# Uber Cars Nearby Scraper (`saswave/uber-cars-nearby-scraper`) Actor Geospatial extraction tool designed to surface real-time vehicle positioning data from Uber's web application layer. Map the active supply of nearby drivers, providing invaluable metadata such as vehicle orientation, estimated times of arrival (ETA), and dispatch types. - **URL**: https://apify.com/saswave/uber-cars-nearby-scraper.md - **Developed by:** [SASWAVE](https://apify.com/saswave) (community) - **Categories:** Automation, Developer tools, Lead generation - **Stats:** 6 total users, 3 monthly users, 16.1% runs succeeded, 1 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. 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/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 ### Uber Nearby Cars Scraper The Uber Nearby Cars Scraper is a high-precision geospatial extraction tool designed to surface real-time vehicle positioning data from Uber's web application layer. By passing specific latitude and longitude coordinates, the scraper maps the active supply of nearby drivers, providing invaluable metadata such as vehicle orientation, estimated times of arrival (ETA), and dispatch types. ### FEATURE Precise Vehicle Positioning: Extract exact coordinate positions (latitude and longitude) of active cars circling a target location. Orientation & Heading Tracking: Capture the vehicle's bearing angle (0° to 360°) to determine which direction the car is facing or traveling. Live Hyper-Local ETAs: Retrieve instant proximity calculations (etaInMin and etaStringShort) to measure immediate driver availability. Vehicle Tier Mapping: Identify vehicle classifications through internal type IDs and secure cloud-hosted vehicle marker icons (mapImageUrl). ### USE CASES Supply & Demand Analytics: Track driver density across different municipal zones or pickup points at varying hours to calculate historical supply trends. Dynamic Competitive Intelligence: Compare Uber's hyper-local vehicle availability and response times directly against alternative ride-hailing applications. Smart City & Traffic Analysis: Monitor real-time fleet movement patterns in major urban bottlenecks to study congestion or spatial distribution. Real Estate Proximity Auditing: Evaluate the "commute score" or transit convenience of properties by calculating average Uber response times at those locations throughout the week. Automated Dispatch Optimization: Build predictive dashboards that notify field teams of the optimal moments to step outside based on immediate vehicle density. ### OUTPUT ```json { "bearing": 316, "coordinate": { "latitude": 48.85504, "longitude": 2.3419, "__typename": "RVWebCommonCoordinate" }, "etaInMin": 4, "etaStringShort": "4 mins", "id": 35, "mapImageUrl": "https://d1a3f4spazzrp4.cloudfront.net/car-types/mapIconsStandard/car_bag_x_2d.png", "__typename": "RVWebCommonVehicle" } ```` ### 🛟 SUPPORT Share your runs with the developer team and create issues on error to help us improve actor quality. You might discover edge case we didn't test yet We stay available anytime # Actor input Schema ## `location` (type: `object`): Use the same text as provided in search url ## Actor input object example ```json { "location": { "latitude": 48.8564826, "longitude": 2.3524135 } } ``` # Actor output Schema ## `results` (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 = { "location": { "latitude": 48.8564826, "longitude": 2.3524135 } }; // Run the Actor and wait for it to finish const run = await client.actor("saswave/uber-cars-nearby-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 = { "location": { "latitude": 48.8564826, "longitude": 2.3524135, } } # Run the Actor and wait for it to finish run = client.actor("saswave/uber-cars-nearby-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 '{ "location": { "latitude": 48.8564826, "longitude": 2.3524135 } }' | apify call saswave/uber-cars-nearby-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=saswave/uber-cars-nearby-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Uber Cars Nearby Scraper", "description": "Geospatial extraction tool designed to surface real-time vehicle positioning data from Uber's web application layer. Map the active supply of nearby drivers, providing invaluable metadata such as vehicle orientation, estimated times of arrival (ETA), and dispatch types.", "version": "0.0", "x-build-id": "TyqX37ePqcXxZLeIv" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/saswave~uber-cars-nearby-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-saswave-uber-cars-nearby-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/saswave~uber-cars-nearby-scraper/runs": { "post": { "operationId": "runs-sync-saswave-uber-cars-nearby-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/saswave~uber-cars-nearby-scraper/run-sync": { "post": { "operationId": "run-sync-saswave-uber-cars-nearby-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "location" ], "properties": { "location": { "title": "From location", "type": "object", "description": "Use the same text as provided in search url" } } }, "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 } } } } } } } } } } ```