diff --git a/gtfs_rt_comparison/compare_vp_tu.ipynb b/gtfs_rt_comparison/compare_vp_tu.ipynb
new file mode 100644
index 000000000..9cc492df4
--- /dev/null
+++ b/gtfs_rt_comparison/compare_vp_tu.ipynb
@@ -0,0 +1,2217 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "724f15b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import datetime\n",
+    "import pandas as pd\n",
+    "from jinja2 import Environment, FileSystemLoader, StrictUndefined\n",
+    "\n",
+    "from calitp_data_analysis import gcs_pandas\n",
+    "from calitp_data_analysis.sql import get_engine\n",
+    "from shared_utils.catalog_utils import get_catalog\n",
+    "from shared_utils.rt_dates import DATES\n",
+    "\n",
+    "g = gcs_pandas.GCSPandas()\n",
+    "\n",
+    "# get jinja environment for substituting in queries\n",
+    "jinja_env = Environment(\n",
+    "    loader=FileSystemLoader(Path(\"queries\")),\n",
+    "    undefined=StrictUndefined,\n",
+    "    autoescape=False,  # nosec B701 - rendering SQL, not markup\n",
+    ")\n",
+    "\n",
+    "jinja_env.filters[\"sql_in\"] = lambda values: \"(\" + \", \".join(f\"'{v}'\" for v in values) + \")\"\n",
+    "jinja_env.filters[\"sql_in_timestamps\"] = (\n",
+    "    lambda values: \"(\" + \", \".join(f\"timestamp('{v}')\" for v in values) + \")\"\n",
+    ")\n",
+    "\n",
+    "# bigquery engine from calitp_date_analysis\n",
+    "engine = get_engine()\n",
+    "\n",
+    "def run_query(name: str, **params) -> pd.DataFrame:\n",
+    "    \"\"\"Render queries/<name>.sql with params and run it against BigQuery.\"\"\"\n",
+    "    return pd.read_sql_query(jinja_env.get_template(f\"{name}.sql\").render(**params), engine)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "505bd04b",
+   "metadata": {},
+   "source": [
+    "## Constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "dee4c1f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "85\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The feed to examine TODO: support multiple\n",
+    "GTFS_DATASET_NAME = \"Bay Area 511 SamTrans Schedule\"\n",
+    "# Sample dates to look at\n",
+    "dates_values = DATES.values()\n",
+    "print(len(dates_values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6808b264",
+   "metadata": {},
+   "source": [
+    "## Get data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "7d247387",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mrtopsyt/data-analyses/.venv/lib/python3.11/site-packages/google/cloud/bigquery/client.py:613: UserWarning: Cannot create BigQuery Storage client, the dependency google-cloud-bigquery-storage is not installed.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tu_base64_url</th>\n",
+       "      <th>schedule_feed_key</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>4dfefaf518766d1cfc3d254a65e4295e</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>0215a1c49f081dc0a7eb1e118ea91229</td>\n",
+       "      <td>2024-12-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>15b3a87c05331f6d0e0b73cf340bfc5b</td>\n",
+       "      <td>2025-02-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>1d276b06a3c6ff0d661e25035982ead7</td>\n",
+       "      <td>2024-03-13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>25c80082b079cb7fab299c7015aa7470</td>\n",
+       "      <td>2023-07-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>c1c5d0b862094fbf640e18075b290861</td>\n",
+       "      <td>2025-09-30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>c1c5d0b862094fbf640e18075b290861</td>\n",
+       "      <td>2025-09-24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>c7ab222b14ca19ad45369c228ac16783</td>\n",
+       "      <td>2025-09-23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>c7bc4bc9de15ff685470b0de7578d313</td>\n",
+       "      <td>2026-01-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>c7bc4bc9de15ff685470b0de7578d313</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>85 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        tu_base64_url  \\\n",
+       "0   aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "1   aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "2   aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "3   aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "4   aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "..                                                ...   \n",
+       "80  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "81  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "82  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "83  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "84  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   \n",
+       "\n",
+       "                   schedule_feed_key       date  \n",
+       "0   4dfefaf518766d1cfc3d254a65e4295e 2026-06-10  \n",
+       "1   0215a1c49f081dc0a7eb1e118ea91229 2024-12-11  \n",
+       "2   15b3a87c05331f6d0e0b73cf340bfc5b 2025-02-12  \n",
+       "3   1d276b06a3c6ff0d661e25035982ead7 2024-03-13  \n",
+       "4   25c80082b079cb7fab299c7015aa7470 2023-07-12  \n",
+       "..                               ...        ...  \n",
+       "80  c1c5d0b862094fbf640e18075b290861 2025-09-30  \n",
+       "81  c1c5d0b862094fbf640e18075b290861 2025-09-24  \n",
+       "82  c7ab222b14ca19ad45369c228ac16783 2025-09-23  \n",
+       "83  c7bc4bc9de15ff685470b0de7578d313 2026-01-14  \n",
+       "84  c7bc4bc9de15ff685470b0de7578d313 2025-12-17  \n",
+       "\n",
+       "[85 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get feed key for the target feed:\n",
+    "feed_keys_to_dates = run_query(\n",
+    "    \"feed_keys\",\n",
+    "    DATES=dates_values,\n",
+    "    GTFS_DATASET_NAME=GTFS_DATASET_NAME,\n",
+    ")\n",
+    "feed_keys_to_dates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "c6cb00ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tu_base64_url</th>\n",
+       "      <th>service_date</th>\n",
+       "      <th>n_stop_time_metrics</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "      <td>59708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>2026-01-14</td>\n",
+       "      <td>59560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>2026-04-08</td>\n",
+       "      <td>36667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>60858</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       tu_base64_url service_date  \\\n",
+       "0  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   2025-12-17   \n",
+       "1  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   2026-01-14   \n",
+       "2  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   2026-04-08   \n",
+       "3  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L3RyaXB1cG...   2026-06-10   \n",
+       "\n",
+       "   n_stop_time_metrics  \n",
+       "0                59708  \n",
+       "1                59560  \n",
+       "2                36667  \n",
+       "3                60858  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Count fct_stop_time_metrics rows per trip updates feed per date\n",
+    "tu_urls = feed_keys_to_dates[\"tu_base64_url\"].dropna().unique()\n",
+    "\n",
+    "stop_time_metrics_counts = run_query(\n",
+    "    \"stop_time_metrics_counts\",\n",
+    "    DATES=dates_values,\n",
+    "    TU_URLS=tu_urls,\n",
+    ")\n",
+    "stop_time_metrics_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "994c75ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the VP based stop times\n",
+    "gtfs_analytics_catalog = get_catalog(\"gtfs_analytics_data\")\n",
+    "stop_time_metrics_counts[\"vp_uri\"] = (\n",
+    "    f\"{gtfs_analytics_catalog['rt_stop_times']['dir']}{gtfs_analytics_catalog['rt_stop_times']['stage3']}_\" \n",
+    "    + stop_time_metrics_counts[\"service_date\"].astype(str)\n",
+    "    + \".parquet\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "8999c6da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The TU based stop times, plus the trips to filter the statewide VP files with.\n",
+    "# Both are limited to the dates that actually have stop time metrics.\n",
+    "stm_dates = stop_time_metrics_counts[\"service_date\"].astype(str).tolist()\n",
+    "stm_urls = stop_time_metrics_counts[\"tu_base64_url\"].unique()\n",
+    "\n",
+    "operator_trips = run_query(\n",
+    "    \"operator_trips\",\n",
+    "    DATES=stm_dates,\n",
+    "    GTFS_DATASET_NAME=GTFS_DATASET_NAME,\n",
+    ")\n",
+    "\n",
+    "tu_stop_times = run_query(\n",
+    "    \"tu_stop_times\",\n",
+    "    DATES=stm_dates,\n",
+    "    TU_URLS=stm_urls,\n",
+    "    GTFS_DATASET_NAME=GTFS_DATASET_NAME,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "79951035",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>service_date</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>tu_arrival_time</th>\n",
