raptor finds the minimal travel time, earliest or latest arrival time for all
stop_times with journeys departing from
raptor(stop_times, transfers, stop_ids, arrival = FALSE, time_range = 3600, max_transfers = NULL, keep = "all")
A (prepared) stop_times table from a gtfs feed. Prepared means
that all stop time rows before the desired journey departure time
should be removed. The table should also only include departures
happening on one day. Use
Transfers table from a gtfs feed. In general no preparation is needed.
Character vector with stop_ids from where journeys should start (or end)
If FALSE (default), all journeys start from
Departure or arrival time range in seconds. All departures from the
first departure of stop_times (not necessarily from stop_id in
Maximum number of transfers allowed, no limit (NULL) as default.
One of c("all", "shortest", "earliest", "latest"). By default,
A data.table with journeys (departure, arrival and travel time) to/from all
stop_ids reachable by
With a modified Round-Based Public Transit Routing Algorithm
(RAPTOR) using data.table, earliest arrival times for all stops are calculated. If two
journeys arrive at the same time, the one with the later departure time and thus shorter
travel time is kept. By default, all journeys departing within
time_range that arrive
at a stop are returned in a table. If you want all journeys arriving at stop_ids within
the specified time range, set
arrival to TRUE.
Journeys are defined by a "from" and "to" stop_id, a departure, arrival and travel time. Note that the exact journeys (with each intermediate stop and route ids for example) is not returned.
For most cases,
stop_times needs to be filtered, as it should only contain trips
happening on a single day and departures later than a given journey start time, see
filter_stop_times(). The algorithm scans all trips until it exceeds
or all trips in
stop_times have been visited.
travel_times() for an easier access to travel time calculations via stop_names.
nyc_path <- system.file("extdata", "google_transit_nyc_subway.zip", package = "tidytransit") nyc <- read_gtfs(nyc_path) # you can use initial walk times to different stops in walking distance (arbitrary example values) stop_ids_harlem_st <- c("301", "301N", "301S") stop_ids_155_st <- c("A11", "A11N", "A11S", "D12", "D12N", "D12S") walk_times <- data.frame(stop_id = c(stop_ids_harlem_st, stop_ids_155_st), walk_time = c(rep(600, 3), rep(410, 6)), stringsAsFactors = F) # Use journeys departing after 7 AM with arrival time before 11 AM on 26th of June stop_times <- filter_stop_times(nyc, "2018-06-26", 7*3600, 9*3600)#># calculate all journeys departing from Harlem St or 155 St between 7:00 and 7:30 rptr <- raptor(stop_times, nyc$transfers, walk_times$stop_id, time_range = 1800, keep = "all") # add walk times to travel times rptr <- left_join(rptr, walk_times, by=c("from_stop_id" = "stop_id")) rptr$travel_time_incl_walk <- rptr$travel_time + rptr$walk_time # get minimal travel times (with walk times) for all stop_ids shortest_travel_times <- setDT(rptr)[order(travel_time_incl_walk)][, .SD, by = "to_stop_id"] hist(shortest_travel_times$travel_time, breaks = 360)