Calculate travel time matrix between origin destination pairs considering a departure time

Fast computation of travel time estimates between one or multiple origin destination pairs. This function considers a departure time set by the user. If you want to calculate travel times considering a time of arrival, have a look at the arrival_travel_time_matrix() function.

Usage

travel_time_matrix(
  r5r_network,
  origins,
  destinations,
  mode = "WALK",
  mode_egress = "WALK",
  departure_datetime = Sys.time(),
  time_window = 10L,
  percentiles = 50L,
  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,
  fare_structure = NULL,
  max_fare = Inf,
  new_carspeeds = NULL,
  carspeed_scale = 1,
  new_lts = NULL,
  draws_per_minute = 5L,
  n_threads = Inf,
  verbose = FALSE,
  progress = FALSE,
  output_dir = NULL,
  r5r_core = deprecated()
)

Arguments

r5r_network

A routable transport network created with build_network().

origins, destinations

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. 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.

departure_datetime

A POSIXct object. Only affects public transport legs; when routing with public transport, it must fall within the service period of the GTFS feeds (calendar.txt; see check_transit_availability()). Defaults to Sys.time(). See details for how datetimes are parsed.

time_window

An integer. The time window in minutes. Departures are simulated every minute from departure_datetime until time_window minutes later, and travel times are summarized over these departures using percentiles (the median by default). Defaults to 10. See vignette("time_window", package = "r5r").

percentiles

An integer vector (max length of 5). Specifies the percentile to use when returning travel time estimates within the given time window. For example, if the 25th travel time percentile between A and B is 15 minutes, 25% of all trips taken between these points within the specified time window are shorter than 15 minutes. Defaults to 50, returning the median travel time. If a vector with length bigger than 1 is passed, the output contains an additional column for each percentile specifying the percentile travel time estimate. Due to upstream restrictions, only 5 percentiles can be specified at a time. See the R5 documentation at https://docs.conveyal.com/analysis/methodology#accounting-for-variability.

max_walk_time

An integer. The maximum walking time (in minutes) to access and egress the transit network, make transfers, or complete walk-only trips. Applies to each leg separately (e.g. 15 allows up to 15 minutes to reach transit and another 15 after leaving it). Defaults to Inf (no limit besides max_trip_duration). In walk-only trips, the lower of max_walk_time and max_trip_duration applies.

max_bike_time

An integer. The maximum cycling time (in minutes) to access and egress the transit network, make transfers, or complete bicycle-only trips. Applies to each leg separately (e.g. 15 allows up to 15 minutes to reach transit and another 15 after leaving it). Defaults to Inf (no limit besides max_trip_duration). In bicycle-only trips, the lower of max_bike_time and max_trip_duration applies.

max_car_time

An integer. The maximum driving time (in minutes) to access and egress the transit network, or to complete car-only trips. Applies to each leg separately (e.g. 15 allows up to 15 minutes to reach transit and another 15 after leaving it). Defaults to Inf (no limit besides max_trip_duration). In car-only trips, the lower of max_car_time and max_trip_duration applies.

max_trip_duration

An integer. The maximum trip duration in minutes. Defaults to 120.

walk_speed

A numeric. Average walk speed in km/h. Defaults to 3.6.

bike_speed

A numeric. Average cycling speed in km/h. Defaults to 12.

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. See details.

fare_structure

A fare structure object, following the convention set in setup_fare_structure(). This object describes how transit fares should be calculated. See vignette("fare_structure", package = "r5r") for its structure.

max_fare

A number. The maximum value that trips can cost when calculating the fastest journey between each origin and destination pair. Defaults to Inf (no limit). A finite value requires a fare_structure; an error is raised otherwise.

new_carspeeds

A data.frame specifying the new car speed for each OSM edge id. This table must contain columns osm_id, max_speed and speed_type. The "speed_type" column is of class character and it indicates whether the values in "max_speed" should be interpreted as percentages of original speeds ("scale") or as absolute speeds ("km/h"). Alternatively, the new_carspeeds parameter can receive an sf data.frame with POLYGON geometry that indicates the new car speed for all the roads that fall within each polygon. In this case, the table must contain the columns poly_id with a unique id for each polygon, scale with the new speed scaling factors and priority, which is a number ranking which polygon should be considered in case of overlapping polygons. See more info in the scenarios vignette (vignette("scenarios", package = "r5r")).

carspeed_scale

Numeric. The scaling factor applied to the car speed of road segments not specified in new_carspeeds. Defaults to 1, which keeps the speeds of the unlisted roads unchanged.

new_lts

A data.frame specifying the new LTS levels for each OSM edge id. The table must contain columns osm_id and lts. Alternatively, the new_lts parameter can receive an sf data.frame with LINESTRING geometry. R5 will then find the nearest road for each LINESTRING and update its LTS value accordingly.

draws_per_minute

An integer. The number of Monte Carlo draws to perform per minute of time_window. 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 Inf (all available threads).

verbose

A logical. Whether to show R5 informative messages when running the function. Defaults to FALSE (R5 error messages are still shown). TRUE shows detailed output, 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 FALSE. May slightly slow computation, as the counter is synchronized across threads.

output_dir

Either NULL (the default) or a path to an existing directory. When a path is given, the function writes the results as .csv files to that directory and returns the path instead of the results. Useful in memory-constrained settings, as results are not loaded into RAM. Missing values (NA) are written as empty fields.

r5r_core

The r5r_core argument is deprecated as of r5r v2.3.0. Use the r5r_network argument instead.

Value

A data.table with travel time estimates (in minutes) between origin and destination pairs. Pairs whose trips couldn't be completed within the maximum travel time and/or whose origin is too far from the street network are not returned in the data.table. 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.

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.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BICYCLE 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(), arrival_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

See also

Other routing: arrival_travel_time_matrix(), detailed_itineraries(), expanded_travel_time_matrix(), pareto_frontier()

Examples

library(r5r)

# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)
#> Using cached R5 version from /home/runner/.cache/R/r5r/r5_jar_v7.5.1/r5-v7.5-1-gf3631e9-all.jar
#> ℹ Using cached network from
#>   /home/runner/work/_temp/Library/r5r/extdata/poa/network.dat.

# load origin/destination points
points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))

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

ttm <- travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  max_trip_duration = 60
)
head(ttm)
#>          from_id               to_id travel_time_p50
#>           <char>              <char>           <int>
#> 1: public_market       public_market               0
#> 2: public_market bus_central_station              14
#> 3: public_market    gasometer_museum              12
#> 4: public_market santa_casa_hospital              15
#> 5: public_market            townhall               3
#> 6: public_market     piratini_palace              17

# using a larger time window
ttm <- travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  time_window = 30,
  max_trip_duration = 60
)
head(ttm)
#>          from_id               to_id travel_time_p50
#>           <char>              <char>           <int>
#> 1: public_market       public_market               0
#> 2: public_market bus_central_station              14
#> 3: public_market    gasometer_museum              13
#> 4: public_market santa_casa_hospital              15
#> 5: public_market            townhall               3
#> 6: public_market     piratini_palace              17

# selecting different percentiles
ttm <- travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  time_window = 30,
  percentiles = c(25, 50, 75),
  max_trip_duration = 60
)
head(ttm)
#>          from_id               to_id travel_time_p25 travel_time_p50
#>           <char>              <char>           <int>           <int>
#> 1: public_market       public_market               0               0
#> 2: public_market bus_central_station              13              14
#> 3: public_market    gasometer_museum              12              13
#> 4: public_market santa_casa_hospital              15              15
#> 5: public_market            townhall               3               3
#> 6: public_market     piratini_palace              17              17
#>    travel_time_p75
#>              <int>
#> 1:               0
#> 2:              14
#> 3:              14
#> 4:              15
#> 5:               3
#> 6:              17

# use a fare structure and set a max fare to take monetary constraints into
# account
fare_structure <- read_fare_structure(
  file.path(data_path, "fares/fares_poa.zip")
)
ttm <- travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  fare_structure = fare_structure,
  max_fare = 5,
  max_trip_duration = 60,
)
head(ttm)
#>          from_id               to_id travel_time_p50
#>           <char>              <char>           <int>
#> 1: public_market       public_market               0
#> 2: public_market bus_central_station              14
#> 3: public_market    gasometer_museum              13
#> 4: public_market santa_casa_hospital              15
#> 5: public_market            townhall               3
#> 6: public_market     piratini_palace              17

stop_r5(r5r_network)
#> r5r_network has been successfully stopped.