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#' Detailed itineraries between origin-destination pairs
#'
#' Returns detailed trip information between origin-destination pairs. The
#' output includes the waiting and moving time in each trip leg, as well as some
#' info such as the distance traveled, the routes used and the geometry of each
#' leg. Please note that this function was originally conceptualized as a trip
#' planning functionality, similar to other commercial and non-commercial APIs
#' and apps (e.g. Moovit, Google's Directions API, OpenTripPlanning's
#' PlannerResource API). Thus, it consumes much more time and memory than the
#' other (more analytical) routing functions included in the package.
#'
#' @template r5r_core
#' @template common_arguments
#' @template verbose
#' @template fare_structure
#' @template max_fare
#' @param time_window An integer. The time window in minutes for which `r5r`
#' will calculate multiple itineraries departing each minute. Defaults to 10
#' minutes. If the same sequence of routes appear in different minutes of the
#' time window, only the fastest of them will be kept in the output. This
#' happens because the result is not aggregated by percentile, as opposed to
#' other routing functions in the package. Because of that, the output may
#' contain trips departing after the specified `departure_datetime`, but
#' still within the time window. Please read the time window vignette for
#' more details on how this argument affects the results of each routing
#' function: `vignette("time_window", package = "r5r")`.
#' @param suboptimal_minutes A number. The difference in minutes that each
#' non-optimal RAPTOR branch can have from the optimal branch without being
#' disregarded by the routing algorithm. If, for example, users set
#' `suboptimal_minutes = 10`, the routing algorithm will consider sub-optimal
#' routes that arrive up to 10 minutes after the arrival of the optimal one.
#' This argument emulates the real-life behaviour that makes people want to
#' take a path that is technically not optimal in terms of travel time, for
#' example, for some practical reasons (e.g. mode preference, safety, etc).
#' In practice, the higher this value, the more itineraries will be returned
#' in the final result.
#' @param shortest_path A logical. Whether the function should only return the
#' fastest itinerary between each origin and destination pair (the default)
#' or multiple alternatives.
#' @param all_to_all A logical. Whether to query routes between the 1st origin
#' to the 1st destination, then the 2nd origin to the 2nd destination, and so
#' on (`FALSE`, the default) or to query routes between all origins to all
#' destinations (`TRUE`).
#' @param drop_geometry A logical. Whether the output should include the
#' geometry of each trip leg or not. The default value of `FALSE` keeps the
#' geometry column in the result.
#'
#' @template transport_modes_section
#' @template lts_section
#' @template datetime_parsing_section
#' @template mcraptor_algorithm_section
#'
#' @return When `drop_geometry` is `FALSE`, the function outputs a `LINESTRING
#' sf` with detailed information on the itineraries between the specified
#' origins and destinations. When `TRUE`, the output is a `data.table`. All
#' distances are in meters and travel times are in minutes. 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.
#'
#' @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"))
#'
#' # inputs
#' departure_datetime <- as.POSIXct(
#' "13-05-2019 14:00:00",
#' format = "%d-%m-%Y %H:%M:%S"
#' )
#'
#' det <- detailed_itineraries(
#' r5r_core,
#' origins = points[10,],
#' destinations = points[12,],
#' mode = c("WALK", "TRANSIT"),
#' departure_datetime = departure_datetime,
#' max_trip_duration = 60
#' )
#' head(det)
#'
#' stop_r5(r5r_core)
#' @export
detailed_itineraries <- function(r5r_core,
origins,
destinations,
mode = "WALK",
mode_egress = "WALK",
departure_datetime = Sys.time(),
time_window = 10L,
suboptimal_minutes = 0L,
fare_structure = NULL,
max_fare = Inf,
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,
shortest_path = TRUE,
all_to_all = FALSE,
n_threads = Inf,
verbose = FALSE,
progress = FALSE,
drop_geometry = 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")
od_list <- expand_od_pairs(origins, destinations, all_to_all)
origins <- od_list$origins
destinations <- od_list$destinations
mode_list <- assign_mode(mode, mode_egress)
# detailed itineraries via public transport cannot be computed on frequencies-based GTFS
if (mode_list$transit_mode != "" & r5r_core$hasFrequencies()) {
stop(
"Assertion on 'r5r_core' failed: None of the GTFS feeds used to create ",
"the transit network can contain a 'frequencies' table. Try using ",
"gtfstools::frequencies_to_stop_times() to create a suitable feed."
)
}
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
)
shortest_path <- assign_shortest_path(shortest_path)
drop_geometry <- assign_drop_geometry(drop_geometry)
set_time_window(r5r_core, time_window)
set_monte_carlo_draws(r5r_core, 1, 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_fare_structure(r5r_core, fare_structure)
set_max_fare(r5r_core, max_fare)
set_output_dir(r5r_core, output_dir)
set_suboptimal_minutes(
r5r_core,
suboptimal_minutes,
fare_structure,
shortest_path
)
# call r5r_core method and process result -------------------------------
path_options <- r5r_core$detailedItineraries(
origins$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,
drop_geometry,
shortest_path
)
if (!is.null(output_dir)) return(output_dir)
path_options <- java_to_dt(path_options)
if (!drop_geometry) {
if (nrow(path_options) > 0) {
path_options[, geometry := sf::st_as_sfc(geometry)]
} else {
path_options[, geometry := sf::st_sfc(sf::st_linestring(), crs = 4326)[0]]
}
path_options <- sf::st_sf(path_options, crs = 4326)
}
return(path_options)
}
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