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_targets.R
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# Instructions
# 1) library(targets)
# 2) Optional: tar_visnetwork(TRUE) - See the current status of the targets
# 3) Optional: update_github_packages = TRUE, see line 12
# 4) Optional: uncomment controller = crew_controller_local(workers = 4) for multicore running, line 23
# 5) Optional: tar_watch(seconds = 60, targets_only = TRUE, supervise = FALSE, poll_connection = FALSE)
# 6) tar_make()
# Options
# Do you want to reinstall github packages, set to TRUE for first run
update_github_packages = TRUE
# Run the install script
if (!"corenet" %in% installed.packages()) {
source("code/install.R")
}
# Load minimum of libraries (Should use package::function in most cases)
library(targets) # Needed to make targets work
library(magrittr) # Light load of |>
library(sf) # Needed for sf support
set.seed(2023)
httr::set_config(httr::timeout(seconds = 60000))
tar_source()
pkgs = get_pkgs()
tar_option_set(
controller = crew::crew_controller_local(workers = 1),
memory = "transient",
garbage_collection = TRUE,
storage = "worker",
retrieval = "worker",
packages = pkgs,
format = "rds" # default storage format
)
# Targets -----------------------------------------------------------------
if (!file.exists("outputdata")) {
stop("You don't have the outputdata folder")
}
list(
# Detect when parameter file has changed:
tar_target(name = param_file, command = "parameters.json", format = "file"),
# Check Renviron exists, create output directory, load params:
tar_target(parameters, {
renviron_exists = file.exists(".Renviron")
if (!renviron_exists) {
warning("No .Renviron file, routing may not work")
}
jsonlite::read_json(param_file, simplifyVector = TRUE)
}),
tar_target(
dir_local,
{
dir_local = paste0("outputdata/", parameters$date_routing)
dir.create(dir_local, showWarnings = FALSE)
dir_local
}
),
tar_target(
region_folder,
{
region_name_lower = snakecase::to_snake_case(parameters$region)
folder_name = file.path(dir_local, region_name_lower)
dir.create(file.path(folder_name), recursive = TRUE, showWarnings = FALSE)
folder_name
}
),
tar_target(aadt_file, command = "data-raw/AADT_factors.csv", format = "file"),
tar_target(aadt_parameters, {
readr::read_csv(aadt_file)
}),
# See build.R:
tar_target(boundaries_file, "la_regions_scotland_bfe_simplified_2023.geojson", format = "file"),
tar_target(
local_authorities,
sf::read_sf(boundaries_file)
),
tar_target(
region_names,
unique(local_authorities$Region)
),
# Case study area:
tar_target(
local_authorities_region,
{
local_authorities_region = local_authorities |>
filter(Region == parameters$region)
sf::write_sf(local_authorities_region, file.path(region_folder, "local_authorities_region.geojson"), delete_dsn = TRUE)
local_authorities_region
}
),
tar_target(
region_boundary,
local_authorities_region
|> sf::st_union()
),
tar_target(
region_boundary_buffered,
region_boundary |>
stplanr::geo_buffer(dist = parameters$region_buffer_distance)
),
tar_target(zones_boundary_buffered, {
z = readRDS("inputdata/DataZones.Rds") # 6976 zones
z_centroids = sf::st_point_on_surface(z)
z_centroids_within = z_centroids[region_boundary_buffered, ]
z[z[[1]] %in% z_centroids_within[[1]], ]
}),
tar_target(zones, {
z = zones_boundary_buffered
z_centroids = sf::st_point_on_surface(z)
z_centroids_within = z_centroids[region_boundary, ]
z[z[[1]] %in% z_centroids_within[[1]], ]
}),
tar_target(od_national, {
if (parameters$open_data_build) {
od_raw = read_csv("data-raw/od_data_dz_synthetic.csv")
# % cycling in national od data
# sum(od_raw$bicycle) / sum(od_raw$all) # 0.15
} else {
desire_lines_raw = read_TEAMS("secure_data/commute/commute_dl_sub30km.Rds")
od_raw = as_tibble(sf::st_drop_geometry(desire_lines_raw))
}
od_subset = od_raw |>
filter(geo_code1 %in% zones$DataZone) |>
filter(geo_code2 %in% zones$DataZone) |>
filter(dist_euclidean < 20000) |>
filter(dist_euclidean > 1000) |>
filter(all >= parameters$min_flow) |>
mutate_od_commute()
od_subset
}),
tar_target(subpoints_origins, {
# source("data-raw/get_wpz.R")
# sf::read_sf("data-raw/oas.geojson")
if (parameters$open_data_build) {
# create a sample of randomly located points in each zone:
spo = st_sample(zones, size = nrow(zones) * 20, by_polygon = TRUE)
} else {
spo = readRDS("inputdata/oas.Rds")
}
spo[region_boundary, ]
}),
tar_target(subpoints_destinations, {
# source("data-raw/get_wpz.R")
# sf::read_sf("data-raw/workplaces_simple_edinburgh.geojson")
# readRDS("inputdata/workplaces_simple.Rds") #Not enough points when using DataZones
message("Getting destinations")
spd = NULL
if (parameters$open_data_build) {
# message("Getting destinations for ", nrow(zones), " zones")
# spd = st_sample(zones, size = nrow(zones) * 20, by_polygon = TRUE)
spd = subpoints_origins
} else {
spd = read_TEAMS("secure_data/OS/os_poi.Rds")
spd = spd[spd$workplace, ]
}
spd[region_boundary_buffered, ]
}),
tar_target(od_commute_jittered, {
# od_jittered = od_national # for no jittering
# Install the Rust crate and the associated R package:
# system("cargo install --git https://github.com/dabreegster/odjitter")
z = zones
z = z[subpoints_destinations, ]
od = od_national |>
filter(geo_code1 %in% z$DataZone) |>
filter(geo_code2 %in% z$DataZone)
odj = odjitter::jitter(
od = od,
zones = z,
subpoints_origins = subpoints_origins,
subpoints_destinations = subpoints_destinations,
disaggregation_threshold = 30,
deduplicate_pairs = FALSE
)
odj$dist_euclidean_jittered = as.numeric(sf::st_length(odj))
odj
}),
tar_target(od_commute_subset, {
odcs = od_commute_jittered |>
filter(dist_euclidean_jittered < 16000) |>
filter(dist_euclidean_jittered > 1000) |>
slice_max(n = parameters$max_to_route, order_by = all, with_ties = FALSE)
odcs
}),
# School OD ---------------------------------------------------------------
tar_target(od_school, {
# Get School OD
if (parameters$open_data_build) {
schools_dl = sf::read_sf("data-raw/school_desire_lines_open.geojson")
} else {
schools_dl = read_TEAMS("secure_data/schools/school_dl_sub30km.Rds")
}
schools_dl = schools_dl[region_boundary_buffered, op = sf::st_within]
schools_dl$dist_euclidean_jittered = round(as.numeric(sf::st_length(schools_dl)))
schools_dl = schools_dl |>
filter(dist_euclidean_jittered < 10000) |>
filter(dist_euclidean_jittered > 1000) |>
slice_max(order_by = all, n = parameters$max_to_route, with_ties = FALSE) |>
mutate_od_school()
schools_dl
}),
# School routing ----------------------------------------------------------
tar_target(rs_school_fastest, {
rs = get_routes(
od = od_school |> slice_max(n = parameters$max_to_route, order_by = all, with_ties = FALSE),
plans = "fastest",
purpose = "school",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_school_fastest, {
length(rs_school_fastest) # Hack for scheduling
}),
tar_target(rs_school_quietest, {
length(done_school_fastest)
rs = get_routes(
od = od_school |> slice_max(n = parameters$max_to_route, order_by = all, with_ties = FALSE),
plans = "quietest",
purpose = "school",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_school_quietest, {
length(rs_school_quietest) # Hack for scheduling
}),
# Balanced:
tar_target(rs_school_balanced, {
length(done_school_quietest)
rs = get_routes(
od = od_school |> slice_max(n = parameters$max_to_route, order_by = all, with_ties = FALSE),
