diff --git a/_shared_utils/setup.py b/_shared_utils/setup.py
index 00c6f6f40..686f5f105 100644
--- a/_shared_utils/setup.py
+++ b/_shared_utils/setup.py
@@ -4,7 +4,7 @@
 setup(
     name="shared_utils",
     packages=find_packages(),
-    version="2.5",
+    version="2.5.1",
     description="Shared utility functions for data analyses",
     author="Cal-ITP",
     license="Apache",
diff --git a/_shared_utils/shared_utils/gtfs_utils_v2.py b/_shared_utils/shared_utils/gtfs_utils_v2.py
index 3e08d5701..4929d362c 100644
--- a/_shared_utils/shared_utils/gtfs_utils_v2.py
+++ b/_shared_utils/shared_utils/gtfs_utils_v2.py
@@ -503,3 +503,23 @@ def get_stop_times(
         )
 
     return stop_times
+
+
+def filter_to_public_schedule_gtfs_dataset_keys(get_df: bool = False) -> list:
+    """
+    Return a list of schedule_gtfs_dataset_keys that have
+    private_dataset == None.
+    private_dataset holds values:True or None, no False.
+    """
+    dim_gtfs_datasets = schedule_rt_utils.filter_dim_gtfs_datasets(
+        keep_cols=["key", "name", "private_dataset"],
+        custom_filtering={
+            "type": ["schedule"],
+        },
+        get_df=True,
+    ) >> filter(_.private_dataset != True)
+
+    if get_df:
+        return dim_gtfs_datasets
+    else:
+        return dim_gtfs_datasets.gtfs_dataset_key.unique().tolist()
diff --git a/_shared_utils/shared_utils/publish_utils.py b/_shared_utils/shared_utils/publish_utils.py
index 098d29238..deb9e0697 100644
--- a/_shared_utils/shared_utils/publish_utils.py
+++ b/_shared_utils/shared_utils/publish_utils.py
@@ -3,6 +3,7 @@
 from typing import Union
 
 import gcsfs
+import pandas as pd
 
 fs = gcsfs.GCSFileSystem()
 PUBLIC_BUCKET = "gs://calitp-publish-data-analysis/"
@@ -47,3 +48,14 @@ def if_exists_then_delete(filepath: str):
             fs.rm(filepath)
 
     return
+
+
+def exclude_private_datasets(
+    df: pd.DataFrame,
+    col: str = "schedule_gtfs_dataset_key",
+    public_gtfs_dataset_keys: list = [],
+) -> pd.DataFrame:
+    """
+    Filter out private datasets.
+    """
+    return df[df[col].isin(public_gtfs_dataset_keys)].reset_index(drop=True)
diff --git a/gtfs_digest/merge_data.py b/gtfs_digest/merge_data.py
index 71e5125db..c0648b89a 100644
--- a/gtfs_digest/merge_data.py
+++ b/gtfs_digest/merge_data.py
@@ -8,6 +8,7 @@
 
 from calitp_data_analysis import utils
 from segment_speed_utils import gtfs_schedule_wrangling, time_series_utils
+from shared_utils import gtfs_utils_v2, publish_utils
 from update_vars import GTFS_DATA_DICT, SEGMENT_GCS, RT_SCHED_GCS, SCHED_GCS
 
 route_time_cols = ["schedule_gtfs_dataset_key", 
@@ -220,16 +221,21 @@ def set_primary_typology(df: pd.DataFrame) -> pd.DataFrame:
     
     return df3
 
+
 if __name__ == "__main__":
     
     from shared_utils import rt_dates
     
-    analysis_date_list = (rt_dates.y2024_dates + rt_dates.y2023_dates
-            )
+    analysis_date_list = (
+        rt_dates.y2024_dates + rt_dates.y2023_dates
+    )
     
     DIGEST_RT_SCHED = GTFS_DATA_DICT.digest_tables.route_schedule_vp 
     DIGEST_SEGMENT_SPEEDS = GTFS_DATA_DICT.digest_tables.route_segment_speeds
     