+       "      <th>n_predictions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>341081</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2026-06-10 07:18:00</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>341185</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2026-06-10 07:18:17</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>341622</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2026-06-10 07:22:00</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>341060</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2026-06-10 07:18:26</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>341957</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2026-06-10 07:19:20</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  trip_instance_key service_date stop_id  stop_sequence  \\\n",
+       "0  0008d298e3f42cea9e00cfddb076fdd0   2026-06-10  341081              1   \n",
+       "1  0008d298e3f42cea9e00cfddb076fdd0   2026-06-10  341185              2   \n",
+       "2  0008d298e3f42cea9e00cfddb076fdd0   2026-06-10  341622              3   \n",
+       "3  0008d298e3f42cea9e00cfddb076fdd0   2026-06-10  341060              4   \n",
+       "4  0008d298e3f42cea9e00cfddb076fdd0   2026-06-10  341957              5   \n",
+       "\n",
+       "      tu_arrival_time  n_predictions  \n",
+       "0 2026-06-10 07:18:00             79  \n",
+       "1 2026-06-10 07:18:17             85  \n",
+       "2 2026-06-10 07:22:00             90  \n",
+       "3 2026-06-10 07:18:26             90  \n",
+       "4 2026-06-10 07:19:20             90  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tu_stop_times.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a1a58f2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>service_date</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>schedule_arrival_time</th>\n",
+       "      <th>schedule_departure_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "      <td>332014</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2025-12-18 00:15:00</td>\n",
+       "      <td>2025-12-18 00:15:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "      <td>332170</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2025-12-18 00:17:01</td>\n",
+       "      <td>2025-12-18 00:17:01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "      <td>332232</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2025-12-18 00:18:03</td>\n",
+       "      <td>2025-12-18 00:18:03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "      <td>332238</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2025-12-18 00:18:42</td>\n",
+       "      <td>2025-12-18 00:18:42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "      <td>332215</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2025-12-18 00:19:49</td>\n",
+       "      <td>2025-12-18 00:19:49</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  trip_instance_key service_date stop_id  stop_sequence  \\\n",
+       "0  00003a69e91a4ef117840d081cdbe355   2025-12-17  332014              1   \n",
+       "1  00003a69e91a4ef117840d081cdbe355   2025-12-17  332170              2   \n",
+       "2  00003a69e91a4ef117840d081cdbe355   2025-12-17  332232              3   \n",
+       "3  00003a69e91a4ef117840d081cdbe355   2025-12-17  332238              4   \n",
+       "4  00003a69e91a4ef117840d081cdbe355   2025-12-17  332215              5   \n",
+       "\n",
+       "  schedule_arrival_time schedule_departure_time  \n",
+       "0   2025-12-18 00:15:00     2025-12-18 00:15:00  \n",
+       "1   2025-12-18 00:17:01     2025-12-18 00:17:01  \n",
+       "2   2025-12-18 00:18:03     2025-12-18 00:18:03  \n",
+       "3   2025-12-18 00:18:42     2025-12-18 00:18:42  \n",
+       "4   2025-12-18 00:19:49     2025-12-18 00:19:49  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the scheduled stop times.\n",
+    "# One feed version can span several service dates, so this is fewer feeds than dates.\n",
+    "schedule_feed_keys = feed_keys_to_dates.loc[\n",
+    "    feed_keys_to_dates[\"date\"].astype(str).isin(stm_dates), \"schedule_feed_key\"\n",
+    "].unique()\n",
+    "\n",
+    "schedule_feeds = run_query(\"schedule_feeds\", FEED_KEYS=schedule_feed_keys)\n",
+    "\n",
+    "schedule_stop_times = run_query(\n",
+    "    \"schedule_stop_times\",\n",
+    "    DATES=stm_dates,\n",
+    "    GTFS_DATASET_NAME=GTFS_DATASET_NAME,\n",
+    "    FEED_VALID_FROMS=schedule_feeds[\"feed_valid_from\"].astype(str),\n",
+    "    FEED_KEYS=schedule_feed_keys,\n",
+    ")\n",
+    "schedule_stop_times.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "d26c12c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source        vp_only  tu_only   both  schedule_only\n",
+      "service_date                                        \n",
+      "2025-12-17       1594    10608  49100           2212\n",
+      "2026-01-14       1151    10809  48751           2775\n",
+      "2026-04-08      19911     6742  29925           6907\n",
+      "2026-06-10       1465    13132  47726           3871\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>vp_arrival_time</th>\n",
+       "      <th>stop_meters</th>\n",
+       "      <th>shape_array_key</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>tu_arrival_time</th>\n",
+       "      <th>n_predictions</th>\n",
+       "      <th>source</th>\n",
+       "      <th>schedule_arrival_time</th>\n",
+       "      <th>schedule_departure_time</th>\n",
+       "      <th>has_schedule</th>\n",
+       "      <th>service_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2025-12-17 00:18:17</td>\n",
+       "      <td>1.613893</td>\n",
+       "      <td>fd6a664f2ca715017d46996e1cf0c8e0</td>\n",
+       "      <td>332014</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>vp_only</td>\n",
+       "      <td>2025-12-18 00:15:00</td>\n",
+       "      <td>2025-12-18 00:15:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2025-12-17 00:20:53</td>\n",
+       "      <td>734.456238</td>\n",
+       "      <td>fd6a664f2ca715017d46996e1cf0c8e0</td>\n",
+       "      <td>332170</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>vp_only</td>\n",
+       "      <td>2025-12-18 00:17:01</td>\n",
+       "      <td>2025-12-18 00:17:01</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>332232</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>schedule_only</td>\n",
+       "      <td>2025-12-18 00:18:03</td>\n",
+       "      <td>2025-12-18 00:18:03</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2025-12-17 00:21:28</td>\n",
+       "      <td>1359.918670</td>\n",
+       "      <td>fd6a664f2ca715017d46996e1cf0c8e0</td>\n",
+       "      <td>332238</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>vp_only</td>\n",
+       "      <td>2025-12-18 00:18:42</td>\n",
+       "      <td>2025-12-18 00:18:42</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>00003a69e91a4ef117840d081cdbe355</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2025-12-17 00:22:40</td>\n",
+       "      <td>1772.076525</td>\n",
+       "      <td>fd6a664f2ca715017d46996e1cf0c8e0</td>\n",
+       "      <td>332215</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>vp_only</td>\n",
+       "      <td>2025-12-18 00:19:49</td>\n",
+       "      <td>2025-12-18 00:19:49</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  trip_instance_key  stop_sequence     vp_arrival_time  \\\n",
+       "0  00003a69e91a4ef117840d081cdbe355              1 2025-12-17 00:18:17   \n",
+       "1  00003a69e91a4ef117840d081cdbe355              2 2025-12-17 00:20:53   \n",
+       "2  00003a69e91a4ef117840d081cdbe355              3                 NaT   \n",
+       "3  00003a69e91a4ef117840d081cdbe355              4 2025-12-17 00:21:28   \n",
+       "4  00003a69e91a4ef117840d081cdbe355              5 2025-12-17 00:22:40   \n",
+       "\n",
+       "   stop_meters                   shape_array_key stop_id tu_arrival_time  \\\n",
+       "0     1.613893  fd6a664f2ca715017d46996e1cf0c8e0  332014             NaT   \n",
+       "1   734.456238  fd6a664f2ca715017d46996e1cf0c8e0  332170             NaT   \n",
+       "2          NaN                               NaN  332232             NaT   \n",
+       "3  1359.918670  fd6a664f2ca715017d46996e1cf0c8e0  332238             NaT   \n",
+       "4  1772.076525  fd6a664f2ca715017d46996e1cf0c8e0  332215             NaT   \n",
+       "\n",
+       "   n_predictions         source schedule_arrival_time schedule_departure_time  \\\n",
+       "0            NaN        vp_only   2025-12-18 00:15:00     2025-12-18 00:15:00   \n",
+       "1            NaN        vp_only   2025-12-18 00:17:01     2025-12-18 00:17:01   \n",
+       "2            NaN  schedule_only   2025-12-18 00:18:03     2025-12-18 00:18:03   \n",
+       "3            NaN        vp_only   2025-12-18 00:18:42     2025-12-18 00:18:42   \n",
+       "4            NaN        vp_only   2025-12-18 00:19:49     2025-12-18 00:19:49   \n",
+       "\n",
+       "   has_schedule service_date  \n",
+       "0          True   2025-12-17  \n",
+       "1          True   2025-12-17  \n",
+       "2          True   2025-12-17  \n",
+       "3          True   2025-12-17  \n",
