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Additional Observed Demand Summaries for Assigning the On-board Survey #39

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Additional Observed Demand Summaries for Assigning the On-board Survey #39

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6cc7570
update observed demand summaries with additional summary variations
DavidOry Mar 8, 2024
05a20e8
update acceptance criteria to new version, observed demand variations
DavidOry Apr 2, 2024
34ca26a
use MAZ data to correct for missing SamTrans TAZs
DavidOry Apr 12, 2024
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66 changes: 45 additions & 21 deletions summaries/make-acceptance-criteria-summaries.R
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Original file line number Diff line number Diff line change
Expand Up @@ -15,24 +15,30 @@ for (package in packages_vector) {

# Remote I-O -------------------------------------------------------------------
box_dir <- "~/Box Sync/"
survey_filename <- paste0(box_dir, "Survey_Database_122717/survey.Rdata")
survey_filename <- paste0(box_dir, "Survey_Database_090221/TPS_Model_Version_PopulationSim_Weights2021-09-02.Rdata")
output_filename <- paste0(
box_dir,
"Survey_Database_122717/acceptance-criteria-summaries-year-2015.csv"
"Survey_Database_090221/acceptance-criteria-summaries-year-2015.csv"
)

output_access_filename <- paste0(box_dir, "Survey_Database_122717/acceptance-criteria-access-summaries-year-2015.csv")
output_access_filename <- paste0(box_dir, "Survey_Database_090221/acceptance-criteria-access-summaries-year-2015.csv")

output_flows_filename <- paste0(box_dir, "Survey_Database_122717/acceptance-criteria-spatial-flows-year-2015.csv")
output_flows_filename <- paste0(box_dir, "Survey_Database_090221/acceptance-criteria-spatial-flows-year-2015.csv")

# Parameters -------------------------------------------------------------------
time_period_dict_df <- tibble(
day_part = c("EARLY AM", "AM PEAK", "MIDDAY", "PM PEAK", "EVENING", "NIGHT"),
time_period = c("ea", "am", "md", "pm", "ev", "ev")
)

mode_dict_df <- tibble(
SURVEY_MODE = c("LB", "EB", "LR", "FR", "HR", "CR"),
survey_tech = c("local bus", "express bus", "light rail", "ferry", "heavy rail", "commuter_rail")
)

rail_operators_vector <- c("BART","Caltrain","ACE","Sonoma-Marin Area Rail Transit")
ALL_DAY_WORD <- "daily"
survey_years_to_summarise <- seq(from = 2012, to = 2017)
survey_years_to_summarise <- seq(from = 2012, to = 2019)

# Methods ----------------------------------------------------------------------
make_direction_from_route <- function(input_df, input_reg_ex_word, brackets_bool) {
Expand All @@ -57,10 +63,12 @@ make_direction_from_route <- function(input_df, input_reg_ex_word, brackets_bool
load(survey_filename)

# Reductions 00: Common --------------------------------------------------------
common_df <- survey %>%
common_df <- TPS %>%
filter(weekpart != "WEEKEND") %>%
select(-time_period) %>%
filter(survey_year %in% survey_years_to_summarise) %>%
left_join(., time_period_dict_df, by = c("day_part"))
left_join(., time_period_dict_df, by = c("day_part")) %>%
left_join(., mode_dict_df, by = c("SURVEY_MODE"))


# Reductions 01: Boardings by route --------------------------------------------
Expand All @@ -75,19 +83,17 @@ by_time_period_df <- common_df %>%
make_direction_from_route(., "\\[Southbound\\]", TRUE) %>%
make_direction_from_route(., "NORTHBOUND", FALSE) %>%
make_direction_from_route(., "SOUTHBOUND", FALSE) %>%
group_by(operator, route, direction, time_period) %>%
summarise(survey_boardings = sum(weight), .groups = "drop")
group_by(operator, survey_tech, route, time_period) %>%
summarise(survey_boardings = sum(final_boardWeight_2015), .groups = "drop")

daily_df <- by_time_period_df %>%
group_by(operator, route) %>%
summarise(survey_boardings = sum(survey_boardings), .groups = "drop") %>%
mutate(direction = NA) %>%
mutate(time_period = ALL_DAY_WORD)

output_df <- bind_rows(by_time_period_df, daily_df) %>%
rename(survey_operator = operator,
survey_route = route,
survey_direction = direction)
survey_route = route)

# Reductions 02: Access shares for rail stations -------------------------------
access_df <- common_df %>%
Expand All @@ -104,22 +110,33 @@ access_df <- common_df %>%