+    # These are public schedule_gtfs_dataset_keys
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
+    
     # Get cardinal direction for each route
     df_sched = concatenate_schedule_by_route_direction(analysis_date_list)
     
@@ -274,11 +280,15 @@ def set_primary_typology(df: pd.DataFrame) -> pd.DataFrame:
         df_crosswalk,
         on = ["schedule_gtfs_dataset_key", "name", "service_date"],
         how = "left"
+    ).pipe(
+        # Find the most common cardinal direction
+        gtfs_schedule_wrangling.top_cardinal_direction
+    ).pipe(
+        # Drop any private datasets before exporting
+        publish_utils.exclude_private_datasets, 
+        public_gtfs_dataset_keys= public_feeds
     )
-    
-    # Find the most common cardinal direction
-    df = gtfs_schedule_wrangling.top_cardinal_direction(df)
-    
+     
     integrify = [
         "n_scheduled_trips", "n_vp_trips",
         "minutes_atleast1_vp", "minutes_atleast2_vp",
@@ -304,7 +314,9 @@ def set_primary_typology(df: pd.DataFrame) -> pd.DataFrame:
         primary_typology,
         on = route_time_cols,
         how = "left"
-    )
+    ).pipe(
+        publish_utils.exclude_private_datasets, 
+        public_gtfs_dataset_keys= public_feeds)
 
     utils.geoparquet_gcs_export(
         segment_speeds2,
diff --git a/gtfs_digest/merge_operator_data.py b/gtfs_digest/merge_operator_data.py
index 6f580a500..04efad49a 100644
--- a/gtfs_digest/merge_operator_data.py
+++ b/gtfs_digest/merge_operator_data.py
@@ -8,6 +8,7 @@
 
 from calitp_data_analysis import utils
 from segment_speed_utils import time_series_utils
+from shared_utils import publish_utils
 from merge_data import merge_in_standardized_route_names
 from update_vars import GTFS_DATA_DICT, SCHED_GCS, RT_SCHED_GCS
 
@@ -100,6 +101,7 @@ def operator_category_counts_by_date() -> pd.DataFrame:
     
     return operator_category_counts
 
+
 if __name__ == "__main__":
 
     from shared_utils import rt_dates
@@ -111,6 +113,8 @@ def operator_category_counts_by_date() -> pd.DataFrame:
     SCHED_RT_CATEGORY = GTFS_DATA_DICT.digest_tables.operator_sched_rt
     CROSSWALK = GTFS_DATA_DICT.schedule_tables.gtfs_key_crosswalk
     
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
+    
     # Concat operator profiles
     df = concatenate_operator_stats(analysis_date_list)
     
@@ -141,14 +145,20 @@ def operator_category_counts_by_date() -> pd.DataFrame:
     
     # Merge
     merge_cols = ["schedule_gtfs_dataset_key", "service_date"]
-    op_profiles_df1 = pd.merge(df, 
-                               crosswalk_df, 
-                               on = merge_cols, 
-                               how = "left")
+    op_profiles_df1 = pd.merge(
+        df, 
+        crosswalk_df, 
+        on = merge_cols, 
+        how = "left"
+    )
     
     # Drop duplicates created after merging
     op_profiles_df2 = (op_profiles_df1
-                       .drop_duplicates(subset = list(op_profiles_df1.columns))
+                       .pipe(
+                           publish_utils.exclude_private_datasets, 
+                           col = "schedule_gtfs_dataset_key", 
+                           public_gtfs_dataset_keys = public_feeds
+                       ).drop_duplicates(subset = list(op_profiles_df1.columns))
                        .reset_index(drop = True))
 
     op_profiles_df2.to_parquet(
@@ -157,7 +167,13 @@ def operator_category_counts_by_date() -> pd.DataFrame:
     
     gdf = concatenate_operator_routes(
         analysis_date_list
-    ).pipe(merge_in_standardized_route_names)
+    ).pipe(
+        merge_in_standardized_route_names
+    ).pipe(
+        publish_utils.exclude_private_datasets, 
+        col = "schedule_gtfs_dataset_key", 
+        public_gtfs_dataset_keys = public_feeds
+    )
 
     utils.geoparquet_gcs_export(
         gdf,
@@ -165,7 +181,12 @@ def operator_category_counts_by_date() -> pd.DataFrame:
         OPERATOR_ROUTE
     )
     
