# Tokyo Train Delay & Disruption Data (`shiokoshi356/tokyo-train-delay`) Actor Real-time Tokyo train delay and disruption data from ODPT. Covers Toei, Metro, JR East and private railways. All output in English with operator and line details. - **URL**: https://apify.com/shiokoshi356/tokyo-train-delay.md - **Developed by:** [Shiokoshi356](https://apify.com/shiokoshi356) (community) - **Categories:** Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Tokyo Train Delay Real-time Tokyo train delay and disruption data from ODPT. Covers Toei, Metro, JR East and private railways. All output in English with operator and line details. ### What does this actor do? This actor extracts structured data and outputs it in a clean, machine-readable JSON format. All results are available via the Apify API and can be exported to CSV, JSON, Excel, and other formats. ### Input parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | Operator | string | No | - | Filter by operator name (e.g. 'Toei', 'Tokyo Metro', 'JR East'). Leave empty for all operators. | | Include Normal | boolean | No | False | Include lines with no delays (normal operation). | | Max Results | integer | No | 100 | Maximum number of results to return. | | Log Level | string | No | INFO | Logging verbosity. Options: `DEBUG`, `INFO`, `WARNING`, `ERROR` | ### Output format Each result item contains the following fields: ```json { "record_id": "...", "operator": "...", "operator_id": "...", "railway_line": "...", "railway_id": "...", "status": "...", "status_text": "...", "status_text_ja": "...", "cause": "...", "direction": "...", "transfer_railways": "...", "valid_from": "...", "recorded_at": "...", "data_source": "...", "OPERATOR_NAMES": "...", "RAILWAY_NAMES": "...", "status_text_ja": "...", "transfer_railways": "..." } ```` ### How to use #### Via Apify Console 1. Navigate to this actor's page on Apify Store 2. Configure the input parameters 3. Click **Start** and wait for the run to complete 4. Download results from the **Dataset** tab #### Via Apify API ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("").call(run_input={ "max_results": 100 }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` ### Integrations This actor can be connected to various cloud services and web apps using integrations. You can integrate with services like Google Sheets, Slack, Zapier, Make, GitHub, and many more. ### Pricing This actor uses a pay-per-result pricing model at approximately $5 per 1,000 results. Actual costs may vary based on the complexity of the data extraction. # Actor input Schema ## `operator` (type: `string`): Filter by operator name (e.g. 'Toei', 'Tokyo Metro', 'JR East'). Leave empty for all operators. ## `include_normal` (type: `boolean`): Include lines with no delays (normal operation). ## `max_results` (type: `integer`): Maximum number of results to return. ## `log_level` (type: `string`): Logging verbosity. ## Actor input object example ```json { "include_normal": false, "max_results": 100, "log_level": "INFO" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("shiokoshi356/tokyo-train-delay").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("shiokoshi356/tokyo-train-delay").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 '{}' | apify call shiokoshi356/tokyo-train-delay --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shiokoshi356/tokyo-train-delay", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Tokyo Train Delay & Disruption Data", "description": "Real-time Tokyo train delay and disruption data from ODPT. Covers Toei, Metro, JR East and private railways. All output in English with operator and line details.", "version": "0.1", "x-build-id": "AV3zcexLgY9Ya0Kq3" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shiokoshi356~tokyo-train-delay/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shiokoshi356-tokyo-train-delay", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/shiokoshi356~tokyo-train-delay/runs": { "post": { "operationId": "runs-sync-shiokoshi356-tokyo-train-delay", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/shiokoshi356~tokyo-train-delay/run-sync": { "post": { "operationId": "run-sync-shiokoshi356-tokyo-train-delay", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "operator": { "title": "Operator", "type": "string", "description": "Filter by operator name (e.g. 'Toei', 'Tokyo Metro', 'JR East'). Leave empty for all operators." }, "include_normal": { "title": "Include Normal", "type": "boolean", "description": "Include lines with no delays (normal operation).", "default": false }, "max_results": { "title": "Max Results", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of results to return.", "default": 100 }, "log_level": { "title": "Log Level", "enum": [ "DEBUG", "INFO", "WARNING", "ERROR" ], "type": "string", "description": "Logging verbosity.", "default": "INFO" } } }, "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 } } } } } } } } } } ```