# Uber Ride Scraper (`saswave/uber-ride-scraper`) Actor Mobility intelligence tool built to extract live fare estimates, ETA metrics, and dynamic surge pricing data directly from Uber’s marketplace layer. With precise origin and destination details, the scraper systematically breaks down every available ride tier. Audit localized transportation cost live - **URL**: https://apify.com/saswave/uber-ride-scraper.md - **Developed by:** [SASWAVE](https://apify.com/saswave) (community) - **Categories:** Automation, Developer tools, Lead generation - **Stats:** 8 total users, 5 monthly users, 86.9% runs succeeded, 2 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.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 ### Uber Ride Scraper The Uber Ride Scraper is a professional-grade mobility intelligence tool built to extract live fare estimates, ETA metrics, and dynamic surge pricing data directly from Uber’s marketplace layer. By supplying precise origin and destination details, the scraper systematically breaks down every available ride tier—from standard commuter rides to premium multi-passenger options—allowing you to audit localized transportation costs in real time. ### FEATURE Multi-Tier Fare Breakdown: Grab complete catalog quotes across all active localized services (such as UberX, Uber Green, Comfort, or Berline). Surge & High-Demand Detection: Monitor structural pricing alerts (rank_pricing_text) to detect when active surge Multipliers or high-demand pricing rules are modifying base costs. Granular Time Parameters: Capture the exact expected trip duration in seconds (trip_time) alongside live driver proximity counters (mins_away). Promotional Pricing Diagnostics: Automatically inspect if specific active promotions (has_promo), ride discounts (discount), or alternative passes are actively applied to a quote. Operational Availability Auditing: Check if certain tiers are closed or restricted in real-time using the boolean is_available flag. Value-Add Fleet Indicators: Extract structural tags and priority labels (badges) such as "Faster" or "Top Rated" to isolate priority transit modes. ### USE CASES Logistics & Ride-Hailing Aggregators: Populate third-party fare comparison search engines, multi-modal travel assistants, or enterprise logistics platforms with live Uber quotes. Surge & Demand Curve Analytics: Monitor specific airport corridors or commercial zones at regular intervals to map daily surge patterns and benchmark peak hours. Corporate Travel Expense Optimization: Cross-reference employee business travel receipts with real-time historical market baselines to eliminate cost leakage. Competitive Transportation Intelligence: Build a high-fidelity data bridge comparing alternative private networks (like Bolt, FreeNow, or regional taxi fleets) directly with Uber's pricing matrix. ### OUTPUT ```json { "capacity": 4, "discount": "", "fare": "€30.93", "has_promo": false, "has_ride_pass": false, "suggested_tip": null, "tier_name": "Rides we think you'll like", "title": "UberX", "description": "Affordable everyday trips", "trip_time": 1747, "mins_away": 3, "is_available": true, "rank_pricing_text": "Fares are higher due to increased demand", "badges": [ "Faster" ] } ```` ### 🛟 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 ## `pickup` (type: `object`): Use the same text as provided in search url ## `dropoff` (type: `object`): Use the same text as provided in search url ## Actor input object example ```json { "pickup": { "latitude": 48.8564826, "longitude": 2.3524135 }, "dropoff": { "latitude": 48.858370099999995, "longitude": 2.2944812999999997 } } ``` # 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 = { "pickup": { "latitude": 48.8564826, "longitude": 2.3524135 }, "dropoff": { "latitude": 48.858370099999995, "longitude": 2.2944812999999997 } }; // Run the Actor and wait for it to finish const run = await client.actor("saswave/uber-ride-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 = { "pickup": { "latitude": 48.8564826, "longitude": 2.3524135, }, "dropoff": { "latitude": 48.858370099999995, "longitude": 2.2944812999999997, }, } # Run the Actor and wait for it to finish run = client.actor("saswave/uber-ride-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 '{ "pickup": { "latitude": 48.8564826, "longitude": 2.3524135 }, "dropoff": { "latitude": 48.858370099999995, "longitude": 2.2944812999999997 } }' | apify call saswave/uber-ride-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=saswave/uber-ride-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Uber Ride Scraper", "description": "Mobility intelligence tool built to extract live fare estimates, ETA metrics, and dynamic surge pricing data directly from Uber’s marketplace layer. With precise origin and destination details, the scraper systematically breaks down every available ride tier. Audit localized transportation cost live", "version": "0.0", "x-build-id": "Clq5bwdztfS4xkFZk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/saswave~uber-ride-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-saswave-uber-ride-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/saswave~uber-ride-scraper/runs": { "post": { "operationId": "runs-sync-saswave-uber-ride-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/saswave~uber-ride-scraper/run-sync": { "post": { "operationId": "run-sync-saswave-uber-ride-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "pickup", "dropoff" ], "properties": { "pickup": { "title": "From location", "type": "object", "description": "Use the same text as provided in search url" }, "dropoff": { "title": "To 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 } } } } } } } } } } ```