# Amtrak, Greyhound & Megabus Scraper — US Intercity Rail & Bus (`jungle_synthesizer/amtrak-greyhound-us-intercity-scraper`) Actor Scrape US intercity bus schedules and fares: Megabus and FlixBus (Greyhound/FlixBus network) with per-carrier trip IDs, origin/destination cities, departure/arrival times, fares, seat availability, amenities, and booking links. Covers 734+ city pairs and 500+ routes across the continental US. - **URL**: https://apify.com/jungle\_synthesizer/amtrak-greyhound-us-intercity-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Travel, Business, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Megabus & FlixBus US Intercity Scraper Scrapes live departure schedules and fares from [Megabus](https://us.megabus.com) and the [FlixBus/Greyhound](https://shop.flixbus.com) network for any US city pair. Returns structured trip data — origin, destination, departure time, arrival time, duration, fare, available seats, and amenities — across both services in a single run. ### Megabus & FlixBus Scraper Features - **Queries two networks in one run** — Megabus and FlixBus (which operates the Greyhound intercity network in the US) - **Returns 16+ fields per trip** including departure/arrival stations, duration in minutes, cheapest fare, seat availability, and amenities like WiFi and power sockets - **Defaults to tomorrow's departures** if no date is specified — useful for scheduled monitoring - **Filter by service** — run just Megabus, just FlixBus, or both - **Pure API scraping** — no browser required, runs fast - **Fuzzy city matching** — resolves "New York, NY" or "NYC" to the correct station ID in both networks ### What Can You Do With US Intercity Bus Data? - **Price trackers** — monitor fare changes across the Megabus and Greyhound/FlixBus network for a specific corridor - **Travel researchers** — compare fares, schedules, and seat availability across carriers for a given city pair - **Yield management teams** — track competitor pricing on overlapping routes - **Data journalists** — build datasets on affordable intercity travel options between US metros - **Budget travel apps** — feed live bus schedules into trip-planning tools without paying aggregator fees ### How It Works 1. Configure your origin city, destination city, and travel date. City names are matched against each carrier's city database — "New York, NY", "New York City", and "NYC" all resolve correctly. 2. The scraper queries Megabus and/or FlixBus APIs directly. Megabus uses its own journey search endpoint; FlixBus/Greyhound routes through the global FlixBus API. 3. Each trip is normalized to a common schema — same fields regardless of carrier, so you can compare Megabus and FlixBus results without hand-stitching two different formats. 4. Results are written to the dataset. Run it once for a snapshot or schedule it to track fare changes over time. ### Megabus & FlixBus Scraper Output Fields ```json { "trip_id": "megabus-123-456-2025-06-15", "service": "megabus", "origin_station": "New York - Port Authority Bus Terminal", "origin_city": "New York", "origin_state": "NY", "destination_station": "Washington, DC - Union Station Bus Plaza", "destination_city": "Washington", "destination_state": "DC", "departure_time": "2025-06-15T07:00:00", "arrival_time": "2025-06-15T11:00:00", "duration_minutes": 240, "transfers": 0, "cheapest_fare_usd": 12.50, "seats_available": 8, "amenities": ["WIFI", "POWER_SOCKETS"], "booking_url": "https://us.megabus.com/journey-planner/journeys?..." } ```` | Field | Type | Description | |-------|------|-------------| | `trip_id` | String | Unique trip identifier combining service, internal IDs, and date | | `service` | String | `megabus` or `flixbus` | | `origin_station` | String | Full station name at origin | | `origin_city` | String | Origin city name | | `origin_state` | String | Origin state abbreviation | | `destination_station` | String | Full station name at destination | | `destination_city` | String | Destination city name | | `destination_state` | String | Destination state abbreviation | | `departure_time` | String | Departure datetime (ISO 8601) | | `arrival_time` | String | Arrival datetime (ISO 8601) | | `duration_minutes` | Integer | Total trip duration in minutes | | `transfers` | Integer | Number of transfers (0 = direct) | | `cheapest_fare_usd` | Number | Lowest available fare in USD | | `seats_available` | Integer | Available seats at the quoted fare (-1 if not reported) | | `amenities` | Array | List of amenities, e.g. `["WIFI", "POWER_SOCKETS"]` | | `booking_url` | String | Direct link to book on the carrier's site | ### Input Configuration ```json { "services": ["megabus", "flixbus"], "originCity": "New York, NY", "destinationCity": "Washington, DC", "date": "2025-06-15", "passengers": 1, "maxItems": 50 } ``` | Field | Type | Default | Description | |-------|------|---------|-------------| | `services` | Array | `["megabus", "flixbus"]` | Which carriers to include. Options: `megabus`, `flixbus` | | `originCity` | String | `"New York, NY"` | Origin city name. Fuzzy-matched against carrier city lists | | `destinationCity` | String | `"Washington, DC"` | Destination city name | | `date` | String | Tomorrow | Travel date in `YYYY-MM-DD` format. Defaults to tomorrow if blank | | `passengers` | Integer | `1` | Number of adult passengers | | `maxItems` | Integer | `10` | Maximum trip records to return across all services | ### 🔍 FAQ #### How do I scrape bus schedules between two US cities? Megabus & FlixBus US Intercity Scraper handles this with three fields: `originCity`, `destinationCity`, and `date`. City names don't need to be exact — the scraper resolves common abbreviations and partial names against each carrier's city database. #### What data can I get from Megabus and FlixBus? Megabus & FlixBus US Intercity Scraper returns departure and arrival times, station names, trip duration, number of transfers, the cheapest available fare in USD, seat availability at that fare, amenities like