plans = "balanced",
purpose = "school",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_school_balanced, {
length(rs_school_balanced) # Hack for scheduling
}),
tar_target(rs_school_ebike, {
length(done_school_quietest)
rs = get_routes(
od = od_school |> slice_max(n = parameters$max_to_route, order_by = all, with_ties = FALSE),
plans = "ebike",
purpose = "school",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_school_ebike, {
length(rs_school_ebike) # Hack for scheduling
}),
# Commute routing ---------------------------------------------------------
tar_target(rs_commute_fastest, {
length(done_school_ebike) # Do school routing first
rs = get_routes(
od = od_commute_subset,
plans = "fastest",
purpose = "commute",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_commute_fastest, {
length(rs_commute_fastest) # Hack for scheduling
}),
tar_target(rs_commute_balanced, {
length(done_commute_fastest)
rs = get_routes(
od = od_commute_subset,
plans = "balanced",
purpose = "commute",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_commute_balanced, {
length(rs_commute_balanced) # Hack for scheduling
}),
tar_target(rs_commute_quietest, {
length(done_commute_fastest)
rs = get_routes(
od = od_commute_subset,
plans = "quietest",
purpose = "commute",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_commute_quietest, {
length(rs_commute_quietest) # Hack for scheduling
}),
tar_target(rs_commute_ebike, {
length(done_commute_quietest)
rs = get_routes(
od = od_commute_subset,
plans = "ebike",
purpose = "commute",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_commute_ebike, {
length(rs_commute_ebike) # Hack for scheduling
}),
# Commute routing post-processing -----------------------------------------
tar_target(r_commute_fastest, {
rs_commute_fastest[[1]]$routes
}),
tar_target(r_commute_quietest, {
rs_commute_quietest[[1]]$routes
}),
tar_target(r_commute_ebike, {
rs_commute_ebike[[1]]$routes
}),
tar_target(r_commute_balanced, {
rs_commute_balanced[[1]]$routes
}),
tar_target(rnet_gq_commute_fastest, {
segments2rnet(rs_commute_fastest[[1]]$segments)
}),
tar_target(rnet_gq_commute_quietest, {
segments2rnet(rs_commute_quietest[[1]]$segments)
}),
tar_target(rnet_gq_commute_ebike, {
segments2rnet(rs_commute_ebike[[1]]$segments)
}),
tar_target(rnet_gq_commute_balanced, {
segments2rnet(rs_commute_balanced[[1]]$segments)
}),
# School routing post-processing -----------------------------------------
tar_target(r_school_fastest, {
rs_school_fastest[[1]]$routes
}),
tar_target(r_school_quietest, {
rs_school_quietest[[1]]$routes
}),
tar_target(r_school_ebike, {
rs_school_ebike[[1]]$routes
}),
tar_target(r_school_balanced, {
rs_school_balanced[[1]]$routes
}),
tar_target(rnet_gq_school_fastest, {
segments2rnet(rs_school_fastest[[1]]$segments)
}),
tar_target(rnet_gq_school_quietest, {
segments2rnet(rs_school_quietest[[1]]$segments)
}),
tar_target(rnet_gq_school_ebike, {
segments2rnet(rs_school_ebike[[1]]$segments)
}),
tar_target(rnet_gq_school_balanced, {
segments2rnet(rs_school_balanced[[1]]$segments)
}),
# Commute Uptake ----------------------------------------------------------
tar_target(uptake_commute_fastest, {
r_commute_fastest |>
aadt_adjust(purpose = "commute", aadt_parameters = aadt_parameters) |>
get_uptake_scenarios()
}),
tar_target(uptake_commute_quietest, {
r_commute_quietest |>
aadt_adjust(purpose = "commute", aadt_parameters = aadt_parameters) |>
get_uptake_scenarios()
}),
tar_target(uptake_commute_ebike, {
r_commute_ebike |>
aadt_adjust(purpose = "commute", aadt_parameters = aadt_parameters) |>
get_uptake_scenarios()
}),
tar_target(uptake_commute_balanced, {