+       "4          True   2025-12-17  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get matching VP stop times\n",
+    "\n",
+    "VP_COLS = [\"trip_instance_key\", \"stop_sequence\", \"arrival_time\", \"stop_meters\", \"shape_array_key\"]\n",
+    "\n",
+    "# trip_instance_key is unique per trip per service date, so it both filters the\n",
+    "# statewide VP files down to this operator and carries the date back onto the\n",
+    "# joined rows. Pushing the filter into the read gets ~200k rows off GCS instead\n",
+    "# of the ~9.6M statewide rows these four files hold.\n",
+    "trip_dates = operator_trips.set_index(\"trip_instance_key\")[\"service_date\"]\n",
+    "\n",
+    "vp_stop_times = g.read_parquet(\n",
+    "    stop_time_metrics_counts[\"vp_uri\"].tolist(),\n",
+    "    columns=VP_COLS,\n",
+    "    filters=[(\"trip_instance_key\", \"in\", set(trip_dates.index))],\n",
+    ").rename(columns={\"arrival_time\": \"vp_arrival_time\"})\n",
+    "\n",
+    "vp_tu_stop_times = vp_stop_times.merge(\n",
+    "    tu_stop_times.drop(columns=[\"service_date\"]),\n",
+    "    on=[\"trip_instance_key\", \"stop_sequence\"],\n",
+    "    how=\"outer\",\n",
+    "    indicator=\"source\",\n",
+    ")\n",
+    "vp_tu_stop_times[\"source\"] = vp_tu_stop_times[\"source\"].cat.rename_categories(\n",
+    "    {\"left_only\": \"vp_only\", \"right_only\": \"tu_only\"}\n",
+    ")\n",
+    "\n",
+    "# Outer again, so scheduled stops that neither VP nor TU ever reported survive.\n",
+    "vp_tu_stop_times = vp_tu_stop_times.merge(\n",
+    "    schedule_stop_times.drop(columns=[\"service_date\"]),\n",
+    "    on=[\"trip_instance_key\", \"stop_sequence\"],\n",
+    "    how=\"outer\",\n",
+    "    indicator=\"has_schedule\",\n",
+    "    suffixes=(\"\", \"_schedule\"),\n",
+    ")\n",
+    "vp_tu_stop_times[\"has_schedule\"] = vp_tu_stop_times[\"has_schedule\"] != \"left_only\"\n",
+    "# The two stop_id columns agree wherever both are populated, so keep one.\n",
+    "vp_tu_stop_times[\"stop_id\"] = vp_tu_stop_times[\"stop_id\"].fillna(\n",
+    "    vp_tu_stop_times[\"stop_id_schedule\"]\n",
+    ")\n",
+    "vp_tu_stop_times = vp_tu_stop_times.drop(columns=[\"stop_id_schedule\"])\n",
+    "# Rows only the schedule knows about have no VP/TU source yet.\n",
+    "vp_tu_stop_times[\"source\"] = (\n",
+    "    vp_tu_stop_times[\"source\"].cat.add_categories(\"schedule_only\").fillna(\"schedule_only\")\n",
+    ")\n",
+    "\n",
+    "vp_tu_stop_times[\"service_date\"] = vp_tu_stop_times[\"trip_instance_key\"].map(trip_dates)\n",
+    "\n",
+    "print(\n",
+    "    vp_tu_stop_times.groupby([\"service_date\", \"source\"], observed=True)\n",
+    "    .size()\n",
+    "    .unstack(fill_value=0)\n",
+    ")\n",
+    "vp_tu_stop_times.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a6d1268",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "### Data availability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "2568c165",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Wr1evnnHJJZcYqampbtOfe+45Iy4uznU77/XauXOna9o777xjREVFGQcPHiw0l40bNxoRERHGjh073Nb50EMPFfsx5bnqqqsKvF9ERIRxww03uE27++67jYYNG7pu571HzvX31ltvFbr+b775xrjooouMGTNmuE1fs2aNUbVqVePw4cOudV977bXn9djOfr6LMmnSJCMqKsr46aefCpz/xRdfGBEREcamTZsKnO9wOIyoqChjzpw555UjAJQVERERxvTp0/NNL2r7+ssvvxgRERHGF198UWjcslL75Cnq+Srqe2/GjBlGRETEea2L+uc0M+qftLQ0o06dOsYbb7xhGMa564q8XAr6zBTHzp07jbi4OOPRRx91TUtJSTEiIyONqVOnui07fPhwIyIiwli2bFm+OAV9Js5Wr14944knnrigPAGgtNiyZYtxxRVXuL4D6tevbzzyyCNGcnKya5kTJ04YFStWNEaOHFlonLzaZO7cua5pqampRsWKFY0XXnjBMAzDyM3NNerVq5dv39DHH39sREZGGlu3bjUMwzDmz59vXHTRRW7ft//973+Niy66yOjYsaNrWkG/tY8fP57vu+zs79wOHToY7dq1c6sHfvvtNyMyMtL48MMPC32MM2fONCIjI41t27a5ph09etSIi4szxowZ47a+yy67zMjMzHRNe+6554zy5csbTqfTMIz836dOp9OoW7eu8dRTT7nuk7fPbf369YXmZLbp06cbl1xySaHzd+zYYURGRhqTJ08udJnnnnvOiIiIcHsu876XlyxZYhiGYZw8edK4+OKLjXHjxrndd/78+UZsbKxx5MgRwzAMY+zYsUalSpWMEydOuJbZsGGDERERYdx9992uaR07djRuu+02t1gF1eNn1ihmvx/PJTc313A4HMbIkSONtm3buqbnvRceeOABt+XznrNbbrklX6yzH+9jjz1mXHbZZW7v6XvvvbfQfVF5uUyaNMmoVatWvnUWVT8V5MCBAwXeL++xPfnkk27rvvzyy4377rvPNS1v+3GuvzO3S2cqqq4eNmyYsWLFCuOrr74y5s6da9SsWdOoU6eOcezYsWI/viNHjhjly5fP935NTEw0IiIijEqVKhljxowxPv/8c2PKlClGdHS08fTTT7s9Pw8++KDx/vvvG19++aUxdepUo3LlykazZs2M7Oxst5h5r8G6deuKnR9Q1nGNPw8FBQXp1ltvVVJSkqZOnarg4GAtX75c4eHh6tatW77lBw8e7Pq/zWbTrbfeWuSwhxs3blRUVJTb0AZ2u10333yzEhMT3Za94oor3E4dr1u3rqTTQ0oUdh2WIUOG6P7779exY8dUoUIFLVu2THa7Pd9QAGfKzc29oFPezxzr+WwNGzZU3bp1tXz5ctcZfklJSWrWrJkuueSSfMvXqlXL9f+wsDD961//0pAhQwqNbxiGZs2apUWLFmn37t2u6+lI0q5du1S/fn3X7Q4dOshms+WLcf311+c7yv1s7du3V/ny5ZWUlOQ6+jspKUmtWrVyGyZkyZIlevXVV/XHH3+4Hf313//+t9Choorr4MGDBQ4PJeUfT7tu3bpuRz9Vrlz5nMM9SFK1atUKnL5v3z4NHjxYrVq10vDhw13TMzMz9dhjj+mJJ54oNDczffbZZ5owYYLGjBmjRo0aXVCMoKAglStXzjWcFgCgZJSV2qckUP+c5o36Z9KkSYqLi9PAgQMvMOPiO3nypPr3769q1aq5hqWSTl8vsm/fvpo2bZrq16+vxo0ba926da7h8wt6PYsjJiaG+gdAmVe/fn39+OOP2rBhg9avX6+vv/5as2fP1ttvv62PPvpIV1xxhX744Qelp6e77ecpzJnDMkdEROiSSy7Rvn37JEk7d+7Unj179OKLL7qdvdOyZUsFBATo//7v/3T55ZfrP//5jy6//HJdeumlrmVq1aplyog66enp+u677/T888+7DSlZu3ZtVa1aVZs2bVLHjh0LvO/GjRt1+eWXu+owSapQoYLatGmjb7/91m3Zli1bus5Ml05/JzscDh0+fLjAoVUDAgI0aNAgLViwQOPGjVNQUJDeeustJSQkqHXr1oU+HqfTec6RigpSWO125ZVX6tixY7rnnnvUp08fNW3aVOHh4a75X3zxhQzD0KBBg4qMHxAQ4FaXVKtWTWFhYa73wvfff6/U1FTdeuutbu+FNm3aKCMjQ7/99puuv/56/fjjj2rVqpXKlSvnWqZ169amnAFZEu/H48ePa8KECVqzZo3279/ves8VlH+nTp0KjFHY+/FMvXr10owZM7Rx40a1aNFC2dnZev/9993OHMzMzNRLL72kxMRE/fXXX25npKWmpioyMvKc6ylMXj1VWI165nbBZrOpTp06rveCJHXu3LlYNWpRI6AVZt68ea7/t2zZUs2bN1eLFi30+uuv66GHHipWjJUrV8rhcLjOnsyT99lr3bq163qDN9xwg06dOqVXXnlFY8aMUVhYmCpVqqTp06e77nf99derXr166tmzp9577z23y2fljZRHjQoUH40/E/Tu3Vvz58/XJ598oq5duyopKUldunQp8Mvh3//+t2644QaFhYWpWrVqboVCQU6cOOG2QytPXFycjh8/7jbtzC986Z9hf87cyXO2W2+9Vf/617+0dOlS3XfffXr77bd1yy235Bui8UzDhw+/oHGVt27dWmjDSDr9PE6cOFFHjx5Vamqqvv/+e7cvgDO9//77KleunMqXL6+EhIRz7libOXOmnnjiCT300ENq1aqVypcvr02bNmn06NH5rnNT2PCs5xq2VTr9nHfv3t214+vo0aP67LPP9Oqrr7qWee+99zRs2DANGTJE48aNU4UKFXTw4EH169evyNequDIzM90K6TOVL18+X75nPv7g4GBdccUV51xHQWN0nzhxQrfeeqsqVKigd955x21c8ZkzZyogIEC9evXSiRMnJEnZ2dnKzc3ViRMnFB4enm+Yqgv1888/a8CAAerdu7cef/xxj2KFhISY8poAQFmS95189nVIzpxmt9sLvX9Zqn3OJSgoqMDnUTr9XFL//KMk6589e/bolVde0ZIlS1xDfOU1MlNTUz3eSXSm7Oxs9evXTydOnNBnn32Wrwn7wgsv6O+//1aPHj0knd4pMnbsWD3xxBOqVKnSBa0zJCREGRkZHucOAFYXHBysjh07uhoMn376qW677TZNnDhRS5YscQ1HV5yd7gXVLHnfRXnDPPbr16/A++YNl3nw4MFCayRPt9snTpyQ0+nUY489psceeyzf/DObEQXdt6B6IS4uLt/1ky+kdhs0aJBeeOEFffTRR+rQoYOWLl2qYcOGFXkts65du+a7PnRxnHn5mTO1bt1aCxYs0KxZs9S9e3eFhobqlltu0YsvvqgKFSro2LFjCgoKOmfdFBYWlm/fR3BwsOvx570XWrRoUeD9816HgwcPuh0Qn8eMA61L4v1477336rvvvtOYMWNUr149RUVFacGCBVqxYkWB8QpSnBr1mmuuUc2aNbV8+XK1aNFCH3/8sU6cOOHWqBo7dqxef/11Pf7442rUqJHKlSunDz74QJMmTVJmZqZHNV3e61pYjVrQ5yFvn5l0uhF69jIFMeNgwwYNGqh27dr5Lq1QlMTERDVo0MDtYELpn9o779qLeVq3bq3Jkyfrjz/+UIMGDQqM2bFjR0VEROjnn392a/zlPYfUqEDx0fgzQdOmTVWtWjUtX75cFStWVHJysiZNmlTgstWrV9fVV19d7NjR0dEFjl986NAhRUdHX3DOecLCwtS7d2+9/fbbat68uTZv3qzJkycXeZ8nnnhC99xzz3mv61zFcO/evfXss89q9erVOnHihOtsyoI0bNjwvAqaVatWqUuXLm5jop85nv6ZCjs6urhHTffs2VNvvPGGtmzZou+//16BgYG6+eab3XK54oor3HaGmXkdlejo6HwXjy6u3bt35/vCLsicOXN0++23u25nZGSoV69eOnnypD777LN8hcmOHTv0xx9/uK5Jc6aqVatq+vTpRV6Tqbj++OMP3XrrrWrSpIlmzpzpcbyUlJQSu2YAAJQWed/PBV0P58CBA5JU4E6CPGWp9jmX2NhYZWZm6sSJE/maVwcOHCjyeZSof4rrfOuf5ORkZWdnu+2MyNOlSxc1btxYGzZsuKBczpSbm6uhQ4fq559/1kcffaSqVavmWyYmJkbvvfeeDhw4oGPHjunSSy/VmjVrFBwcrKuuuuqC1nvixAm3szYAAKfdeOONatiwoesaqnm/FQ8cOFDoSAPFkVffTJkyRY0bN843P6+eqFSpkn7++ed88w8dOuR2AFNISEi+62id2UwoSLly5WSz2fTII48UOHpV3tk2heW/c+fOAvMyo3aLj49X+/bt9dZbbyknJ0dHjx495xn3r7zySqFNvAvVt29f9e3bV0eOHNGaNWs0ZswY2e12zZo1SxUqVFBOTo4OHTpUrIZUYfKer8WLFxf4vZ93QFmlSpUKrJfPvrZkaGiosrOz3aadfSBdYTmY9X48W2ZmptatW6eJEye6jRRV2OgaZtSoixYt0ksvvaSkpCQ1btxYNWrUcM1ftWqVhg4dqtGjR7umffjhh8WKfS55z+WF1qjvvPOO7r333nMu5+nBhhfir7/+0nfffef2OyPPuerIsw9ALI68bVhR2yIA7mj8mcBms6lXr16aNWuWwsPDVaFCBbVv396U2M2aNdP06dO1fv161yngOTk5ev/999WsWTNT1jFkyBDNmzdPjz32mC699NJCjyzKU61aNa98oVSvXl1NmjRRYmKiTpw4ofbt25vWdMnIyMh3VNWyZctMiX22vGGtli9fru+//17t27d3a4R5O5fatWsrOTn5gu57IUNd5eTkaNCgQfr999/10UcfqUqVKvmWHz16tAYMGOA2berUqdq5c6dmz57t8fBe0umjzbp3765LLrlEb7/9dpFnkxTH4cOHlZ6ebkpuAFCWXHrppapUqZLWrFmjm26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si0qurffK4OxbfEOVzznx5bKlWrHiG5lMJtWoWVMDBgxS0EWeGi7uHrHtpx81b/5cnTp5UuHh1fTkU88oJCSkRNcPAAAA9onCHwAAwHUsPStH5ty84hv+B04ORnm6u5RpDpSOvKzTsuZayjSHwcFRRvdKZZoDpSMtO0vmvNwyzeFkdJCXa8FCGa5NVnO6ZL34U6z/mcFJBifPss0BAAAA2DEKfwAAAAAAAAAAAIAd4Dv+AAAAAAAAAAAAADtA4Q8AAAAAAAAAAACwAxT+AAAAAAAAAAAAADtA4Q8AAAAAAAAAAACwAxT+AAAAAAAAAAAAADtA4Q8AAAAAAAAAAACwAxT+AAAAAAAAAAAAADtA4Q8AAAAAAAAAAACwAxT+AAAAAAAAAAAAADtA4Q8AAAAAAAAAAACwAxT+AAAAAAAAAAAAADtA4Q8AAAAAAAAAAACwA/8HLj8lXd18jOwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1800x800 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Hour of day distribution of stop time entries, stacked by service date\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from calitp_data_analysis.calitp_color_palette import CALITP_CATEGORY_BOLD_COLORS\n",
+    "\n",
+    "# Light mode, pinned explicitly so the figure does not inherit a dark notebook\n",
+    "# theme (a transparent background would put this near-black text on dark gray).\n",
