diff --git a/_shared_utils/shared_utils/gtfs_analytics_data.yml b/_shared_utils/shared_utils/gtfs_analytics_data.yml
index db0b364d4..a745e0e1e 100644
--- a/_shared_utils/shared_utils/gtfs_analytics_data.yml
+++ b/_shared_utils/shared_utils/gtfs_analytics_data.yml
@@ -81,6 +81,7 @@ stop_segments:
   shape_stop_cols: ["shape_array_key", "shape_id", "stop_sequence"]
   stop_pair_cols: ["stop_pair", "stop_pair_name"]
   route_dir_cols: ["route_id", "direction_id"]
+  segment_cols: ["schedule_gtfs_dataset_key", "route_id", "direction_id", "stop_pair", "geometry"]
   shape_stop_single_segment: "rollup_singleday/speeds_shape_stop_segments" #-- stop after Oct 2024
   route_dir_single_segment: "rollup_singleday/speeds_route_dir_segments"
   route_dir_multi_segment: "rollup_multiday/speeds_route_dir_segments"
diff --git a/rt_segment_speeds/scripts/quarter_year_averages.py b/rt_segment_speeds/scripts/quarter_year_averages.py
index 7d2d2bd13..afe2ec907 100644
--- a/rt_segment_speeds/scripts/quarter_year_averages.py
+++ b/rt_segment_speeds/scripts/quarter_year_averages.py
@@ -1,49 +1,68 @@
+"""
+Average segment speeds over longer time periods,
+a quarter or a year.
+"""
+import dask.dataframe as dd
 import geopandas as gpd
 import pandas as pd
 
 from dask import delayed, compute
 
-from update_vars import SEGMENT_GCS, GTFS_DATA_DICT
+from calitp_data_analysis import utils
 from segment_speed_utils import time_series_utils
+from update_vars import SEGMENT_GCS, GTFS_DATA_DICT
 
-from average_segment_speeds import concatenate_trip_segment_speeds
-
-def concatenate_single_day_summaries(
-    speed_file: str,
-    analysis_date_list: list,
-    group_cols: list
+def segment_speeds_one_day(
+    segment_type: str,
+    analysis_date: list,
+    segment_cols: list,
+    org_cols: list
 ):
     """
-    Concatenate several single day averages
-    and we'll take the average over that.
+    Concatenate segment geometry (from rt_segment_speeds)
+    for all the dates we have 
+    and get it to route-direction-segment grain
+    """
+    speed_file = GTFS_DATA_DICT[segment_type]["route_dir_single_segment"]
+    segment_file = GTFS_DATA_DICT[segment_type]["segments_file"]
+    
+    speeds_df = pd.read_parquet(
+        f"{SEGMENT_GCS}{speed_file}_{analysis_date}.parquet",
+        columns = segment_cols + org_cols + [
+            "schedule_gtfs_dataset_key",
+            "p20_mph", "p50_mph", "p80_mph", "n_trips"]
+    ).assign(
+        service_date = pd.to_datetime(analysis_date)
+    )
     
-    If we have 6 dates of segment p20/p50/p80 speeds,
-    we'll treat each date as an independent entity
-    and average the p20/p50/p80 speeds over that time period.
+    segment_gdf = gpd.read_parquet(
+        f"{SEGMENT_GCS}{segment_file}_{analysis_date}.parquet",
+        columns = segment_cols + [
+            "schedule_gtfs_dataset_key", "geometry"]
+    ).drop_duplicates().reset_index(drop=True)
+
+    merge_cols = [c for c in speeds_df.columns if c in segment_gdf.columns]
     
-    We will not go back to trip segment speeds for each date
-    and do a weighted average.
-    In an extreme case, if one date had 1,000 trips and another date
-    had 100 trips, one date would have 10x the weight of another date,
-    and here, we just want to see where the p20 speed typically is.
-    """
-    df = time_series_utils.concatenate_datasets_across_dates(
-        SEGMENT_GCS,
-        speed_file,
-        analysis_date_list,
-        data_type  = "df",
-        columns = group_cols + ["p20_mph", "p50_mph", "p80_mph", "n_trips"],
-        get_pandas = False
+    df = pd.merge(
+        segment_gdf[merge_cols + ["geometry"]].drop_duplicates(),
+        speeds_df,
+        on = merge_cols,
+        how = "inner"
     )
     
     df = df.assign(
         year = df.service_date.dt.year,
         quarter = df.service_date.dt.quarter,
     )
-
+    
     return df
 
