Nothing
devtools::load_all(".")
# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path, elevation = "TOBLER", overwrite = T)
# load origin/destination points
points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))
# points <- read.csv(file.path(data_path, "poa_hexgrid.csv")) %>%
# dplyr::sample_n(50)
# load fare structure object
fare_structure_path <- system.file(
"extdata/poa/fares/fares_poa.zip",
package = "r5r"
)
fare_structure <- read_fare_structure(fare_structure_path)
# inputs
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S")
# r5r_core$setDetailedItinerariesV2(TRUE)
# r5r_core$setDetailedItinerariesV2(FALSE)
# a <- capture.output(
system.time(
det_new <- detailed_itineraries(r5r_core,
origins = points[1:5,],
destinations = points[1:5,],
mode = c("WALK", "TRANSIT"),
departure_datetime = departure_datetime,
max_walk_time = 30,
max_trip_duration = 90,
suboptimal_minutes = 5,
# fare_structure = fare_structure,
# max_fare = 9,
time_window = 1,
all_to_all = T,
progress = T,
shortest_path = F,
verbose = F,
drop_geometry = F)
)
# )
det_new$dist <- sf::st_length(det_new)
mapview::mapview(det_new, zcol = "option") +
mapview::mapview(points, xcol="lon", ycol="lat", crs=4326)
mapview::mapview(dplyr::filter(det_new, option == 2), zcol = "route")
mapview::mapview(dplyr::filter(det2,
from_id == "beira_rio_stadium",
to_id == "bus_central_station",
option == 2), zcol = "mode")
r5r::select_mode(c("WALK", "BICYCLE"), "WALK", style = "dit")
set_fare_structure(r5r_core, fare_structure)
r5r_core$setMaxFare(rJava::.jfloat(10.0))
r5r_core$dropFareCalculator()
library("tidyverse")
saveRDS(det, "det_v2.rds")
saveRDS(det2, "det_v1.rds")
library(sf)
library(tidyverse)
det_new %>%
st_set_geometry(NULL) %>%
group_by(option) %>%
summarise(mode = paste(mode, collapse = "|"),
routes = paste(route, collapse = "|"))
det_new %>%
st_set_geometry(NULL) %>%
group_by(option) %>%
summarise(mode = paste(mode, collapse = "|"),
routes = paste(route, collapse = "|"),
total_fare = mean(total_fare))
a <- det_new %>%
st_set_geometry(NULL) %>%
group_by(option) %>%
mutate(sum_dur = sum(segment_duration + wait),
is_diff = sum_dur != total_duration)
suppressWarnings()
a <- r5r_core$message("bla")
# load libraries
library("r5r")
library("data.table")
library("tidyverse")
# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path)
# inputs
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S")
# size <- 15
compute_paths <- function(sm, tw) {
# sample_data <- fread(here::here("data", "sample_15.csv"))
t <- system.time(
dit <- detailed_itineraries(r5r_core,
origins = sample_data[1,],
destinations = sample_data[12,],
mode = c("WALK", "TRANSIT"),
departure_datetime = departure_datetime,
suboptimal_minutes = sm,
time_window = tw,
max_walk_dist = 1000,
max_trip_duration = 120,
progress = T,
shortest_path = F)
)
l <- dit$option |> unique() |> length()
rm(dit)
rJava::.jgc()
return(data.table(suboptimal_minutes = sm,
time_window = tw,
n_options = l,
time = t[3]))
}
# compute paths
# times_old <- lapply(c(15, 25), compute_paths) |> rbindlist()
df <- NULL
for (sm in 0:15) {
for (tw in 1:15) {
df1 <- compute_paths(sm, tw)
if (is.null(df)) {
df <- df1
} else {
df <- rbind(df, df1)
}
}
}
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