# Reductions 03: Flows by technology -------------------------------------------
working_df <- common_df %>%
filter(orig_taz > 0) %>%
filter(dest_taz > 0) %>%
mutate(temp = (first_board_tech == "local bus") | (last_alight_tech == "local bus") | (survey_tech == "local bus")) %>%
filter(orig_tm2_taz > 0) %>%
filter(dest_tm2_taz > 0) %>%
mutate(temp = (first_board_tech == "LB") | (last_alight_tech == "LB") | (SURVEY_MODE == "LB")) %>%
mutate(is_loc_in_path = if_else(temp, 1.0, 0.0)) %>%
mutate(temp = (first_board_tech == "express bus") | (last_alight_tech == "express bus") | (survey_tech == "express bus")) %>%
mutate(temp = (first_board_tech == "EB") | (last_alight_tech == "EB") | (SURVEY_MODE == "EB")) %>%
mutate(is_exp_in_path = if_else(temp, 1.0, 0.0)) %>%
mutate(temp = (first_board_tech == "light rail") | (last_alight_tech == "light rail") | (survey_tech == "light rail")) %>%
mutate(temp = (first_board_tech == "LR") | (last_alight_tech == "LR") | (SURVEY_MODE == "LR")) %>%
mutate(is_lrt_in_path = if_else(temp, 1.0, 0.0)) %>%
mutate(temp = (first_board_tech == "ferry") | (last_alight_tech == "ferry") | (survey_tech == "ferry")) %>%
mutate(temp = (first_board_tech == "FR") | (last_alight_tech == "FR") | (SURVEY_MODE == "FR")) %>%
mutate(is_fry_in_path = if_else(temp, 1.0, 0.0)) %>%
mutate(temp = (first_board_tech == "heavy rail") | (last_alight_tech == "heavy rail") | (survey_tech == "heavy rail")) %>%
mutate(temp = (first_board_tech == "HR") | (last_alight_tech == "HR") | (SURVEY_MODE == "HR")) %>%
mutate(is_hvy_in_path = if_else(temp, 1.0, 0.0)) %>%
mutate(temp = (first_board_tech == "commuter rail") | (last_alight_tech == "commuter rail") | (survey_tech == "commuter rail")) %>%
mutate(temp = (first_board_tech == "CR") | (last_alight_tech == "CR") | (SURVEY_MODE == "CR")) %>%
mutate(is_com_in_path = if_else(temp, 1.0, 0.0)) %>%
mutate(techs_in_path = is_loc_in_path + is_exp_in_path + is_lrt_in_path + is_fry_in_path + is_hvy_in_path + is_com_in_path) %>%
select(Unique_ID, orig_taz, dest_taz, time_period, is_loc_in_path, is_exp_in_path, is_lrt_in_path, is_fry_in_path, is_hvy_in_path, is_com_in_path, techs_in_path, trip_weight)
select(ID,
orig_taz = orig_tm2_taz,
dest_taz = dest_tm2_taz,
time_period,
is_loc_in_path,
is_exp_in_path,
is_lrt_in_path,
is_fry_in_path,
is_hvy_in_path,
is_com_in_path,
techs_in_path,
trip_weight = final_tripWeight_2015)

flows_df <- working_df %>%
group_by(orig_taz, dest_taz, time_period) %>%
Expand All @@ -142,3 +159,10 @@ write_csv(output_df, output_filename)
write_csv(access_df, output_access_filename)
write_csv(flows_df, output_flows_filename)

sum_df <- output_df %>%
filter(time_period == "am") %>%
group_by(survey_operator) %>%
summarise(boardings = sum(survey_boardings), .groups = "drop")

sam_df <- TPS %>%
filter(str_detect(operator, "SamTrans"))
189 changes: 187 additions & 2 deletions summaries/make-observed-demand-csv.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,12 +14,20 @@ for (package in packages_vector) {
}

# Remote I-O -------------------------------------------------------------------
box_dir <- "~/Box Sync/"
box_dir <- "~/Box Sync/"
github_dir <- "../../"
survey_filename <- paste0(box_dir, "Survey_Database_090221/TPS_Model_Version_PopulationSim_Weights2021-09-02.Rdata")
output_filename <- paste0(
box_dir,
"Survey_Database_090221/observed-demand-year-2015.csv"
)
output_01_filename <- paste0(box_dir, "Survey_Database_090221/observed-demand-year-2015-emme-taz-by-path.csv")
output_02_filename <- paste0(box_dir, "Survey_Database_090221/observed-demand-am-year-2015-emme-taz-by-path.csv")
output_03_filename <- paste0(box_dir, "Survey_Database_090221/observed-demand-ea-year-2015-emme-taz-by-path.csv")

