# Google Distance Matrix Scraper (`seemuapps/google-distance-matrix-scraper`) Actor Calculate travel time, distance, and traffic-aware ETAs between any list of origins and destinations using the official Google Distance Matrix API. - **URL**: https://apify.com/seemuapps/google-distance-matrix-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** AI, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $12.00 / 1,000 origin destination pairs 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 ## Google Distance Matrix Scraper Bulk-calculate travel time and distance between any list of origins and destinations using the official Google Distance Matrix API. Get traffic-aware driving ETAs, transit fares, walking and cycling times — one dataset row per origin×destination pair. ### What you get - **One dataset row per origin×destination pair** — every origin paired with every destination - **Travel distance** in meters and human-readable text - **Travel duration** in seconds and human-readable text - **Traffic-aware ETAs** when you set a departure time — duration in traffic for driving mode - **Transit fares** with value and currency when mode is Transit - **All four travel modes** — driving, walking, bicycling, transit - **Auto-chunking** — large matrices are automatically split into 10×10 batches to respect Google's 100-element-per-request limit, then merged back into one dataset - **Per-pair error handling** — bad inputs or unreachable pairs get a row with `status` like `NOT_FOUND` or `ZERO_RESULTS`; the run continues ### Use cases - **Delivery & logistics routing** — pre-calculate drive times from warehouses to every customer, traffic included - **Site selection** — find the candidate location with the shortest average drive time to a list of points of interest - **Field service dispatch** — match technicians to jobs by drive time - **Real estate commute analysis** — calculate transit/drive time from listings to a list of workplaces, gyms, schools - **Bulk ETA APIs** — feed your CRM with up-to-date drive times to every account - **Travel research** — compare driving vs transit vs cycling time for a list of trips ### How to use 1. Paste your **Origins** — addresses or `lat,lng` pairs, one per line. 2. Paste your **Destinations** — addresses or `lat,lng` pairs, one per line. 3. Pick a **Travel mode** — Driving, Walking, Bicycling, or Transit. 4. (Optional) Toggle **Units** between Metric (km) and Imperial (mi). 5. (Optional) For traffic-aware driving, set **Departure time** to `now` or a Unix-epoch timestamp. 6. (Optional) For transit, set **Departure time** or **Arrival time** and pick **Transit modes** (bus, subway, etc.). 7. (Optional) Set **Avoid** to exclude tolls, highways, ferries, or indoor segments. 8. Run — every origin×destination pair appears as its own row, ready to export as CSV or JSON. ### Examples **Drive-time matrix for a small fleet:** - Origins: 3 warehouse addresses - Destinations: 50 customer addresses - Result: 150 rows, each with driving distance and ETA **Transit fare comparison for one trip across cities:** - Origins: `Penn Station, NYC` - Destinations: `Times Square, NYC`, `JFK Airport, NYC`, `Brooklyn Museum, NYC` - Mode: Transit · Departure time: `now` - Result: 3 rows with transit duration and fare per destination **Traffic-aware drive time at rush hour:** - Set **Departure time** to a future epoch timestamp (e.g. tomorrow 8 AM) - Set **Traffic model** to `pessimistic` for worst-case ETAs ### Output schema | Field | Type | Description | |---|---|---| | `origin` | string | Input origin | | `destination` | string | Input destination | | `originResolved` | string \| null | Google's resolved origin address | | `destinationResolved` | string \| null | Google's resolved destination address | | `mode` | string | Travel mode used | | `status` | string | `OK`, `NOT_FOUND`, `ZERO_RESULTS`, `MAX_ROUTE_LENGTH_EXCEEDED`, `ERROR`, etc. | | `distanceMeters` | number \| null | Distance in meters | | `distanceText` | string \| null | Human-readable distance ("3.2 mi", "5.1 km") | | `durationSeconds` | number \| null | Duration in seconds | | `durationText` | string \| null | Human-readable duration ("12 mins", "1 hour 5 mins") | | `durationInTrafficSeconds` | number \| null | Duration with traffic (driving + departureTime only) | | `durationInTrafficText` | string \| null | Human-readable traffic duration | | `fareValue` | number \| null | Transit fare value | | `fareCurrency` | string \| null | Transit fare currency code | | `fareText` | string \| null | Transit fare display text | | `error` | string \| null | Error message if `status` is not `OK` | ### Tips - **Cost scales with elements**, not requests — 25 origins × 25 destinations is 625 elements regardless of how many calls the actor makes under the hood. - For traffic-aware ETAs you **must** set `departureTime` — without it, Google returns "typical" duration only. - `duration_in_traffic` only appears for **driving** mode with **departureTime** set. - `lat,lng` inputs skip address resolution and are slightly faster + more reliable than free-text addresses. - For very large matrices (1000+ pairs), run multiple jobs in parallel — each Apify run handles its own chunking. # Actor input Schema ## `origins` (type: `array`): Origin addresses or 'lat,lng' pairs. The actor auto-chunks large lists into 10x10 batches to respect the API's 100-element-per-request limit. ## `destinations` (type: `array`): Destination addresses or 'lat,lng' pairs. Every origin is paired with every destination, producing one dataset row per pair. ## `mode` (type: `string`): How the route between each origin and destination is calculated. Use Transit for public-transport ETAs and fares. ## `units` (type: `string`): Units used for the human-readable distance and duration text. Numeric