diff --git a/tm2py/acceptance/observed.py b/tm2py/acceptance/observed.py
index 9a736565..8fb2d23a 100644
--- a/tm2py/acceptance/observed.py
+++ b/tm2py/acceptance/observed.py
@@ -22,7 +22,7 @@ class Observed:
     census_tract_centroids_gdf: gpd.GeoDataFrame
 
     RELEVANT_PEMS_OBSERVED_YEARS_LIST = [2014, 2015, 2016]
-    RELEVANT_BRIDGE_TRANSACTIONS_YEARS_LIST = [2014, 2015, 2016] # ch
+    RELEVANT_BRIDGE_TRANSACTIONS_YEARS_LIST = [2014, 2015, 2016] 
     RELEVANT_PEMS_VEHICLE_CLASSES_FOR_LARGE_TRUCK = [6, 7, 8, 9, 10, 11, 12]
 
     ohio_rmse_standards_df = pd.DataFrame(
@@ -220,6 +220,7 @@ def _join_standard_route_id(self, input_df: pd.DataFrame) -> pd.DataFrame:
         df["survey_agency"] = df["survey_agency"].map(
             self.c.canonical_agency_names_dict
         )
+
         join_df = df[~df["survey_agency"].isin(self.c.rail_operators_vector)].copy()
 
         join_all_df = join_df.copy()
@@ -329,6 +330,7 @@ def reduce_on_board_survey(self, read_file_from_disk=True):
             temp_df = in_df[
                 (in_df["weekpart"].isna()) | (in_df["weekpart"] != "WEEKEND")
             ].copy()
+
             temp_df["time_period"] = temp_df["day_part"].map(time_period_dict)
             temp_df["route"] = np.where(
                 temp_df["operator"].isin(self.c.rail_operators_vector),
@@ -553,11 +555,11 @@ def _make_district_to_district_transit_flows_by_technology(self):
         o_df = self.reduced_transit_spatial_flow_df.copy()
         o_df = o_df[o_df["time_period"] == "am"].copy()
 
-        tm1_district_dict = self.c.taz_to_district_df.set_index("taz_tm1")[
-            "district_tm1"
+        tm2_district_dict = self.c.taz_to_district_df.set_index("taz_tm2")[
+            "district_tm2"
         ].to_dict()
-        o_df["orig_district"] = o_df["orig_taz"].map(tm1_district_dict)
-        o_df["dest_district"] = o_df["dest_taz"].map(tm1_district_dict)
+        o_df["orig_district"] = o_df["orig_taz"].map(tm2_district_dict)
+        o_df["dest_district"] = o_df["dest_taz"].map(tm2_district_dict)
 
         for prefix in self.c.transit_technology_abbreviation_dict.keys():
             o_df["{}".format(prefix.lower())] = (
@@ -796,7 +798,6 @@ def reduce_bridge_transactions(self, read_file_from_disk=True):
             return_df.to_pickle(os.path.join(file_root, out_file))
 
         self.observed_bridge_transactions_df = return_df
-
         return
 
     def reduce_traffic_counts(self, read_file_from_disk=True):
@@ -872,20 +873,19 @@ def _reduce_pems_counts(self, read_file_from_disk=True):
                 self.c.pems_to_link_crosswalk_df,
                 out_df,
                 how="left",
-                left_on="pems_station_id",
-                right_on="station_id",
-            ).drop(columns=["pems_station_id"])
+                on = "station_id"
+            )
+
 
-            out_df = self._join_tm2_node_ids(out_df)
+            out_df = self._join_tm2_node_ids(out_df)  
 
             # take median across multiple stations on same link
             median_df = (
                 out_df.groupby(
                     [
-                        "A",
-                        "B",
-                        "emme_a_node_id",
-                        "emme_b_node_id",
+                        "emme_a_node_id", 
+                        "emme_b_node_id", 
+                        "model_link_id",
                         "time_period",
                         "vehicle_class",
                     ]
@@ -894,15 +894,15 @@ def _reduce_pems_counts(self, read_file_from_disk=True):
                 .reset_index()
             )
             join_df = out_df[
-                ["A", "B", "observed_flow", "station_id", "type", "vehicle_class"]
+                 ["emme_a_node_id","emme_b_node_id","model_link_id", "time_period", "station_id", "type", "vehicle_class"]
             ].copy()
             return_df = pd.merge(
                 median_df,
                 join_df,
                 how="left",
-                on=["A", "B", "observed_flow", "vehicle_class"],
+                on = ["emme_a_node_id","emme_b_node_id","model_link_id", "time_period", "vehicle_class"]
             ).reset_index(drop=True)
-
+            return_df = return_df.rename(columns = {"model_link_id" : "standard_link_id"})
             return_df = self._join_ohio_standards(return_df)
             return_df = self._identify_key_arterials_and_bridges(return_df)