Calculate travel time matrix between origin destination pairs
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, max_walk_dist = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, n_threads = Inf, verbose = TRUE )
r5r_core |
a rJava object to connect with R5 routing engine |
origins, destinations |
a spatial sf POINT object, or a data.frame containing the columns 'id', 'lon', 'lat' |
mode |
string. Transport modes allowed for the trips. Defaults to "WALK". See details for other options. |
mode_egress |
string. Transport mode used after egress from public transport. It can be either 'WALK', 'BICYCLE', or 'CAR'. Defaults to "WALK". |
departure_datetime |
POSIXct object. If working with public transport
networks, please check |
time_window |
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. |
percentiles |
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' |
max_walk_dist |
numeric. Maximum walking distance (in meters) for the
whole trip. Defaults to no restrictions on walking, as
long as |
max_trip_duration |
numeric. Maximum trip duration in minutes. Defaults to 120 minutes (2 hours). |
walk_speed |
numeric. Average walk speed in km/h. Defaults to 3.6 km/h. |
bike_speed |
numeric. Average cycling speed in km/h. Defaults to 12 km/h. |
max_rides |
numeric. The max number of public transport rides allowed in the same trip. Defaults to 3. |
max_lts |
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. |
n_threads |
numeric. The number of threads to use in parallel computing. Defaults to use all available threads (Inf). |
verbose |
logical. TRUE to show detailed output messages (the default) or FALSE to show only eventual ERROR messages. |
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.
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 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.
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.
Other routing:
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)
# 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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