# FTA Monthly Transit Ridership Change Monitor (`aether_studio/fta-ridership-change-monitor`) Actor Compares the newest two official FTA National Transit Database monthly observations by agency, mode, and service type, with ridership and service-productivity changes. - **URL**: https://apify.com/aether\_studio/fta-ridership-change-monitor.md - **Developed by:** [AetherPromptStudio](https://apify.com/aether_studio) (community) - **Categories:** Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## FTA Monthly Transit Ridership Change Monitor Turn the official Federal Transit Administration National Transit Database monthly release into analysis-ready changes by agency, mode, and type of service. The Actor compares each series with its own immediately prior month and returns ridership deltas, percent changes, service quantities, and trips per vehicle revenue hour. Use filters to focus on agencies, states, or FTA mode codes. ### Common uses - Transit agency and municipal performance dashboards - Mobility-market and public-policy research - Service planning and peer-agency comparisons - Automated monthly change alerts ### Output Each default dataset record includes a stable series identifier, agency and FTA identifiers, current and previous months, ridership values and changes, service measures, productivity ratios, official source links, and retrieval time. The `SUMMARY` record explains source coverage, filters, and the number of matches. ### Source and limitations Data comes directly from the U.S. Department of Transportation's public **Complete Monthly Ridership with Adjustments and Estimates** dataset. FTA NTD monthly data may contain estimates, adjustments, reporting differences, seasonal effects, and later revisions. Changes are descriptive, not causal findings, forecasts, or service-quality judgments. This independent product is not affiliated with or endorsed by FTA. # Actor input Schema ## `agencyContains` (type: `string`): Case-insensitive agency-name filter. Leave empty for all agencies. ## `states` (type: `array`): Two-letter state codes. Leave empty for all. ## `modes` (type: `array`): Examples: MB for motor bus, HR for heavy rail, CR for commuter rail. ## `minPercentChange` (type: `number`): Exclude ridership changes smaller than this percentage. ## `maxAgeDays` (type: `integer`): Exclude series whose newest observation is older than this many days. ## `maxResults` (type: `integer`): Maximum number of changed series written to the dataset. ## Actor input object example ```json { "agencyContains": "", "states": [], "modes": [], "minPercentChange": 5, "maxAgeDays": 120, "maxResults": 50 } ```` # Actor output Schema ## `results` (type: `string`): No description ## `summary` (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 = { "agencyContains": "", "states": [], "modes": [], "minPercentChange": 5, "maxAgeDays": 120, "maxResults": 50 }; // Run the Actor and wait for it to finish const run = await client.actor("aether_studio/fta-ridership-change-monitor").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 = { "agencyContains": "", "states": [], "modes": [], "minPercentChange": 5, "maxAgeDays": 120, "maxResults": 50, } # Run the Actor and wait for it to finish run = client.actor("aether_studio/fta-ridership-change-monitor").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 '{ "agencyContains": "", "states": [], "modes": [], "minPercentChange": 5, "maxAgeDays": 120, "maxResults": 50 }' | apify call aether_studio/fta-ridership-change-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=aether_studio/fta-ridership-change-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FTA Monthly Transit Ridership Change Monitor", "description": "Compares the newest two official FTA National Transit Database monthly observations by agency, mode, and service type, with ridership and service-productivity changes.", "version": "0.1", "x-build-id": "Kf1cd1jieuflfggYi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/aether_studio~fta-ridership-change-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-aether_studio-fta-ridership-change-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/aether_studio~fta-ridership-change-monitor/runs": { "post": { "operationId": "runs-sync-aether_studio-fta-ridership-change-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/aether_studio~fta-ridership-change-monitor/run-sync": { "post": { "operationId": "run-sync-aether_studio-fta-ridership-change-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "agencyContains": { "title": "Agency Contains", "type": "string", "description": "Case-insensitive agency-name filter. Leave empty for all agencies.", "default": "" }, "states": { "title": "States", "type": "array", "description": "Two-letter state codes. Leave empty for all.", "default": [], "items": { "type": "string" } }, "modes": { "title": "FTA Mode Codes", "type": "array", "description": "Examples: MB for motor bus, HR for heavy rail, CR for commuter rail.", "default": [], "items": { "type": "string" } }, "minPercentChange": { "title": "Minimum Absolute Percent Change", "minimum": 0, "type": "number", "description": "Exclude ridership changes smaller than this percentage.", "default": 1 }, "maxAgeDays": { "title": "Maximum Observation Age", "minimum": 1, "maximum": 730, "type": "integer", "description": "Exclude series whose newest observation is older than this many days.", "default": 120 }, "maxResults": { "title": "Maximum Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of changed series written to the dataset.", "default": 200 } } }, "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 } } } } } } } } } } ```