Testing out r5r's routing and travel time matrix functions

routing_test.R

# Increase Java memory
options(java.parameters = "-Xmx10G")

# Load libraries
library(data.table)
library(dplyr)
library(ggplot2)
library(ggspatial)
library(osmdata)
library(osmextract)
library(r5r)
library(sf)
library(tigris)

# Populate an input/ dir with a .pbf file. For Chicago:
# 	wget --no-use-server-timestamps -O input/chicago.osm https://overpass-api.de/api/map?bbox=-87.8558,41.6229,-87.5085,42.0488
#   osmium cat input/chicago.osm -o input/chicago.osm.pbf
r5r_core <- setup_r5(data_path = "input/")

# Setup some input parameters for a route
mode <- "BICYCLE"
departure_datetime <- Sys.time()
origin <- data.frame(
  id = "Trader Joe's",
  lat = 41.95085364900005,
  lon = -87.67506868730602
)
dest <- data.frame(
  id = "Aldi",
  lat = 41.96381358872797,
  lon = -87.65687040135536
)

# Create a single detailed route and corresponding map
route_single_df <- detailed_itineraries(
  r5r_core = r5r_core,
  origins = origin,
  destinations = dest,
  departure_datetime = departure_datetime,
  mode = mode,
  max_lts = 1,
  shortest_path = TRUE,
  progress = TRUE
)

ggplot() +
  ggspatial::annotation_map_tile(zoomin = -1, type = "cartolight") +
  geom_sf(data = route_single_df, color = "blue")

# Load all 2020 Census block centroids and clip them to the Chicago boundary
blocks_gdf <- tigris::blocks(state = "IL", county = "Cook", year = 2020) %>%
  st_centroid()
chicago_gdf <- tigris::places(state = "IL", year = 2020) %>%
  filter(NAME == "Chicago")

blocks_gdf_clipped <- blocks_gdf %>%
  st_intersection(chicago_gdf) %>%
  st_transform(4326)
blocks_df <- blocks_gdf_clipped %>%
  st_coordinates() %>%
  as.data.frame() %>%
  setNames(c("lon", "lat")) %>%
  mutate(id = blocks_gdf_clipped$GEOID20)

# Route from 2 origins to a sample of Chicago Census block centroids
# Takes ~580s for 20K OD pairs w 60 min cutoff on an M2 Macbook Air
tictoc::tic()
route_multi_df <- detailed_itineraries(
  r5r_core = r5r_core,
  origins = rbind(origin, dest),
  destinations = blocks_df %>% slice_sample(n = 1000),
  departure_datetime = departure_datetime,
  mode = mode,
  max_bike_time = 60,
  max_lts = 1,
  all_to_all = TRUE,
  shortest_path = TRUE,
  progress = TRUE
)
tictoc::toc()

ggplot() +
  ggspatial::annotation_map_tile(zoom = 14, type = "cartolight") +
  geom_sf(data = route_multi_df, aes(color = from_id), alpha = 0.5)

# Route from 2 origins to a sample of Chicago Census block centroids
# Takes ~1.2s for 20K OD pairs w 60 min cutoff on an M2 Macbook Air
tictoc::tic()
travel_time_df <- travel_time_matrix(
  r5r_core = r5r_core,
  origins = rbind(origin, dest),
  destinations = blocks_df,
  departure_datetime = departure_datetime,
  mode = mode,
  max_bike_time = 60,
  max_lts = 1,
  progress = TRUE
)
tictoc::toc()