+    "SURFACE = \"#ffffff\"\n",
+    "INK = \"#0b0b0b\"\n",
+    "INK_MUTED = \"#52514e\"\n",
+    "GRID = \"#d9d9d9\"\n",
+    "AXIS = \"#b0b0b0\"\n",
+    "\n",
+    "# Slots 1, 2, 4, 3: green sits between orange and yellow so the two warmest\n",
+    "# hues are never adjacent segments within a stack.\n",
+    "DATE_COLORS = [CALITP_CATEGORY_BOLD_COLORS[i] for i in (0, 1, 3, 2)]\n",
+    "\n",
+    "# grid position -> (source, which arrival time to bin, panel title)\n",
+    "PANELS = {\n",
+    "    (0, 0): (\"both\", \"vp_arrival_time\", \"Matched (both) — VP arrival\"),\n",
+    "    (0, 1): (\"both\", \"tu_arrival_time\", \"Matched (both) — TU arrival\"),\n",
+    "    (1, 0): (\"vp_only\", \"vp_arrival_time\", \"VP only — VP arrival\"),\n",
+    "    (1, 1): (\"tu_only\", \"tu_arrival_time\", \"TU only — TU arrival\"),\n",
+    "    (1, 2): (\"schedule_only\", \"schedule_arrival_time\", \"Schedule only — scheduled arrival\"),\n",
+    "}\n",
+    "\n",
+    "service_dates = sorted(vp_tu_stop_times[\"service_date\"].unique())\n",
+    "color_by_date = dict(zip(service_dates, DATE_COLORS))\n",
+    "\n",
+    "# Rows share a y axis: the two \"both\" panels are the same matched rows counted\n",
+    "# two ways, and the bottom row is the three mutually exclusive misses. The rows\n",
+    "# differ in magnitude by ~5x, so their scales are left independent.\n",
+    "fig, axes = plt.subplots(\n",
+    "    2, 3, figsize=(18, 8), sharex=True, sharey=\"row\", facecolor=SURFACE\n",
+    ")\n",
+    "fig.delaxes(axes[0, 2])  # only two ways to count a matched row\n",
+    "\n",
+    "for (row, col), (src, time_col, title) in PANELS.items():\n",
+    "    ax = axes[row, col]\n",
+    "    subset = vp_tu_stop_times.loc[vp_tu_stop_times[\"source\"] == src]\n",
+    "    hourly = (\n",
+    "        subset.groupby([subset[time_col].dt.hour, \"service_date\"], observed=True)\n",
+    "        .size()\n",
+    "        .unstack(fill_value=0)\n",
+    "        .reindex(index=range(24), columns=service_dates, fill_value=0)\n",
+    "    )\n",
+    "\n",
+    "    bottom = np.zeros(24)\n",
+    "    for service_date in service_dates:\n",
+    "        heights = hourly[service_date].to_numpy()\n",
+    "        ax.bar(\n",
+    "            hourly.index,\n",
+    "            heights,\n",
+    "            bottom=bottom,\n",
+    "            width=0.82,\n",
+    "            color=color_by_date[service_date],\n",
+    "            label=str(service_date),\n",
+    "            edgecolor=SURFACE,\n",
+    "            linewidth=0.6,\n",
+    "        )\n",
+    "        bottom += heights\n",
+    "\n",
+    "    ax.set_facecolor(SURFACE)\n",
+    "    ax.set_title(f\"{title}  (n={len(subset):,})\", fontsize=11, loc=\"left\", color=INK)\n",
+    "    ax.set_xticks(range(0, 24, 2))\n",
+    "    ax.grid(axis=\"y\", color=GRID, linewidth=0.6)\n",
+    "    ax.set_axisbelow(True)\n",
+    "    for side in (\"top\", \"right\"):\n",
+    "        ax.spines[side].set_visible(False)\n",
+    "    for side in (\"left\", \"bottom\"):\n",
+    "        ax.spines[side].set_color(AXIS)\n",
+    "    ax.tick_params(colors=INK_MUTED, labelsize=9, labelbottom=True)\n",
+    "\n",
+    "for ax in axes[:, 0]:\n",
+    "    ax.set_ylabel(\"stop time entries\", fontsize=10, color=INK_MUTED)\n",
+    "for ax in axes[1, :]:\n",
+    "    ax.set_xlabel(\"hour of day (Pacific)\", fontsize=10, color=INK_MUTED)\n",
+    "\n",
+    "handles, labels = axes[0, 0].get_legend_handles_labels()\n",
+    "legend = fig.legend(\n",
+    "    handles,\n",
+    "    labels,\n",
+    "    title=\"service date\",\n",
+    "    loc=\"lower center\",\n",
+    "    ncol=len(service_dates),\n",
+    "    frameon=False,\n",
+    "    fontsize=9,\n",
+    "    title_fontsize=9,\n",
+    ")\n",
+    "legend.get_title().set_color(INK_MUTED)\n",
+    "for text in legend.get_texts():\n",
+    "    text.set_color(INK_MUTED)\n",
+    "\n",
+    "fig.suptitle(\n",
+    "    \"When VP, TU and scheduled stop times occur, by hour and service date\",\n",
+    "    fontsize=13,\n",
+    "    x=0.5,\n",
+    "    y=0.98,\n",
+    "    color=INK,\n",
+    ")\n",
+    "fig.tight_layout(rect=[0, 0.05, 1, 0.96])\n",
+    "plt.show()\n",
+    "# this plot seems to show that VP only trips have a strong time pattern (mornings on all dates, evenings specifically on one date. \n",
+    "# this matches hypothesis that TU is producing trips for certain dates only, because it runs on data that is partitioned by UTC date rather than service_date\n",
+    "# however, TU arrivals are occuring for all dates evenly. this suggests that there are significant data availability issues for the VP data, although\n",
+    "# could also infer that erroneous trips are occuring\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "a2355ca5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO - would be interesting to know if missing VP trips are clustered on the same trips and/or same stops\n",
+    "# many clustered on same trip - means that vp is either consistently unavailable for some trips \n",
+    "# or that trips are being dropped by the pipeline\n",
+    "# many clustered on same stop - issue with GPS/cellular outages at specific stops\n",
+    "# no clustering - VP pipeline is potentially dropping stops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b33fa86",
+   "metadata": {},
+   "source": [
+    "### Basic difference analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "8ea66996",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>vp_arrival_time</th>\n",
+       "      <th>stop_meters</th>\n",
+       "      <th>shape_array_key</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>tu_arrival_time</th>\n",
+       "      <th>n_predictions</th>\n",
+       "      <th>source</th>\n",
+       "      <th>schedule_arrival_time</th>\n",
+       "      <th>schedule_departure_time</th>\n",
+       "      <th>has_schedule</th>\n",
+       "      <th>service_date</th>\n",
+       "      <th>vp_tu_difference</th>\n",
+       "      <th>vp_tu_difference_seconds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2026-06-10 07:18:28</td>\n",
+       "      <td>875.071896</td>\n",
+       "      <td>5c9e314c9eb12d2f42a1172287709df2</td>\n",
+       "      <td>341185</td>\n",
+       "      <td>2026-06-10 07:18:17</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:15:17</td>\n",
+       "      <td>2026-06-10 07:15:17</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>0 days 00:00:11</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2026-06-10 07:21:44</td>\n",
+       "      <td>3452.935577</td>\n",
+       "      <td>5c9e314c9eb12d2f42a1172287709df2</td>\n",
+       "      <td>341622</td>\n",
+       "      <td>2026-06-10 07:22:00</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:22:00</td>\n",
+       "      <td>2026-06-10 07:22:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>0 days 00:00:16</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2026-06-10 07:23:34</td>\n",
+       "      <td>4380.034253</td>\n",
+       "      <td>5c9e314c9eb12d2f42a1172287709df2</td>\n",
+       "      <td>341060</td>\n",
+       "      <td>2026-06-10 07:18:26</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:27:26</td>\n",
+       "      <td>2026-06-10 07:27:26</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>0 days 00:05:08</td>\n",
+       "      <td>308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2026-06-10 07:23:50</td>\n",
+       "      <td>4533.545296</td>\n",
+       "      <td>5c9e314c9eb12d2f42a1172287709df2</td>\n",
+       "      <td>341957</td>\n",
+       "      <td>2026-06-10 07:19:20</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:28:20</td>\n",
+       "      <td>2026-06-10 07:28:20</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>0 days 00:04:30</td>\n",
+       "      <td>270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2026-06-10 07:24:41</td>\n",
+       "      <td>4958.540713</td>\n",
+       "      <td>5c9e314c9eb12d2f42a1172287709df2</td>\n",
+       "      <td>341271</td>\n",
+       "      <td>2026-06-10 07:22:49</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:30:49</td>\n",
+       "      <td>2026-06-10 07:30:49</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>0 days 00:01:52</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   trip_instance_key  stop_sequence     vp_arrival_time  \\\n",
+       "81  0008d298e3f42cea9e00cfddb076fdd0              2 2026-06-10 07:18:28   \n",
+       "82  0008d298e3f42cea9e00cfddb076fdd0              3 2026-06-10 07:21:44   \n",
+       "83  0008d298e3f42cea9e00cfddb076fdd0              4 2026-06-10 07:23:34   \n",
+       "84  0008d298e3f42cea9e00cfddb076fdd0              5 2026-06-10 07:23:50   \n",
+       "85  0008d298e3f42cea9e00cfddb076fdd0              6 2026-06-10 07:24:41   \n",
+       "\n",
+       "    stop_meters                   shape_array_key stop_id     tu_arrival_time  \\\n",
+       "81   875.071896  5c9e314c9eb12d2f42a1172287709df2  341185 2026-06-10 07:18:17   \n",
+       "82  3452.935577  5c9e314c9eb12d2f42a1172287709df2  341622 2026-06-10 07:22:00   \n",
+       "83  4380.034253  5c9e314c9eb12d2f42a1172287709df2  341060 2026-06-10 07:18:26   \n",
+       "84  4533.545296  5c9e314c9eb12d2f42a1172287709df2  341957 2026-06-10 07:19:20   \n",
+       "85  4958.540713  5c9e314c9eb12d2f42a1172287709df2  341271 2026-06-10 07:22:49   \n",
+       "\n",
+       "    n_predictions source schedule_arrival_time schedule_departure_time  \\\n",
+       "81           85.0   both   2026-06-10 07:15:17     2026-06-10 07:15:17   \n",
+       "82           90.0   both   2026-06-10 07:22:00     2026-06-10 07:22:00   \n",
+       "83           90.0   both   2026-06-10 07:27:26     2026-06-10 07:27:26   \n",
+       "84           90.0   both   2026-06-10 07:28:20     2026-06-10 07:28:20   \n",
+       "85           90.0   both   2026-06-10 07:30:49     2026-06-10 07:30:49   \n",
+       "\n",
+       "    has_schedule service_date vp_tu_difference  vp_tu_difference_seconds  \n",
+       "81          True   2026-06-10  0 days 00:00:11                        11  \n",
+       "82          True   2026-06-10  0 days 00:00:16                        16  \n",
+       "83          True   2026-06-10  0 days 00:05:08                       308  \n",
+       "84          True   2026-06-10  0 days 00:04:30                       270  \n",
+       "85          True   2026-06-10  0 days 00:01:52                       112  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vp_tu_stop_times_cleaned = vp_tu_stop_times.loc[\n",
+    "    (\n",
+    "        (vp_tu_stop_times[\"service_date\"] != datetime.date(2026, 4, 8))\n",
+    "        & (vp_tu_stop_times[\"source\"] == \"both\")\n",
+    "    )\n",
+    "].copy()\n",
+    "vp_tu_stop_times_cleaned[\"vp_tu_difference\"] = (\n",
+    "    vp_tu_stop_times_cleaned[\"vp_arrival_time\"] - vp_tu_stop_times_cleaned[\"tu_arrival_time\"]\n",
+    ").abs()\n",
+    "vp_tu_stop_times_cleaned[\"vp_tu_difference_seconds\"] = vp_tu_stop_times_cleaned[\"vp_tu_difference\"].dt.seconds\n",
+    "vp_tu_stop_times_cleaned.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "1b5a1d30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>p5</th>\n",
+       "      <th>p25</th>\n",
+       "      <th>p50</th>\n",
+       "      <th>p75</th>\n",
+       "      <th>p90</th>\n",
+       "      <th>p95</th>\n",
+       "      <th>std_dev</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>service_date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2025-12-17</th>\n",
+       "      <td>41.4</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>106.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-01-14</th>\n",
+       "      <td>42.7</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>109.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-06-10</th>\n",
+       "      <td>58.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>194.0</td>\n",
+       "      <td>164.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              mean   p5   p25   p50   p75    p90    p95  std_dev\n",
+       "service_date                                                    \n",
+       "2025-12-17    41.4  2.0  11.0  23.0  42.0   77.0  118.0    106.5\n",
+       "2026-01-14    42.7  2.0  11.0  24.0  43.0   79.0  123.0    109.4\n",
+       "2026-06-10    58.2  2.0  12.0  25.0  49.0  109.0  194.0    164.5"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Headline difference analysis\n",
+    "vp_tu_stop_times_grouped_by_date = vp_tu_stop_times_cleaned.groupby(\"service_date\")[\"vp_tu_difference_seconds\"]\n",
+    "diff_stats = pd.DataFrame(\n",
+    "    {\n",
+    "        \"mean\": vp_tu_stop_times_grouped_by_date.mean(),\n",
+    "        \"p5\": vp_tu_stop_times_grouped_by_date.quantile(0.05),\n",
+    "        \"p25\": vp_tu_stop_times_grouped_by_date.quantile(0.25),\n",
+    "        \"p50\": vp_tu_stop_times_grouped_by_date.quantile(0.50),\n",
+    "        \"p75\": vp_tu_stop_times_grouped_by_date.quantile(0.75),\n",
+    "        \"p90\": vp_tu_stop_times_grouped_by_date.quantile(0.90),\n",
+    "        \"p95\": vp_tu_stop_times_grouped_by_date.quantile(0.95),\n",
+    "        \"std_dev\": vp_tu_stop_times_grouped_by_date.std(),\n",
+    "    }\n",
+    ")\n",
+    "diff_stats.index.name = \"service_date\"\n",
+    "diff_stats.round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "7f724710",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "SMALL_HIST = 60\n",
+    "# The tail runs to ~85 min but 99.4% of rows are under 10 min, so the axis stops\n",
+    "MAX_SECONDS = 600\n",
+    "TICK_SECONDS = 120  # a multiple of the bin width that divides MAX_SECONDS evenly\n",
+    "\n",
+    "diff_seconds = vp_tu_stop_times_cleaned[\"vp_tu_difference\"].dt.total_seconds()\n",
+    "bins = np.arange(0, MAX_SECONDS + 2 * SMALL_HIST, SMALL_HIST)\n",
+    "within_noise = (diff_seconds < SMALL_HIST).mean()\n",
+    "\n",
+    "# Reuse color_by_date so a date keeps the color it has in the hour of day plot.\n",
+    "# 2026-04-08 is filtered out of the cleaned frame; the remaining dates keep their\n",
+    "# own colors rather than being repainted.\n",
+    "cleaned_dates = [\n",
+    "    d for d in service_dates if d in set(vp_tu_stop_times_cleaned[\"service_date\"])\n",
+    "]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 6), facecolor=SURFACE)\n",
+    "ax.hist(\n",
+    "    [\n",
+    "        diff_seconds[vp_tu_stop_times_cleaned[\"service_date\"] == d].clip(upper=MAX_SECONDS)\n",
+    "        for d in cleaned_dates\n",
+    "    ],\n",
+    "    bins=bins,\n",
+    "    stacked=True,\n",
+    "    color=[color_by_date[d] for d in cleaned_dates],\n",
+    "    label=[str(d) for d in cleaned_dates],\n",
+    "    edgecolor=SURFACE,\n",
+    "    linewidth=0.6,\n",
+    ")\n",
+    "\n",
+    "# The first bin is the noise floor; mark its right edge.\n",
+    "ax.axvline(SMALL_HIST, color=INK, linewidth=1.2, linestyle=\"--\")\n",
+    "ax.annotate(\n",
+    "    f\"{SMALL_HIST}s in the next chart \\n{within_noise:.1%} within\",\n",
+    "    xy=(SMALL_HIST, ax.get_ylim()[1]),\n",
+    "    xytext=(8, -22),\n",
+    "    textcoords=\"offset points\",\n",
+    "    fontsize=9,\n",
+    "    color=INK,\n",
+    ")\n",
+    "\n",
+    "ax.set_facecolor(SURFACE)\n",
+    "ax.set_title(\n",
+    "    f\"VP vs TU arrival time difference  (n={len(diff_seconds):,})\",\n",
+    "    fontsize=12,\n",
+    "    loc=\"left\",\n",
+    "    color=INK,\n",
+    ")\n",
+    "ax.set_xlabel(\n",
+    "    f\"|VP arrival − TU arrival| (seconds, {SMALL_HIST}s bins)\",\n",
+    "    fontsize=10,\n",
+    "    color=INK_MUTED,\n",
+    ")\n",
+    "ax.set_ylabel(\"stop time entries\", fontsize=10, color=INK_MUTED)\n",
+    "# The last tick sits on the overflow bin, so it gets a \"+\" label.\n",
+    "ticks = np.arange(0, MAX_SECONDS + TICK_SECONDS, TICK_SECONDS)\n",
+    "ax.set_xticks(ticks)\n",
+    "ax.set_xticklabels([f\"{t:.0f}\" for t in ticks[:-1]] + [f\"{MAX_SECONDS}+\"])\n",
+    "ax.grid(axis=\"y\", color=GRID, linewidth=0.6)\n",
+    "ax.set_axisbelow(True)\n",
+    "for side in (\"top\", \"right\"):\n",
+    "    ax.spines[side].set_visible(False)\n",
+    "for side in (\"left\", \"bottom\"):\n",
+    "    ax.spines[side].set_color(AXIS)\n",
+    "ax.tick_params(colors=INK_MUTED, labelsize=9)\n",
+    "\n",
+    "legend = ax.legend(title=\"service date\", frameon=False, fontsize=9, title_fontsize=9)\n",
+    "legend.get_title().set_color(INK_MUTED)\n",
+    "for text in legend.get_texts():\n",
+    "    text.set_color(INK_MUTED)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "9b7479fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Zoom in on the sub-minute differences to examine distribution there\n",
+    "SMALL_HIST_SECONDS = 5\n",
+    "CUTOFF_SECONDS = 60\n",
+    "\n",
+    "diff_under_minute = diff_seconds[diff_seconds <= CUTOFF_SECONDS]\n",
+    "fine_bins = np.arange(0, CUTOFF_SECONDS + SMALL_HIST_SECONDS, SMALL_HIST_SECONDS)\n",
+    "cleaned_service_dates = vp_tu_stop_times_cleaned[\"service_date\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 6), facecolor=SURFACE)\n",
+    "ax.hist(\n",
+    "    [\n",
+    "        diff_under_minute[cleaned_service_dates.loc[diff_under_minute.index] == d]\n",
+    "        for d in cleaned_dates\n",
+    "    ],\n",
+    "    bins=fine_bins,\n",
+    "    stacked=True,\n",
+    "    color=[color_by_date[d] for d in cleaned_dates],\n",
+    "    label=[str(d) for d in cleaned_dates],\n",
+    "    edgecolor=SURFACE,\n",
+    "    linewidth=0.6,\n",
+    ")\n",
+    "\n",
+    "ax.set_facecolor(SURFACE)\n",
+    "ax.set_title(\n",
+    "    f\"VP vs TU difference under {CUTOFF_SECONDS}s  \"\n",
+    "    f\"(n={len(diff_under_minute):,} of {len(diff_seconds):,})\",\n",
+    "    fontsize=12,\n",
+    "    loc=\"left\",\n",
+    "    color=INK,\n",
+    ")\n",
+    "ax.set_xlabel(\n",
+    "    f\"|VP arrival − TU arrival| (seconds, {SMALL_HIST_SECONDS}s bins)\",\n",
