Abstract
This vignette shows how to use the travel_time_matrix() and expanded_travel_time_matrix() functions in r5r.1. Introduction
Many transport planning and modeling tasks need travel time estimates
between origins and destinations. R5 computes realistic
door-to-door travel times in multimodal networks very fast, and
r5r offers three functions for it:
This vignette shows, with a reproducible example, how they work and how they differ.
2. Build routable transport network with
build_network()
We use the Porto Alegre (Brazil) sample data included in
r5r.
# increase Java memory
options(java.parameters = "-Xmx2G")
# load libraries
library(r5r)
library(data.table)
library(ggplot2)
# build a routable transport network with r5r
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)
# routing inputs
mode <- c('walk', 'transit')
max_trip_duration <- 60 # minutes
# departure time
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S")
# load origin/destination points
points <- fread(file.path(data_path, "poa_points_of_interest.csv"))3. The travel_time_matrix() function
travel_time_matrix() quickly computes travel times
between all origin/destination pairs for a departure time and transport
mode. Other parameters include:
-
max_trip_duration: maximum trip duration -
max_rides: maximum number of public transport rides -
max_walk_timeandmax_bike_time: maximum walking or cycling time to and from public transport -
walk_speedandbike_speed: average walking or cycling speed (km/h) -
max_fare: maximum monetary cost in public transport. See this vignette.
# estimate travel time matrix
ttm <- travel_time_matrix(
r5r_network,
origins = points,
destinations = points,
mode = mode,
max_trip_duration = max_trip_duration,
departure_datetime = departure_datetime
)
head(ttm, n = 10)
#> 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
#> 7: public_market metropolitan_cathedral 17
#> 8: public_market farroupilha_park 18
#> 9: public_market moinhos_de_vento_hospital 20
#> 10: public_market farrapos_station 21Travel times can vary significantly across the day with public
transport service levels. The time_window and
percentiles parameters handle this efficiently:
R5 computes travel times for departures every minute within
time_window and returns the selected percentiles (time
window vignette).
4. The expanded_travel_time_matrix() function
expanded_travel_time_matrix() returns more than the
total travel time: by default, it also lists the public transport routes
taken between each origin/destination pair. With
breakdown = TRUE, it adds each trip’s number of public
transport rides and its access, waiting, in-vehicle, transfer and egress
times, which can be slower for large data sets.
A general call to expanded_travel_time_matrix()
ettm <- expanded_travel_time_matrix(
r5r_network,
origins = points,
destinations = points,
mode = mode,
max_trip_duration = max_trip_duration,
departure_datetime = departure_datetime
)
head(ettm, n = 10)
#> from_id to_id departure_time draw_number routes total_time
#> <char> <char> <char> <int> <char> <num>
#> 1: public_market public_market 14:00:00 1 [WALK] 0
#> 2: public_market public_market 14:01:00 1 [WALK] 0
#> 3: public_market public_market 14:02:00 1 [WALK] 0
#> 4: public_market public_market 14:03:00 1 [WALK] 0
#> 5: public_market public_market 14:04:00 1 [WALK] 0
#> 6: public_market public_market 14:05:00 1 [WALK] 0
#> 7: public_market public_market 14:06:00 1 [WALK] 0
#> 8: public_market public_market 14:07:00 1 [WALK] 0
#> 9: public_market public_market 14:08:00 1 [WALK] 0
#> 10: public_market public_market 14:09:00 1 [WALK] 0Calling expanded_travel_time_matrix() with
breakdown = TRUE
ettm2 <- expanded_travel_time_matrix(
r5r_network,
origins = points,
destinations = points,
mode = mode,
max_trip_duration = max_trip_duration,
departure_datetime = departure_datetime,
breakdown = TRUE
)
head(ettm2, n = 10)
#> from_id to_id departure_time draw_number access_time
#> <char> <char> <char> <int> <num>
#> 1: public_market public_market 14:00:00 1 0
#> 2: public_market public_market 14:01:00 1 0
#> 3: public_market public_market 14:02:00 1 0
#> 4: public_market public_market 14:03:00 1 0
#> 5: public_market public_market 14:04:00 1 0
#> 6: public_market public_market 14:05:00 1 0
#> 7: public_market public_market 14:06:00 1 0
#> 8: public_market public_market 14:07:00 1 0
#> 9: public_market public_market 14:08:00 1 0
#> 10: public_market public_market 14:09:00 1 0
#> wait_time ride_time transfer_time egress_time routes n_rides total_time
#> <num> <num> <num> <num> <char> <int> <num>
#> 1: 0 0 0 0 [WALK] 0 0
#> 2: 0 0 0 0 [WALK] 0 0
#> 3: 0 0 0 0 [WALK] 0 0
#> 4: 0 0 0 0 [WALK] 0 0
#> 5: 0 0 0 0 [WALK] 0 0
#> 6: 0 0 0 0 [WALK] 0 0
#> 7: 0 0 0 0 [WALK] 0 0
#> 8: 0 0 0 0 [WALK] 0 0
#> 9: 0 0 0 0 [WALK] 0 0
#> 10: 0 0 0 0 [WALK] 0 0Over its time_window (10 minutes by default),
expanded_travel_time_matrix() returns the fastest route
departing at each minute. This can be very memory intensive for large
data sets and time windows.
