R Notebook

#nyc_path <- system.file("extdata", "google_transit_nyc_subway.zip", package = "tidytransit")
nyc <- gtfs

# 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)