# FRA Railroad Accidents Scraper (`automation-lab/fra-railroad-accidents-scraper`) Actor Extract FRA railroad accident records with railroads, locations, causes, casualties, damage costs, hazmat flags, and source report links. - **URL**: https://apify.com/automation-lab/fra-railroad-accidents-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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. 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 ## FRA Railroad Accidents Scraper Extract public Federal Railroad Administration (FRA) rail equipment accident and incident records from the DOT open-data catalog. Use it to monitor railroad safety events, casualty counts, damage costs, hazardous-material indicators, accident causes, and source report links without manually exporting data from government portals. ### What does FRA Railroad Accidents Scraper do? FRA Railroad Accidents Scraper collects rail equipment accident/incident records from the official DOT/FRA dataset. It lets you filter records by: - πŸ“… Accident date range - πŸ—ΊοΈ State and county - πŸš† Reporting railroad code or name - ⚠️ Accident type - πŸ’΅ Minimum damage cost - πŸ“ Optional narrative text Each output item is a normalized accident record with typed costs, casualty counts, cause fields, hazmat indicators, and source links. ### Who is it for? This actor is useful for teams that need repeatable railroad accident data workflows. - πŸ›‘οΈ Rail safety and compliance teams tracking incidents by territory - 🧾 Insurance and claims analysts monitoring high-damage events - βš–οΈ Legal and litigation-support teams researching specific railroads or regions - πŸ“Š Transportation market researchers building trend dashboards - πŸ›οΈ Public-sector analysts reviewing accident causes and locations - πŸ§ͺ Data teams enriching internal risk models with FRA source records ### Why use this actor? The FRA and DOT portals are valuable but are not optimized for repeatable automation. This actor gives you: - Structured JSON records ready for APIs, exports, and dashboards - Date, railroad, location, and accident-type filters - Direct report URLs for audit trails - Clean numeric damage and casualty fields - Optional narrative extraction - Pay-per-result pricing so small checks stay inexpensive ### Data source The actor uses the public DOT/FRA Socrata dataset: - Rail Equipment Accident/Incident Data (Form 54) - Dataset ID: `85tf-25kj` - Source page: https://data.transportation.gov/Railroads/Rail-Equipment-Accident-Incident-Data/85tf-25kj The FRA Safety Data homepage links this dataset as Rail Equipment Accident/Incident Data. ### What data can you extract? | Field | Description | | --- | --- | | `reportNumber` | FRA accident report number | | `reportKey` | FRA report/incident key | | `railroad` | Reporting railroad name | | `railroadCode` | Reporting railroad code | | `date` / `time` | Accident date and time | | `state` / `county` | Location fields | | `cityOrLocation` | Station or location text | | `accidentType` | Type such as derailment or collision | | `causeCode` | FRA cause code | | `causeDescription` | FRA cause description | | `fatalities` / `injuries` | Total persons killed or injured | | `damageCost` | Total reported damage cost | | `hazmatReleased` | Boolean hazardous material release indicator | | `sourceUrl` | Direct FRA report link when available | ### How much does it cost to scrape FRA railroad accident data? This actor uses pay-per-event pricing. You pay a small start fee per run and then a per-record fee for saved accident records. For example: - A quick 100-record state check is inexpensive. - A large national date-range export costs more because it saves more records. - Empty-result searches only incur the run start event. Exact live prices are shown on the Apify Store pricing panel. ### How to use 1. Open the actor on Apify. 2. Set a `startDate` and `endDate`. 3. Optionally add `state`, `county`, `railroad`, or `accidentType` filters. 4. Set `maxItems`. 5. Run the actor. 6. Download results from the Dataset tab as JSON, CSV, Excel, XML, or RSS. ### Input example ```json { "startDate": "2024-01-01", "endDate": "2024-12-31", "state": "TX", "railroad": "BNSF", "accidentType": "Derailment", "includeNarrative": true, "maxItems": 100 } ```` ### Input fields #### `startDate` Earliest accident date to include. Use `YYYY-MM-DD` format. #### `endDate` Latest accident date to include. If omitted, the actor uses the current date. #### `state` Optional state abbreviation or full state name. Examples: `TX`, `Texas`, `CA`, `Illinois`. #### `county` Optional county-name filter. #### `railroad` Optional reporting railroad code or name. Examples: `BNSF`, `CSX`, `Union Pacific`, `Norfolk Southern`. #### `accidentType` Optional text filter for FRA