r_commute_balanced |>
aadt_adjust(purpose = "commute", aadt_parameters = aadt_parameters) |>
get_uptake_scenarios()
}),
# School Uptake ----------------------------------------------------------
tar_target(uptake_school_fastest, {
routes = r_school_fastest |>
aadt_adjust(purpose = "school", aadt_parameters = aadt_parameters) |>
get_uptake_scenarios(purpose = "school")
routes
}),
tar_target(uptake_school_quietest, {
routes = r_school_quietest |>
aadt_adjust(purpose = "school", aadt_parameters = aadt_parameters) |>
get_uptake_scenarios(purpose = "school")
routes
}),
tar_target(uptake_school_ebike, {
routes = r_school_ebike |>
aadt_adjust(purpose = "school", aadt_parameters = aadt_parameters) |>
get_uptake_scenarios(purpose = "school")
routes
}),
tar_target(uptake_school_balanced, {
routes = r_school_balanced |>
aadt_adjust(purpose = "school", aadt_parameters = aadt_parameters) |>
get_uptake_scenarios(purpose = "school")
routes
}),
# Commute RNets -----------------------------------------------------------
tar_target(rnet_commute_fastest, {
stplanr::overline2(uptake_commute_fastest, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
}),
tar_target(rnet_commute_quietest, {
stplanr::overline2(uptake_commute_quietest, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
}),
tar_target(rnet_commute_ebike, {
stplanr::overline2(uptake_commute_ebike, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
}),
tar_target(rnet_commute_balanced, {
stplanr::overline2(uptake_commute_balanced, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
}),
# Primary School RNets -----------------------------------------------------------
tar_target(rnet_primary_fastest, {
rnet = uptake_school_fastest
rnet = rnet[rnet$schooltype == "primary", ]
rnet = stplanr::overline2(rnet, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
saveRDS(rnet, paste0(region_folder, "/rnet_primary_school_fastest.Rds"))
rnet
}),
tar_target(rnet_primary_quietest, {
rnet = uptake_school_quietest
rnet = rnet[rnet$schooltype == "primary", ]
rnet = stplanr::overline2(rnet, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
saveRDS(rnet, paste0(region_folder, "/rnet_primary_school_quietest.Rds"))
rnet
}),
tar_target(rnet_primary_ebike, {
rnet = uptake_school_ebike
rnet = rnet[rnet$schooltype == "primary", ]
rnet = stplanr::overline2(rnet, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
saveRDS(rnet, paste0(region_folder, "/rnet_primary_school_ebike.Rds"))
rnet
}),
tar_target(rnet_primary_balanced, {
rnet = uptake_school_balanced
rnet = rnet[rnet$schooltype == "primary", ]
rnet = stplanr::overline2(rnet, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
saveRDS(rnet, paste0(region_folder, "/rnet_primary_school_balanced.Rds"))
rnet
}),
# Secondary School RNets -----------------------------------------------------------
tar_target(rnet_secondary_fastest, {
rnet = uptake_school_fastest
rnet = rnet[rnet$schooltype == "secondary", ]
rnet = stplanr::overline2(rnet, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
saveRDS(rnet, paste0(region_folder, "/rnet_primary_school_fastest.Rds"))
rnet
}),
tar_target(rnet_secondary_quietest, {
rnet = uptake_school_quietest
rnet = rnet[rnet$schooltype == "secondary", ]
rnet = stplanr::overline2(rnet, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
saveRDS(rnet, paste0(region_folder, "/rnet_primary_school_quietest.Rds"))
rnet
}),
tar_target(rnet_secondary_ebike, {
rnet = uptake_school_ebike
rnet = rnet[rnet$schooltype == "secondary", ]
rnet = stplanr::overline2(rnet, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
saveRDS(rnet, paste0(region_folder, "/rnet_primary_school_ebike.Rds"))