+    "    fontsize=10,\n",
+    "    color=INK_MUTED,\n",
+    ")\n",
+    "ax.set_ylabel(\"stop time entries\", fontsize=10, color=INK_MUTED)\n",
+    "ax.set_xticks(np.arange(0, CUTOFF_SECONDS + 10, 10))\n",
+    "ax.grid(axis=\"y\", color=GRID, linewidth=0.6)\n",
+    "ax.set_axisbelow(True)\n",
+    "for side in (\"top\", \"right\"):\n",
+    "    ax.spines[side].set_visible(False)\n",
+    "for side in (\"left\", \"bottom\"):\n",
+    "    ax.spines[side].set_color(AXIS)\n",
+    "ax.tick_params(colors=INK_MUTED, labelsize=9)\n",
+    "\n",
+    "legend = ax.legend(title=\"service date\", frameon=False, fontsize=9, title_fontsize=9)\n",
+    "legend.get_title().set_color(INK_MUTED)\n",
+    "for text in legend.get_texts():\n",
+    "    text.set_color(INK_MUTED)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87ae9865",
+   "metadata": {},
+   "source": [
+    "### Outlier Investigation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "12f67118",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9201\n"
+     ]
+    }
+   ],
+   "source": [
+    "OUTLIER_DIFFERENCE = datetime.timedelta(seconds=120)\n",
+    "vp_tu_stop_times_outliers = vp_tu_stop_times_cleaned.loc[\n",
+    "    vp_tu_stop_times_cleaned[\"vp_tu_difference\"] > OUTLIER_DIFFERENCE\n",
+    "].copy()\n",
+    "print(len(vp_tu_stop_times_outliers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "ddd35f2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.06320366541417943\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(vp_tu_stop_times_outliers) / len(vp_tu_stop_times_cleaned))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "018fac51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>vp_arrival_time</th>\n",
+       "      <th>stop_meters</th>\n",
+       "      <th>shape_array_key</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>tu_arrival_time</th>\n",
+       "      <th>n_predictions</th>\n",
+       "      <th>source</th>\n",
+       "      <th>schedule_arrival_time</th>\n",
+       "      <th>schedule_departure_time</th>\n",
+       "      <th>has_schedule</th>\n",
+       "      <th>service_date</th>\n",
+       "      <th>vp_tu_difference</th>\n",
+       "      <th>vp_tu_difference_seconds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2026-06-10 07:23:34</td>\n",
+       "      <td>4380.034253</td>\n",
+       "      <td>5c9e314c9eb12d2f42a1172287709df2</td>\n",
+       "      <td>341060</td>\n",
+       "      <td>2026-06-10 07:18:26</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:27:26</td>\n",
+       "      <td>2026-06-10 07:27:26</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>0 days 00:05:08</td>\n",
+       "      <td>308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2026-06-10 07:23:50</td>\n",
+       "      <td>4533.545296</td>\n",
+       "      <td>5c9e314c9eb12d2f42a1172287709df2</td>\n",
+       "      <td>341957</td>\n",
+       "      <td>2026-06-10 07:19:20</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:28:20</td>\n",
+       "      <td>2026-06-10 07:28:20</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>0 days 00:04:30</td>\n",
+       "      <td>270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2026-06-10 07:25:47</td>\n",
+       "      <td>5505.371128</td>\n",
+       "      <td>5c9e314c9eb12d2f42a1172287709df2</td>\n",
+       "      <td>341097</td>\n",
+       "      <td>2026-06-10 07:29:01</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:34:01</td>\n",
+       "      <td>2026-06-10 07:34:01</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>0 days 00:03:14</td>\n",
+       "      <td>194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2026-06-10 07:26:59</td>\n",
+       "      <td>5829.093790</td>\n",
+       "      <td>5c9e314c9eb12d2f42a1172287709df2</td>\n",
+       "      <td>341031</td>\n",
+       "      <td>2026-06-10 07:30:55</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:35:55</td>\n",
+       "      <td>2026-06-10 07:35:55</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2026-06-10</td>\n",
+       "      <td>0 days 00:03:56</td>\n",
+       "      <td>236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>000e8ebad1f93d750cc8f215289b4ce1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2025-12-17 16:54:01</td>\n",
+       "      <td>2543.385058</td>\n",
+       "      <td>cbfa2dd041603fde52a091d42b038725</td>\n",
+       "      <td>363001</td>\n",
+       "      <td>2025-12-17 16:56:20</td>\n",
+       "      <td>178.0</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2025-12-17 16:52:20</td>\n",
+       "      <td>2025-12-17 16:52:20</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2025-12-17</td>\n",
+       "      <td>0 days 00:02:19</td>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   trip_instance_key  stop_sequence     vp_arrival_time  \\\n",
+       "83  0008d298e3f42cea9e00cfddb076fdd0              4 2026-06-10 07:23:34   \n",
+       "84  0008d298e3f42cea9e00cfddb076fdd0              5 2026-06-10 07:23:50   \n",
+       "86  0008d298e3f42cea9e00cfddb076fdd0              7 2026-06-10 07:25:47   \n",
+       "87  0008d298e3f42cea9e00cfddb076fdd0              8 2026-06-10 07:26:59   \n",
+       "96  000e8ebad1f93d750cc8f215289b4ce1              8 2025-12-17 16:54:01   \n",
+       "\n",
+       "    stop_meters                   shape_array_key stop_id     tu_arrival_time  \\\n",
+       "83  4380.034253  5c9e314c9eb12d2f42a1172287709df2  341060 2026-06-10 07:18:26   \n",
+       "84  4533.545296  5c9e314c9eb12d2f42a1172287709df2  341957 2026-06-10 07:19:20   \n",
+       "86  5505.371128  5c9e314c9eb12d2f42a1172287709df2  341097 2026-06-10 07:29:01   \n",
+       "87  5829.093790  5c9e314c9eb12d2f42a1172287709df2  341031 2026-06-10 07:30:55   \n",
+       "96  2543.385058  cbfa2dd041603fde52a091d42b038725  363001 2025-12-17 16:56:20   \n",
+       "\n",
+       "    n_predictions source schedule_arrival_time schedule_departure_time  \\\n",
+       "83           90.0   both   2026-06-10 07:27:26     2026-06-10 07:27:26   \n",
+       "84           90.0   both   2026-06-10 07:28:20     2026-06-10 07:28:20   \n",
+       "86           90.0   both   2026-06-10 07:34:01     2026-06-10 07:34:01   \n",
+       "87           90.0   both   2026-06-10 07:35:55     2026-06-10 07:35:55   \n",
+       "96          178.0   both   2025-12-17 16:52:20     2025-12-17 16:52:20   \n",
+       "\n",
+       "    has_schedule service_date vp_tu_difference  vp_tu_difference_seconds  \n",
+       "83          True   2026-06-10  0 days 00:05:08                       308  \n",
+       "84          True   2026-06-10  0 days 00:04:30                       270  \n",
+       "86          True   2026-06-10  0 days 00:03:14                       194  \n",
+       "87          True   2026-06-10  0 days 00:03:56                       236  \n",
+       "96          True   2025-12-17  0 days 00:02:19                       139  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vp_tu_stop_times_outliers.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "2fcd677d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vp_tu_stop_times_outliers[\"vp_schedule_difference\"] = (\n",
+    "    vp_tu_stop_times_outliers[\"schedule_arrival_time\"] - vp_tu_stop_times_outliers[\"vp_arrival_time\"]\n",
+    ").abs()\n",
+    "vp_tu_stop_times_outliers[\"tu_schedule_difference\"] = (\n",
+    "    vp_tu_stop_times_outliers[\"schedule_arrival_time\"] - vp_tu_stop_times_outliers[\"tu_arrival_time\"]\n",
+    ").abs()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "ac037f56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>p5</th>\n",
+       "      <th>p10</th>\n",
+       "      <th>p15</th>\n",
+       "      <th>p25</th>\n",
+       "      <th>p50</th>\n",
+       "      <th>p75</th>\n",
+       "      <th>p90</th>\n",
+       "      <th>p95</th>\n",
+       "      <th>std_dev</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>metric</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>vp_schedule_difference</th>\n",
+       "      <td>368.5</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>224.0</td>\n",
+       "      <td>432.0</td>\n",
+       "      <td>859.0</td>\n",
+       "      <td>1240.0</td>\n",
+       "      <td>442.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tu_schedule_difference</th>\n",
+       "      <td>339.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>240.0</td>\n",
+       "      <td>420.0</td>\n",
+       "      <td>720.0</td>\n",
+       "      <td>960.0</td>\n",
+       "      <td>341.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         mean    p5   p10   p15    p25    p50    p75    p90  \\\n",
+       "metric                                                                        \n",
+       "vp_schedule_difference  368.5  32.0  57.0  83.0  119.0  224.0  432.0  859.0   \n",
+       "tu_schedule_difference  339.4   0.0   0.0  60.0  120.0  240.0  420.0  720.0   \n",
+       "\n",
+       "                           p95  std_dev  \n",
