diff --git a/server/rapid/gen_latest_rapid_data.sh b/server/rapid/gen_latest_rapid_data.sh
deleted file mode 100755
index a91e48147..000000000
--- a/server/rapid/gen_latest_rapid_data.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash -x
-
-# step 1: download and inflate from the feed (see appropriate line in setup_rapid_input.sh)
-# stop 2: run this script with the new files
-
-newfile=$1
-
-poetry run python process_events.py $newfile data/output
-
-# step 3: upload to aws
-# sample upload command below. note that an aws cp --recursive will be faster (if you only have one month).
-# aws s3 sync --dryrun data/output/Events/ s3://tm-mbta-performance/Events/
-# aws s3 cp --recursive --dryrun data/output/Events/ s3://tm-mbta-performance/Events/
diff --git a/server/rapid/gen_rapid_events.sh b/server/rapid/gen_rapid_events.sh
deleted file mode 100755
index 4503ef2f2..000000000
--- a/server/rapid/gen_rapid_events.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/bash -x
-
-for y in `seq 2016 2024`; do
-    for f in $(find data/input/$y/ -name '*.csv'); do
-        echo "Generating stop data from $f"
-        poetry run python process_events.py $f data/output
-    done
-done
diff --git a/server/rapid/process_events.py b/server/rapid/process_events.py
deleted file mode 100644
index 0370ab622..000000000
--- a/server/rapid/process_events.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import argparse
-import pandas as pd
-import pathlib
-
-
-def process_events(input_csv, outdir, nozip=False):
-    columns = [
-        "service_date",
-        "route_id",
-        "trip_id",
-        "direction_id",
-        "stop_id",
-        "stop_sequence",
-        "vehicle_id",
-        "vehicle_label",
-        "event_type",
-        "event_time_sec",
-    ]
-
-    df = pd.read_csv(
-        input_csv,
-        usecols=columns,
-        parse_dates=["service_date"],
-        dtype={
-            "route_id": "str",
-            "trip_id": "str",
-            "stop_id": "str",
-            "vehicle_id": "str",
-            "vehicle_label": "str",
-            "event_time": "int",
-        },
-    )
-
-    df["event_time"] = df["service_date"] + pd.to_timedelta(df["event_time_sec"], unit="s")
-    df.drop(columns=["event_time_sec"], inplace=True)
-
-    service_date_month = pd.Grouper(key="service_date", freq="1M")
-    grouped = df.groupby([service_date_month, "stop_id"])
-
-    for name, events in grouped:
-        service_date, stop_id = name
-
-        fname = pathlib.Path(
-            outdir,
-            "Events",
-            "monthly-data",
-            str(stop_id),
-            f"Year={service_date.year}",
-            f"Month={service_date.month}",
-            "events.csv.gz",
-        )
-        fname.parent.mkdir(parents=True, exist_ok=True)
-        # set mtime to 0 in gzip header for determinism (so we can re-gen old routes, and rsync to s3 will ignore)
-        events.to_csv(fname, index=False, compression={"method": "gzip", "mtime": 0} if not nozip else None)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("input", metavar="INPUT_CSV")
-    parser.add_argument("output", metavar="OUTPUT_DIR")
-
-    parser.add_argument("--nozip", "-nz", action="store_true", help="debug feature to skip gzipping")
-
-    args = parser.parse_args()
-    input_csv = args.input
-    output_dir = args.output
-    no_zip = args.nozip
-
-    pathlib.Path(output_dir).mkdir(exist_ok=True)
-
-    process_events(input_csv, output_dir, no_zip)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/server/rapid/setup_rapid_input.sh b/server/rapid/setup_rapid_input.sh
deleted file mode 100755
index 22b5ce152..000000000
--- a/server/rapid/setup_rapid_input.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/sh -x
-
-mkdir -p data/input
-
-# 2016 is a weird case- seems to be tts, headways, etc. not ARR DEP events.
-wget -N -O data/input/2016.zip https://www.arcgis.com/sharing/rest/content/items/3e892be850fe4cc4a15d6450de4bd318/data
-wget -N -O data/input/2017.zip https://www.arcgis.com/sharing/rest/content/items/cde60045db904ad299922f4f8759dcad/data
-wget -N -O data/input/2018.zip https://www.arcgis.com/sharing/rest/content/items/25c3086e9826407e9f59dd9844f6c975/data
-wget -N -O data/input/2019.zip https://www.arcgis.com/sharing/rest/content/items/11bbb87f8fb245c2b87ed3c8a099b95f/data
-wget -N -O data/input/2020.zip https://www.arcgis.com/sharing/rest/content/items/cb4cf52bafb1402b9b978a424ed4dd78/data
-wget -N -O data/input/2021.zip https://www.arcgis.com/sharing/rest/content/items/611b8c77f30245a0af0c62e2859e8b49/data
-wget -N -O data/input/2022.zip https://www.arcgis.com/sharing/rest/content/items/99094a0c59e443cdbdaefa071c6df609/data
-wget -N -O data/input/2023.zip https://www.arcgis.com/sharing/rest/content/items/9a7f5634db72459ab731b6a9b274a1d4/data
-wget -N -O data/input/2024.zip https://www.arcgis.com/sharing/rest/content/items/4adbec39db40498a8530496d8c63a924/data
-
-cd data/input
-for i in `seq 2017 2024`; do
-    unzip -o -d $i $i.zip
-done
-
-# The following years only have single csv files
-# These are too large to process at once, so we use this sed script
-# to split it into monthly files.
-for y in 2016 2017 2018; do
-    awk -v year=$y -v outdir="$y/" -F "-" '
-        NR==1 {header=$0}; 
-        NF>1 && NR>1 {
-            if(! files[$2]) {
-                print header >> (outdir year "_" $2 ".csv");
-                files[$2] = 1;
-            };
-            print $0 >> (outdir year "_" $2 ".csv");
-        }' $y/Events$y.csv;
-    
-    rm $y/Events$y.csv;
-done