expanded_travel_time_matrix {r5r} | R Documentation |
Calculate minute-by-minute travel times between origin destination pairs
Description
Detailed computation of travel time estimates between one or multiple origin destination pairs. Results show the travel time of the fastest route alternative departing each minute within a specified time window. Please note this function can be very memory intensive for large data sets and time windows.
Usage
expanded_travel_time_matrix(
r5r_core,
origins,
destinations,
mode = "WALK",
mode_egress = "WALK",
departure_datetime = Sys.time(),
time_window = 10L,
breakdown = FALSE,
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 = FALSE,
output_dir = NULL
)
Arguments
r5r_core |
An object to connect with the R5 routing engine, created with
|
origins , destinations |
Either a |
mode |
A character vector. The transport modes allowed for access,
transfer and vehicle legs of the trips. Defaults to |
mode_egress |
A character vector. The transport mode used after egress
from the last public transport. It can be either |
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 |
time_window |
An integer. The time window in minutes for which |
breakdown |
A logical. Whether to include detailed information about
each trip in the output. If |
max_walk_time |
An integer. The maximum walking time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete walk-only trips. Defaults to no restrictions (numeric value
of |
max_bike_time |
An integer. The maximum cycling time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete bicycle-only trips. Defaults to no restrictions (numeric
value of |
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 |
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 |
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 |
progress |
A logical. Whether to show a progress counter when running
the router. Defaults to |
output_dir |
Either |
Value
A data.table
with travel time estimates (in minutes) and the
routes used in each trip between origin and destination pairs, for each
minute of the specified time window. Each set of origin, destination and
departure minute can appear up to N times, where N is the number of Monte
Carlo draws specified in the function arguments (please note that this
only applies when the GTFS feeds that describe the transit network include
a frequencies table, otherwise only a single draw is performed). A pair is
completely absent from the final output if no trips could be completed in
any of the minutes of the time window. If for a single pair trips could be
completed in some of the minutes of the time window, but not for all of
them, the minutes in which trips couldn't be completed will have NA
travel time and routes used. 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 optionTRANSIT
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
See Also
Other routing:
detailed_itineraries()
,
pareto_frontier()
,
travel_time_matrix()
Examples
library(r5r)
# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path)
# 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"
)
# by default only returns the total time between each pair in each minute of
# the specified time window
ettm <- expanded_travel_time_matrix(
r5r_core,
origins = points,
destinations = points,
mode = c("WALK", "TRANSIT"),
time_window = 20,
departure_datetime = departure_datetime,
max_trip_duration = 60
)
head(ettm)
# when breakdown = TRUE the output contains much more information
ettm <- expanded_travel_time_matrix(
r5r_core,
origins = points,
destinations = points,
mode = c("WALK", "TRANSIT"),
time_window = 20,
departure_datetime = departure_datetime,
max_trip_duration = 60,
breakdown = TRUE
)
head(ettm)
stop_r5(r5r_core)