+       "metric                                   \n",
+       "vp_schedule_difference  1240.0    442.2  \n",
+       "tu_schedule_difference   960.0    341.7  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Spread of the schedule differences among the vp/tu outliers (signed, seconds)\n",
+    "OUTLIER_DIFF_COLS = [\"vp_schedule_difference\", \"tu_schedule_difference\"]\n",
+    "PERCENTILES = {\"p5\": 0.05, \"p10\": 0.1, \"p15\": 0.15, \"p25\": 0.25, \"p50\": 0.50, \"p75\": 0.75, \"p90\": 0.90, \"p95\": 0.95}\n",
+    "\n",
+    "outlier_diff_seconds = vp_tu_stop_times_outliers[OUTLIER_DIFF_COLS].apply(\n",
+    "    lambda column: column.dt.total_seconds()\n",
+    ")\n",
+    "outlier_stats = pd.DataFrame(\n",
+    "    {\n",
+    "        \"mean\": outlier_diff_seconds.mean(),\n",
+    "        **{name: outlier_diff_seconds.quantile(q) for name, q in PERCENTILES.items()},\n",
+    "        \"std_dev\": outlier_diff_seconds.std(),\n",
+    "    }\n",
+    ")\n",
+    "outlier_stats.index.name = \"metric\"\n",
+    "outlier_stats.round(1)\n",
+    "# there isn't really a clear pattern here, \n",
+    "# except the p5/p10 is interesting. seems likely that the schedule rt will \"snap\" to schedule times. \n",
+    "# also TU times are all within 1 min of schedule times. this decreases the usefulness of TU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "71a5dcea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>source</th>\n",
+       "      <th>vp_arrival_time</th>\n",
+       "      <th>vp_arrival_time_lag1</th>\n",
+       "      <th>vp_arrival_time_lag2</th>\n",
+       "      <th>vp_arrival_time_lag3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:18:28</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:21:44</td>\n",
+       "      <td>2026-06-10 07:18:28</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:23:34</td>\n",
+       "      <td>2026-06-10 07:21:44</td>\n",
+       "      <td>2026-06-10 07:18:28</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:23:50</td>\n",
+       "      <td>2026-06-10 07:23:34</td>\n",
+       "      <td>2026-06-10 07:21:44</td>\n",
+       "      <td>2026-06-10 07:18:28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:24:41</td>\n",
+       "      <td>2026-06-10 07:23:50</td>\n",
+       "      <td>2026-06-10 07:23:34</td>\n",
+       "      <td>2026-06-10 07:21:44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:25:47</td>\n",
+       "      <td>2026-06-10 07:24:41</td>\n",
+       "      <td>2026-06-10 07:23:50</td>\n",
+       "      <td>2026-06-10 07:23:34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>0008d298e3f42cea9e00cfddb076fdd0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2026-06-10 07:26:59</td>\n",
+       "      <td>2026-06-10 07:25:47</td>\n",
+       "      <td>2026-06-10 07:24:41</td>\n",
+       "      <td>2026-06-10 07:23:50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>000e8ebad1f93d750cc8f215289b4ce1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2025-12-17 16:47:12</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>000e8ebad1f93d750cc8f215289b4ce1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2025-12-17 16:48:19</td>\n",
+       "      <td>2025-12-17 16:47:12</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>000e8ebad1f93d750cc8f215289b4ce1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>both</td>\n",
+       "      <td>2025-12-17 16:51:15</td>\n",
+       "      <td>2025-12-17 16:48:19</td>\n",
+       "      <td>2025-12-17 16:47:12</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   trip_instance_key  stop_sequence source  \\\n",
+       "81  0008d298e3f42cea9e00cfddb076fdd0              2   both   \n",
+       "82  0008d298e3f42cea9e00cfddb076fdd0              3   both   \n",
+       "83  0008d298e3f42cea9e00cfddb076fdd0              4   both   \n",
+       "84  0008d298e3f42cea9e00cfddb076fdd0              5   both   \n",
+       "85  0008d298e3f42cea9e00cfddb076fdd0              6   both   \n",
+       "86  0008d298e3f42cea9e00cfddb076fdd0              7   both   \n",
+       "87  0008d298e3f42cea9e00cfddb076fdd0              8   both   \n",
+       "90  000e8ebad1f93d750cc8f215289b4ce1              2   both   \n",
+       "91  000e8ebad1f93d750cc8f215289b4ce1              3   both   \n",
+       "93  000e8ebad1f93d750cc8f215289b4ce1              5   both   \n",
+       "\n",
+       "       vp_arrival_time vp_arrival_time_lag1 vp_arrival_time_lag2  \\\n",
+       "81 2026-06-10 07:18:28                  NaT                  NaT   \n",
+       "82 2026-06-10 07:21:44  2026-06-10 07:18:28                  NaT   \n",
+       "83 2026-06-10 07:23:34  2026-06-10 07:21:44  2026-06-10 07:18:28   \n",
+       "84 2026-06-10 07:23:50  2026-06-10 07:23:34  2026-06-10 07:21:44   \n",
+       "85 2026-06-10 07:24:41  2026-06-10 07:23:50  2026-06-10 07:23:34   \n",
+       "86 2026-06-10 07:25:47  2026-06-10 07:24:41  2026-06-10 07:23:50   \n",
+       "87 2026-06-10 07:26:59  2026-06-10 07:25:47  2026-06-10 07:24:41   \n",
+       "90 2025-12-17 16:47:12                  NaT                  NaT   \n",
+       "91 2025-12-17 16:48:19  2025-12-17 16:47:12                  NaT   \n",
+       "93 2025-12-17 16:51:15  2025-12-17 16:48:19  2025-12-17 16:47:12   \n",
+       "\n",
+       "   vp_arrival_time_lag3  \n",
+       "81                  NaT  \n",
+       "82                  NaT  \n",
+       "83                  NaT  \n",
+       "84  2026-06-10 07:18:28  \n",
+       "85  2026-06-10 07:21:44  \n",
+       "86  2026-06-10 07:23:34  \n",
+       "87  2026-06-10 07:23:50  \n",
+       "90                  NaT  \n",
+       "91                  NaT  \n",
+       "93                  NaT  "
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Jump detection: attach the previous 3 arrival times within each trip.\n",
+    "# trip_instance_key is the per trip per service date key, so grouping on it keeps\n",
+    "# one trip's last stops from leaking into the next trip's first stops.\n",
+    "# The frame already arrives in trip/stop order (both queries order by them and the\n",
+    "# outer merges sort on the join keys), so this asserts that rather than re-sorting.\n",
+    "LAG_STEPS = (1, 2, 3)\n",
+    "ARRIVAL_COLS = [\"vp_arrival_time\", \"tu_arrival_time\"]\n",
+    "\n",
+    "trips = vp_tu_stop_times_cleaned.groupby(\"trip_instance_key\", sort=False)\n",
+    "assert trips[\"stop_sequence\"].is_monotonic_increasing.all(), \"not in stop_sequence order\"\n",
+    "\n",
+    "vp_tu_stop_times_lagged = vp_tu_stop_times_cleaned.assign(\n",
+    "    **{\n",
+    "        f\"{col}_lag{lag}\": trips[col].shift(lag)\n",
+    "        for col in ARRIVAL_COLS\n",
+    "        for lag in LAG_STEPS\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "vp_tu_stop_times_lagged[\n",
+    "    [\"trip_instance_key\", \"stop_sequence\", \"source\", \"vp_arrival_time\"]\n",
+    "    + [f\"vp_arrival_time_lag{lag}\" for lag in LAG_STEPS]\n",
+    "].head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b959954",
+   "metadata": {},
+   "source": [
+    "### Analyze backwards jumps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "813dbd1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>entries</th>\n",
+       "      <th>backwards</th>\n",
+       "      <th>pct_backwards</th>\n",
+       "      <th>mean_backwards_time_difference_seconds</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>metric</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>vp_jump1_seconds</th>\n",
+       "      <td>140419</td>\n",
+       "      <td>549</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>370.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp_jump2_seconds</th>\n",
+       "      <td>135282</td>\n",
+       "      <td>346</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>549.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vp_jump3_seconds</th>\n",
+       "      <td>130164</td>\n",
+       "      <td>207</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>792.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tu_jump1_seconds</th>\n",
+       "      <td>140419</td>\n",
+       "      <td>7800</td>\n",
+       "      <td>5.55</td>\n",
+       "      <td>70.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tu_jump2_seconds</th>\n",
+       "      <td>135282</td>\n",
+       "      <td>2142</td>\n",
+       "      <td>1.58</td>\n",
+       "      <td>113.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tu_jump3_seconds</th>\n",
+       "      <td>130164</td>\n",
+       "      <td>1167</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>166.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  entries  backwards  pct_backwards  \\\n",
+       "metric                                                \n",
+       "vp_jump1_seconds   140419        549           0.39   \n",
+       "vp_jump2_seconds   135282        346           0.26   \n",
+       "vp_jump3_seconds   130164        207           0.16   \n",
+       "tu_jump1_seconds   140419       7800           5.55   \n",