rnet
}),
tar_target(rnet_secondary_balanced, {
rnet = uptake_school_balanced
rnet = rnet[rnet$schooltype == "secondary", ]
rnet = stplanr::overline2(rnet, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
saveRDS(rnet, paste0(region_folder, "/rnet_primary_school_balanced.Rds"))
rnet
}),
# Commute Zone stats ---------------------------------------------------------
tar_target(commute_stats_baseline, {
stats = sf::st_drop_geometry(od_commute_subset)
stats = aadt_adjust(stats, purpose = "commute", aadt_parameters = aadt_parameters)
stats_from = dplyr::group_by(stats, geo_code1) |>
dplyr::summarise(
all = sum(all, na.rm = TRUE),
bicycle = sum(bicycle, na.rm = TRUE),
car = sum(car, na.rm = TRUE),
foot = sum(foot, na.rm = TRUE),
public_transport = sum(public_transport, na.rm = TRUE),
taxi = sum(taxi, na.rm = TRUE)
)
stats_to = dplyr::group_by(stats, geo_code2) |>
dplyr::summarise(
all = sum(all, na.rm = TRUE),
bicycle = sum(bicycle, na.rm = TRUE),
car = sum(car, na.rm = TRUE),
foot = sum(foot, na.rm = TRUE),
public_transport = sum(public_transport, na.rm = TRUE),
taxi = sum(taxi, na.rm = TRUE)
)
names(stats_from)[1] = "DataZone"
names(stats_to)[1] = "DataZone"
names(stats_from)[2:ncol(stats_from)] = paste0("comm_orig_", names(stats_from)[2:ncol(stats_from)])
names(stats_to)[2:ncol(stats_to)] = paste0("comm_dest_", names(stats_to)[2:ncol(stats_to)])
stats = dplyr::full_join(stats_from, stats_to, by = "DataZone")
stats
}),
tar_target(commute_stats_fastest, {
make_commute_stats(uptake_commute_fastest, "fastest")
}),
tar_target(commute_stats_quietest, {
make_commute_stats(uptake_commute_quietest, "quietest")
}),
tar_target(commute_stats_ebike, {
make_commute_stats(uptake_commute_ebike, "ebike")
}),
tar_target(commute_stats_balanced, {
make_commute_stats(uptake_commute_balanced, "balanced")
}),
# School Zone stats ---------------------------------------------------------
tar_target(school_stats_baseline, {
stats = sf::st_drop_geometry(od_school)
stats = aadt_adjust(stats, purpose = "school", aadt_parameters = aadt_parameters)
stats = dplyr::group_by(stats, SeedCode, schooltype) |>
dplyr::summarise(
all = sum(all, na.rm = TRUE),
bicycle = sum(bicycle, na.rm = TRUE),
car = sum(car, na.rm = TRUE),
foot = sum(foot, na.rm = TRUE),
public_transport = sum(public_transport, na.rm = TRUE),
other = sum(other, na.rm = TRUE)
)
stats$schooltype = paste0("schl_", stats$schooltype, "_dest")
stats = tidyr::pivot_wider(stats,
id_cols = c("SeedCode"),
names_from = c("schooltype"),
names_glue = "{schooltype}_{.value}",
values_from = names(stats)[3:ncol(stats)]
)
stats
}),
tar_target(school_stats_from_baseline, {
stats = sf::st_drop_geometry(od_school)
stats = aadt_adjust(stats, purpose = "school", aadt_parameters = aadt_parameters)
stats = dplyr::group_by(stats, DataZone, schooltype) |>
dplyr::summarise(
all = sum(all, na.rm = TRUE),
bicycle = sum(bicycle, na.rm = TRUE),
car = sum(car, na.rm = TRUE),
foot = sum(foot, na.rm = TRUE),
public_transport = sum(public_transport, na.rm = TRUE),
other = sum(other, na.rm = TRUE)
)
stats$schooltype = paste0("schl_", stats$schooltype, "_orig")
stats = tidyr::pivot_wider(stats,
id_cols = c("DataZone"),
names_from = c("schooltype"),
names_glue = "{schooltype}_{.value}",
values_from = names(stats)[3:ncol(stats)]
)
stats
}),
tar_target(school_stats_fastest, {
make_school_stats(uptake_school_fastest, "fastest")
}),
tar_target(school_stats_quietest, {
make_school_stats(uptake_school_quietest, "quietest")
}),
tar_target(school_stats_ebike, {
make_school_stats(uptake_school_ebike, "ebike")
}),
tar_target(school_stats_balanced, {
make_school_stats(uptake_school_balanced, "balanced")
