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# options(java.parameters = '-Xmx2G')
# library(r5r)
devtools::load_all(".")
library(data.table)
library(tidyverse)
# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path = data_path, verbose = TRUE, overwrite = FALSE,
temp_dir = FALSE)
# load origin/destination points
departure_datetime <- as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S")
points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))
r5r_core$setBenchmark(TRUE)
# r5r_core$setTravelTimesBreakdown(FALSE)
ttm <- expanded_travel_time_matrix(r5r_core,
origins = points,
destinations = points,
departure_datetime = departure_datetime,
breakdown = T,
mode = c("WALK", "TRANSIT"),
max_trip_duration = 60,
max_walk_dist = 800,
time_window = 15,
verbose = FALSE,
progress = TRUE)
# r5r_core$setTravelTimesBreakdown(TRUE)
t_ttm_breakdown <- system.time(
ttm_b_mean <- travel_time_matrix(r5r_core,
origins = points,
destinations = points,
departure_datetime = departure_datetime,
breakdown = TRUE,
breakdown_stat = "mean",
mode = c("WALK", "TRANSIT"),
max_trip_duration = 60,
max_walk_dist = 800,
time_window = 30,
# percentiles = c(25),
percentiles = c(25, 50, 75),
verbose = FALSE,
progress = FALSE)
)
# t_ttm_breakdown <- system.time(
# ttm_b_min <- travel_time_matrix(r5r_core,
# origins = points[1:100, ],
# destinations = points,
# departure_datetime = departure_datetime,
# breakdown = TRUE,
# breakdown_stat = "minimum",
# mode = c("WALK", "TRANSIT"),
# max_trip_duration = 60,
# max_walk_dist = 800,
# time_window = 30,
# # percentiles = c(25),
# percentiles = c(25, 50, 75),
# verbose = FALSE)
# )
View(ttm_b_mean)
# ttm_first <- read_csv("ttm_first_option.csv")
# ttm_b_mean %>% write_csv(file = "ttm_first_option.csv")
# t_ttm_normal
# t_ttm_breakdown
# rbind(
# ttm_n %>% select(fromId, execution_time) %>% distinct() %>% mutate(method = "normal"),
# ttm_b_mean %>% select(fromId, execution_time) %>% distinct() %>% mutate(method = "breakdown")
# ) %>%
# ggplot(aes(execution_time)) + geom_histogram() + facet_wrap(~method, scales = "free")
#
# max_trip_duration = 60
#
# travel_times = data.table::copy(ttm)
# for(j in seq(from = 3, to = (length(percentiles) + 3))){
# data.table::set(travel_times, i=which(travel_times[[j]]>max_trip_duration), j=j, value=NA_integer_)
# }
#
# names
# colnames <- names(travel_times)[startsWith(names(travel_times), "travel_time")]
# for(j in colnames){
# data.table::set(travel_times, i=which(travel_times[j]>max_trip_duration), j=j, value=NA_integer_)
# }
#
# length(c(25, 50, 75))
#
# ttm_n$saveToCsv("teste_ttm.csv")
#
# s_ttm <- read_csv("teste_ttm.csv")
#
#
ttm_b_mean %>%
select(fromId, execution_time) %>%
distinct() %>%
summarise(t = sum(execution_time) / 1000 / 12)
t_ttm_breakdown
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