interim_dir <- "../output/"
+
+person_filename <- paste0(interim_dir, "final_persons.csv")
+tour_filename <- paste0(interim_dir, "final_tours.csv")
+ptype_dict <- tibble(ptype = seq(1,8),
+ label = c("Full-time Worker",
+ "Part-time Worker",
+ "University Student",
+ "Unemployed",
+ "Retired",
+ "Driving age Student",
+ "Non-driving age Student",
+ "Preschool Student"))person_df <- read_csv(person_filename, col_types = cols(
+ person_id = col_double(),
+ household_id = col_double(),
+ age = col_double(),
+ PNUM = col_double(),
+ sex = col_double(),
+ pemploy = col_double(),
+ pstudent = col_double(),
+ ptype = col_double(),
+ educ = col_double(),
+ naics2_original_code = col_character(),
+ soc2 = col_double(),
+ age_16_to_19 = col_logical(),
+ age_16_p = col_logical(),
+ adult = col_logical(),
+ male = col_logical(),
+ female = col_logical(),
+ has_non_worker = col_logical(),
+ has_retiree = col_logical(),
+ has_preschool_kid = col_logical(),
+ has_driving_kid = col_logical(),
+ has_school_kid = col_logical(),
+ has_full_time = col_logical(),
+ has_part_time = col_logical(),
+ has_university = col_logical(),
+ student_is_employed = col_logical(),
+ nonstudent_to_school = col_logical(),
+ is_student = col_logical(),
+ is_preschool = col_logical(),
+ is_gradeschool = col_logical(),
+ is_highschool = col_logical(),
+ is_university = col_logical(),
+ school_segment = col_double(),
+ is_worker = col_logical(),
+ is_fulltime_worker = col_logical(),
+ is_parttime_worker = col_logical(),
+ is_internal_worker = col_logical(),
+ is_external_worker = col_logical(),
+ home_zone_id = col_double(),
+ time_factor_work = col_double(),
+ time_factor_nonwork = col_double(),
+ naics_code = col_double(),
+ occupation = col_character(),
+ is_income_less25K = col_logical(),
+ is_income_25K_to_60K = col_logical(),
+ is_income_60K_to_120K = col_logical(),
+ is_income_greater60K = col_logical(),
+ is_income_greater120K = col_logical(),
+ is_non_worker_in_HH = col_logical(),
+ is_all_adults_full_time_workers = col_logical(),
+ is_pre_drive_child_in_HH = col_logical(),
+ work_from_home = col_logical(),
+ is_out_of_home_worker = col_logical(),
+ external_workplace_zone_id = col_double(),
+ external_workplace_location_logsum = col_double(),
+ external_workplace_modechoice_logsum = col_double(),
+ school_zone_id = col_double(),
+ school_location_logsum = col_double(),
+ school_modechoice_logsum = col_double(),
+ distance_to_school = col_double(),
+ roundtrip_auto_time_to_school = col_double(),
+ workplace_zone_id = col_double(),
+ workplace_location_logsum = col_double(),
+ workplace_modechoice_logsum = col_double(),
+ distance_to_work = col_double(),
+ workplace_in_cbd = col_logical(),
+ work_zone_area_type = col_double(),
+ auto_time_home_to_work = col_double(),
+ roundtrip_auto_time_to_work = col_double(),
+ work_auto_savings = col_double(),
+ exp_daily_work = col_double(),
+ non_toll_time_work = col_double(),
+ toll_time_work = col_double(),
+ toll_dist_work = col_double(),
+ toll_cost_work = col_double(),
+ toll_travel_time_savings_work = col_double(),
+ transit_pass_subsidy = col_double(),
+ transit_pass_ownership = col_double(),
+ free_parking_at_work = col_logical(),
+ telecommute_frequency = col_character(),
+ cdap_activity = col_character(),
+ travel_active = col_logical(),
+ under16_not_at_school = col_logical(),
+ has_preschool_kid_at_home = col_logical(),
+ has_school_kid_at_home = col_logical(),
+ mandatory_tour_frequency = col_character(),
+ work_and_school_and_worker = col_logical(),
+ work_and_school_and_student = col_logical(),
+ num_mand = col_double(),
+ num_work_tours = col_double(),
+ has_pre_school_child_with_mandatory = col_logical(),
+ has_driving_age_child_with_mandatory = col_logical(),
+ num_joint_tours = col_double(),
+ non_mandatory_tour_frequency = col_double(),
+ num_non_mand = col_double(),
+ num_escort_tours = col_double(),
+ num_eatout_tours = col_double(),
+ num_shop_tours = col_double(),