}),
tar_target(school_stats_from_fastest, {
make_school_stats_from(uptake_school_fastest, "fastest")
}),
tar_target(school_stats_from_quietest, {
make_school_stats_from(uptake_school_quietest, "quietest")
}),
tar_target(school_stats_from_ebike, {
make_school_stats_from(uptake_school_ebike, "ebike")
}),
tar_target(school_stats_from_balanced, {
make_school_stats_from(uptake_school_balanced, "balanced")
}),
# Combine stats ---------------------------------------------------------
tar_target(school_stats, {
# Ebike routes for ebike scenario
ebike = school_stats_ebike
fastest = school_stats_fastest
ebike = ebike[, !grepl("go_dutch", names(ebike))]
# Can't use quietness/hilliness for ebike
ebike = ebike[, !grepl("(quietness|hilliness)", names(ebike))]
fastest = fastest[, !grepl("ebike", names(fastest))]
names(ebike) = gsub("_ebike$", "_fastest", names(ebike))
stats = dplyr::left_join(school_stats_baseline, fastest, by = "SeedCode")
stats = dplyr::left_join(stats, ebike, by = "SeedCode")
stats = dplyr::left_join(stats, school_stats_quietest, by = "SeedCode")
stats
}),
tar_target(school_stats_from, {
# Ebike routes for ebike scenario
ebike = school_stats_from_ebike
fastest = school_stats_from_fastest
ebike = ebike[, !grepl("go_dutch", names(ebike))]
# Can't use quietness/hilliness for ebike
ebike = ebike[, !grepl("(quietness|hilliness)", names(ebike))]
fastest = fastest[, !grepl("ebike", names(fastest))]
names(ebike) = gsub("_ebike$", "_fastest", names(ebike))
stats = dplyr::left_join(school_stats_from_baseline, fastest, by = "DataZone")
stats = dplyr::left_join(stats, ebike, by = "DataZone")
stats = dplyr::left_join(stats, school_stats_from_quietest, by = "DataZone")
stats
}),
tar_target(commute_stats, {
# Ebike routes for ebike scenario
ebike = commute_stats_ebike
fastest = commute_stats_fastest
ebike = ebike[, !grepl("go_dutch", names(ebike))]
# Can't use quietness/hilliness for ebike
ebike = ebike[, !grepl("(quietness|hilliness)", names(ebike))]
fastest = fastest[, !grepl("ebike", names(fastest))]
names(ebike) = gsub("_ebike$", "_fastest", names(ebike))
stats = dplyr::left_join(commute_stats_baseline, fastest, by = "DataZone")
stats = dplyr::left_join(stats, ebike, by = "DataZone")
stats = dplyr::left_join(stats, commute_stats_quietest, by = "DataZone")
stats
}),
tar_target(zones_stats, {
stats = dplyr::full_join(commute_stats, school_stats_from, by = "DataZone")
stats = dplyr::full_join(stats, utility_stats, by = "DataZone")
stats
}),
# Now covered in build.R:
# tar_target(zones_stats_json, {
# export_zone_json(zones_stats, "DataZone", path = region_folder)
# }),
# tar_target(school_stats_json, {
# export_zone_json(school_stats, "SeedCode", path = region_folder)
# }),
# Shopping OD ---------------------------------------------------------
tar_target(trip_purposes, {
trip_purposes = read.csv("./data-raw/scottish-household-survey-2012-19.csv")
go_home = trip_purposes$Mean[trip_purposes$Purpose == "Go Home"]
trip_purposes = trip_purposes |>
filter(Purpose != "Sample size (=100%)") |>
mutate(adjusted_mean = Mean / (sum(Mean) - go_home) * sum(Mean))
trip_purposes
}),
tar_target(os_pois, {
check = length(parameters)
check = length(region_boundary)
# Get shopping destinations from secure OS data
path_teams = Sys.getenv("NPT_TEAMS_PATH")
os_pois_raw = readRDS(file.path(path_teams, "secure_data/OS/os_poi.Rds"))
os_pois_subset = os_pois_raw |>
mutate(groupname = as.character(groupname))
os_pois_subset[region_boundary_buffered, ] |>
sf::st_transform("EPSG:27700")
}),
tar_target(grid, {
grid = readRDS("./inputdata/grid_scot.Rds")
grid = sf::st_transform(grid, "EPSG:4326")