WiFi and power outlets, and a direct booking URL. Sixteen fields per trip. #### Does this scraper include Greyhound routes? Yes — under FlixBus. Greyhound's US intercity network is now operated by FlixBus, and its routes appear in the FlixBus API. Set `services: ["flixbus"]` to query just the Greyhound/FlixBus network. #### How much does it cost to run? Megabus & FlixBus US Intercity Scraper uses residential proxies for reliability. A typical city-pair query — both carriers, one date — costs a few cents. Runs are short: 30-60 seconds for most pairs. #### Can I track fare changes over time? The scraper doesn't track changes natively, but it's built for scheduled runs. Set it up with Apify's scheduler to query the same city pair daily — you'll get a historical fare record with no extra code. *** ### Need More Features? Need additional carriers, bulk route queries, or custom output fields? [File an issue](https://console.apify.com/actors/issues) or get in touch. ### Why Use Megabus & FlixBus US Intercity Scraper? - **Two networks, one dataset** — Megabus and FlixBus/Greyhound in a normalized schema, so you're not reconciling two different API shapes yourself - **Direct API access** — queries carrier APIs directly without going through an aggregator that charges for the data or throttles your queries - **Field consistency** — every trip record has the same fields regardless of carrier, which means your downstream analysis works without conditionals # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `services` (type: `array`): Which US intercity services to include: megabus (direct API), flixbus (covers Greyhound/FlixBus network). ## `originCity` (type: `string`): City name for the origin, e.g. 'New York, NY'. ## `destinationCity` (type: `string`): City name for the destination, e.g. 'Washington, DC'. ## `date` (type: `string`): Date of travel. Defaults to tomorrow if empty. ## `passengers` (type: `integer`): Number of adult passengers. ## `maxItems` (type: `integer`): Maximum trip records to return across all services. ## `proxyConfiguration` (type: `object`): No description ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "services": [ "megabus", "flixbus" ], "originCity": "New York, NY", "destinationCity": "Washington, DC", "passengers": 1, "maxItems": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "countryCode": "US" } } ``` # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "services": [ "megabus", "flixbus" ], "originCity": "New York, NY", "destinationCity": "Washington, DC", "date": "", "passengers": 1, "maxItems": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "countryCode": "US" } }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/amtrak-greyhound-us-intercity-scraper").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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "services": [ "megabus", "flixbus", ], "originCity": "New York, NY", "destinationCity": "Washington, DC", "date": "", "passengers": 1, "maxItems": 10, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], "countryCode": "US", }, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/amtrak-greyhound-us-intercity-scraper").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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "services": [ "megabus", "flixbus" ], "originCity": "New York, NY", "destinationCity": "Washington, DC", "date": "", "passengers": 1, "maxItems": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "countryCode": "US" } }' | apify call jungle_synthesizer/amtrak-greyhound-us-intercity-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/amtrak-greyhound-us-intercity-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amtrak, Greyhound & Megabus Scraper — US Intercity Rail & Bus", "description": "Scrape US intercity bus schedules and fares: Megabus and FlixBus (Greyhound/FlixBus network) with per-carrier trip IDs, origin/destination cities, departure/arrival times, fares, seat availability, amenities, and booking links. Covers 734+ city pairs and 500+ routes across the continental US.", "version": "0.1", "x-build-id": "WNWS2QEjAs6TxdYm5" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~amtrak-greyhound-us-intercity-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-amtrak-greyhound-us-intercity-scraper", "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/jungle_synthesizer~amtrak-greyhound-us-intercity-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-amtrak-greyhound-us-intercity-scraper", "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/jungle_synthesizer~amtrak-greyhound-us-intercity-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-amtrak-greyhound-us-intercity-scraper", "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": [ "sp_intended_usage", "sp_improvement_suggestions", "services", "originCity", "destinationCity" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "services": { "title": "Services to Query", "type": "array", "description": "Which US intercity services to include: megabus (direct API), flixbus (covers Greyhound/FlixBus network).", "items": { "type": "string", "enum": [ "megabus", "flixbus" ], "enumTitles": [ "Megabus", "FlixBus / Greyhound" ] } }, "originCity": { "title": "Origin City", "type": "string", "description": "City name for the origin, e.g. 'New York, NY'." }, "destinationCity": { "title": "Destination City", "type": "string", "description": "City name for the destination, e.g. 'Washington, DC'." }, "date": { "title": "Travel Date (YYYY-MM-DD)", "type": "string", "description": "Date of travel. Defaults to tomorrow if empty." }, "passengers": { "title": "Passengers", "type": "integer", "description": "Number of adult passengers.", "default": 1 }, "maxItems": { "title": "Max Trips", "type": "integer", "description": "Maximum trip records to return across all services.", "default": 10 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "" } } }, "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 } } } } } } } } } } ```