# GTFS Feed Extractor (`datamule/gtfs-feed-extractor`) Actor Point at ANY GTFS-static feed URL (BART, MBTA, and thousands of transit agencies worldwide — one global standard) and get flat dataset rows: agency, stops, routes, trips, calendar, stop\_times and more. Discovery mode lists a feed's tables. Pay per record. - **URL**: https://apify.com/datamule/gtfs-feed-extractor.md - **Developed by:** [Datamule](https://apify.com/datamule) (community) - **Categories:** Travel - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.35 / 1,000 records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## GTFS Feed Extractor **Point at ANY GTFS-static feed URL and get flat, structured dataset rows — one actor for every transit agency on Earth.** GTFS (the **General Transit Feed Specification**, originally Google Transit) is *the* single global standard for public-transit schedule data. Thousands of agencies on every continent — subways, buses, trams, ferries, rail — publish the identical **ZIP-of-CSVs**: `agency.txt`, `stops.txt`, `routes.txt`, `trips.txt`, `stop_times.txt`, `calendar.txt`, `calendar_dates.txt`, `shapes.txt`, `fare_*.txt`, `feed_info.txt` and more. Every other "transit" scraper wraps **one** agency. This actor is the **generic runner**: give it any GTFS-static ZIP URL and it turns the tables you ask for into dataset rows — one row per CSV record, the columns preserved exactly, plus a lossless copy of each original record. ### What it does - **Extract mode (default):** downloads the feed ZIP and parses the tables you choose into flat rows. Each row carries the CSV columns as-is, plus meta columns `_table` (which file), `_feed_url`, `_row` (0-based index in the table), and `_raw` (the verbatim original record). - **Discovery mode (`listTablesOnly`):** emits one row per table the feed contains — its name, row count and column names — so you can see what a feed holds before pulling the big tables. Robust to real-world feed variation: it matches table names case-insensitively against whatever the ZIP actually contains, parses with a proper CSV reader (quoted fields, embedded commas), strips UTF-8 BOMs, tolerates non-UTF-8 feeds and non-standard extension columns, and **skips missing tables silently** instead of crashing. ### Input | Field | Type | Description | |-------|------|-------------| | `gtfs_url` *(required)* | string | Direct URL to a GTFS-static ZIP feed, e.g. `https://www.bart.gov/dev/schedules/google_transit.zip`. | | `tables` | array | Tables to extract, by name (with or without `.txt`): `stops`, `routes`, `trips`, `calendar`… Leave empty for a sensible default set (`agency`, `feed_info`, `calendar`, `calendar_dates`, `routes`, `stops`, `trips` — **not** `stop_times`, which is often 100k+ rows). Use `["all"]` to extract every table including `stop_times`/`shapes`. | | `maxRecords` | integer | Optional **global** cap on rows emitted across *all* requested tables. Keeps a sample cheap. | | `listTablesOnly` | boolean | Discovery mode — list the feed's tables (name, row count, columns) instead of extracting. | | `bearer` | string (secret) | Optional bearer token for gated feeds. Never logged. | | `extraHeaders` | object | Optional extra HTTP headers for gated feeds, e.g. `{"x-api-key": "…"}`. Never logged. | ### Output One dataset item per CSV record. For a `stops` row from BART, for example: ```json { "_table": "stops", "_feed_url": "https://www.bart.gov/dev/schedules/google_transit.zip", "_row": 0, "stop_id": "A10-1", "stop_code": "902101", "stop_name": "Lake Merritt", "stop_lat": "37.797296", "stop_lon": "-122.265287", "zone_id": "LAKE", "location_type": "0", "parent_station": "LAKE", "_raw": { "stop_id": "A10-1", "stop_code": "902101", "stop_name": "Lake Merritt", "…": "…" } } ```` Empty CSV cells become `null` in the flat columns; `_raw` preserves the verbatim record (including any values beyond the header). ### Finding feeds Almost every public agency's GTFS feed is cataloged in the **MobilityData Mobility Database** (), the ~2,000-feed successor to TransitFeeds. Point this actor at any feed's direct download URL. ### Pricing Pay-per-event: **$0.0005 per record** (one emitted dataset row). Use `maxRecords` and a focused `tables` list to control cost — you rarely need every row of `stop_times`. ### Notes & limits - **GTFS-static only.** GTFS-Realtime (the protobuf `.pb` trip-update/vehicle-position feeds) is a different format and is not covered here. - Feeds up to 512 MB are supported; larger feeds should be sampled with `maxRecords` / `tables`. - A URL that 404s or isn't a ZIP returns a clear error; a feed missing an optional table simply skips it. # Actor input Schema ## `gtfs_url` (type: `string`): A direct URL to a GTFS-static ZIP feed — the single global public-transit standard, published by thousands of agencies worldwide as an identical zip-of-CSVs. Example: https://www.bart.gov/dev/schedules/google\_transit.zip (Bay Area Rapid Transit). Find feeds for almost any agency via the MobilityData Mobility Database (mobilitydatabase.org). ## `tables` (type: `array`): Which GTFS tables (files) to extract, by name (with or without .txt): e.g. stops, routes, trips, calendar. Leave EMPTY for a sensible default set (agency, feed\_info, calendar, calendar\_dates, routes, stops, trips — deliberately NOT stop\_times, which is often 100k+ rows). Use a single entry "all" to extract every table in the feed (including the large stop\_times and shapes). Names not present in the feed are skipped silently. ## `maxRecords` (type: `integer`): Optional GLOBAL cap on the number of rows emitted across ALL requested tables (each emitted row is one billable record). stop\_times alone can be 100k+ rows, so a cap keeps a sample cheap. Default 25; set a larger value only when you intentionally want a broader extraction. ## `listTablesOnly` (type: `boolean`): When on, ignore "Tables to extract" and instead emit one row per table the feed contains — its name, row count and column names. A cheap way to inspect what a feed holds before pulling the big tables. ## `bearer` (type: `string`): Optional bearer token for gated feeds (sent as Authorization: Bearer \*\*\*). Not required for public GTFS feeds. Never logged. ## `extraHeaders` (type: `object`): Optional extra HTTP headers as a JSON object, e.g. {"x-api-key": "..."} for gated feeds. Not required for public feeds. Header values are never logged. ## Actor input object example ```json { "gtfs_url": "https://www.bart.gov/dev/schedules/google_transit.zip", "tables": [ "agency", "stops", "routes", "trips" ], "maxRecords": 25, "listTablesOnly": false } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "gtfs_url": "https://www.bart.gov/dev/schedules/google_transit.zip", "tables": [ "agency", "stops", "routes", "trips" ], "maxRecords": 25 }; // Run the Actor and wait for it to finish const run = await client.actor("datamule/gtfs-feed-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "gtfs_url": "https://www.bart.gov/dev/schedules/google_transit.zip", "tables": [ "agency", "stops", "routes", "trips", ], "maxRecords": 25, } # Run the Actor and wait for it to finish run = client.actor("datamule/gtfs-feed-extractor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "gtfs_url": "https://www.bart.gov/dev/schedules/google_transit.zip", "tables": [ "agency", "stops", "routes", "trips" ], "maxRecords": 25 }' | apify call datamule/gtfs-feed-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamule/gtfs-feed-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GTFS Feed Extractor", "description": "Point at ANY GTFS-static feed URL (BART, MBTA, and thousands of transit agencies worldwide — one global standard) and get flat dataset rows: agency, stops, routes, trips, calendar, stop_times and more. Discovery mode lists a feed's tables. Pay per record.", "version": "0.1", "x-build-id": "TxoQilCq9IygdUMNp" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamule~gtfs-feed-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamule-gtfs-feed-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamule~gtfs-feed-extractor/runs": { "post": { "operationId": "runs-sync-datamule-gtfs-feed-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamule~gtfs-feed-extractor/run-sync": { "post": { "operationId": "run-sync-datamule-gtfs-feed-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "gtfs_url" ], "properties": { "gtfs_url": { "title": "GTFS-static feed URL", "type": "string", "description": "A direct URL to a GTFS-static ZIP feed — the single global public-transit standard, published by thousands of agencies worldwide as an identical zip-of-CSVs. Example: https://www.bart.gov/dev/schedules/google_transit.zip (Bay Area Rapid Transit). Find feeds for almost any agency via the MobilityData Mobility Database (mobilitydatabase.org)." }, "tables": { "title": "Tables to extract", "type": "array", "description": "Which GTFS tables (files) to extract, by name (with or without .txt): e.g. stops, routes, trips, calendar. Leave EMPTY for a sensible default set (agency, feed_info, calendar, calendar_dates, routes, stops, trips — deliberately NOT stop_times, which is often 100k+ rows). Use a single entry \"all\" to extract every table in the feed (including the large stop_times and shapes). Names not present in the feed are skipped silently.", "items": { "type": "string" } }, "maxRecords": { "title": "Max records", "minimum": 1, "type": "integer", "description": "Optional GLOBAL cap on the number of rows emitted across ALL requested tables (each emitted row is one billable record). stop_times alone can be 100k+ rows, so a cap keeps a sample cheap. Default 25; set a larger value only when you intentionally want a broader extraction.", "default": 25 }, "listTablesOnly": { "title": "List tables only (discovery)", "type": "boolean", "description": "When on, ignore \"Tables to extract\" and instead emit one row per table the feed contains — its name, row count and column names. A cheap way to inspect what a feed holds before pulling the big tables.", "default": false }, "bearer": { "title": "Bearer token", "type": "string", "description": "Optional bearer token for gated feeds (sent as Authorization: Bearer ***). Not required for public GTFS feeds. Never logged." }, "extraHeaders": { "title": "Extra request headers", "type": "object", "description": "Optional extra HTTP headers as a JSON object, e.g. {\"x-api-key\": \"...\"} for gated feeds. Not required for public feeds. Header values are never logged." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```