abstr provides an R interface to the A/B Street transport system simulation and network editing software. It provides functions for converting origin-destination data, combined with data on buildings representing origin and destination locations, into
.json files that can be directly imported into the A/B Street city simulation.
See the formats page in the A/B Street documentation for details of the schema that the package outputs.
You can install the released version of abstr from CRAN with:
Install the development version from GitHub as follows:
The example below shows how
abstr can be used. The input datasets include
sf objects representing buildings, origin-destination (OD) data represented as desire lines and administrative zones representing the areas within which trips in the desire lines start and end. With the exception of OD data, each of the input datasets is readily available for most cities. The input datasets are illustrated in the plots below, which show example data shipped in the package, taken from the Seattle, U.S.
library(abstr) library(tmap) # for map making tm_shape(montlake_zones) + tm_polygons(col = "grey") + tm_shape(montlake_buildings) + tm_polygons(col = "blue") + tm_style("classic")
The map above is a graphical representation of the Montlake residential neighborhood in central Seattle, Washington. Here,
montlake_zones represents neighborhood residential zones declared by Seattle local government and
montlake_buildings being the accumulation of buildings listed in OpenStreetMap
The final piece of the
abstr puzzle is OD data.
head(montlake_od) #> # A tibble: 6 × 6 #> o_id d_id Drive Transit Bike Walk #> <dbl> <dbl> <int> <int> <int> <int> #> 1 281 361 23 1 2 14 #> 2 282 361 37 4 0 11 #> 3 282 369 14 3 0 8 #> 4 301 361 27 4 3 15 #> 5 301 368 6 2 1 16 #> 6 301 369 14 2 0 13
In this example, the first two columns correspond to the origin and destination zones in Montlake, with the subsequent columns representing the transport mode share between these zones.
Let’s combine each of the elements outlined above, the zone, building and OD data. We do this using the
ab_scenario() function in the
abstr package, which generates a data frame representing tavel between the
montlake_buildings. While the OD data contains information on origin and destination zone,
ab_scenario() ‘disaggregates’ the data and randomly selects building within each origin and destination zone to simulate travel at the individual level, as illustrated in the chunk below which uses only a sample of the
montlake_od data, showing travel between three pairs of zones, to illustrate the process:
set.seed(42) montlake_od_minimal = subset(montlake_od, o_id == "373" |o_id == "402" | o_id == "281" | o_id == "588" | o_id == "301" | o_id == "314") output_sf = ab_scenario( od = montlake_od_minimal, zones = montlake_zones, zones_d = NULL, origin_buildings = montlake_buildings, destination_buildings = montlake_buildings, pop_var = 3, time_fun = ab_time_normal, output = "sf", modes = c("Walk", "Bike", "Drive", "Transit") )
output_sf object created above can be further transformed to match A/B Street’s schema and visualised in A/B Street, or visualised in R (using the
tmap package in the code chunk below):
Each line in the plot above represents a single trip, with the color representing each transport mode. Moreover, each trip is configured with an associated departure time, that can be represented in A/B Street.
ab_json functions conclude the
abstr workflow by outputting a local JSON file, matching the A/B Street’s schema.
Let’s see what is in the file:
The first trip schedule should look something like this, matching A/B Street’s schema.
After generating a
ab_scenario.json, you can import and simulate it as follows.
After you successfully import this file once, it will be available in the list of scenarios, under the “Montlake Example” name, or whatever
name specified by the JSON file.
You can generate scenarios for any city in the world. See here for how to import new cities into A/B Street.
Note: Instead of installing a pre-built version of A/B Street in the first step, feel free to build from source, but it’s not necessary for any integration with the
If you’re generating many JSON scenarios, you might not want to manually use A/B Street’s user interface to import each file. You can instead run a command to do the import. See the docs at a-b-street.github.io/docs/tech/dev/ for details, but the basic steps are:
These steps can be achieved by running the following lines of code (run the commented lines of code to install Rust, clone the A/B Street repo and set the working directory, you can also replace
../montlake.json with a different path to the scenario file):
# curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh # install rust # git clone firstname.lastname@example.org:a-b-street/abstreet # cd abstreet # cargo run --bin updater -- download --minimal cargo run --bin cli -- import-scenario --input ../montlake.json --map data/system/us/seattle/maps/montlake.bin cargo run --bin game --release
If you’re using Windows, you’ll instead run
cli.exe. If you’re building from source use the following command:
For a more comprehensive guide in the art of collecting, transforming and saving data for A/B Street, check out the
abstr documentation. The package website, hosted at a-b-street.github.io/abstr, contains articles that will help you get going with
abstr. See the following articles for reproducible examples that will help you getting your valuable origin-destination and activity data into a dynamic transport simulation environment for visualisation, model exaperiments and more: