Create a transport network used for routing in R5

Source: R/setup_r5.R

Builds a multimodal transport network used for routing in R5, combining multiple data inputs present in the directory where the network should be saved to. The directory must contain only one street network file (in .osm.pbf format). It may optionally contain one or more public transport GTFS feeds (in GTFS.zip format, where GTFS is the name of your feed), when used for public transport routing, and a .tif file describing the elevation profile of the study area. If there is more than one GTFS feed in the directory, all feeds are merged. If there is already a 'network.dat' file in the directory, the function will simply read it and load it to memory (unless specified not to do so).

setup_r5(
  data_path,
  verbose = FALSE,
  temp_dir = FALSE,
  elevation = "TOBLER",
  overwrite = FALSE
)

Arguments

data_path

A string pointing to the directory where data inputs are stored and where the built network.dat will be saved.

verbose

A logical. Whether to show R5 informative messages when running the function. Defaults to FALSE (please note that in such case R5 error messages are still shown). Setting verbose to TRUE shows detailed output, which can be useful for debugging issues not caught by r5r.

temp_dir

A logical. Whether the R5 Jar file should be saved to a temporary directory. Defaults to FALSE.

elevation

A string. The name of the impedance function to be used to calculate impedance for walking and cycling based on street slopes. Available options include TOBLER (Default) and MINETTI, or NONE to ignore elevation. R5 loads elevation data from .tif files saved inside the data_path directory. See more info in the Details below.

overwrite

A logical. Whether to overwrite an existing network.dat or to use a cached file. Defaults to FALSE (i.e. use a cached network).

Value

An rJava object to connect with R5 routing engine.

Details

More information about the TOBLER and MINETTI options to calculate the effects of elevation on travel times can be found in the references below:

  • Campbell, M. J., et al (2019). Using crowdsourced fitness tracker data to model the relationship between slope and travel rates. Applied geography, 106, 93-107. doi:10.1016/j.apgeog.2019.03.008 .

  • Minetti, A. E., et al (2002). Energy cost of walking and running at extreme uphill and downhill slopes. Journal of applied physiology. doi:10.1152/japplphysiol.01177.2001 .

  • Tobler, W. (1993). Three presentations on geographical analysis and modeling: Non-isotropic geographic modeling speculations on the geometry of geography global spatial analysis. Technical Report. National center for geographic information and analysis. 93 (1). https://escholarship.org/uc/item/05r820mz.

See also

Other setup: download_r5()

Examples

library(r5r)

# directory with street network and gtfs files
data_path <- system.file("extdata/poa", package = "r5r")

r5r_core <- setup_r5(data_path)
#> Using cached R5 version from /home/runner/.cache/R/r5r/r5_jar_v7.1.0/r5-v7.1-all.jar
#> 
#> Using cached network.dat from /home/runner/work/_temp/Library/r5r/extdata/poa/network.dat