library(tidyverse)
library(tidyr)
library(stringr)
library(xml2)

# compile a big file of baseline, and doubling capacity, with and without one lane less, short termm and long term

baseline <-
  read_csv("output/experiments-pieter/set2/baseline/analysis/linkCounts.csv") %>%
  tibble() %>%
  rename(longterm = volume) %>%
  left_join(
    read_csv(
      "output/experiments-pieter/set2/baseline-nomc/analysis/linkCounts.csv"
    ) %>%
      tibble() %>%
      rename(shortterm = volume)
  )

baseline_cap <-
  read_xml(
    "output/experiments-pieter/set2/baseline/duesseldorf-25pct-dc_114-no-lanes.output_network.xml.gz"
  )
baseline_cap <-
  tibble(
    id = baseline_cap %>%
      xml_find_all("//link") %>%
      xml_attr("id") ,
    cap = baseline_cap %>%
      xml_find_all("//link") %>%
      xml_attr("capacity")
  )


doubled <-
  read_csv("output/experiments-pieter/set2/dbl-no-redux/analysis/linkCounts.csv") %>%
  tibble() %>%
  rename(longterm = volume) %>%
  left_join(
    read_csv(
      "output/experiments-pieter/set2/dbl-no-redux-nomc/analysis/linkCounts.csv"
    ) %>%
      tibble() %>%
      rename(shortterm = volume)
  )


doubled_cap <-
  read_xml(
    "output/experiments-pieter/set2/dbl-no-redux/duesseldorf-25pct-dc_114-no-lanes.output_network.xml.gz"
  )
doubled_cap <-
  tibble(
    id = doubled_cap %>%
      xml_find_all("//link") %>%
      xml_attr("id") ,
    cap = doubled_cap %>%
      xml_find_all("//link") %>%
      xml_attr("capacity")
  )


doubled_reduced <-
  read_csv("output/experiments-pieter/set2/dbl-redux/analysis/linkCounts.csv") %>%
  tibble() %>%
  rename(longterm = volume) %>%
  left_join(
    read_csv(
      "output/experiments-pieter/set2/dbl-redux-nomc/analysis/linkCounts.csv"
    ) %>%
      tibble() %>%
      rename(shortterm = volume)
  )


reduced_cap <-
  read_xml(
    "output/experiments-pieter/set2/dbl-redux/duesseldorf-25pct-dc_114-no-lanes.output_network.xml.gz"
  )
reduced_cap <-
  tibble(
    id = reduced_cap %>%
      xml_find_all("//link") %>%
      xml_attr("id") ,
    cap = reduced_cap %>%
      xml_find_all("//link") %>%
      xml_attr("capacity")
  )

write.csv(
  baseline %>% left_join(baseline_cap),
  "projects/komodnext/website/corridor/baseline_volume.csv",
  row.names = F,
  quote = F
)
write.csv(
  doubled %>% left_join(doubled_cap),
  "projects/komodnext/website/corridor/doubled_volume.csv",
  row.names = F,
  quote = F
)
write.csv(
  doubled_reduced %>% left_join(reduced_cap),
  "projects/komodnext/website/corridor/doubled_reduced_volume.csv",
  row.names = F,
  quote = F
)


corridor <-
  read.csv("scenarios/input/northeast_corridor_grafenberger_allee.csv") %>%
  select(id = ID)

longterm.traveltimes <-
  read_csv("output/experiments-pieter/set2/baseline/analysis/linkTimesVolumes.csv") %>%
  tibble() %>%
  select(id = linkId, time, tt = avgTravelTime) %>%
  right_join(corridor) %>%
  mutate(hour = round(time / 3600)) %>%
  group_by(id, hour) %>%
  summarise(baseline = mean(tt)) %>%
  ungroup() %>%
  left_join(
    read_csv(
      "output/experiments-pieter/set2/dbl-no-redux/analysis/linkTimesVolumes.csv"
    ) %>%
      tibble() %>%
      select(id = linkId, time, tt = avgTravelTime) %>%
      right_join(corridor) %>%
      mutate(hour = round(time / 3600)) %>%
      group_by(id, hour) %>%
      summarise(doubled = mean(tt)) %>%
      ungroup()
  ) %>%
  left_join(
    read_csv(
      "output/experiments-pieter/set2/dbl-redux/analysis/linkTimesVolumes.csv"
    ) %>%
      tibble() %>%
      select(id = linkId, time, tt = avgTravelTime) %>%
      right_join(corridor) %>%
      mutate(hour = round(time / 3600)) %>%
      group_by(id, hour) %>%
      summarise(reduced = mean(tt)) %>%
      ungroup()
  )

write.csv(
  longterm.traveltimes %>%
    group_by(hour) %>%
    summarise(across(baseline:reduced, ~ sum(.x, na.rm = T) / 60)) %>%
    filter(complete.cases(.)),
  file = "projects/komodnext/website/corridor/longterm_tt.csv",
  row.names = F,
  quote = F
)