ettm_window <- expanded_travel_time_matrix(
r5r_network,
origins = points,
destinations = points,
mode = mode,
max_trip_duration = max_trip_duration,
departure_datetime = departure_datetime,
breakdown = TRUE,
time_window = 10
)
ettm_window[15:25,]
#> from_id to_id departure_time draw_number access_time
#> <char> <char> <char> <int> <num>
#> 1: public_market bus_central_station 14:04:00 1 1.5
#> 2: public_market bus_central_station 14:05:00 1 4.8
#> 3: public_market bus_central_station 14:06:00 1 4.1
#> 4: public_market bus_central_station 14:07:00 1 4.4
#> 5: public_market bus_central_station 14:08:00 1 2.3
#> 6: public_market bus_central_station 14:09:00 1 2.3
#> 7: public_market gasometer_museum 14:00:00 1 2.9
#> 8: public_market gasometer_museum 14:01:00 1 6.0
#> 9: public_market gasometer_museum 14:02:00 1 6.0
#> 10: public_market gasometer_museum 14:03:00 1 3.5
#> 11: public_market gasometer_museum 14:04:00 1 3.5
#> wait_time ride_time transfer_time egress_time routes n_rides total_time
#> <num> <num> <num> <num> <char> <int> <num>
#> 1: 1.5 3.5 0 6.7 525 1 13.2
#> 2: 1.2 1.6 0 6.2 LINHA1 1 13.8
#> 3: 1.9 2.0 0 6.7 495 1 14.7
#> 4: 1.6 2.0 0 6.7 493 1 14.7
#> 5: 4.7 1.1 0 7.4 D72 1 15.5
#> 6: 3.7 1.1 0 7.4 D72 1 14.5
#> 7: 1.1 4.5 0 1.8 2821 1 10.3
#> 8: 3.0 4.3 0 1.8 346 1 15.1
#> 9: 2.0 4.3 0 1.8 346 1 14.1
#> 10: 3.5 4.9 0 1.8 244 1 13.7
#> 11: 2.5 4.9 0 1.8 244 1 12.75. The arrival_travel_time_matrix() function
travel_time_matrix() and
expanded_travel_time_matrix() take a
departure time. To route by arrival
time, use arrival_travel_time_matrix(): given the latest
acceptable arrival time and a maximum trip duration, it returns the
travel time of the trip with the latest departure that still arrives in
time.
This models trips where arriving by a set time matters, such as getting to work or school by 9 a.m.: people usually take the latest departure that gets them there on time, not the fastest trip that leaves them waiting at the destination.
As in expanded_travel_time_matrix(), the output has
additional columns (and breakdown = TRUE is available).
A general call to arrival_travel_time_matrix()
arrival_datetime <- as.POSIXct(
"13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S"
)
arrival_ttm <- arrival_travel_time_matrix(
r5r_network,
origins = points,
destinations = points,
mode = c("WALK", "TRANSIT"),
arrival_datetime = arrival_datetime,
max_trip_duration = 60
)
head(arrival_ttm, n = 10)
#> from_id to_id departure_time draw_number routes
#> <char> <char> <char> <int> <char>
#> 1: public_market public_market 13:59:00 1 [WALK]
#> 2: public_market bus_central_station 13:45:00 1 LINHA1
#> 3: public_market gasometer_museum 13:45:00 1 2441
#> 4: public_market santa_casa_hospital 13:44:00 1 [WALK]
#> 5: public_market townhall 13:56:00 1 [WALK]
#> 6: public_market piratini_palace 13:42:00 1 [WALK]
#> 7: public_market metropolitan_cathedral 13:42:00 1 [WALK]
#> 8: public_market farroupilha_park 13:40:00 1 R41
#> 9: public_market moinhos_de_vento_hospital 13:36:00 1 731|637
#> 10: public_market farrapos_station 13:36:00 1 731
#> total_time
#> <num>
#> 1: 0.0
#> 2: 13.8
#> 3: 11.7
#> 4: 15.4
#> 5: 3.6
#> 6: 17.4
#> 7: 18.0
#> 8: 16.5
#> 9: 20.3
#> 10: 21.4Cleaning up after usage
Stop the network and run Java’s garbage collector to free the memory it used:
If you have any suggestions or want to report an error, please visit the package GitHub page.