+ num_maint_tours = col_double(),
+ num_discr_tours = col_double(),
+ num_social_tours = col_double(),
+ num_non_escort_tours = col_double(),
+ num_shop_maint_tours = col_double(),
+ num_shop_maint_escort_tours = col_double(),
+ num_add_shop_maint_tours = col_double(),
+ num_soc_discr_tours = col_double(),
+ num_add_soc_discr_tours = col_double(),
+ model = col_character()
+))
+
+tour_df <- read_csv(tour_filename, col_types = cols(
+ tour_id = col_double(),
+ person_id = col_double(),
+ tour_type = col_character(),
+ tour_type_count = col_double(),
+ tour_type_num = col_double(),
+ tour_num = col_double(),
+ tour_count = col_double(),
+ tour_category = col_character(),
+ number_of_participants = col_double(),
+ destination = col_double(),
+ origin = col_double(),
+ household_id = col_double(),
+ start = col_double(),
+ end = col_double(),
+ duration = col_double(),
+ school_esc_outbound = col_character(),
+ school_esc_inbound = col_character(),
+ num_escortees = col_double(),
+ tdd = col_double(),
+ tour_id_temp = col_double(),
+ composition = col_character(),
+ is_external_tour = col_logical(),
+ is_internal_tour = col_logical(),
+ destination_logsum = col_double(),
+ vehicle_occup_1 = col_character(),
+ vehicle_occup_2 = col_character(),
+ vehicle_occup_3.5 = col_character(),
+ tour_mode = col_character(),
+ mode_choice_logsum = col_double(),
+ selected_vehicle = col_character(),
+ atwork_subtour_frequency = col_character(),
+ parent_tour_id = col_double(),
+ stop_frequency = col_character(),
+ primary_purpose = col_character(),
+ model = col_character()
+))| person_type | +none | +work_1 | +work_2 | +school_1 | +school_2 | +work_and_school | +
|---|---|---|---|---|---|---|
| Full-time Worker | +0.7793215 | +0.1993803 | +0.0212982 | +0.0000000 | +0.0000000 | +0.0000000 | +
| Part-time Worker | +0.7374583 | +0.2314355 | +0.0311062 | +0.0000000 | +0.0000000 | +0.0000000 | +
| University Student | +0.7873440 | +0.0076863 | +0.0159111 | +0.1704447 | +0.0079819 | +0.0106320 | +
| Unemployed | +1.0000000 | +0.0000000 | +0.0000000 | +0.0000000 | +0.0000000 | +0.0000000 | +
| Retired | +1.0000000 | +0.0000000 | +0.0000000 | +0.0000000 | +0.0000000 | +0.0000000 | +
| Driving age Student | +0.8328733 | +0.0002269 | +0.0053503 | +0.1524559 | +0.0072031 | +0.0018906 | +
| Non-driving age Student | +0.8154858 | +0.0000000 | +0.0000000 | +0.1819527 | +0.0025614 | +0.0000000 | +
| Preschool Student | +0.9033079 | +0.0000000 | +0.0000000 | +0.0489189 | +0.0477732 | +0.0000000 | +
summary_df %>%
+ kbl() %>%
+ kable_styling()
+The model does show the right types of categories for each of the +person types. It seems to have a much higher than expected share of +non-mandatory and home activity patterns for workers and students, so +the model does not appear to be well calibrated. But the implementation +appears correct. ActivitySim files reviewed include:
+mandatory_tour_frequency_alternatives.csvmandatory_tour_frequency_coeffs.csvmandatory_tour_frequency.csvmandatory_tour_frequency.yamlannotate_persons_mtf.csvinterim_dir <- "../output/"
+
+person_filename <- paste0(interim_dir, "final_persons.csv")
+trips_filename <- paste0(interim_dir, "final_trips.csv")
+
+temp_trips_filename <- paste0(interim_dir, "final_trips.RDS")
+ptype_dict <- tibble(ptype = seq(1,8),
+ label = c("Full-time Worker",
+ "Part-time Worker",
+ "University Student",
+ "Unemployed",
+ "Retired",
+ "Driving age Student",
+ "Non-driving age Student",
+ "Preschool Student"))trips_df <- read_csv(trips_filename)
+Rows: 9229920 Columns: 258── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+Delimiter: ","
+chr (7): primary_purpose, escort_participants, school_escort_direction, purpose, trip_mode, trip_period, model
+dbl (242): trip_id, person_id, household_id, trip_num, trip_count, destination, origin, tour_id, destination_logsum, depart, mode_choice_logsum, vot_da, vot_s2, vot_s3, parking_zone, tour_participants, distance, vot1, vot2, vot3, DRIVEALONE_N...