grid = grid[region_boundary_buffered, ]
grid |> sf::st_transform("EPSG:27700")
}),
tar_target(oas, {
oas = readRDS("./inputdata/oas.Rds")
oas[region_boundary, ]
}),
tar_target(intermediate_zones, {
izs = sf::read_sf("inputdata/SG_IntermediateZone_Bdry_2011.gpkg")
izs_centroids = sf::st_centroid(izs)
izs_centroids_within = izs_centroids[region_boundary |> sf::st_transform(27700), ]
izs[izs[[1]] %in% izs_centroids_within[[1]], ]
}),
# Utility OD -------------------------------------------------------------
tar_target(od_shopping, {
od_shopping = make_od(
oas, os_pois, grid,
purpose = "shopping",
trip_purposes,
zones, parameters
)
od_shopping
}),
tar_target(od_visiting, {
od_visiting = make_od(
oas, os_pois, grid,
purpose = "visiting",
trip_purposes,
zones, parameters
)
od_visiting
}),
tar_target(od_leisure, {
od_leisure = make_od(
oas, os_pois, grid,
purpose = "leisure",
trip_purposes,
zones, parameters
)
od_leisure
}),
# Combined utility trip purposes --------------------------------------------
tar_target(od_utility_combined, {
od_utility_combined = rbind(od_shopping, od_visiting, od_leisure) |>
dplyr::slice_max(n = parameters$max_to_route, order_by = all, with_ties = FALSE)
sum(od_utility_combined$bicycle) / sum(od_utility_combined$all)
# Get % cycling for commuting per zone
# pcycle_regional = sum(commute_stats$comm_orig_bicycle, na.rm = TRUE) /
# sum(commute_stats$comm_orig_all, na.rm = TRUE)
pcycle_national = 0.016
commute_stats_minimal = commute_stats |>
dplyr::select(DataZone, comm_orig_bicycle, comm_orig_all)
cycling_multiplier = commute_stats_minimal |>
dplyr::transmute(
DataZone,
multiplier = (comm_orig_bicycle / comm_orig_all) /
pcycle_national
) |>
# 0 to 0.1:
dplyr::mutate(multiplier = case_when(
multiplier == 0 ~ 0.1,
TRUE ~ multiplier
))
# summary(cycling_multiplier$multiplier)
# Add new cycling multiplier column to od_utility_combined
od_utility_combined = od_utility_combined |>
dplyr::left_join(cycling_multiplier, by = join_by(geo_code1 == DataZone)) |>
# Convert NAs to 1:
dplyr::mutate(multiplier = case_when(
is.na(multiplier) ~ 1,
TRUE ~ multiplier
)) |>
dplyr::mutate(
bicycle_new = bicycle * multiplier,
car = car - (bicycle_new - bicycle),
bicycle = bicycle_new
) |>
dplyr::select(-multiplier, -bicycle_new)
# Check new % cycling:
# sum(od_utility_combined$bicycle) / sum(od_utility_combined$all)
# Ensure the columns and distance units are identical to the other routing types
# (apart from the additional trip purpose column)
od_utility_combined = od_utility_combined |>
dplyr::mutate(
dist_euclidean = length_euclidean_unjittered * 1000,
dist_euclidean_jittered = length_euclidean_jittered * 1000
) |>
dplyr::select(
geo_code1, geo_code2, car, foot, bicycle, all,
dist_euclidean, public_transport, taxi, geometry,
dist_euclidean_jittered, purpose
)
# Add Start and End DataZones for stats
# geo_code1 and 2 refere to non-Data Zone ids
end_point = lwgeom::st_endpoint(od_utility_combined)
end_point = sf::st_join(sf::st_as_sf(end_point), zones)
od_utility_combined$endDZ = end_point$DataZone
start_point = lwgeom::st_startpoint(od_utility_combined)
start_point = sf::st_join(sf::st_as_sf(start_point), zones)
od_utility_combined$startDZ = start_point$DataZone
od_utility_combined
}),
tar_target(rs_utility_fastest, {
length(done_commute_ebike) # Do school routing first
rs = get_routes(
od = od_utility_combined |> dplyr::slice_max(n = parameters$max_to_route, order_by = all, with_ties = FALSE),
plans = "fastest",
purpose = "utility",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_utility_fastest, {
length(rs_utility_fastest) # Hack for scheduling
}),
tar_target(rs_utility_quietest, {
length(done_utility_fastest)
