Travel time matrices

In r5r: Rapid Realistic Routing with 'R5'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = identical(tolower(Sys.getenv("NOT_CRAN")), "true"),
  out.width = "100%"
)

## removes files previously created by 'setup_r5()'
#data_path <- system.file("extdata/poa", package = "r5r")
#existing_files <- list.files(data_path)
#files_to_keep <- c(
#  "poa_hexgrid.csv",
#  "poa_osm.pbf",
#  "poa_points_of_interest.csv",
#  "poa_eptc.zip",
#  "poa_trensurb.zip",
#  'fares'
#  )
#files_to_remove <- existing_files[! existing_files %in% files_to_keep]
#invisible(file.remove(file.path(data_path, files_to_remove)))

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 packages has two functions that allow users to leverage the computing power of R5: - travel_time_matrix() - expanded_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 setup_r5()

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_core <- setup_r5(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_core,   
                          origins = points,
                          destinations = points,    
                          mode = mode,
                          max_trip_duration = max_trip_duration,
                          departure_datetime = departure_datetime )

head(ttm, n = 10)

Now 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_core,   
                                    origins = points,
                                    destinations = points,    
                                    mode = mode,
                                    max_trip_duration = max_trip_duration,
                                    departure_datetime = departure_datetime )

head(ettm, n = 10)

Calling expanded_travel_time_matrix() with breakdown = TRUE

ettm2 <- expanded_travel_time_matrix(r5r_core,   
                                    origins = points,
                                    destinations = points,    
                                    mode = mode,
                                    max_trip_duration = max_trip_duration,
                                    departure_datetime = departure_datetime,
                                    breakdown = TRUE)

head(ettm2, n = 10)

You 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_core,   
                                           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,]

Cleaning 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:

r5r::stop_r5(r5r_core)
rJava::.jgc(R.gc = TRUE)

If you have any suggestions or want to report an error, please visit the package GitHub page.

References

r5r documentation built on Aug. 8, 2023, 9:07 a.m.