# Google Maps Traffic Scraper (`romy/google-maps-traffic-scraper`) Actor Fetch real-time and historical traffic data from Google Maps as GeoJSON. Returns road-level speed levels, congestion status, and road segments for any coordinate and zoom level. - **URL**: https://apify.com/romy/google-maps-traffic-scraper.md - **Developed by:** [Romy](https://apify.com/romy) (community) - **Categories:** Automation, Developer tools, Integrations - **Stats:** 21 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 tile fetcheds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 Traffic Scraper Fetch real-time traffic data from Google Maps as GeoJSON — road-level speed levels, congestion status, and road segments for any coordinate and zoom level. Powered by Google Maps internal tile API. Data is returned as a **GeoJSON FeatureCollection** with LineString geometries, ready to plug into Mapbox, Leaflet, QGIS, or any GIS tool. --- ### Input ```json { "lat": "-6.219320790773214", "lon": "106.81002294222947", "zoom": 17, "radius": 2, "seconds": 391039 } ```` | Field | Type | Required | Description | | --------- | ------ | -------- | -------------------------------------------------- | | `lat` | string | ✅ | Latitude of the center point | | `lon` | string | ✅ | Longitude of the center point | | `zoom` | int | ✅ | Map zoom level (10–20). Higher = more detail | | `radius` | int | ✅ | Tile radius around center. Total tiles = `(2r+1)²` | | `seconds` | int | ❌ | Seconds into week (see below). `-1` = current time | *** #### `zoom` — choosing the right level | Zoom | Coverage per tile | Best for | | ----- | ------------------ | ---------------------------------- | | 10–12 | City-wide | Heatmap, macro overview | | 13–15 | District / kawasan | Area analysis | | 16–17 | Street level | Per-road congestion ✅ recommended | | 18–20 | Lane level | High-detail, niche use cases | > **Recommended:** `zoom: 17` for street-level detail with reasonable tile count. *** #### `radius` — area coverage Radius controls how many tiles are fetched around the center point. | Radius | Tiles fetched | Approx. area (zoom 17) | | ------ | ------------- | ----------------------------- | | 0 | 1 | ~300m × 300m | | 1 | 9 | ~900m × 900m | | 2 | 25 | ~1.5km × 1.5km ✅ recommended | | 3 | 49 | ~2.2km × 2.2km | | 5 | 121 | ~3.7km × 3.7km | > Higher radius = more HTTP requests = higher cost and longer run time. *** #### `seconds` — historical traffic Pass `-1` to get **current live traffic**. To fetch traffic for a **specific time in the week**, convert a datetime to seconds-into-week (Sunday 00:00 UTC = 0): ```python from datetime import datetime, timezone def datetime_to_seconds_into_week(dt: datetime) -> int: """Convert datetime (aware) → seconds_into_week (Sunday 00:00 UTC = 0).""" dt_utc = dt.astimezone(timezone.utc) day_utc = (dt_utc.weekday() + 1) % 7 # Sun=0, Mon=1, ..., Sat=6 return (day_utc * 86400 + dt_utc.hour * 3600 + dt_utc.minute * 60 + dt_utc.second) ## Example: Monday 08:00 WIB (UTC+7) → UTC = Monday 01:00 dt = datetime(2024, 1, 8, 8, 0, 0, tzinfo=timezone.utc) # already UTC here seconds = datetime_to_seconds_into_week(dt) ## → 86400 + 3600 = 90000 (Monday 01:00 UTC) ``` > **Note:** Google Maps traffic data is based on historical weekly patterns. The `seconds` value represents a point in the weekly cycle, not an absolute timestamp — so the same value will return the same traffic pattern every week. *** ### Output Returns a **GeoJSON FeatureCollection**: ![Screenshot-2026-02-23-005343](https://i.ibb.co.com/wZdFVYYF/Screenshot-2026-02-23-005343.png) ![Screenshot-2026-02-23-005418](https://i.ibb.co.com/tw3kKQ1B/Screenshot-2026-02-23-005418.png) ```json { "type": "FeatureCollection", "metadata": { "center_lat": -6.219320790773214, "center_lon": 106.81002294222947, "zoom": 17, "tile_radius": 2, "tiles_fetched": 25, "total_features": 487, "seconds_in_week": 391039, "speed_summary": { "Free Flow": 210, "Slow Traffic": 180, "Heavy Congestion": 97 } }, "features": [ { "type": "Feature", "geometry": { "type": "LineString", "coordinates": [ [106.8100123, -6.2193456], [106.8101234, -6.2194567] ] }, "properties": { "speed_level": 2, "speed_label": "Slow Traffic", "color": "#FF8C00", "road_class": 32, "stroke_weight": 5, "seconds_in_week": 391039 } } ] } ``` #### Speed levels | `speed_level` | Label | Color | | ------------- | ------------------- | ------------ | | 1 | Free Flow | `#2DB82D` 🟢 | | 2 | Slow Traffic | `#FF8C00` 🟠 | | 3 | Moderate Congestion | `#FF4500` 🔴 | | 4 | Heavy Congestion | `#CC0000` 