# TODO: need to update to more stable path
tm2_zone_system_filename <- paste0(github_dir, "tm2py/examples/temp_acceptance/inputs/landuse/taz_data.csv")
maz_filename <- paste0(github_dir, "tm2py/examples/temp_acceptance/inputs/landuse/maz_data.csv")


# Parameters -------------------------------------------------------------------
Expand All @@ -31,14 +39,95 @@ time_period_dict_df <- tibble(
# Methods ----------------------------------------------------------------------

# Data Reads -------------------------------------------------------------------
load(survey_filename)
load(survey_filename)

taz_df <- read_csv(tm2_zone_system_filename, col_types = cols(
TAZ = col_integer(),
index = col_double(),
TAZ_ORIGINAL = col_integer(),
AVGTTS = col_double(),
DIST = col_double(),
PCTDETOUR = col_double(),
TERMINALTIME = col_double()
))

maz_df <- read_csv(maz_filename, col_types = cols(
MAZ_ORIGINAL = col_integer(),
TAZ_ORIGINAL = col_integer(),
DistID = col_double(),
DistName = col_character(),
CountyID = col_double(),
CountyName = col_character(),
ACRES = col_double(),
HH = col_double(),
POP = col_double(),
ag = col_double(),
art_rec = col_double(),
constr = col_double(),
eat = col_double(),
ed_high = col_double(),
ed_k12 = col_double(),
ed_oth = col_double(),
fire = col_double(),
gov = col_double(),
health = col_double(),
hotel = col_double(),
info = col_double(),
lease = col_double(),
logis = col_double(),
man_bio = col_double(),
man_lgt = col_double(),
man_hvy = col_double(),
man_tech = col_double(),
natres = col_double(),
prof = col_double(),
ret_loc = col_double(),
ret_reg = col_double(),
serv_bus = col_double(),
serv_pers = col_double(),
serv_soc = col_double(),
transp = col_double(),
util = col_double(),
emp_total = col_double(),
publicEnrollGradeKto8 = col_double(),
privateEnrollGradeKto8 = col_double(),
publicEnrollGrade9to12 = col_double(),
privateEnrollGrade9to12 = col_double(),
comm_coll_enroll = col_double(),
EnrollGradeKto8 = col_double(),
EnrollGrade9to12 = col_double(),
collegeEnroll = col_double(),
otherCollegeEnroll = col_double(),
AdultSchEnrl = col_double(),
hstallsoth = col_double(),
hstallssam = col_double(),
dstallsoth = col_double(),
dstallssam = col_double(),
mstallsoth = col_double(),
mstallssam = col_double(),
park_area = col_double(),
hparkcost = col_double(),
numfreehrs = col_double(),
dparkcost = col_double(),
mparkcost = col_double(),
ech_dist = col_double(),
hch_dist = col_double(),
parkarea = col_double(),
TERMINAL = col_double()
))

# Reductions -------------------------------------------------------------------

# SamTrans records are missing TAZ codes, use MAZ and update
output_df <- TPS %>%
filter(weekpart != "WEEKEND") %>%
left_join(., time_period_dict_df, by = c("day_part")) %>%
select(-orig_tm2_taz, -dest_tm2_taz) %>%
left_join(., select(maz_df, orig_tm2_taz = TAZ_ORIGINAL, orig_tm2_maz = MAZ_ORIGINAL), by = c("orig_tm2_maz")) %>%
left_join(., select(maz_df, dest_tm2_taz = TAZ_ORIGINAL, dest_tm2_maz = MAZ_ORIGINAL), by = c("dest_tm2_maz")) %>%
filter(!is.na(orig_tm2_taz)) %>%
filter(!is.na(dest_tm2_taz)) %>%
filter(!is.na(model_time)) %>%
group_by(model_time, access_mode_model, egress_mode_model, orig_tm2_taz, dest_tm2_taz) %>%
summarise(trips = sum(final_tripWeight_2015), .groups = "drop")

Expand All @@ -47,3 +136,99 @@ sum(output_df$trips)
# Write ------------------------------------------------------------------------
write_csv(output_df, output_filename)


# Variations -------------------------------------------------------------------
taz_dict_df <- select(taz_df, external_taz = TAZ_ORIGINAL, emme_taz = TAZ)

