(
folder_name,
current_year,
boundaries_latlon,
gtfs_filenames,
gtfs_wednesdays,
access_resolution = 2000, #m
min_pop = 2000, #defaults to 500 ppl/km2
):
for file in [folder_name+'temp/access/grid_pop.geojson',
folder_name+'temp/access/city_ltstagged.pbf']:
if os.path.exists(file):
os.remove(file)
#prep pop -- it would probably be better to do this straight from GHSL, ugh
grid_gdf_latlon = prep_pop_ghsl.setup_grid_ghsl(
boundaries_latlon.unary_union,
access_resolution,
folder_name+"geodata/population/pop_2020.tif",
'ESRI:54009',
adjust_pop = True
)
grid_gdf_latlon['id'] = grid_gdf_latlon.index
selection = grid_gdf_latlon.population > min_pop
grid_gdf_latlon = grid_gdf_latlon[selection]
grid_gdf_latlon.to_file(folder_name+'temp/access/grid_pop.geojson')
#prep osm (add LTS values)
original_filename = folder_name+"temp/city.pbf"
biketagged_filename = folder_name+"temp/access/city_ltstagged.pbf"
prep_bike_osm.add_lts_tags(original_filename, biketagged_filename)
full_gtfs_filenames = [folder_name+'temp/gtfs/'+name for name in gtfs_filenames]
print(full_gtfs_filenames)
try:
transport_network = TransportNetwork(
biketagged_filename,
full_gtfs_filenames
)
except:
print('r5py ERROR')
wednesday_mornings = [datetime.datetime.strptime(wed+' 08:30:00', '%Y%m%d %H:%M:%S') for wed in gtfs_wednesdays]
latest_wednesday = max(wednesday_mornings)
mode_settings=prepare_mode_settings(departure = latest_wednesday)
points_gdf_latlon = grid_gdf_latlon.copy()
points_gdf_latlon.geometry = grid_gdf_latlon.centroid
ttms = {}
try:
for mode in ['TRANSIT', 'BIKE_LTS1', 'CAR']:#mode_settings.keys():
print(f'computing ttm for {mode}')
ttm_computer = TravelTimeMatrixComputer(transport_network, points_gdf_latlon,**mode_settings[mode])
ttm_long = ttm_computer.compute_travel_times()
ttm_wide = pd.pivot(ttm_long, index='from_id', columns='to_id', values='travel_time')
ttms[mode] = ttm_wide
ttms[mode].to_csv(folder_name+'temp/access/'+mode+'_ttm.csv')
except:
print('FAILED to calculate ttm for', mode)
return False
#3 versions - cumsum, time, value
print('calculating indicators for journey gaps')
for origin_id in tqdm(list(grid_gdf_latlon.index)):
origin_pop = grid_gdf_latlon.loc[origin_id, 'population']
grid_gdf_latlon.loc[origin_id, 'time_ratio_weighted_sum'] = 0
if origin_pop > 0:
#we weight by both the destination pop and by the gravity model
# factor, by multiplying by both of them
# see https://docs.google.com/spreadsheets/d/11SpKFZfN-pr3ieftGoyNBPx5lcqY0b0Wg6RBgdevkMY/edit#gid=0
weighting_factor_sum = 0
for dest_id in grid_gdf_latlon.index:
dest_pop = grid_gdf_latlon.loc[dest_id, 'population']
if dest_pop > 0 and not origin_id == dest_id:
car_time = ttms['CAR'].loc[origin_id, dest_id]
sustrans_time = min(ttms['TRANSIT'].loc[origin_id, dest_id],ttms['BIKE_LTS1'].loc[origin_id, dest_id])
time_ratio = (sustrans_time/car_time)
#we pretend origin is 1 for now, just to get the weighting factor
#we'll weight by origin pop when we calculate analysis-area-wide indicators
weighting_factor = value_of_cxn(1, dest_pop, sustrans_time)
time_ratio_with_weighting = time_ratio * weighting_factor
if not np.isnan(time_ratio_with_weighting):
weighting_factor_sum += weighting_factor
grid_gdf_latlon.loc[origin_id, 'time_ratio_weighted_sum'] += time_ratio_with_weighting
journey_gap = grid_gdf_latlon.loc[origin_id, 'time_ratio_weighted_sum'] / weighting_factor_sum
grid_gdf_latlon.loc[origin_id, 'journey_gap_unweighted'] = journey_gap
grid_gdf_latlon.loc[origin_id, 'journey_gap_weighted'] = journey_gap * origin_pop
grid_gdf_latlon.to_file(f'{folder_name}geodata/access/grid_pop_evaluated_{current_year}.geojson')
return True