-    operator_category_counts = operator_category_counts_by_date()
+    operator_category_counts = operator_category_counts_by_date().pipe(
+        publish_utils.exclude_private_datasets, 
+        col = "schedule_gtfs_dataset_key", 
+        public_gtfs_dataset_keys = public_feeds    
+    )
+    
     operator_category_counts.to_parquet(
         f"{RT_SCHED_GCS}{SCHED_RT_CATEGORY}.parquet"
     )
diff --git a/gtfs_digest/merge_operator_service.py b/gtfs_digest/merge_operator_service.py
index 4574b3948..ac59ceb50 100644
--- a/gtfs_digest/merge_operator_service.py
+++ b/gtfs_digest/merge_operator_service.py
@@ -1,12 +1,3 @@
-import pandas as pd
-import numpy as np
-from segment_speed_utils import helpers, time_series_utils, gtfs_schedule_wrangling
-from segment_speed_utils.project_vars import (COMPILED_CACHED_VIEWS, RT_SCHED_GCS, SCHED_GCS)
-
-from shared_utils import catalog_utils, rt_dates
-
-GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data")
-
 """
 Finding the total number of scheduled service hours for 
 an operator across its routes for a full week. The data is
@@ -14,6 +5,16 @@
 
 Grain is operator-service_date-route
 """
+import pandas as pd
+
+from segment_speed_utils import (gtfs_schedule_wrangling, helpers, 
+                                 time_series_utils) 
+from segment_speed_utils.project_vars import (
+    COMPILED_CACHED_VIEWS, weeks_available)
+from shared_utils import gtfs_utils_v2, publish_utils, rt_dates
+from update_vars import GTFS_DATA_DICT, RT_SCHED_GCS
+
+
 def concatenate_trips(
     date_list: list,
 ) -> pd.DataFrame:
@@ -44,29 +45,6 @@ def concatenate_trips(
 
     return df
 
-def get_day_type(date):
-    """
-    Function to return the day type (e.g., Monday, Tuesday, etc.) from a datetime object.
-    """
-    days_of_week = ["Monday", 
-                    "Tuesday", 
-                    "Wednesday", 
-                    "Thursday", 
-                    "Friday", 
-                    "Saturday", 
-                    "Sunday"]
-    return days_of_week[date.weekday()]
-
-def weekday_or_weekend(row):
-    """
-    Tag if a day is a weekday or Saturday/Sunday
-    """
-    if row.day_type == "Sunday":
-        return "Sunday"
-    if row.day_type == "Saturday":
-        return "Saturday"
-    else:
-        return "Weekday"
 
 def total_service_hours(date_list: list) -> pd.DataFrame:
     """
@@ -76,67 +54,81 @@ def total_service_hours(date_list: list) -> pd.DataFrame:
     # Combine all the days' data for a week.
     df = concatenate_trips(date_list)
     
-    # Find day type aka Monday, Tuesday, Wednesday based on service date.
-    df['day_type'] = df['service_date'].apply(get_day_type)
-    
-    # Tag if the day is a weekday, Saturday, or Sunday.
-    df["weekday_weekend"] = df.apply(weekday_or_weekend, axis=1)
+    WEEKDAY_DICT = {
+        **{k: "Weekday" for k in ["Monday", "Tuesday", "Wednesday",
+                             "Thursday", "Friday"]},
+        "Saturday": "Saturday",
+        "Sunday": "Sunday"
+    }
     
-    # df = gtfs_schedule_wrangling.add_weekday_weekend_column(df)
-    
-    # Find the minimum departure hour.
-    df["departure_hour"] = df.trip_first_departure_datetime_pacific.dt.hour
+    # Find day type (Monday, Tuesday, etc), departure hour, month_year, and weekday_weekend
+    df = df.assign(
+        day_type = df.service_date.dt.day_name(),
+        departure_hour = df.trip_first_departure_datetime_pacific.dt.hour.astype("Int64"),
+        # get month_year that's 2024-04 for Apr2024 format
+        month_year = (df.service_date.dt.year.astype(str) + 
+                      "-" +  df.service_date.dt.month.astype(str).str.zfill(2)),
+    ).pipe(
+        gtfs_schedule_wrangling.add_weekday_weekend_column, WEEKDAY_DICT
+    )
     