+       "tu_jump2_seconds   135282       2142           1.58   \n",
+       "tu_jump3_seconds   130164       1167           0.90   \n",
+       "\n",
+       "                  mean_backwards_time_difference_seconds  \n",
+       "metric                                                    \n",
+       "vp_jump1_seconds                                   370.5  \n",
+       "vp_jump2_seconds                                   549.5  \n",
+       "vp_jump3_seconds                                   792.5  \n",
+       "tu_jump1_seconds                                    70.7  \n",
+       "tu_jump2_seconds                                   113.0  \n",
+       "tu_jump3_seconds                                   166.4  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Jump size: how far each arrival moved relative to the 1-3 entries before it.\n",
+    "# Within a trip, arrival times should only ever increase with stop_sequence, so a\n",
+    "# negative jump is time running backwards - the \"came back down\" half of a jump.\n",
+    "jump_columns = {\n",
+    "    f\"{col.removesuffix('_arrival_time')}_jump{lag}_seconds\": (\n",
+    "        vp_tu_stop_times_lagged[col] - vp_tu_stop_times_lagged[f\"{col}_lag{lag}\"]\n",
+    "    ).dt.total_seconds()\n",
+    "    for col in ARRIVAL_COLS\n",
+    "    for lag in LAG_STEPS\n",
+    "}\n",
+    "vp_tu_stop_times_lagged = vp_tu_stop_times_lagged.assign(**jump_columns)\n",
+    "JUMP_COLS = list(jump_columns)\n",
+    "\n",
+    "# How often does each feed run backwards? The denominator is entries that have a\n",
+    "# lag to compare against, so trip starts and gaps from the other feed drop out.\n",
+    "backwards_steps = pd.DataFrame(\n",
+    "    {\n",
+    "        \"entries\": vp_tu_stop_times_lagged[JUMP_COLS].notna().sum(),\n",
+    "        \"backwards\": (vp_tu_stop_times_lagged[JUMP_COLS] < 0).sum(),\n",
+    "    }\n",
+    ")\n",
+    "backwards_steps[\"pct_backwards\"] = (\n",
+    "    backwards_steps[\"backwards\"] / backwards_steps[\"entries\"] * 100\n",
+    ").round(2)\n",
+    "# How far time runs backwards when it does, in seconds. Only the negative jumps\n",
+    "# are averaged, so this is the typical size of a backwards step rather than a\n",
+    "# net drift across all entries.\n",
+    "jumps = vp_tu_stop_times_lagged[JUMP_COLS]\n",
+    "backwards_steps[\"mean_backwards_time_difference_seconds\"] = (\n",
+    "    jumps.where(jumps < 0).abs().mean().round(1)\n",
+    ")\n",
+    "backwards_steps.index.name = \"metric\"\n",
+    "backwards_steps\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cal-itp-data-analyses (3.11.x)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/gtfs_rt_comparison/queries/feed_keys.sql b/gtfs_rt_comparison/queries/feed_keys.sql
new file mode 100644
index 000000000..638a45ff8
--- /dev/null
+++ b/gtfs_rt_comparison/queries/feed_keys.sql
@@ -0,0 +1,15 @@
+-- Schedule feed keys for the target feed, and the trip updates feed that goes
+-- with each one. Left join because a schedule feed can exist on a date with no
+-- trip updates feed at all - those rows are the dates to drop later.
+select
+    tu.base64_url as tu_base64_url,
+    sched.feed_key as schedule_feed_key,
+    sched.date
+from mart_gtfs.fct_daily_schedule_feeds as sched
+left join mart_gtfs.fct_daily_rt_feed_files as tu
+    on sched.feed_key = tu.schedule_feed_key
+    and sched.date = tu.date
+    and tu.feed_type = 'trip_updates'
+where
+    sched.date in {{ DATES | sql_in }}
+    and sched.gtfs_dataset_name = '{{ GTFS_DATASET_NAME }}'
diff --git a/gtfs_rt_comparison/queries/operator_trips.sql b/gtfs_rt_comparison/queries/operator_trips.sql
new file mode 100644
index 000000000..8367a0333
--- /dev/null
+++ b/gtfs_rt_comparison/queries/operator_trips.sql
@@ -0,0 +1,8 @@
+-- Every scheduled trip this operator ran on the dates of interest. The VP
+-- parquets are statewide, so these keys are what filters them down; keeping all
+-- of them also keeps trips that VP saw but TU never predicted.
+select trip_instance_key, trip_id, service_date
+from mart_gtfs.fct_scheduled_trips
+where
+    service_date in {{ DATES | sql_in }}
+    and name = '{{ GTFS_DATASET_NAME }}'
diff --git a/gtfs_rt_comparison/queries/schedule_feeds.sql b/gtfs_rt_comparison/queries/schedule_feeds.sql
new file mode 100644
index 000000000..33e7731cf
--- /dev/null
+++ b/gtfs_rt_comparison/queries/schedule_feeds.sql
@@ -0,0 +1,7 @@
+-- The _valid_from of each schedule feed version. dim_stop_arrivals is
+-- partitioned by _feed_valid_from, so this is what lets that query prune.
+select
+    key as feed_key,
+    _valid_from as feed_valid_from
+from mart_gtfs.dim_schedule_feeds
+where key in {{ FEED_KEYS | sql_in }}
diff --git a/gtfs_rt_comparison/queries/schedule_stop_times.sql b/gtfs_rt_comparison/queries/schedule_stop_times.sql
new file mode 100644
index 000000000..d1095d6ea
--- /dev/null
+++ b/gtfs_rt_comparison/queries/schedule_stop_times.sql
@@ -0,0 +1,49 @@
+-- Scheduled stop times, keyed by trip_instance_key + service_date + stop_sequence.
+-- dim_stop_arrivals is per feed version, not per date. fct_scheduled_trips
+-- expands a feed's trips out per service date and carries trip_instance_key,
+-- which is the key the VP stop arrivals use.
+--
+-- dim_stop_arrivals is partitioned by _feed_valid_from and clustered by
+-- feed_key, so both have to be literals here.
+with sched_trips as (
+    select trip_instance_key, feed_key, trip_id, service_date
+    from mart_gtfs.fct_scheduled_trips
+    where
+        service_date in {{ DATES | sql_in }}
+        and name = '{{ GTFS_DATASET_NAME }}'
+),
+arrivals as (
+    select feed_key, trip_id, stop_id, stop_sequence, feed_timezone,
+           arrival_sec, departure_sec
+    from mart_gtfs.dim_stop_arrivals
+    where
+        _feed_valid_from in {{ FEED_VALID_FROMS | sql_in_timestamps }}
+        and feed_key in {{ FEED_KEYS | sql_in }}
+)
+-- arrival_sec/departure_sec are gtfs time - count from midnight and are allowed to run past
+-- 24h on owl trips, so they are added to midnight in the feed's own timezone
+-- and converted to Pacific, matching how the VP and TU arrival times read.
+select
+    sched_trips.trip_instance_key,
+    sched_trips.service_date,
+    arrivals.stop_sequence,
+    arrivals.stop_id,
+    datetime(
+        timestamp_add(
+            timestamp(sched_trips.service_date, arrivals.feed_timezone),
+            interval arrivals.arrival_sec second
+        ),
+        'America/Los_Angeles'
+    ) as schedule_arrival_time,
+    datetime(
+        timestamp_add(
+            timestamp(sched_trips.service_date, arrivals.feed_timezone),
+            interval arrivals.departure_sec second
+        ),
+        'America/Los_Angeles'
+    ) as schedule_departure_time
+from arrivals
+inner join sched_trips
+    on sched_trips.feed_key = arrivals.feed_key
+    and sched_trips.trip_id = arrivals.trip_id
+order by trip_instance_key, stop_sequence
diff --git a/gtfs_rt_comparison/queries/stop_time_metrics_counts.sql b/gtfs_rt_comparison/queries/stop_time_metrics_counts.sql
new file mode 100644
index 000000000..d8e6a133e
--- /dev/null
+++ b/gtfs_rt_comparison/queries/stop_time_metrics_counts.sql
@@ -0,0 +1,13 @@
+-- Count fct_stop_time_metrics rows per trip updates feed per date. The dates
+-- with a significant number of rows are the dates where both TU and VP based
+-- stop times are available.
+select
+    base64_url as tu_base64_url,
+    service_date,
+    count(*) as n_stop_time_metrics
+from mart_gtfs.fct_stop_time_metrics
+where
+    service_date in {{ DATES | sql_in }}
+    and base64_url in {{ TU_URLS | sql_in }}
+group by base64_url, service_date
+order by service_date
diff --git a/gtfs_rt_comparison/queries/tu_stop_times.sql b/gtfs_rt_comparison/queries/tu_stop_times.sql
new file mode 100644
index 000000000..ebe0a2e45
--- /dev/null
+++ b/gtfs_rt_comparison/queries/tu_stop_times.sql
@@ -0,0 +1,31 @@
+-- TU based stop times, keyed the way the VP stop times are (schedule trip_instance_key + service_date + stop_sequence).
+
+-- Ordered by trip and stop so the frame arrives ready for within-trip lags.
+with stm as (
+    select service_date, schedule_base64_url, trip_id, stop_id, stop_sequence,
+           actual_arrival_pacific, n_predictions
+    from mart_gtfs.fct_stop_time_metrics
+    where
+        service_date in {{ DATES | sql_in }}
+        and base64_url in {{ TU_URLS | sql_in }}
+),
+sched as (
+    select trip_instance_key, trip_id, base64_url, service_date
+    from mart_gtfs.fct_scheduled_trips
+    where
+        service_date in {{ DATES | sql_in }}
+        and name = '{{ GTFS_DATASET_NAME }}'
+)
+select
+    sched.trip_instance_key,
+    stm.service_date,
+    stm.stop_sequence,
+    stm.stop_id,
+    stm.actual_arrival_pacific as tu_arrival_time,
+    stm.n_predictions
+from stm
+left join sched
+    on sched.service_date = stm.service_date
+    and sched.trip_id = stm.trip_id
+    and sched.base64_url = stm.schedule_base64_url
+order by trip_instance_key, stop_sequence