🔴 | | 6 | Road Closed | `#000000` ⚫ | *** ### Pricing This Actor uses **Pay Per Event** billing — you only pay for what you use. | Event | Price | Description | | ------------------- | -------- | ---------------------------- | | `apify-actor-start` | $0.00005 | Per run start | | `tile-fetched` | $0.010 | Per tile fetched from Google | Cost is fully **predictable before you run** — it depends only on the `radius` you choose: | Radius | Tiles | Cost per request | | ------ | ----- | ---------------- | | 0 | 1 | ~$0.01 | | 1 | 9 | ~$0.09 | | 2 | 25 | ~$0.25 ✅ | | 3 | 49 | ~$0.49 | | 5 | 121 | ~$1.21 | > No surprise charges — tile count is always `(2 × radius + 1)²`, known upfront. *** ### Use cases - **Logistics & delivery** — route optimization based on real congestion data - **Urban planning** — traffic pattern analysis by time of day / day of week - **Real estate** — congestion scoring for property valuation - **Retail analytics** — foot traffic and accessibility scoring near stores - **Insurance** — road risk scoring by location and time *** ### Notes - Data reflects Google Maps traffic patterns and is updated periodically by Google. - Historical data (`seconds` parameter) is based on weekly recurring patterns, not archived snapshots. - Higher zoom levels and larger radius values increase both cost and run time proportionally. # Actor input Schema ## `lat` (type: `string`): Latitude of the center point (e.g. -6.2193) ## `lon` (type: `string`): Longitude of the center point (e.g. 106.8100) ## `zoom` (type: `integer`): Map zoom level (10-20). 10-12 = city wide, 13-15 = district, 16-17 = street level (recommended), 18-20 = lane level. ## `radius` (type: `integer`): Tile radius around center. Total tiles = (2r+1)^2. Cost = tiles x $0.010. radius 0=1 tile ($0.01), radius 1=9 tiles ($0.09), radius 2=25 tiles ($0.25), radius 3=49 tiles ($0.49), radius 5=121 tiles ($1.21). ## `seconds` (type: `integer`): Seconds into the week for historical traffic (Sunday 00:00 UTC = 0). Use -1 for current live traffic. ## Actor input object example ```json { "lat": "-6.219320790773214", "lon": "106.81002294222947", "zoom": 17, "radius": 2, "seconds": -1 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("romy/google-maps-traffic-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("romy/google-maps-traffic-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 '{}' | apify call romy/google-maps-traffic-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=romy/google-maps-traffic-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Maps Traffic Scraper", "description": "Fetch real-time and historical traffic data from Google Maps as GeoJSON. Returns road-level speed levels, congestion status, and road segments for any coordinate and zoom level.", "version": "0.0", "x-build-id": "l7aHsgGkkQwCXShVC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/romy~google-maps-traffic-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-romy-google-maps-traffic-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/romy~google-maps-traffic-scraper/runs": { "post": { "operationId": "runs-sync-romy-google-maps-traffic-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/romy~google-maps-traffic-scraper/run-sync": { "post": { "operationId": "run-sync-romy-google-maps-traffic-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "lat", "lon", "zoom", "radius" ], "properties": { "lat": { "title": "Latitude", "type": "string", "description": "Latitude of the center point (e.g. -6.2193)", "default": "-6.219320790773214" }, "lon": { "title": "Longitude", "type": "string", "description": "Longitude of the center point (e.g. 106.8100)", "default": "106.81002294222947" }, "zoom": { "title": "Zoom Level", "minimum": 10, "maximum": 20, "type": "integer", "description": "Map zoom level (10-20). 10-12 = city wide, 13-15 = district, 16-17 = street level (recommended), 18-20 = lane level.", "default": 17 }, "radius": { "title": "Tile Radius", "minimum": 0, "maximum": 5, "type": "integer", "description": "Tile radius around center. Total tiles = (2r+1)^2. Cost = tiles x $0.010. radius 0=1 tile ($0.01), radius 1=9 tiles ($0.09), radius 2=25 tiles ($0.25), radius 3=49 tiles ($0.49), radius 5=121 tiles ($1.21).", "default": 2 }, "seconds": { "title": "Seconds into Week", "type": "integer", "description": "Seconds into the week for historical traffic (Sunday 00:00 UTC = 0). Use -1 for current live traffic.", "default": -1 } } }, "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 } } } } } } } } } } ```