+
 def get_aggregation(df: pd.DataFrame, group_cols: list):
+    """
+    Aggregating across days, take the (mean)p20/p50/p80 speed
+    and count number of trips across those days.
+    """
     speed_cols = [c for c in df.columns if "_mph" in c]
 
     df2 = (df
@@ -56,36 +75,83 @@ def get_aggregation(df: pd.DataFrame, group_cols: list):
     
     return df2
 
-if __name__ == "__main__":
-    
-    from shared_utils import rt_dates
-
-    group_cols = [
+def average_by_time(date_list: list, time_cols: list):
+    """
+    """
+    # These define segments, it's route-dir-stop_pair
+    segment_stop_cols = [
         "route_id", "direction_id", 
-        "stop_pair", "stop_pair_name", 
+        "stop_pair", 
+    ]
+    
+    # These are the other columns we need, from speeds, but not in segments
+    org_cols = [
+        "stop_pair_name",
         "time_period",
-        'name', # do not use schedule_gtfs_dataset_key, which can differ over time
+        "name",
         'caltrans_district', 'organization_source_record_id',
         'organization_name', 'base64_url'
-    ] 
+    ]
     
-    FILE = GTFS_DATA_DICT["stop_segments"]["route_dir_single_segment"]
-
-    quarter_df = concatenate_single_day_summaries(
-        FILE,
-        all_dates,
-        group_cols
-    ).pipe(get_aggregation, group_cols + ["year", "quarter"])
-
-    quarter_df = compute(quarter_df)[0]
-    quarter_df.to_parquet(f"{SEGMENT_GCS}{FILE}_quarter.parquet")
+    delayed_dfs = [
+        delayed(segment_speeds_one_day)(
+            "stop_segments", 
+            one_date,
+            segment_stop_cols,
+            org_cols
+       ) for one_date in date_list
+    ]
+    
+    ddf = dd.from_delayed(delayed_dfs)
     
-    year_df = concatenate_single_day_summaries(
-        FILE,
-        all_dates,
-        group_cols
-    ).pipe(get_aggregation, group_cols + ["year"])
+    group_cols = [
+        c for c in segment_stop_cols + org_cols 
+        if c not in ["schedule_gtfs_dataset_key"]
+    ] + time_cols
+    
+    speed_averages = get_aggregation(ddf, group_cols)
+    speed_averages = speed_averages.compute()
+    
+    segment_geom = ddf[
+        ["name", "geometry"] + segment_stop_cols + time_cols
+    ].drop_duplicates().compute()
+    
+    speed_gdf = pd.merge(
+        segment_geom,
+        speed_averages,
+        on = ["name"] + segment_stop_cols + time_cols,
+        how = "inner"
+    )
     
-    year_df = compute(year_df)[0]
-    year_df.to_parquet(f"{SEGMENT_GCS}{FILE}_year.parquet")
-    
\ No newline at end of file
+    return speed_gdf
+
+
+if __name__ == "__main__":
+    import datetime
+    from shared_utils import rt_dates
+
+    segment_type = "stop_segments"
+    EXPORT = GTFS_DATA_DICT[segment_type]["route_dir_multi_segment"]
+    all_dates = rt_dates.y2024_dates + rt_dates.y2023_dates
+    '''
+    # quarter averages take x min
+    speeds_by_quarter = average_by_time(all_dates, ["year", "quarter"])
+
+    utils.geoparquet_gcs_export(
+        speeds_by_quarter,
+        SEGMENT_GCS,
+        f"{EXPORT}_quarter"
+    )
+    del speeds_by_quarter
+    '''
+    # year averages take 14 min
+    t0 = datetime.datetime.now()
+    speeds_by_year = average_by_time(all_dates, ["year"])
+
+    utils.geoparquet_gcs_export(
+        speeds_by_year,
+        SEGMENT_GCS,
+        f"{EXPORT}_year"
+    )    
+    t1 = datetime.datetime.now()
+    print(f"execution: {t1 - t0}")
\ No newline at end of file