raptor | R Documentation |
raptor
finds the minimal travel time, earliest or latest arrival time for all
stops in stop_times
with journeys departing from stop_ids
within
time_range
.
raptor(
stop_times,
transfers,
stop_ids,
arrival = FALSE,
time_range = 3600,
max_transfers = NULL,
keep = "all"
)
stop_times |
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 |
Transfers table from a gtfs feed. In general no preparation
is needed. Can be omitted if stop_times has been prepared with
|
stop_ids |
Character vector with stop_ids from where journeys should start (or end). It is recommended to only use stop_ids that are related to each other, like different platforms in a train station or bus stops that are reasonably close to each other. |
arrival |
If FALSE (default), all journeys start from |
time_range |
Either a range in seconds or a vector containing the minimal and maximal
departure time (i.e. earliest and latest possible journey departure time)
as seconds or "HH:MM:SS" character. If |
max_transfers |
Maximum number of transfers allowed, no limit (NULL) as default. |
keep |
One of c("all", "shortest", "earliest", "latest"). By default, |
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 exact journeys (with each intermediate stop and route ids for example) are not returned.
For most cases, stop_times
needs to be filtered, as it should only contain trips
happening on a single day, see filter_stop_times()
. The algorithm scans all trips
until it exceeds max_transfers
or all trips in stop_times
have been visited.
A data.table with journeys (departure, arrival and travel time) to/from all
stop_ids reachable by stop_ids
.
travel_times()
for an easier access to travel time calculations via stop_names.
nyc_path <- system.file("extdata", "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 = FALSE)
# 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 <- merge(rptr, walk_times, by.x = "from_stop_id", by.y = "stop_id")
rptr$travel_time_incl_walk <- rptr$travel_time + rptr$walk_time
# get minimal travel times (with walk times) for all stop_ids
library(data.table)
shortest_travel_times <- setDT(rptr)[order(travel_time_incl_walk)][, .SD[1], by = "to_stop_id"]
hist(shortest_travel_times$travel_time, breaks = seq(0,2*60)*60)
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