-    # Delete out the specific day, leave only month & year.
-    df["month"] = df.service_date.astype(str).str.slice(stop=7)
     
-    # Total up service hours by weekday, Sunday, and Saturday.
+    # Total up hourly service hours by weekday, Sunday, and Saturday.
     df2 = (
         df.groupby(["name", 
-                    "month", 
+                    "month_year", 
                     "weekday_weekend", 
                     "departure_hour"])
-        .agg(
-            {
-                "service_hours": "sum",
-            }
-        )
+        .agg({"service_hours": "sum"})
         .reset_index()
     )
     
-    # For weekday hours, divide by 5.
-    df2["weekday_service_hours"] = df2.service_hours/5
+    # weekday hours should be divided by 5, while keeping sat/sun intact
+    df2 = df2.assign(
+        daily_service_hours = df2.apply(
+            lambda x: round(x.service_hours / 5, 2) 
+            if x.weekday_weekend=="Weekday"
+            else round(x.service_hours, 2), axis=1
+        ),
+        service_hours = df2.service_hours.round(2),
+    )
     
-    # Rename projects.
-    df2 = df2.rename(columns = {'service_hours':'weekend_service_hours'})
     return df2
 
-def total_service_hours_all_months() -> pd.DataFrame:
+
+def total_service_hours_all_months(week_list: list[list]) -> pd.DataFrame:
     """
     Find service hours for a full week for one operator
     and for the months we have a full week's worth of data downloaded.
-    As of 5/2024, we have April 2023 and October 2023.
-    """
-    # Grab the dataframes with a full week's worth of data. 
-    apr_23week = rt_dates.get_week(month="apr2023", exclude_wed=False)
-    oct_23week = rt_dates.get_week(month="oct2023", exclude_wed=False)
-    apr_24week = rt_dates.get_week(month="apr2024", exclude_wed=False)
-    
-    # Sum up total service_hours
-    apr_23df = total_service_hours(apr_23week)
-    oct_23df = total_service_hours(oct_23week)
-    apr_24df = total_service_hours(apr_24week)
+    As of 5/2024, we have April 2023, October 2023, and April 2024.
+    """   
+    public_datasets = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys(
+        get_df=True
+    )
+    public_feeds = public_datasets.gtfs_dataset_name.unique().tolist()
     
     # Combine everything
-    all_df = pd.concat([apr_23df, oct_23df, apr_24df])
-   
+    all_df = pd.concat(
+        [total_service_hours(one_week) for one_week in week_list]
+    ).pipe(
+        publish_utils.exclude_private_datasets, 
+        col = "name", 
+        public_gtfs_dataset_keys = public_feeds
+    )
+
     return all_df
 
 
 if __name__ == "__main__":
     
-    # Save service hours.
-    SERVICE_EXPORT = f"{GTFS_DATA_DICT.digest_tables.dir}{GTFS_DATA_DICT.digest_tables.scheduled_service_hours}.parquet"
-    service_hours = total_service_hours_all_months()
-    service_hours.to_parquet(SERVICE_EXPORT) 
+    print(f"Aggregating for dates: {weeks_available}")
+    
+    # Save service hours
+    SERVICE_EXPORT = GTFS_DATA_DICT.digest_tables.scheduled_service_hours
+    
+    service_hours = total_service_hours_all_months(weeks_available)
+    
+    service_hours.to_parquet(
+        f"{RT_SCHED_GCS}{SERVICE_EXPORT}.parquet"
+    ) 
     
\ No newline at end of file
diff --git a/high_quality_transit_areas/D1_assemble_hqta_points.py b/high_quality_transit_areas/D1_assemble_hqta_points.py
index 177bf9bf7..4226bedb5 100644
--- a/high_quality_transit_areas/D1_assemble_hqta_points.py
+++ b/high_quality_transit_areas/D1_assemble_hqta_points.py
@@ -20,6 +20,7 @@
 from A1_rail_ferry_brt_stops import clip_to_ca, get_rail_ferry_brt_extract
 from calitp_data_analysis import geography_utils, utils
 from segment_speed_utils import helpers
+from shared_utils import gtfs_utils_v2
 from update_vars import analysis_date, GCS_FILE_PATH, PROJECT_CRS
 
 catalog = intake.open_catalog("*.yml")
@@ -147,7 +148,7 @@ def get_agency_info(df: pd.DataFrame, date: str) -> pd.DataFrame:
         "organization_source_record_id": "org_id"
     })[["schedule_gtfs_dataset_key", 
      "agency", "org_id", "base64_url"]]
-        
+
     return crosswalk
     
 
@@ -211,6 +212,13 @@ def add_agency_names_hqta_details(
 
 
 def final_processing(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
+    """
+    Final steps for getting dataset ready for Geoportal.
+    Subset to columns, drop duplicates, sort for readability,
+    always project into WGS84.
+    """
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
+    
     keep_cols = [
         "agency_primary", 
         "hqta_type", "stop_id", "route_id",
@@ -222,12 +230,14 @@ def final_processing(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
         "geometry"
     ]
     
-    gdf2 = (gdf.reindex(columns = keep_cols)
-            .drop_duplicates(
-                subset=["agency_primary", "hqta_type", "stop_id", "route_id"])
-            .sort_values(["agency_primary", "hqta_type", "stop_id"])
-            .reset_index(drop=True)
-            .to_crs(geography_utils.WGS84)
+    gdf2 = (
+        gdf[gdf.schedule_gtfs_dataset_key.isin(public_feeds)]
+        .reindex(columns = keep_cols)
+        .drop_duplicates(
+            subset=["agency_primary", "hqta_type", "stop_id", "route_id"])
+        .sort_values(["agency_primary", "hqta_type", "stop_id"])
+        .reset_index(drop=True)
+        .to_crs(geography_utils.WGS84)
     )
     
     return gdf2
diff --git a/high_quality_transit_areas/D2_assemble_hqta_polygons.py b/high_quality_transit_areas/D2_assemble_hqta_polygons.py
index 47d62ebc5..7d68be922 100644
--- a/high_quality_transit_areas/D2_assemble_hqta_polygons.py
+++ b/high_quality_transit_areas/D2_assemble_hqta_polygons.py
@@ -15,6 +15,7 @@
 import D1_assemble_hqta_points as assemble_hqta_points
 from calitp_data_analysis import utils, geography_utils
 from D1_assemble_hqta_points import (EXPORT_PATH, add_route_info)
+from shared_utils import gtfs_utils_v2
 from update_vars import GCS_FILE_PATH, analysis_date, PROJECT_CRS
 
 catalog = intake.open_catalog("*.yml")
@@ -108,6 +109,7 @@ def final_processing(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
     Drop extra columns, get sorting done.
     Used to drop bad stops, but these all look ok.
     """
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
     
     keep_cols = [
         "agency_primary", "agency_secondary",
@@ -118,7 +120,8 @@ def final_processing(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
     ]
     
     # Drop bad stops, subset columns
-    gdf2 = (gdf[keep_cols]
+    gdf2 = (
+        gdf[gdf.schedule_gtfs_dataset_key.isin(public_feeds)][keep_cols]
             .drop_duplicates()
             .sort_values(["hqta_type", "agency_primary", 
                           "agency_secondary",
diff --git a/open_data/create_routes_data.py b/open_data/create_routes_data.py
index 6d7961536..9b4da38b0 100644
--- a/open_data/create_routes_data.py
+++ b/open_data/create_routes_data.py
@@ -2,9 +2,6 @@
 Create routes file with identifiers including
 route_id, route_name, operator name.
 """
-import os
-os.environ['USE_PYGEOS'] = '0'
-
 import geopandas as gpd
 import pandas as pd
 