accident type. Examples: `Derailment`, `Collision`, `Fire`, `Obstruction`. #### `minDamageCost` Optional minimum total damage cost in USD. Use it to focus on severe incidents. #### `includeNarrative` Set to `true` to include FRA narrative text when available. Set to `false` for smaller exports. #### `maxItems` Maximum number of accident records to save. ### Output example ```json { "reportNumber": "RD1224131", "railroad": "BNSF Railway Company", "railroadCode": "BNSF", "date": "2024-12-30", "stateAbbr": "TX", "county": "JEFFERSON", "cityOrLocation": "BEAUMONT", "accidentType": "Derailment", "causeCode": "T220", "fatalities": 0, "injuries": 0, "damageCost": 41654, "hazmatReleased": false, "sourceUrl": "https://safetydata.fra.dot.gov/...", "scrapedAt": "2026-07-06T03:42:05.621Z" } ``` ### Tips for better results - Use a date range first, then add location or railroad filters. - Start with a lower `maxItems` while designing your workflow. - Use `minDamageCost` to find higher-severity events. - Keep `includeNarrative` enabled when reviewing incident context. - Disable narrative for compact dashboard exports. ### Common workflows #### State accident monitoring Run the actor monthly for one or more states and append the dataset to your BI tool. #### Railroad-specific analysis Filter by railroad code or name to track events involving a carrier. #### Damage-cost review Set `minDamageCost` to focus on major incidents for claims, risk, or compliance review. #### Cause-code research Export `causeCode` and `causeDescription` for trend analysis. ### Integrations You can connect the output to: - Google Sheets or Excel for incident lists - BigQuery, Snowflake, or PostgreSQL for analytics - Slack or email alerts for newly detected severe accidents - BI dashboards for monthly safety trend reporting - Internal risk or underwriting systems ### API usage #### Node.js ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/fra-railroad-accidents-scraper').call({ startDate: '2024-01-01', endDate: '2024-12-31', state: 'TX', maxItems: 100, }); console.log(run.defaultDatasetId); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient('YOUR_APIFY_TOKEN') run = client.actor('automation-lab/fra-railroad-accidents-scraper').call(run_input={ 'startDate': '2024-01-01', 'endDate': '2024-12-31', 'state': 'TX', 'maxItems': 100, }) print(run['defaultDatasetId']) ``` #### cURL ```bash curl -X POST 'https://api.apify.com/v2/acts/automation-lab~fra-railroad-accidents-scraper/runs?token=YOUR_APIFY_TOKEN' \ -H 'Content-Type: application/json' \ -d '{"startDate":"2024-01-01","endDate":"2024-12-31","state":"TX","maxItems":100}' ``` ### MCP usage Use this actor from MCP-compatible tools through Apify MCP. MCP URL: ```text https://mcp.apify.com/?tools=automation-lab/fra-railroad-accidents-scraper ``` Claude Code setup: ```bash claude mcp add apify-fra-railroad-accidents "https://mcp.apify.com/?tools=automation-lab/fra-railroad-accidents-scraper" ``` Generic MCP JSON config: ```json { "mcpServers": { "apify-fra-railroad-accidents": { "url": "https://mcp.apify.com/?tools=automation-lab/fra-railroad-accidents-scraper" } } } ``` Example prompts: - "Run the FRA railroad accidents scraper for Texas derailments in 2024." - "Find FRA accident records for BNSF with damage above 100000 dollars." - "Export rail equipment accident data for Illinois for the last year." ### Reliability notes The actor uses the DOT open-data API rather than scraping a fragile visual search form. This keeps runs fast, stable, and inexpensive. ### Limitations - The actor depends on fields published by the DOT/FRA dataset. - Very recent accidents may appear only after FRA/DOT data refreshes. - Source report links are included when the dataset provides them. - Historical field completeness can vary by year. ### Legality This actor extracts public government open-data records. You are responsible for using the data in compliance with applicable laws, platform terms, and internal policies. Do not use the actor to make regulated decisions without validating the source records and methodology. ### FAQ #### Is this official FRA data? The actor uses the public DOT/FRA open-data dataset linked from the FRA Safety Data homepage. Always verify critical decisions against source records. #### Can I scrape all historical records? Yes, set a broad date range and a high `maxItems` value. For very large exports, run by year or state to keep exports easier to manage. ### Troubleshooting #### Why did I get zero records? Your filters may be too narrow. Try removing `county`, `railroad`, or `accidentType`, or expand the date range. #### Why is a narrative missing? Some source records may not include narrative text. Make sure `includeNarrative` is enabled. #### Why are some source URLs long? FRA report