+lgl (9): outbound, ownsTransponder, is_ea, is_am, is_md, is_pm, is_ev, ownTrp, inbound
+ℹ Use `spec()` to retrieve the full column specification for this data.
+ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.saveRDS(trips_df, temp_trips_filename)summary_df <- person_df %>%
+ select(person_id, ptype, free_parking_at_work) %>%
+ left_join(., ptype_dict, by = c("ptype")) %>%
+ rename(person_type = label) %>%
+ mutate(free_parking_at_work = if_else(free_parking_at_work, "Free Parking at Work", "Not Free Parking at Work")) %>%
+ group_by(ptype, person_type, free_parking_at_work) %>%
+ summarise(count = n(), .groups = "drop") %>%
+ group_by(ptype, person_type) %>%
+ mutate(share = count/sum(count)) %>%
+ ungroup() %>%
+ arrange(ptype) %>%
+ select(person_type, free_parking_at_work, share) %>%
+ pivot_wider(names_from = free_parking_at_work, values_from = share, values_fill = 0.0)
+
+
+summary_df %>%
+ kbl() %>%
+ kable_styling()| person_type | +Free Parking at Work | +Not Free Parking at Work | +
|---|---|---|
| Full-time Worker | +0.4530072 | +0.5469928 | +
| Part-time Worker | +0.5140448 | +0.4859552 | +
| University Student | +0.1371233 | +0.8628767 | +
| Unemployed | +0.0000000 | +1.0000000 | +
| Retired | +0.0000000 | +1.0000000 | +
| Driving age Student | +0.0413185 | +0.9586815 | +
| Non-driving age Student | +0.0000000 | +1.0000000 | +
| Preschool Student | +0.0000000 | +1.0000000 | +
NAThe model does show the right types of categories for each of the +person types. The implementation appears correct. ActivitySim files +reviewed include:
+free_parking_annotate_persons_preprocessor.csvfree_parking_coefficients.csv – For SANDAG: still has
+coefficients specific to SF Bay Areafree_parking.csv: not much intelligence in the
+specfree_parking.yaml: appears# need to check what choices those with free parking make in regards to parking locations
+summary_df <- trips_df %>%
+ select(trip_id, person_id, purpose, parking_zone, destination) %>%
+ filter(purpose == "work") %>%
+ left_join(., select(person_df, person_id, free_parking_at_work), by = c("person_id")) %>%
+ mutate(parking_choice_made = (parking_zone > 0)) %>%
+ mutate(parking_zone_is_destination = if_else(parking_zone == destination, "Parked at Destination", "Parked and Walked to Destination")) %>%
+ mutate(parking_zone_is_destination = if_else(parking_choice_made, parking_zone_is_destination, "No Parking Choice Made")) %>%
+ mutate(free_parking_at_work = if_else(free_parking_at_work, "Free Parking at Work", "Not Free Parking at Work")) %>%
+ group_by(free_parking_at_work, parking_zone_is_destination) %>%
+ summarise(count = n(), .groups = "drop") %>%
+ group_by(free_parking_at_work) %>%
+ mutate(share = count/sum(count)) %>%
+ ungroup() %>%
+ select(-count) %>%
+ pivot_wider(names_from = parking_zone_is_destination, values_from = share, values_fill = 0.0)
+
+summary_df %>%
+ kbl() %>%
+ kable_styling()| free_parking_at_work | +No Parking Choice Made | +Parked at Destination | +
|---|---|---|
| Free Parking at Work | +0.9386687 | +0.0613313 | +
| Not Free Parking at Work | +0.9406610 | +0.0593390 | +
NA
+NA
+NA
+NAThe parking location choice model does not appeared to be applied +correctly. Emailed Joe F. follow-up question on 10/26.
+ +