@@ -12,7 +9,7 @@
 
 import prep_traffic_ops
 from calitp_data_analysis import utils, geography_utils
-from shared_utils import portfolio_utils
+from shared_utils import gtfs_utils_v2, portfolio_utils
 from segment_speed_utils import helpers
 from update_vars import analysis_date, TRAFFIC_OPS_GCS
 
@@ -90,6 +87,8 @@ def finalize_export_df(df: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
     """
     Suppress certain columns used in our internal modeling for export.
     """
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
+    
     # Change column order
     route_cols = [
         'organization_source_record_id', 'organization_name',
@@ -98,9 +97,10 @@ def finalize_export_df(df: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
     agency_ids = ['base64_url']
     
     col_order = route_cols + shape_cols + agency_ids + ['geometry']
-    df2 = (df[col_order]
+    df2 = (df[df.schedule_gtfs_dataset_key.isin(public_feeds)][col_order]
            .reindex(columns = col_order)
            .rename(columns = prep_traffic_ops.RENAME_COLS)
+           .reset_index(drop=True)
     )
     
     return df2
diff --git a/open_data/create_stops_data.py b/open_data/create_stops_data.py
index 7795f0056..0a4fccea1 100644
--- a/open_data/create_stops_data.py
+++ b/open_data/create_stops_data.py
@@ -2,10 +2,6 @@
 Create stops file with identifiers including
 route_id, route_name, agency_id, agency_name.
 """
-import os
-os.environ['USE_PYGEOS'] = '0'
-
-import dask.dataframe as dd
 import geopandas as gpd
 import pandas as pd
 
@@ -13,7 +9,7 @@
 
 import prep_traffic_ops
 from calitp_data_analysis import utils, geography_utils
-from shared_utils import schedule_rt_utils
+from shared_utils import gtfs_utils_v2, schedule_rt_utils
 from segment_speed_utils import helpers
 from update_vars import analysis_date, TRAFFIC_OPS_GCS
 
@@ -21,7 +17,7 @@
 def attach_route_info_to_stops(
     stops: gpd.GeoDataFrame, 
     trips: pd.DataFrame, 
-    stop_times: dd.DataFrame
+    stop_times: pd.DataFrame
 ) -> gpd.GeoDataFrame:
     """
     Attach all the various route information (route_id, route_type)
@@ -35,7 +31,7 @@ def attach_route_info_to_stops(
                 ]
     
     stops_with_route_info = (
-        dd.merge(
+        pd.merge(
             stop_times,
             trips[trip_cols], 
             on = ["feed_key", "trip_id"]
@@ -43,9 +39,9 @@ def attach_route_info_to_stops(
                                   "route_id", "route_type"])
         .drop(columns = "trip_id")
         .reset_index(drop=True)
-    ).compute()
+    )
     
-    stops_with_geom = dd.merge(
+    stops_with_geom = pd.merge(
         stops,
         stops_with_route_info,
         on = ["feed_key", "stop_id"],
@@ -69,6 +65,8 @@ def finalize_export_df(df: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
     """
     Suppress certain columns used in our internal modeling for export.
     """
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
+    
     # Change column order
     route_cols = [
         'organization_source_record_id', 'organization_name',
@@ -78,9 +76,10 @@ def finalize_export_df(df: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
     
     col_order = route_cols + stop_cols + agency_ids + ['geometry']
     
-    df2 = (df[col_order]
+    df2 = (df[df.schedule_gtfs_dataset_key.isin(public_feeds)][col_order]
            .reindex(columns = col_order)
            .rename(columns = prep_traffic_ops.RENAME_COLS)
+           .reset_index(drop=True)
     )
     
     return df2
@@ -105,7 +104,8 @@ def create_stops_file_for_export(date: str) -> gpd.GeoDataFrame:
     
     stop_times = helpers.import_scheduled_stop_times(
         date,
-        columns = prep_traffic_ops.keep_stop_time_cols
+        columns = prep_traffic_ops.keep_stop_time_cols,
+        get_panda = True
     )
         
     stops_assembled = attach_route_info_to_stops(stops, trips, stop_times)
diff --git a/rt_segment_speeds/scripts/publish_open_data.py b/rt_segment_speeds/scripts/publish_open_data.py
index 84efbf6c8..5f285d777 100644
--- a/rt_segment_speeds/scripts/publish_open_data.py
+++ b/rt_segment_speeds/scripts/publish_open_data.py
@@ -7,6 +7,7 @@
 from pathlib import Path
 
 from calitp_data_analysis import utils
+from shared_utils import gtfs_utils_v2
 from update_vars import GTFS_DATA_DICT, SEGMENT_GCS
 