rs = get_routes(
od = od_utility_combined,
plans = "quietest",
purpose = "utility",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_utility_quietest, {
length(rs_utility_quietest) # Hack for scheduling
}),
tar_target(rs_utility_ebike, {
length(done_utility_quietest)
rs = get_routes(
od = od_utility_combined,
plans = "ebike",
purpose = "utility",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_utility_ebike, {
length(rs_utility_ebike) # Hack for scheduling
}),
tar_target(rs_utility_balanced, {
length(done_commute_balanced)
rs = get_routes(
od = od_utility_combined,
plans = "balanced",
purpose = "utility",
folder = region_folder,
date = parameters$date_routing,
segments = "both"
)
rs
}),
tar_target(done_utility_balanced, {
length(rs_utility_balanced) # Hack for scheduling
}),
# Utility routing post-processing -----------------------------------------
tar_target(r_utility_fastest, {
rs_utility_fastest[[1]]$routes
}),
tar_target(r_utility_quietest, {
rs_utility_quietest[[1]]$routes
}),
tar_target(r_utility_ebike, {
rs_utility_ebike[[1]]$routes
}),
tar_target(r_utility_balanced, {
rs_utility_balanced[[1]]$routes
}),
tar_target(rnet_gq_utility_fastest, {
segments2rnet(rs_utility_fastest[[1]]$segments)
}),
tar_target(rnet_gq_utility_quietest, {
segments2rnet(rs_utility_quietest[[1]]$segments)
}),
tar_target(rnet_gq_utility_ebike, {
segments2rnet(rs_utility_ebike[[1]]$segments)
}),
tar_target(rnet_gq_utility_balanced, {
segments2rnet(rs_utility_balanced[[1]]$segments)
}),
# Utility Uptake ----------------------------------------------------------
tar_target(uptake_utility_fastest, {
routes = r_utility_fastest |>
filter(distances < 10000) |>
get_uptake_scenarios(purpose = "utility")
routes
}),
tar_target(uptake_utility_quietest, {
routes = r_utility_quietest |>
filter(distances < 10000) |>
get_uptake_scenarios(purpose = "utility")
routes
}),
tar_target(uptake_utility_ebike, {
routes = r_utility_ebike |>
filter(distances < 10000) |>
get_uptake_scenarios(purpose = "utility")
routes
}),
tar_target(uptake_utility_balanced, {
routes = r_utility_balanced |>
filter(distances < 10000) |>
get_uptake_scenarios(purpose = "utility")
routes
}),
# Utility Rnets -----------------------------------------------------------
tar_target(rnet_utility_fastest, {
stplanr::overline2(uptake_utility_fastest, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
}),
tar_target(rnet_utility_quietest, {
stplanr::overline2(uptake_utility_quietest, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
}),
tar_target(rnet_utility_ebike, {
stplanr::overline2(uptake_utility_ebike, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
}),
tar_target(rnet_utility_balanced, {
stplanr::overline2(uptake_utility_balanced, c("bicycle", "bicycle_go_dutch", "bicycle_ebike"))
}),
# Utility Zone stats ---------------------------------------------------------
tar_target(utility_stats_baseline, {
stats = sf::st_drop_geometry(od_utility_combined)
stats = stats[, c(
"startDZ", "endDZ", "purpose", "all", "car",
"foot", "bicycle", "public_transport", "taxi"
)]
stats_shopping = aadt_adjust(stats[stats$purpose == "shopping", ],
purpose = "shopping",
aadt_parameters = aadt_parameters
)
stats_leisure = aadt_adjust(stats[stats$purpose == "leisure", ],
purpose = "leisure",
aadt_parameters = aadt_parameters
)
stats_visiting = aadt_adjust(stats[stats$purpose == "visiting", ],
purpose = "visiting",
aadt_parameters = aadt_parameters
)
stats = rbind(stats_shopping, stats_leisure, stats_visiting)
stats_orig = stats |>
dplyr::select(!endDZ) |>
dplyr::group_by(startDZ, purpose) |>
dplyr::summarise_all(sum, na.rm = TRUE)
stats_dest = stats |>