Creating Custom Road Datasets with trafficCAR

Overview

This vignette is written for users of trafficCAR who want to work with road networks beyond the bundled examples.

You will learn how to:

1. Obtain road network data for a city
2. Save it as a GeoJSON file
3. Convert the GeoJSON into an .rda dataset (recommended for packages)
4. Save an .rds dataset (recommended for non-package projects)
5. Organize multiple city datasets (small / medium / large)
6. Use the datasets with trafficCAR

The recommended workflows are:

User project: GeoJSON (raw) → .rds (fast)
R package: GeoJSON (raw) → .rda (CRAN-friendly)

Step 1: Obtain road data for a city

A common source of road data is OpenStreetMap, accessed via the osmdata package.

# install.packages(c("osmdata", "sf"))
library(osmdata)
library(sf)

Choose a city name or bounding box.

city <- "College Station, Texas, USA"

q <- opq(city) |>
  add_osm_feature(key = "highway")

osm <- osmdata_sf(q)

Extract road geometries:

roads <- osm$osm_lines

Step 2: Clean, validate, and inspect geometries

roads <- roads[, c("highway", "name", "geometry")]
roads <- sf::st_make_valid(roads)

# Optional: simplify to reduce object size.
# Note: dTolerance is in the units of your coordinate reference system (CRS).
# For best results, consider transforming to a projected CRS with meter units first.
roads <- sf::st_simplify(roads, dTolerance = 10)

unique(sf::st_geometry_type(roads))

Only LINESTRING and MULTILINESTRING geometries are supported by trafficCAR.

Step 3: Write the dataset to GeoJSON (raw source)

sf::st_write(
  roads,
  "roads_cstat.geojson",
  delete_dsn = TRUE,
  quiet = TRUE
)

Step 4A: Save as .rds (recommended for user projects)

roads_cstat <- sf::st_read("roads_cstat.geojson", quiet = TRUE)

saveRDS(roads_cstat, "roads_cstat.rds")
roads_cstat <- readRDS("roads_cstat.rds")

Step 4B: Convert GeoJSON to .rda (recommended for R packages)

library(usethis)

roads_cstat <- sf::st_read("roads_cstat.geojson", quiet = TRUE)
use_data(roads_cstat, overwrite = TRUE, compress = "xz")

Step 5: Use the dataset in trafficCAR

library(trafficCAR)

data(roads_small, package = "trafficCAR")
roads_cstat <- roads_small

segs <- roads_to_segments(
  roads_cstat,
  split_at_intersections = TRUE
)

adj <- build_adjacency(segs)

Organizing multiple city datasets

data-raw/
  roads_cstat_small.geojson
  roads_cstat_small.R
data/
  roads_cstat_small.rda

library(sf)
library(usethis)

roads_cstat_small <- sf::st_read(
  "data-raw/roads_cstat_small.geojson",
  quiet = TRUE
)

use_data(roads_cstat_small, overwrite = TRUE, compress = "xz")

Using external GeoJSON files directly

library(sf)
library(trafficCAR)

roads <- sf::st_read("roads_cstat.geojson", quiet = TRUE)
segs <- roads_to_segments(roads)

Summary

• Use GeoJSON as the authoritative raw format
• Store raw files in data-raw/ or outside the package
• Use .rds for user projects and .rda for packages
• Apply geometry simplification and xz compression to reduce size