Fast computation of access to opportunities given a selected
decay function. See details for the available decay functions.
accessibility(
r5r_core,
origins,
destinations,
opportunities_colname = "opportunities",
mode = "WALK",
mode_egress = "WALK",
departure_datetime = Sys.time(),
time_window = 1L,
percentiles = 50L,
decay_function = "step",
cutoffs = 30L,
decay_value = 1,
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 accessibility estimates for all origin points, by a given transport mode, and per travel time cutoff and percentile.
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. The column name in the destinations
input that tells the number of opportunities in each location.
Defaults to "opportunities".
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 travel times departing each minute. When using frequency-based GTFS files, 5 Monte Carlo simulations will be run for each minute in the time window. See details for further information.
numeric vector. Defaults to '50', returning the accessibility value for the median travel time computed for a given time_window. If a numeric vector is passed, for example c(25, 50, 75), the function will return accessibility estimates for each percentile, by travel time cutoff. 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'
string. Choice of one of the following decay functions:
'step', 'exponential', 'fixed_exponential', 'linear',
and 'logistic'. Defaults to 'step', which yields
cumulative opportunities accessibility metrics.
More info in details.
numeric. Cutoff times in minutes for calculating cumulative opportunities accessibility when using the 'step decay function'. This parameter has different effects for each of the other decay functions: it indicates the 'median' (or inflection point) of the decay curves in the 'logistic' and 'linear' functions, and the 'half-life' in the 'exponential' function. It has no effect when using the 'fixed exponential' function.
numeric. Extra parameter to be passed to the selected
decay_function.
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 decay functions. More info in the original R5 documentation from Conveyal, at https://docs.conveyal.com/learn-more/decay-functions The options include:
step (cumulative opportunities)
A binary decay function used to calculate cumulative opportunities metrics.
logistic
This is the logistic function, i.e. the cumulative distribution function of the logistic distribution, expressed such that its parameters are the median (inflection point) and standard deviation. This function applies a sigmoid rolloff that has a convenient relationship to discrete choice theory. Its parameters can be set to reflect a whole population's tolerance for making trips with different travel times. The function's value represents the probability that a randomly chosen member of the population would accept making a trip, given its duration. Opportunities are then weighted by how likely it is a person would consider them "reachable".
The median parameter is controlled by the cutoff parameter, leaving only
the standard deviation to configure through the decay_value parameter.
fixed_exponential
This function is of the form e-Lt where L is a single fixed decay constant in the range (0, 1). It is constrained to be positive to ensure weights decrease (rather than grow) with increasing travel time.
This function is controlled exclusively by the L constant, given by the
decay_value parameter. Values provided in cutoffs are ignored.
exponential
This is similar to the fixed-exponential option above, but in this case the
decay parameter is inferred from the cutoffs parameter values, which is
treated as the half-life of the decay.
linear
This is a simple, vaguely sigmoid option, which may be useful when you have a sense of a maximum travel time that would be tolerated by any traveler, and a minimum time below which all travel is perceived to be equally easy.
The transition region is transposable and symmetric around the cutoffs
parameter values, taking decay_value minutes to taper down from one to zero.
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 accessibility() 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:
detailed_itineraries(),
travel_time_matrix()
if (interactive()) {
library(r5r)
# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path = data_path, temp_dir = TRUE)
# load origin/destination points
points <- read.csv(file.path(data_path, "poa_hexgrid.csv"))
access <- accessibility(r5r_core,
origins = points,
destinations = points,
opportunities_colname = "schools",
mode = "WALK",
cutoffs = c(25, 30),
max_trip_duration = 30,
verbose = FALSE)
stop_r5(r5r_core)
}
Run the code above in your browser using DataLab