Estimate isochrones from a given location

Fast computation of isochrones from a given location. The function can return either polygon-based or line-based isochrones. Polygon-based isochrones are generated as concave polygons based on the travel times from the trip origin to all nodes in the transport network. Meanwhile, line-based isochronesare based on travel times from each origin to the centroids of all segments in the transport network.

Usage

isochrone(
  r5r_core,
  origins,
  mode = "transit",
  mode_egress = "walk",
  cutoffs = c(0, 15, 30),
  sample_size = 0.8,
  departure_datetime = Sys.time(),
  polygon_output = TRUE,
  time_window = 10L,
  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 = TRUE
)

Arguments

r5r_core: An object to connect with the R5 routing engine, created with setup_r5().
origins: Either a POINT sf object with WGS84 CRS, or a data.frame containing the columns id, lon and lat.
mode: A character vector. The transport modes allowed for access, transfer and vehicle legs of the trips. Defaults to WALK. Please see details for other options.
mode_egress: A character vector. The transport mode used after egress from the last public transport. It can be either WALK, BICYCLE or CAR. Defaults to WALK. Ignored when public transport is not used.
cutoffs: numeric vector. Number of minutes to define the time span of each Isochrone. Defaults to c(0, 15, 30).
sample_size: numeric. Sample size of nodes in the transport network used to estimate isochrones. Defaults to 0.8 (80% of all nodes in the transport network). Value can range between 0.2 and 1. Smaller values increase computation speed but return results with lower precision. This parameter has no effect when polygon_output = FALSE.
departure_datetime: A POSIXct object. Please note that the departure time only influences public transport legs. When working with public transport networks, please check the calendar.txt within your GTFS feeds for valid dates. Please see details for further information on how datetimes are parsed.
polygon_output: A Logical. If TRUE, the function outputs polygon-based isochrones (the default) based on travel times from each origin to a sample of a random sample nodes in the transport network (see parameter sample_size). If FALSE, the function outputs line-based isochrones based on travel times from each origin to the centroids of all segments in the transport network.
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")
max_walk_time: An integer. The maximum walking time (in minutes) 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 time is considered separately for each leg (e.g. if you set max_walk_time to 15, you could potentially walk up to 15 minutes to reach transit, and up to another 15 minutes to reach the destination after leaving transit). Defaults to Inf, no limit.
max_bike_time: An integer. The maximum cycling time (in minutes) to access and egress the transit network. Defaults to no restrictions, as long as max_trip_duration is respected. The max time is considered separately for each leg (e.g. if you set max_bike_time to 15 minutes, you could potentially cycle up to 15 minutes to reach transit, and up to another 15 minutes to reach the destination after leaving transit). Defaults to Inf, no limit.
max_car_time: An integer. The maximum driving time (in minutes) to access and egress the transit network. Defaults to no restrictions, as long as max_trip_duration is respected. The max time is considered separately for each leg (e.g. if you set max_car_time to 15 minutes, you could potentially drive up to 15 minutes to reach transit, and up to another 15 minutes to reach the destination after leaving transit). Defaults to Inf, no limit.
max_trip_duration: An integer. The maximum trip duration in minutes. Defaults to 120 minutes (2 hours).
walk_speed: A numeric. Average walk speed in km/h. Defaults to 3.6 km/h.
bike_speed: A numeric. Average cycling speed in km/h. Defaults to 12 km/h.
max_rides: An integer. The maximum number of public transport rides allowed in the same trip. Defaults to 3.
max_lts: An integer 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. Please see details for more information.
draws_per_minute: An integer. The number of Monte Carlo draws to perform per time window minute when calculating travel time matrices and when estimating accessibility. Defaults to 5. This would mean 300 draws in a 60-minute time window, for example. This parameter only affects the results when the GTFS feeds contain a frequencies.txt table. If the GTFS feed does not have a frequency table, r5r still allows for multiple runs over the set time_window but in a deterministic way.
n_threads: An integer. The number of threads to use when running the router in parallel. Defaults to use all available threads (Inf).
verbose: A logical. Whether to show R5 informative messages when running the function. Defaults to FALSE (please note that in such case R5 error messages are still shown). Setting verbose to TRUE shows detailed output, which can be useful for debugging issues not caught by r5r.
progress: A logical. Whether to show a progress counter when running the router. Defaults to FALSE. 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.

Value

A POLYGON "sf" "data.frame" for each isochrone of each origin.

