Fast computation of travel time estimates between one or multiple origin destination pairs.
travel_time_matrix(
r5r_core,
origins,
destinations,
mode = "WALK",
mode_egress = "WALK",
departure_datetime = Sys.time(),
time_window = 1L,
percentiles = 50L,
breakdown = FALSE,
breakdown_stat = "MEAN",
max_walk_dist = Inf,
max_bike_dist = Inf,
max_trip_duration = 120L,
walk_speed = 3.6,
bike_speed = 12,
max_rides = 3,
max_lts = 2,
n_threads = Inf,
verbose = TRUE,
progress = TRUE
)A data.table with travel time estimates (in minutes) between origin destination pairs by a given transport mode. Note that origins/destinations that were beyond the maximum travel time, and/or origins that were far from the street network are not returned in the data.table.
a rJava object to connect with R5 routing engine
a spatial sf POINT object with WGS84 CRS, or a data.frame containing the columns 'id', 'lon', 'lat'.
string. Transport modes allowed for the trips. Defaults to "WALK". See details for other options.
string. Transport mode used after egress from public transport. It can be either 'WALK', 'BICYCLE', or 'CAR'. Defaults to "WALK".
POSIXct object. If working with public transport
networks, please check calendar.txt within
the GTFS file for valid dates. See details for
further information on how datetimes are parsed.
numeric. Time window in minutes for which r5r will calculate multiple travel time matrices departing each minute. By default, the number of simulations is 5 times the size of 'time_window' set by the user. Defaults window size to '1', the function only considers 5 departure times. This parameter is only used with frequency-based GTFS files. See details for further information.
numeric vector. Defaults to '50', returning the median travel time for a given time_window. If a numeric vector is passed, for example c(25, 50, 75), the function will return additional columns with the travel times within percentiles of trips. For example, if the 25 percentile of trips between A and B is 15 minutes, this means that 25% of all trips taken between A and B within the set time window are shorter than 15 minutes. Only the first 5 cut points of the percentiles are considered. For more details, see R5 documentation at 'https://docs.conveyal.com/analysis/methodology#accounting-for-variability'
logic. If FALSE (default), the function returns a simple
output with columns origin, destination and travel time
percentiles. If TRUE, r5r breaks down the trip information
and returns more columns with estimates of access_time,
waiting_time, ride_time, transfer_time, total_time , n_rides
and route. Warning: Setting TRUE makes the function
significantly slower.
string. If min, all the brokendown trip informantion
is based on the trip itinerary with the smallest waiting time in the
time window. If breakdown_stat = mean, the information is based on
the trip itinerary whose waiting time is the closest to the average
waiting time in the time window.
numeric. Maximum walking distance (in meters) to access
and egress the transit network, or to make transfers
within the network. Defaults to no restrictions as long
as max_trip_duration is respected. The max distance is
considered separately for each leg (e.g. if you set
max_walk_dist to 1000, you could potentially walk up
to 1 km to reach transit, and up to another 1 km to
reach the destination after leaving transit). Obs: if you
want to set the maximum walking distance considering
walking-only trips you have to set the max_trip_duration
accordingly (e.g. to set a distance of 1 km assuming a
walking speed of 3.6 km/h you have to set max_trip_duration = 1 / 3.6 * 60).
numeric. Maximum cycling distance (in meters) to access
and egress the transit network. Defaults to no
restrictions as long as max_trip_duration is respected.
The max distance is considered separately for each leg
(e.g. if you set max_bike_dist to 1000, you could
potentially cycle up to 1 km to reach transit, and up
to another 1 km to reach the destination after leaving
transit). Obs: if you want to set the maximum cycling
distance considering cycling-only trips you have to set
the max_trip_duration accordingly (e.g. to set a
distance of 5 km assuming a cycling speed of 12 km/h you
have to set max_trip_duration = 5 / 12 * 60).
numeric. Maximum trip duration in minutes. Defaults to 120 minutes (2 hours).
numeric. Average walk speed in km/h. Defaults to 3.6 km/h.
numeric. Average cycling speed in km/h. Defaults to 12 km/h.
numeric. The max number of public transport rides allowed in the same trip. Defaults to 3.
numeric (between 1 and 4). The maximum level of traffic stress that cyclists will tolerate. A value of 1 means cyclists will only travel through the quietest streets, while a value of 4 indicates cyclists can travel through any road. Defaults to 2. See details for more information.
numeric. The number of threads to use in parallel computing. Defaults to use all available threads (Inf).
logical. TRUE to show detailed output messages (the default).
logical. TRUE to show a progress counter. Only works when
verbose is set to FALSE, so the progress counter does not
interfere with R5's output messages. Setting progress to TRUE
may impose a small penalty for computation efficiency, because
the progress counter must be synchronized among all active
threads.
R5 allows for multiple combinations of transport modes. The options include:
TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option 'TRANSIT' automatically considers all public transport modes available.
WALK, BICYCLE, CAR, BICYCLE_RENT, CAR_PARK
When cycling is enabled in R5, setting max_lts will allow cycling only on
streets with a given level of danger/stress. Setting max_lts to 1, for example,
will allow cycling only on separated bicycle infrastructure or low-traffic
streets; routing will revert to walking when traversing any links with LTS
exceeding 1. Setting max_lts to 3 will allow cycling on links with LTS 1, 2,
or 3.
The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:
LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).
LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.
LTS 3: Tolerable for “enthused and confident” cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.
LTS 4: Tolerable for only “strong and fearless” cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.
For advanced users, you can provide custom LTS values by adding a tag
<key = "lts> to the osm.pbf file
The travel_time_matrix function uses an R5-specific extension to the RAPTOR routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a systematic sample of one departure per minute over the time window set by the user in the 'time_window' parameter. A detailed description of base RAPTOR can be found in Delling et al (2015).
Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53.
Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604.
r5r ignores the timezone attribute of datetime objects when parsing dates
and times, using the study area's timezone instead. For example, let's say
you are running some calculations using Rio de Janeiro, Brazil, as your study
area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in
Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as
the exact same date and time in Rio's local time, perhaps surprisingly,
ignoring the timezone attribute.
Other routing:
accessibility(),
detailed_itineraries()
if (interactive()) {
library(r5r)
# build transport network
data_path <- system.file("extdata/spo", package = "r5r")
r5r_core <- setup_r5(data_path = data_path, temp_dir = TRUE)
# load origin/destination points
points <- read.csv(file.path(data_path, "spo_hexgrid.csv"))[1:5,]
departure_datetime <- as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S")
# estimate travel time matrix
ttm <- travel_time_matrix(r5r_core,
origins = points,
destinations = points,
mode = c("WALK", "TRANSIT"),
departure_datetime = departure_datetime,
max_walk_dist = Inf,
max_trip_duration = 120L)
stop_r5(r5r_core)
}
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