shortterm.traveltimes <-
  read_csv("output/experiments-pieter/set2/baseline-nomc/analysis/linkTimesVolumes.csv") %>%
  tibble() %>%
  select(id = linkId, time, tt = avgTravelTime) %>%
  right_join(corridor) %>%
  mutate(hour = round(time / 3600)) %>%
  group_by(id, hour) %>%
  summarise(baseline = mean(tt)) %>%
  ungroup() %>%
  left_join(
    read_csv(
      "output/experiments-pieter/set2/dbl-no-redux-nomc/analysis/linkTimesVolumes.csv"
    ) %>%
      tibble() %>%
      select(id = linkId, time, tt = avgTravelTime) %>%
      right_join(corridor) %>%
      mutate(hour = round(time / 3600)) %>%
      group_by(id, hour) %>%
      summarise(doubled = mean(tt)) %>%
      ungroup()
  ) %>%
  left_join(
    read_csv(
      "output/experiments-pieter/set2/dbl-redux-nomc/analysis/linkTimesVolumes.csv"
    ) %>%
      tibble() %>%
      select(id = linkId, time, tt = avgTravelTime) %>%
      right_join(corridor) %>%
      mutate(hour = round(time / 3600)) %>%
      group_by(id, hour) %>%
      summarise(reduced = mean(tt)) %>%
      ungroup()
  )

write.csv(
  shortterm.traveltimes %>%
    group_by(hour) %>%
    summarise(across(baseline:reduced, ~ sum(.x, na.rm = T) / 60)) %>%
    filter(complete.cases(.)),
  file = "projects/komodnext/website/corridor/shortterm_tt.csv",
  row.names = F,
  quote = F
)

# switchers ---------
baseline_trips <-
  read_delim(
    "output/experiments-pieter/set2/baseline/duesseldorf-25pct-dc_114-no-lanes.output_trips.csv.gz",
    delim = ";"
  )
baseline_corridorpax <-
  read_csv("output/experiments-pieter/set2/baseline/analysis/corridorpaxplans.csv") %>%
  transmute(person = as.character(personId), trip_id, time)

doubled_trips <-
  read_delim(
    "output/experiments-pieter/set2/dbl-no-redux/duesseldorf-25pct-dc_114-no-lanes.output_trips.csv.gz",
    delim = ";"
  )
doubled_corridorpax <-
  read_csv("output/experiments-pieter/set2/dbl-no-redux/analysis/corridorpaxplans.csv") %>%
  transmute(person = as.character(personId), trip_id, time)

doubled_reduced_trips <-
  read_delim(
    "output/experiments-pieter/set2/dbl-redux/duesseldorf-25pct-dc_114-no-lanes.output_trips.csv.gz",
    delim = ";"
  )
doubled_reduced_corridorpax <-
  read_csv("output/experiments-pieter/set2/dbl-redux/analysis/corridorpaxplans.csv") %>%
  transmute(person = as.character(personId), trip_id, time)

doubled_join <-
  baseline_trips %>%
  select(trip_id, main_mode_x = main_mode) %>%
  left_join(doubled_trips %>%
              select(trip_id, main_mode_y = main_mode)) %>%
  left_join(baseline_corridorpax %>% transmute(trip_id, incorridorbaseline =
                                                 T)) %>%
  left_join(doubled_corridorpax %>% transmute(trip_id, incorridordoubled =
                                                T)) %>%
  filter(!(is.na(incorridorbaseline) &
             is.na(incorridordoubled))) %>%
  mutate(
    main_mode_x = ifelse(
      main_mode_x == "car" &
        is.na(incorridorbaseline),
      "avoided_corridor",
      main_mode_x
    ),
    main_mode_x = ifelse(main_mode_x == "car", "used_corridor", main_mode_x),
    main_mode_y = ifelse(
      main_mode_y == "car" &
        is.na(incorridordoubled),
      "avoided_corridor",
      main_mode_y
    ),
    main_mode_y = ifelse(main_mode_y == "car", "used_corridor", main_mode_y)
  ) %>%
  group_by(main_mode_x, main_mode_y) %>%
  summarise(num = n())

doubled_join %>%
  write.csv(file = "projects/komodnext/website/corridor/longterm_doubled_sankey.csv",
            row.names = F,
            quote = F)


reduced_join <-
  baseline_trips %>%
  select(trip_id, main_mode_x = main_mode) %>%
  left_join(doubled_reduced_trips %>%
              select(trip_id, main_mode_y = main_mode)) %>%
  left_join(baseline_corridorpax %>% transmute(trip_id, incorridorbaseline =
                                                 T)) %>%
  left_join(doubled_reduced_corridorpax %>% transmute(trip_id, incorridordoubled =
                                                        T)) %>%
  filter(!(is.na(incorridorbaseline) &
             is.na(incorridordoubled))) %>%
  mutate(
    main_mode_x = ifelse(
      main_mode_x == "car" &
        is.na(incorridorbaseline),
      "avoided_corridor",
      main_mode_x
    ),
    main_mode_x = ifelse(main_mode_x == "car", "used_corridor", main_mode_x),
    main_mode_y = ifelse(
      main_mode_y == "car" &
        is.na(incorridordoubled),
      "avoided_corridor",
      main_mode_y
    ),
    main_mode_y = ifelse(main_mode_y == "car", "used_corridor", main_mode_y)
  ) %>%
  group_by(main_mode_x, main_mode_y) %>%
  summarise(num = n())

reduced_join %>%
  write.csv(file = "projects/komodnext/website/corridor/longterm_reduced_sankey.csv",
            row.names = F,
            quote = F)