 
@@ -16,6 +17,8 @@ def stage_open_data_exports(analysis_date: str):
     export them to a stable GCS URL so we can always 
     read it in open_data/catalog.yml.
     """
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
+    
     datasets = [
         GTFS_DATA_DICT.stop_segments.route_dir_single_segment,
         #GTFS_DATA_DICT.speedmap_segments.route_dir_single_segment,
@@ -24,7 +27,8 @@ def stage_open_data_exports(analysis_date: str):
 
     for d in datasets:
         gdf = gpd.read_parquet(
-            f"{SEGMENT_GCS}{d}_{analysis_date}.parquet"
+            f"{SEGMENT_GCS}{d}_{analysis_date}.parquet",
+            filters = [[("schedule_gtfs_dataset_key", "in", public_feeds)]]
         )
         
         utils.geoparquet_gcs_export(
diff --git a/rt_segment_speeds/scripts/publish_public_gcs.py b/rt_segment_speeds/scripts/publish_public_gcs.py
index fb1c32c9c..8b361b0e8 100644
--- a/rt_segment_speeds/scripts/publish_public_gcs.py
+++ b/rt_segment_speeds/scripts/publish_public_gcs.py
@@ -8,7 +8,7 @@
 from pathlib import Path
 
 from calitp_data_analysis import utils
-from shared_utils import rt_dates
+from shared_utils import rt_dates, gtfs_utils_v2
 from update_vars import GTFS_DATA_DICT, SEGMENT_GCS, PUBLIC_GCS
 
 if __name__ == "__main__":
@@ -19,11 +19,16 @@
         GTFS_DATA_DICT.speedmap_segments.route_dir_single_segment,
     ]
     
+    public_feeds = gtfs_utils_v2.filter_to_public_schedule_gtfs_dataset_keys()
+    
     for d in datasets:
         
         start = datetime.datetime.now()
 
-        df = gpd.read_parquet(f"{SEGMENT_GCS}{d}_{analysis_date}.parquet")
+        df = gpd.read_parquet(
+            f"{SEGMENT_GCS}{d}_{analysis_date}.parquet",
+            filters = [[("schedule_gtfs_dataset_key", "in", public_feeds)]]
+        )
                 
         utils.geoparquet_gcs_export(
             df,
diff --git a/rt_segment_speeds/segment_speed_utils/gtfs_schedule_wrangling.py b/rt_segment_speeds/segment_speed_utils/gtfs_schedule_wrangling.py
index 9a43a4073..5c467cb2a 100644
--- a/rt_segment_speeds/segment_speed_utils/gtfs_schedule_wrangling.py
+++ b/rt_segment_speeds/segment_speed_utils/gtfs_schedule_wrangling.py
@@ -109,9 +109,13 @@ def add_peak_offpeak_column(df: pd.DataFrame) -> pd.DataFrame:
     
     return df
 
-def add_weekday_weekend_column(df: pd.DataFrame) -> pd.DataFrame:
+def add_weekday_weekend_column(df: pd.DataFrame, category_dict: dict = time_helpers.WEEKDAY_DICT) -> pd.DataFrame:
+    """
+    Add a single weekday_weekend column based on a service date's day_name.
+    day_name gives values like Monday, Tuesday, etc.
+    """
     df = df.assign(
-        weekday_weekend = df.service_date.dt.day_name().map(time_helpers.WEEKDAY_DICT)
+        weekday_weekend = df.service_date.dt.day_name().map(category_dict)
     )
     
     return df