links include query parameters that identify the report key. Keep them intact for auditability. ### Related scrapers Explore related Automation Lab actors: - https://apify.com/automation-lab/fmcsa-crash-scraper - https://apify.com/automation-lab/msha-mine-safety-scraper - https://apify.com/automation-lab/nhtsa-vehicle-safety-scraper - https://apify.com/automation-lab/bls-labor-statistics-scraper ### Support If you need a field added, a workflow example, or help with a filter, open an issue from the Apify actor page. ### Changelog Initial version extracts FRA rail equipment accident/incident records from the DOT open-data dataset. # Actor input Schema ## `startDate` (type: `string`): Earliest accident date to include (YYYY-MM-DD). ## `endDate` (type: `string`): Latest accident date to include (YYYY-MM-DD). Defaults to today if omitted. ## `state` (type: `string`): Optional state abbreviation or full state name, e.g. TX or Texas. ## `county` (type: `string`): Optional county name filter. ## `railroad` (type: `string`): Optional reporting railroad code or name, e.g. BNSF, CSX, Union Pacific. ## `accidentType` (type: `string`): Optional accident type text such as Derailment, Collision, Fire, or Obstruction. ## `minDamageCost` (type: `integer`): Only include records with total damage cost at or above this amount in USD. ## `includeNarrative` (type: `boolean`): Include the FRA narrative text when available. Turn off for smaller exports. ## `maxItems` (type: `integer`): Maximum accident/incident records to save. ## Actor input object example ```json { "startDate": "2024-01-01", "endDate": "2024-12-31", "state": "TX", "includeNarrative": true, "maxItems": 20 } ``` # Actor output Schema ## `overview` (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 = { "startDate": "2024-01-01", "endDate": "2024-12-31", "state": "TX", "includeNarrative": true, "maxItems": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/fra-railroad-accidents-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "startDate": "2024-01-01", "endDate": "2024-12-31", "state": "TX", "includeNarrative": True, "maxItems": 20, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/fra-railroad-accidents-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startDate": "2024-01-01", "endDate": "2024-12-31", "state": "TX", "includeNarrative": true, "maxItems": 20 }' | apify call automation-lab/fra-railroad-accidents-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/fra-railroad-accidents-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FRA Railroad Accidents Scraper", "description": "Extract FRA railroad accident records with railroads, locations, causes, casualties, damage costs, hazmat flags, and source report links.", "version": "0.1", "x-build-id": "8ZaPER9mcEjkHdP4b" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~fra-railroad-accidents-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-fra-railroad-accidents-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/automation-lab~fra-railroad-accidents-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-fra-railroad-accidents-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/automation-lab~fra-railroad-accidents-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-fra-railroad-accidents-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", "properties": { "startDate": { "title": "Start date", "type": "string", "description": "Earliest accident date to include (YYYY-MM-DD).", "default": "2024-01-01" }, "endDate": { "title": "End date", "type": "string", "description": "Latest accident date to include (YYYY-MM-DD). Defaults to today if omitted." }, "state": { "title": "State", "type": "string", "description": "Optional state abbreviation or full state name, e.g. TX or Texas." }, "county": { "title": "County", "type": "string", "description": "Optional county name filter." }, "railroad": { "title": "Railroad", "type": "string", "description": "Optional reporting railroad code or name, e.g. BNSF, CSX, Union Pacific." }, "accidentType": { "title": "Accident type", "type": "string", "description": "Optional accident type text such as Derailment, Collision, Fire, or Obstruction." }, "minDamageCost": { "title": "Minimum damage cost", "minimum": 0, "type": "integer", "description": "Only include records with total damage cost at or above this amount in USD." }, "includeNarrative": { "title": "Include narrative", "type": "boolean", "description": "Include the FRA narrative text when available. Turn off for smaller exports.", "default": true }, "maxItems": { "title": "Maximum records", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Maximum accident/incident records to save.", "default": 20 } } }, "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 } } } } } } } } } } ```