Abstract
This vignette shows how to use the travel_time_matrix() and expanded_travel_time_matrix() functions in r5r.1. Introduction
Some of the most common tasks in transport planning and modeling
involve require having good quality data with travel time estimates
between origins and destinations. R5 is incredibly fast in
generating realistic door-to-door travel time estimates in multimodal
transport systems.
The r5r package has two functions that allow users to
leverage the computing power of R5: -
travel_time_matrix() -
expanded_travel_time_matrix() -
arrival_travel_time_matrix()
This vignette shows a reproducible example to explain how these two functions work and the differences between them.
2. Build routable transport network with
build_network()
First, let’s build the multimodal transport network we’ll be using in
this vignette. In this example we’ll be using the a sample data set for
the city of Porto Alegre (Brazil) 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
The travel_time_matrix() function provides a simple and
really fast way to calculate the travel time between all possible origin
destination pairs at a given departure time using a given transport
mode.
The user can also customize many parameters such as: -
max_trip_duration: maximum trip duration -
max_rides: maximum number of transfer in the public
transport system - max_walk_time and
max_bike_time: maximum walking or cycling time to and from
public transport - walk_speed and bike_speed:
maximum walking or cycling speed - 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 13
#> 3: public_market gasometer_museum 12
#> 4: public_market santa_casa_hospital 13
#> 5: public_market townhall 3
#> 6: public_market piratini_palace 14
#> 7: public_market metropolitan_cathedral 15
#> 8: public_market farroupilha_park 18
#> 9: public_market moinhos_de_vento_hospital 20
#> 10: public_market farrapos_station 20Now remember that travel time estimates can vary significantly across the day because of variations in public transport service levels. In order to account for this, you might want to calculate multiple travel time matrices departing at different times.
This can be done very efficiently by using the
time_window and percentile parameters in the
travel_time_matrix() function. When these parameters are
set, R5 will automatically compute multiple travel times
estimates considering multiple departures per minute within the
time_window selected by the user. More
information about this functionality can found in this vignette.
4. The expanded_travel_time_matrix() function
Sometimes, we want to know more than simply the total travel time
from A to B. This is when the expanded_travel_time_matrix()
function comes in. By default, the output of this function will also
tell which public transport routes were taken between each origin
destination pair.
Nonetheless, you may set the parameter breakdown = TRUE
to gather much more info for each trip. In this case,
expanded_travel_time_matrix() will tell the number of
transfers used to complete each trip and their total access, waiting,
in-vehicle and transfer times. Please note that setting
breakdown = TRUE can make the function 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 0You will notice in the documentation that the
expanded_travel_time_matrix() also has a
time_window parameter. In this case, though, when the user
sets a time_window value, the
expanded_travel_time_matrix() will return the fastest route
alternative departing each minute within the specified time window.
Please note this function 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 1.3
#> 2: public_market bus_central_station 14:05:00 1 3.1
#> 3: public_market bus_central_station 14:06:00 1 3.1
#> 4: public_market bus_central_station 14:07:00 1 4.0
#> 5: public_market bus_central_station 14:08:00 1 2.1
#> 6: public_market bus_central_station 14:09:00 1 2.1
#> 7: public_market gasometer_museum 14:00:00 1 2.5
#> 8: public_market gasometer_museum 14:01:00 1 4.3
#> 9: public_market gasometer_museum 14:02:00 1 4.3
#> 10: public_market gasometer_museum 14:03:00 1 4.3
#> 11: public_market gasometer_museum 14:04:00 1 4.3
#> wait_time ride_time transfer_time egress_time routes n_rides total_time
#> <num> <num> <num> <num> <char> <int> <num>
#> 1: 1.7 3.5 0 6.7 525 1 13.2
#> 2: 2.9 1.6 0 6.2 LINHA1 1 13.8
#> 3: 1.9 1.6 0 6.2 LINHA1 1 12.8
#> 4: 2.0 2.0 0 6.7 493 1 14.7
#> 5: 4.9 1.1 0 7.4 D72 1 15.5
#> 6: 3.9 1.1 0 7.4 D72 1 14.5
#> 7: 1.5 4.5 0 1.8 2821 1 10.3
#> 8: 4.7 4.3 0 1.8 346 1 15.1
#> 9: 3.7 4.3 0 1.8 346 1 14.1
#> 10: 2.7 4.3 0 1.8 346 1 13.1
#> 11: 1.7 4.3 0 1.8 346 1 12.14. The arrival_travel_time_matrix() function
Both of the functions travel_time_matrix() and
expanded_travel_time_matrix() consider a
departure time set by the user. In some cases, though,
you might need to calculate travel times considering an arrival
time. For such cases, you can use
arrival_travel_time_matrix(). In this function, you need to
set the latest arrival time desired and a maximum trip duration. The
function returns the travel time of the trip with the latest departure
time that arrives before the arrival time.
This function is useful when modeling user behavior in situations where arriving by a specific time is important, such as getting to work or school for a set start time (e.g., 9 a.m.). In these scenarios, it is often most convenient for a person to take the latest possible departure that still ensures arrival before their required start time, rather than choosing the trip with the shortest travel time but arriving much earlier and waiting unnecessarily at the destination.
Note that the output of this function includes more information with
additional columns, like in the
expanded_travel_time_matrix() function (you can also use
breakdown = TRUE).
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 13:46:00 1 LINHA1
#> 3: public_market gasometer_museum 13:46:00 1 2441
#> 4: public_market santa_casa_hospital 13:46:00 1 [WALK]
#> 5: public_market townhall 13:56:00 1 [WALK]
#> 6: public_market piratini_palace 13:45:00 1 [WALK]
#> 7: public_market metropolitan_cathedral 13:44:00 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 LINHA1
#> total_time
#> <num>
#> 1: 0.0
#> 2: 12.8
#> 3: 10.7
#> 4: 13.6
#> 5: 3.2
#> 6: 14.9
#> 7: 15.5
#> 8: 16.5
#> 9: 20.2
#> 10: 16.5Cleaning up after usage
r5r objects are still allocated to any amount of memory
previously set after they are done with their calculations. In order to
remove an existing r5r object and reallocate the memory it
had been using, we use the stop_r5 function followed by a
call to Java’s garbage collector, as follows:
If you have any suggestions or want to report an error, please visit the package GitHub page.