meters and seconds are always returned alongside. ## `avoid` (type: `array`): Features to avoid: tolls, highways, ferries, indoor. Driving/walking/bicycling modes only. ## `departureTime` (type: `string`): Departure time for traffic-aware ETAs. Use 'now' or a Unix-epoch timestamp in seconds. Required for transit and for traffic-aware driving. ## `arrivalTime` (type: `string`): Arrival time as a Unix-epoch timestamp in seconds. Transit mode only. Mutually exclusive with departure time. ## `trafficModel` (type: `string`): Traffic prediction model. Only applied when Departure time is set and mode is driving. ## `transitMode` (type: `array`): Transit modes to consider: bus, subway, train, tram, rail. Transit mode only. ## `language` (type: `string`): BCP-47 language code for distance/duration text (e.g. en, es, fr). ## `region` (type: `string`): Two-letter region code to bias address resolution (e.g. us, gb, de). ## Actor input object example ```json { "origins": [ "1600 Amphitheatre Pkwy, Mountain View, CA", "1 Apple Park Way, Cupertino, CA" ], "destinations": [ "SFO Airport, San Francisco, CA", "Stanford University, CA" ], "mode": "driving", "units": "metric", "trafficModel": "best_guess", "language": "en" } ```` # Actor output Schema ## `results` (type: `string`): One record per origin×destination pair: origin, destination, originResolved, destinationResolved, mode, status, distanceMeters, distanceText, durationSeconds, durationText, durationInTrafficSeconds, durationInTrafficText, fareValue, fareCurrency, fareText, error. # 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 = { "origins": [ "1600 Amphitheatre Pkwy, Mountain View, CA", "1 Apple Park Way, Cupertino, CA" ], "destinations": [ "SFO Airport, San Francisco, CA", "Stanford University, CA" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/google-distance-matrix-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 = { "origins": [ "1600 Amphitheatre Pkwy, Mountain View, CA", "1 Apple Park Way, Cupertino, CA", ], "destinations": [ "SFO Airport, San Francisco, CA", "Stanford University, CA", ], } # Run the Actor and wait for it to finish run = client.actor("seemuapps/google-distance-matrix-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 '{ "origins": [ "1600 Amphitheatre Pkwy, Mountain View, CA", "1 Apple Park Way, Cupertino, CA" ], "destinations": [ "SFO Airport, San Francisco, CA", "Stanford University, CA" ] }' | apify call seemuapps/google-distance-matrix-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/google-distance-matrix-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Distance Matrix Scraper", "description": "Calculate travel time, distance, and traffic-aware ETAs between any list of origins and destinations using the official Google Distance Matrix API.", "version": "1.0", "x-build-id": "gSHDuAHHv0Dxby1pH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~google-distance-matrix-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-google-distance-matrix-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~google-distance-matrix-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-google-distance-matrix-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~google-distance-matrix-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-google-distance-matrix-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "origins", "destinations" ], "properties": { "origins": { "title": "Origins", "type": "array", "description": "Origin addresses or 'lat,lng' pairs. The actor auto-chunks large lists into 10x10 batches to respect the API's 100-element-per-request limit.", "items": { "type": "string" } }, "destinations": { "title": "Destinations", "type": "array", "description": "Destination addresses or 'lat,lng' pairs. Every origin is paired with every destination, producing one dataset row per pair.", "items": { "type": "string" } }, "mode": { "title": "Travel mode", "enum": [ "driving", "walking", "bicycling", "transit" ], "type": "string", "description": "How the route between each origin and destination is calculated. Use Transit for public-transport ETAs and fares.", "default": "driving" }, "units": { "title": "Units", "enum": [ "metric", "imperial" ], "type": "string", "description": "Units used for the human-readable distance and duration text. Numeric meters and seconds are always returned alongside.", "default": "metric" }, "avoid": { "title": "Avoid", "type": "array", "description": "Features to avoid: tolls, highways, ferries, indoor. Driving/walking/bicycling modes only.", "items": { "type": "string" } }, "departureTime": { "title": "Departure time", "type": "string", "description": "Departure time for traffic-aware ETAs. Use 'now' or a Unix-epoch timestamp in seconds. Required for transit and for traffic-aware driving." }, "arrivalTime": { "title": "Arrival time (transit only)", "type": "string", "description": "Arrival time as a Unix-epoch timestamp in seconds. Transit mode only. Mutually exclusive with departure time." }, "trafficModel": { "title": "Traffic model", "enum": [ "best_guess", "pessimistic", "optimistic" ], "type": "string", "description": "Traffic prediction model. Only applied when Departure time is set and mode is driving.", "default": "best_guess" }, "transitMode": { "title": "Transit mode", "type": "array", "description": "Transit modes to consider: bus, subway, train, tram, rail. Transit mode only.", "items": { "type": "string" } }, "language": { "title": "Language", "type": "string", "description": "BCP-47 language code for distance/duration text (e.g. en, es, fr).", "default": "en" }, "region": { "title": "Region", "type": "string", "description": "Two-letter region code to bias address resolution (e.g. us, gb, de)." } } }, "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 } } } } } } } } } } ```