## Variation 1: Emme TAZ numbers, aggregate over TAZs by path and time-of-day --
working_df <- output_df %>%
left_join(.,
select(taz_dict_df, orig_tm2_taz = external_taz, emme_taz),
by = c("orig_tm2_taz")) %>%
rename(orig_emme_taz = emme_taz) %>%
left_join(.,
select(taz_dict_df, dest_tm2_taz = external_taz, emme_taz),
by = c("dest_tm2_taz")) %>%
rename(dest_emme_taz = emme_taz) %>%
select(model_time,
access_mode_model,
egress_mode_model,
orig_emme_taz,
dest_emme_taz,
trips) %>%
mutate(path_type = if_else(access_mode_model == "walk" & egress_mode_model == "walk", "wlk_trn_wlk", "drive-to-drive")) %>%
mutate(path_type = if_else(access_mode_model == "pnr" & egress_mode_model == "walk", "pnr_trn_wlk", path_type)) %>%
mutate(path_type = if_else(access_mode_model == "knr" & egress_mode_model == "walk", "knr_trn_wlk", path_type)) %>%
mutate(path_type = if_else(access_mode_model == "tnc" & egress_mode_model == "walk", "knr_trn_wlk", path_type)) %>%
mutate(path_type = if_else(access_mode_model == "walk" & egress_mode_model == "pnr", "wlk_trn_pnr", path_type)) %>%
mutate(path_type = if_else(access_mode_model == "walk" & egress_mode_model == "knr", "wlk_trn_knr", path_type)) %>%
mutate(path_type = if_else(access_mode_model == "walk" & egress_mode_model == "tnc", "wlk_trn_knr", path_type)) %>%
filter(path_type != "drive-to-drive")

output_01_df <- working_df %>%
group_by(model_time, path_type, orig_emme_taz, dest_emme_taz) %>%
summarise(trips = sum(trips), .groups = "drop")

sum(output_01_df$trips)

write_csv(output_01_df, output_01_filename)

## Variation 2: Same as 1 for AM period
output_02_df <- output_01_df %>%
filter(model_time == "am")

sum(output_02_df$trips)

sum(filter(output_02_df, orig_emme_taz < 637 & dest_emme_taz < 637)$trips)

write_csv(output_02_df, output_02_filename)

## Variation 3: Get transfer rate
transfer_df <- TPS %>%
filter(weekpart != "WEEKEND") %>%
left_join(., time_period_dict_df, by = c("day_part")) %>%
filter(!is.na(orig_tm2_taz)) %>%
filter(!is.na(dest_tm2_taz)) %>%
group_by(model_time, access_mode_model, egress_mode_model, orig_tm2_taz, dest_tm2_taz) %>%
summarise(trips = sum(final_tripWeight_2015),
boardings = sum(final_boardWeight_2015),
.groups = "drop") %>%
filter(model_time == "am")

sum(transfer_df$boardings) / sum(transfer_df$trips)

## Variation 4: Same as 1 for EA period
output_03_df <- output_01_df %>%
filter(model_time == "ea")

sum(output_03_df$trips)

write_csv(output_03_df, output_03_filename)

## Debug: Operator Distribution for Marin-SF or SF-Marin Movements
debug_df <- TPS %>%
filter(weekpart != "WEEKEND") %>%
left_join(., time_period_dict_df, by = c("day_part")) %>%
filter(!is.na(orig_tm2_taz)) %>%
filter(!is.na(dest_tm2_taz)) %>%
mutate(orig_in_sf = orig_tm2_taz < 100000) %>%
mutate(orig_in_marin = orig_tm2_taz > 800000) %>%
mutate(dest_in_sf = dest_tm2_taz < 100000) %>%
mutate(dest_in_marin = dest_tm2_taz > 800000) %>%
filter((orig_in_sf & dest_in_marin) | (orig_in_marin & dest_in_sf)) %>%
filter(model_time == "am") %>%
group_by(operator) %>%
summarise(trips = sum(final_tripWeight_2015), .groups = "drop")

debug_df <- TPS %>%
filter(weekpart != "WEEKEND") %>%
left_join(., time_period_dict_df, by = c("day_part")) %>%
filter(!is.na(orig_tm2_taz)) %>%
filter(!is.na(dest_tm2_taz)) %>%
filter(model_time == "am") %>%
group_by(operator, survey_year) %>%
summarise(trips = sum(final_tripWeight_2015), .groups = "drop")