Transport modes

R5 allows for multiple combinations of transport modes. The options include:

Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option TRANSIT automatically considers all public transport modes available.
Non transit modes: WALK, BICYCLE, CAR, BICYCLE_RENT, CAR_PARK.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BIKE to either mode or mode_egress), 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 and 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. Routing also reverts to walking if the street segment is tagged as non-bikable in OSM (e.g. a staircase), independently of the specified max LTS.

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 only for "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.

Datetime parsing

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.

Routing algorithm

The travel_time_matrix(), expanded_travel_time_matrix() and accessibility() functions use 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). However, whenever the user includes transit fares inputs to these functions, they automatically switch to use an R5-specific extension to the McRAPTOR routing algorithm.

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. doi:10.3141/2653-06
Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534

Examples

options(java.parameters = "-Xmx2G")
library(r5r)
library(ggplot2)

# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path = data_path)
#> Using cached R5 version from /home/runner/.cache/R/r5r/r5_jar_v7.1.0/r5-v7.1-all.jar
#> 
#> Using cached network.dat from /home/runner/work/_temp/Library/r5r/extdata/poa/network.dat

# load origin/point of interest
points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))
origin <- points[2,]

departure_datetime <- as.POSIXct(
 "13-05-2019 14:00:00",
 format = "%d-%m-%Y %H:%M:%S"
)

# estimate polygon-based isochrone from origin
iso_poly <- isochrone(
  r5r_core,
  origins = origin,
  mode = "walk",
  polygon_output = TRUE,
  departure_datetime = departure_datetime,
  cutoffs = seq(0, 120, 30)
  )

head(iso_poly)
#> Simple feature collection with 4 features and 2 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -51.2422 ymin: -30.0778 xmax: -51.1602 ymax: -29.9966
#> Geodetic CRS:  WGS 84
#>                    id isochrone                       polygons
#> 1 bus_central_station       120 POLYGON ((-51.2412 -30.0724...
#> 2 bus_central_station        90 POLYGON ((-51.2421 -30.0365...
#> 3 bus_central_station        60 POLYGON ((-51.2421 -30.0365...
#> 4 bus_central_station        30 POLYGON ((-51.2314 -30.0287...


# estimate line-based isochrone from origin
iso_lines <- isochrone(
  r5r_core,
  origins = origin,
  mode = "walk",
  polygon_output = FALSE,
  departure_datetime = departure_datetime,
  cutoffs = seq(0, 100, 25)
  )
#> Warning: st_centroid assumes attributes are constant over geometries

head(iso_lines)
#> Simple feature collection with 6 features and 13 fields
#> Geometry type: LINESTRING
#> Dimension:     XY
#> Bounding box:  xmin: -51.18467 ymin: -30.05426 xmax: -51.17266 ymax: -30.02355
#> Geodetic CRS:  WGS 84
#>   edge_index   osm_id isochrone travel_time_p50 from_vertex to_vertex
#> 1        644 27238056       100             100         443       444
#> 2        645 27238056       100             100         444       443
#> 3       1048 27370379       100             100         717       718
#> 4       1049 27370379       100             100         718       717
#> 5       1058 27370382       100             100         722       723
#> 6       1059 27370382       100             100         723       722
#>   street_class  length walk   car car_speed bicycle bicycle_lts
#> 1     TERTIARY  78.580 TRUE  TRUE    39.996    TRUE           2
#> 2     TERTIARY  78.580 TRUE FALSE    39.996   FALSE           2
#> 3        OTHER 242.560 TRUE  TRUE    40.248    TRUE           4
#> 4        OTHER 242.560 TRUE  TRUE    40.248    TRUE           4
#> 5        OTHER  85.916 TRUE  TRUE    40.248    TRUE           2
#> 6        OTHER  85.916 TRUE  TRUE    40.248    TRUE           2
#>                         geometry
#> 1 LINESTRING (-51.17282 -30.0...
#> 2 LINESTRING (-51.17266 -30.0...
#> 3 LINESTRING (-51.18231 -30.0...
#> 4 LINESTRING (-51.18467 -30.0...
#> 5 LINESTRING (-51.18424 -30.0...
#> 6 LINESTRING (-51.1839 -30.05...


# plot colors
colors <- c('#ffe0a5','#ffcb69','#ffa600','#ff7c43','#f95d6a',
            '#d45087','#a05195','#665191','#2f4b7c','#003f5c')

# polygons
ggplot() +
  geom_sf(data=iso_poly, aes(fill=factor(isochrone))) +
  scale_fill_manual(values = colors) +
  theme_minimal()


# lines
ggplot() +
  geom_sf(data=iso_lines, aes(color=factor(isochrone))) +
  scale_color_manual(values = colors) +
  theme_minimal()


stop_r5(r5r_core)
#> r5r_core has been successfully stopped.