Get aggregated frequency per hour for each OSM way

For each OSM way with GTFS service, returns the number of departures aggregated per hour and direction.

Usage

get_way_frequency_hourly(
  gtfs,
  q,
  date = GTFShift::calendar_nextBusinessWednesday(),
  keep_osm_attributes = FALSE,
  osm_file = NULL
)

Arguments

gtfs

tidygtfs. GTFS feed.

q

osmdata::opq. Overpass query for transit network, to obtain OSM route ways, using GTFShift::osm_shapes_to_routes().

date

Date (Default GTFShift::calendar_nextBusinessWednesday()). Reference date to consider when analyzing the GTFS file.

keep_osm_attributes

Boolean (Default FALSE). Whether to keep all OSM way attributes in the output sf object.

osm_file

character (Optional). Location of OSM extract file with osm.pbf format. Refer to osmextract::oe_download() for more details. If not provided OSM Overpass API is called through osmdata::osmdata_sf().

Value

sf data.frame. Hourly way frequencies, with the following columns:

way_osm_id

The osm_id attribute from OSM way.

hour

The hour for which the frequency applies (24 hour format).

frequency

The number of services for the route that depart from the first stop for the corresponding 60 minutes period.

routes

The list of route_ids that use the way.

shapes

The list of shape_ids that use the way.

geometry

The route shape.

(if keep_osm_attributes = TRUE)

All OSM way attributes.

Details

This method analyses the GTFS feed for a representative day, finding for each route the corresponding OSM ways using GTFShift::osm_shapes_to_routes() (routes not on OSM are ignored), aggregating the number of services per hour and direction for each.

For a detailed example, see the vignette("analyse").

See also

GTFShift::calendar_nextBusinessWednesday()

GTFShift::osm_shapes_to_routes()

Examples

# Subset GTFS for one route only, for demo purposes
gtfs <- GTFShift::load_feed(system.file("extdata/samples",
  "gtfs_tcb_sample.zip", package = "GTFShift")
)
gtfs <- GTFShift::filter_by_route_name(gtfs, c("1", "2", "3", "4"))

# Build query and prepare osm extract (possible to use API as alternative)
q <- osmdata::opq(bbox = sf::st_bbox(tidytransit::shapes_as_sf(gtfs$shapes))) |> 
  osmdata::add_osm_feature(key = "route", value = "bus") |> 
  osmdata::add_osm_feature(key = "operator", value = "Transportes Colectivos do Barreiro")
osm_file <- system.file("extdata/samples", "osmextract_tcb_network.pbf", package = "GTFShift")

# Get frequency
frequency_analysis <- GTFShift::get_way_frequency_hourly(
  gtfs, q, 
  date = gtfs$calendar$start_date[1],
  osm_file = osm_file
)
#> Analysing GTFS for 2026-06-10...
#> > Filtering by reference date 2026-06-10...
#> Matched 12 shapes (100.00% of 12 in GTFS) of 12 routes (100.00% of 12 in GTFS) with OSM routes!

head(frequency_analysis |> sf::st_drop_geometry())
#> # A tibble: 6 × 5
#>   way_osm_id  hour frequency routes    shapes   
#>   <chr>      <int>     <int> <list>    <list>   
#> 1 1020123867     0         1 <chr [1]> <chr [1]>
#> 2 1020123867     1         1 <chr [1]> <chr [1]>
#> 3 1020123867     6         1 <chr [1]> <chr [1]>
#> 4 1020123867    10         1 <chr [1]> <chr [1]>
#> 5 1020123867    23         1 <chr [1]> <chr [1]>
#> 6 1020152005     0         1 <chr [1]> <chr [1]>