raptor finds the minimal travel time and/or earliest arrival time for all stops in stop_times with journeys departing from from_stop_ids within departure_time_range.

raptor(stop_times, transfers, from_stop_ids, departure_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 filter_stop_times() for easier preparation.


Transfers table from a gtfs feed. In general no preparation is needed.


Atomic char vector with stop_ids from where a journey should start


All departures from the first departure of stop_times (not necessarily a from_stop) within departure_time_range (in seconds) are considered.


Maximum number of transfers allowed, no limit (NULL) as default.


One of c("all", "shortest", "earliest"). By default all journeys arriving at a stop are returned. With shortest the journey with shortest travel time is returned. With earliest the journey arriving at a stop the earliest is returned.


A data.table with travel times to all stop_ids reachable from from_stop_ids and their corresponding journey departure and arrival times.


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 within departure_time_range that arrive at a stop are returned in a table. Journeys are defined by a departure stop_id, a departure, arrival and travel time. Note that the exact journeys (with each intermediate stop and route id 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 max_transfers or all trips in stop_times have been visited.

See also


travel_times, filter_stop_times


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, departure_time_range = 1800, keep = "all") # add walk times to travel times rptr <- left_join(rptr, walk_times, by=c("journey_departure_stop_id" = "stop_id")) rptr$travel_time_incl_walk <- rptr$travel_time + rptr$walk_time # get minimal travel times (with walk times) for all stop_ids shortest_travel_times <- setDT(rptr)[order(travel_time_incl_walk)][, .SD[1], by = "stop_id"] hist(shortest_travel_times$travel_time, breaks = 360)