diff --git a/mbta-performance/chalicelib/lamp/ingest.py b/mbta-performance/chalicelib/lamp/ingest.py
index 3675609..858fe48 100644
--- a/mbta-performance/chalicelib/lamp/ingest.py
+++ b/mbta-performance/chalicelib/lamp/ingest.py
@@ -3,7 +3,7 @@
 import os
 import pandas as pd
 import requests
-from typing import Tuple
+from typing import Iterable, Tuple
 
 from .constants import LAMP_COLUMNS, S3_COLUMNS
 from ..date import format_dateint, get_current_service_date
@@ -22,7 +22,6 @@
 S3_BUCKET = "tm-mbta-performance"
 # month and day are not zero-padded
 S3_KEY_TEMPLATE = "Events-lamp/daily-data/{stop_id}/Year={YYYY}/Month={_M}/Day={_D}/events.csv"
-TEMP_GTFS_LOCAL_PREFIX = ".temp/gtfs-feeds/"
 
 COLUMN_RENAME_MAP = {
     "headway_trunk_seconds": "headway_seconds",
@@ -34,6 +33,12 @@
 # that the vehicle is currently on (this can be due to AVL glitches, trip diversions, test train trips, etc.)
 TRIP_IDS_TO_DROP = ("NONREV-", "ADDED-")
 
+# information to fetch from GTFS
+TEMP_GTFS_LOCAL_PREFIX = ".temp/gtfs-feeds/"
+MAX_QUERY_DEPTH = 950  # actually 1000
+# defining these columns in particular becasue we use them everywhere
+RTE_DIR_STOP = ["route_id", "direction_id", "stop_id"]
+
 
 def _local_save(s3_key, stop_events):
     """TODO remove this temp code, it saves the output files locally!"""
@@ -127,40 +132,70 @@ def fetch_pq_file_from_remote(service_date: date) -> pd.DataFrame:
     )
 
 
-def _recalculate_fields_from_gtfs(pq_df: pd.DataFrame, service_date: date):
-    # calculate gtfs traveltimes
+def fetch_from_gtfs(trip_ids: Iterable[str]) -> pd.DataFrame:
     mbta_gtfs = MbtaGtfsArchive(TEMP_GTFS_LOCAL_PREFIX)
+    service_date = get_current_service_date()
     feed = mbta_gtfs.get_feed_for_date(service_date)
     feed.download_or_build()
     session = feed.create_sqlite_session()
-    gtfs_stops = pd.read_sql(
-        session.query(
-            StopTime.trip_id,
-            StopTime.stop_id,
-            StopTime.arrival_time,
-            # func.min(StopTime.arrival_time).label("trip_start_time")
-        )
-        .filter(
-            or_(StopTime.trip_id == tid for tid in pq_df["trip_id"].unique())
-            # ).group_by(StopTime.trip_id).statement,
+
+    gtfs_stops = []
+    for start in range(1, len(trip_ids), MAX_QUERY_DEPTH):
+        gtfs_stops.append(
+            pd.read_sql(
+                session.query(
+                    StopTime.trip_id,
+                    StopTime.stop_id,
+                    StopTime.arrival_time,
+                )
+                .filter(or_(StopTime.trip_id == tid for tid in trip_ids[start : start + MAX_QUERY_DEPTH]))
+                .statement,
+                session.bind,
+            )
         )
-        .statement,
-        session.bind,
-    )
+    return pd.concat(gtfs_stops)
+
+
+def _recalculate_fields_from_gtfs(pq_df: pd.DataFrame, service_date: date):
+    trip_ids = pq_df["trip_id"].unique()
+    gtfs_stops = fetch_from_gtfs(trip_ids)
+
     # we could do this groupby/min/merge in sql, but let's keep our computations in
     # pandas to stay consistent across services
     trip_start_times = gtfs_stops.groupby("trip_id").arrival_time.transform("min")
-    gtfs_stops["scheduled_tt"] = gtfs_stops.arrival_time - trip_start_times
+    gtfs_stops["scheduled_tt"] = gtfs_stops["arrival_time"] - trip_start_times
+    gtfs_stops["arrival_time"] = gtfs_stops["arrival_time"].astype(float)
+    # gtfs_stops["arrival_time"] = pd.to_datetime(gtfs_stops.arrival_time, unit="s")
+
+    # assign each actual trip a scheduled trip_id, based on when it started the route
+    route_starts = pq_df.loc[pq_df.groupby("trip_id").event_time.idxmin()]
+    route_starts["arrival_time"] = (
+        route_starts.event_time - pd.Timestamp(service_date).tz_localize("US/Eastern")
+    ).dt.total_seconds()
+    route_starts = route_starts.sort_values(by="arrival_time")
+
+    trip_id_map = pd.merge_asof(
+        route_starts,
+        gtfs_stops[["trip_id", "stop_id"] + ["arrival_time"]],
+        on="arrival_time",
+        direction="nearest",
+        by=["trip_id", "stop_id"],
+        suffixes=["", "_scheduled"],
+    )
+    trip_id_map = trip_id_map.set_index("trip_id").trip_id_scheduled
+
     # merged
     # TODO check, hamima: can one conceivably return to a stop_id multiple times in a trip?
-    augmented_events = pd.merge(
+    # use the scheduled trip matching to get the scheduled traveltime
+    pq_df["scheduled_trip_id"] = pq_df.trip_id.map(trip_id_map)
+    pq_df = pd.merge(
         pq_df,
         gtfs_stops[["trip_id", "stop_id", "scheduled_tt"]],
         how="left",
         on=["trip_id", "stop_id"],
         suffixes=["", "_gtfs"],
     )
-    return augmented_events
+    return pq_df
 
 
 def ingest_pq_file(pq_df: pd.DataFrame, service_date: date) -> pd.DataFrame: