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#' Calculate minute-by-minute travel times between origin destination pairs
#'
#' Detailed computation of travel time estimates between one or multiple origin
#' destination pairs. Results show the travel time of the fastest route
#' alternative departing each minute within a specified time window. Please
#' note this function can be very memory intensive for large data sets and time
#' windows.
#'
#' @template r5r_core
#' @template common_arguments
#' @param time_window An integer. The time window in minutes for which `r5r`
#' will calculate multiple travel time matrices departing each minute.
#' Defaults to 10 minutes. The function returns the result based on median
#' travel times. Please read the time window vignette for more details on its
#' usage `vignette("time_window", package = "r5r")`
#' @template draws_per_minute
#' @template verbose
#' @param breakdown A logical. Whether to include detailed information about
#' each trip in the output. If `FALSE` (the default), the output lists the
#' total time between each origin-destination pair and the routes used to
#' complete the trip for each minute of the specified time window. If `TRUE`,
#' the output includes the total access, waiting, in-vehicle and transfer
#' time of each trip. Please note that setting this parameter to `TRUE` makes
#' the function significantly slower.
#'
#' @return A `data.table` with travel time estimates (in minutes) and the
#' routes used in each trip between origin and destination pairs, for each
#' minute of the specified time window. Each set of origin, destination and
#' departure minute can appear up to N times, where N is the number of Monte
#' Carlo draws specified in the function arguments (please note that this
#' only applies when the GTFS feeds that describe the transit network include
#' a frequencies table, otherwise only a single draw is performed). A pair is
#' completely absent from the final output if no trips could be completed in
#' any of the minutes of the time window. If for a single pair trips could be
#' completed in some of the minutes of the time window, but not for all of
#' them, the minutes in which trips couldn't be completed will have `NA`
#' travel time and routes used. If `output_dir` is not `NULL`, the function
#' returns the path specified in that parameter, in which the `.csv` files
#' containing the results are saved.
#'
#' @template transport_modes_section
#' @template lts_section
#' @template datetime_parsing_section
#' @template raptor_algorithm_section
#'
#' @family routing
#'
#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
#' library(r5r)
#'
#' # build transport network
#' data_path <- system.file("extdata/poa", package = "r5r")
#' r5r_core <- setup_r5(data_path)
#'
#' # load origin/destination points
#' points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))
#'
#' departure_datetime <- as.POSIXct(
#' "13-05-2019 14:00:00",
#' format = "%d-%m-%Y %H:%M:%S"
#' )
#'
#' # by default only returns the total time between each pair in each minute of
#' # the specified time window
#' ettm <- expanded_travel_time_matrix(
#' r5r_core,
#' origins = points,
#' destinations = points,
#' mode = c("WALK", "TRANSIT"),
#' time_window = 20,
#' departure_datetime = departure_datetime,
#' max_trip_duration = 60
#' )
#' head(ettm)
#'
#' # when breakdown = TRUE the output contains much more information
#' ettm <- expanded_travel_time_matrix(
#' r5r_core,
#' origins = points,
#' destinations = points,
#' mode = c("WALK", "TRANSIT"),
#' time_window = 20,
#' departure_datetime = departure_datetime,
#' max_trip_duration = 60,
#' breakdown = TRUE
#' )
#' head(ettm)
#'
#' stop_r5(r5r_core)
#' @export
expanded_travel_time_matrix <- function(r5r_core,
origins,
destinations,
mode = "WALK",
mode_egress = "WALK",
departure_datetime = Sys.time(),
time_window = 10L,
breakdown = FALSE,
max_walk_time = Inf,
max_bike_time = Inf,
max_car_time = Inf,
max_trip_duration = 120L,
walk_speed = 3.6,
bike_speed = 12,
max_rides = 3,
max_lts = 2,
draws_per_minute = 5L,
n_threads = Inf,
verbose = FALSE,
progress = FALSE,
output_dir = NULL) {
old_options <- options(datatable.optimize = Inf)
on.exit(options(old_options), add = TRUE)
old_dt_threads <- data.table::getDTthreads()
dt_threads <- ifelse(is.infinite(n_threads), 0, n_threads)
data.table::setDTthreads(dt_threads)
on.exit(data.table::setDTthreads(old_dt_threads), add = TRUE)
# check inputs and set r5r options --------------------------------------
checkmate::assert_class(r5r_core, "jobjRef")
origins <- assign_points_input(origins, "origins")
destinations <- assign_points_input(destinations, "destinations")
mode_list <- assign_mode(mode, mode_egress)
departure <- assign_departure(departure_datetime)
max_walk_time <- assign_max_street_time(
max_walk_time,
walk_speed,
max_trip_duration,
"walk"
)
max_bike_time <- assign_max_street_time(
max_bike_time,
bike_speed,
max_trip_duration,
"bike"
)
max_car_time <- assign_max_street_time(
max_car_time,
8, # 8 km/h, R5's default.
max_trip_duration,
"car"
)
max_trip_duration <- assign_max_trip_duration(
max_trip_duration,
mode_list,
max_walk_time,
max_bike_time
)
set_time_window(r5r_core, time_window)
set_monte_carlo_draws(r5r_core, draws_per_minute, time_window)
set_speed(r5r_core, walk_speed, "walk")
set_speed(r5r_core, bike_speed, "bike")
set_max_rides(r5r_core, max_rides)
set_max_lts(r5r_core, max_lts)
set_n_threads(r5r_core, n_threads)
set_verbose(r5r_core, verbose)
set_progress(r5r_core, progress)
set_output_dir(r5r_core, output_dir)
set_expanded_travel_times(r5r_core, TRUE)
set_breakdown(r5r_core, breakdown)
set_fare_structure(r5r_core, NULL)
# call r5r_core method and process result -------------------------------
travel_times <- r5r_core$travelTimeMatrix(
origins$id,
origins$lat,
origins$lon,
destinations$id,
destinations$lat,
destinations$lon,
mode_list$direct_modes,
mode_list$transit_mode,
mode_list$access_mode,
mode_list$egress_mode,
departure$date,
departure$time,
max_walk_time,
max_bike_time,
max_car_time,
max_trip_duration
)
if (!verbose & progress) cat("Preparing final output...", file = stderr())
travel_times <- java_to_dt(travel_times)
# replace travel-times of non-viable trips with NAs
# if breakdown is TRUE, there are more columns in the output
if (nrow(travel_times) > 0) {
if (breakdown) {
travel_times[
total_time > max_trip_duration,
`:=`(
access_time = NA_integer_,
wait_time = NA_integer_,
ride_time = NA_integer_,
transfer_time = NA_integer_,
egress_time = NA_integer_,
routes = NA_character_,
n_rides = NA_integer_,
total_time = NA_integer_
)
]
} else {
travel_times[
total_time > max_trip_duration,
`:=`(routes = NA_character_, total_time = NA_integer_)
]
}
}
if (!verbose & progress) cat(" DONE!\n", file = stderr())
if (!is.null(output_dir)) return(output_dir)
return(travel_times[])
}
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