update news

README.md

@@ -232,7 +232,6 @@ BibTeX:
 	shorttitle = {r5r},
 	url = {https://findingspress.org/article/21262-r5r-rapid-realistic-routing-on-multimodal-transport-networks-with-r-5-in-r},
 	doi = {10.32866/001c.21262},
-	abstract = {Routing is a key step in transport planning and research. Nonetheless, researchers and practitioners often face challenges when performing this task due to long computation times and the cost of licensed software. R{\textasciicircum}5{\textasciicircum} is a multimodal transport network router that offers multiple routing features, such as calculating travel times over a time window and returning multiple itineraries for origin/destination pairs. This paper describes r5r, an open-source R package that leverages R{\textasciicircum}5{\textasciicircum} to efficiently compute travel time matrices and generate detailed itineraries between sets of origins and destinations at no expense using seamless parallel computing.},
 	language = {en},
 	urldate = {2021-03-04},
 	journal = {Findings},

r-package/NEWS.md

@@ -1,3 +1,10 @@
+# r5r 1.1.0999 dev
+
+**Minor changes**
+
+- In the `accessibility()` function, the value of `max_trip_duration` is now capped by the max value passed to the `cutoffs` parameter. Closes [#342](https://github.com/ipeaGIT/r5r/issues/348).
+
+
 # r5r 1.1.0
 
 **Major changes**

r-package/tests/tests_rafa/r5r_arrow.R

@@ -0,0 +1,71 @@
+# allocate RAM memory to Java
+options(java.parameters = "-Xmx2G")
+
+# 1) build transport network, pointing to the path where OSM and GTFS data are stored
+library(r5r)
+library(arrow)
+library(dplyr)
+
+
+path <- system.file("extdata/poa", package = "r5r")
+r5r_core <- setup_r5(data_path = path, verbose = FALSE)
+
+# 2) load origin/destination points and set arguments
+points <- read.csv(system.file("extdata/poa/poa_hexgrid.csv", package = "r5r"))
+mode <- c("WALK", "TRANSIT")
+max_walk_time <- 30   # minutes
+max_trip_duration <- 60 # minutes
+departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
+                                 format = "%d-%m-%Y %H:%M:%S")
+
+# 3.1) calculate a travel time matrix
+ttm <- travel_time_matrix(r5r_core = r5r_core,
+                          origins = points,
+                          destinations = points,
+                          mode = mode,
+                          departure_datetime = departure_datetime,
+                          max_walk_time = max_walk_time,
+                          max_trip_duration = max_trip_duration,
+                          output_dir = './aaaa')
+
+
+
+csv_files <- list.files(path = './aaaa', full.names = TRUE)
+
+# Define the dataset
+DS <- arrow::open_csv_dataset(sources = csv_files)
+
+# Create a scanner
+O <- Scanner$create(DS)
+
+# Load it as Arrow Table in memory
+AT <- SO$ToTable()
+
+
+# prep land use data
+jobs_df <- select(points, c('id', 'jobs', 'healthcare'))
+
+# merge jobs
+AT <- left_join(AT, jobs_df, by = c('to_id' = 'id'))
+
+head(AT) |> collect()
+
+# calculate cumulative access in less than 20 min.
+access_df <- AT |>
+              filter(travel_time_p50 <= 20) |>
+              group_by(from_id) |>
+              summarise(access = sum(jobs)) |>
+              collect()
+
+head(access_df)
+
+
+# calculate access to closest healthcare facility
+access_df2 <- AT |>
+  filter(healthcare > 0) |>
+  group_by(from_id) |>
+  summarise(access = min(jobs)) |>
+  collect()
+
+head(access_df2)
+