🚀 🚗 ParkMe Parking Scraper

The ParkMe Parking Scraper collects parking rates and lot details in a clean format that is easy to analyze. It is designed for teams that need clear visibility into parking costs, lot capacity, amenities, and operating context across city areas.

Whether you are benchmarking local parking rates, building mobility insights, planning location strategy, or tracking market changes over time, this actor helps you move from manual checks to structured data you can use immediately. Instead of reviewing lot pages one by one, you get one organized dataset with rate information, location, and lot context ready for reporting, comparisons, and recurring monitoring.

It is a strong fit for operations teams, analysts, marketplace builders, and research workflows that need reliable parking intelligence in a format that is simple to export and easy to work with.

✨ What Does It Do

This actor collects practical parking data so you can act on it right away:

• Lot identity (name, lot ID, URL, thumbnail): Quickly identify and link to each lot in your reports or dashboards without manual lookup
• Location data (address, latitude, longitude, distance): Map lots, filter by proximity, and integrate location context into route planning or real estate analysis
• Rate data (rate card, calculated rate, structured rates, rate summary): Compare parking costs across lots to optimize spend, set competitive rates, or surface affordability insights for end users
• Operator data (operator name, notes, main phone, phone list): Know who runs each lot so you can reach operators directly or attribute market share by company
• Capacity and usage (total spaces, handicap spaces, occupancy): Understand availability patterns and capacity constraints that affect user decisions
• Availability context (hours, detailed hours, reservation signal): Show users when lots are open and whether they can book in advance, reducing friction in booking flows
• Quality signals (review score, review count): Incorporate ratings into recommendation logic or quality-tier filtering
• Lot context (lot type, lot category, notes, subtitle lines, policies, payment types): Surface key lot details like covered parking, EV charging, or accepted payment methods to support informed comparisons

🎬 Demo Video

Demo video coming soon.

🔧 Input

Use one search area input:

• City (city): Example New York, NY
• Bounding Box (bbox): Format swLat|swLng|neLat|neLng

Then configure run options:

• Max Items (maxItems): Number of lots to collect
• Rate Type (rateRequest): R (Regular), B (Best Available), L (Monthly)
• Parking Duration (duration): Duration in minutes
• Entry Time (entryTime): Optional value in YYYY-MM-DDTHH:MM

Example input:

{
  "maxItems": 10,
  "city": "New York, NY",
  "rateRequest": "R",
  "duration": 60,
  "entryTime": "2026-03-01T09:00"
}

📊 Output

Each dataset item contains rate, location, and detail fields for one parking lot.

Example output:

{
  "thumbnailUrl": "https://images.example.com/lot.jpg",
  "id": "12467",
  "name": "450 W 38th St Garage",
  "address": "450 W 38th St, New York, NY 10018",
  "buildingAddress": "450 W 38th St, New York, NY 10018, US",
  "latitude": 40.7542,
  "longitude": -73.9885,
  "rateCard": ["1 Hour: $21.15", "2 Hours: $38.50"],
  "calculatedRate": 21.15,
  "structuredRates": [],
  "ratesSummary": "Up to 1 hour: $21.15",
  "currency": "USD",
  "phone": "(212) 555-0123",
  "phones": ["(212) 555-0123", "(212) 555-0000"],
  "operator": "Sample Operator",
  "operatorNote": null,
  "amenities": ["Covered", "EV Charging"],
  "paymentTypes": ["Credit Card", "Cash"],
  "spacesTotal": 450,
  "handicapSpacesTotal": 8,
  "occupancy": 27,
  "reviewCount": 327,
  "reviewScore": 4.5,
  "hours": "Mon-Fri: 6am-12am; Sat-Sun: 7am-12am",
  "hoursOfOperation": "06:00-00:00; 07:00-00:00",
  "lotType": "Subterranean",
  "lotCategory": "Public Parking",
  "note": "Valet Only",
  "reservations": true,
  "policies": [],
  "distance": 4657,
  "subtitleOne": "221 W 10th St",
  "subtitleTwo": "New York, NY 10014, US",
  "url": "https://www.parkme.com/lot/16540/221-w-10th-st",
  "scrapedAt": "2026-02-24T22:00:00.000Z"
}

💎 Why Choose This Actor

• Simple setup with practical filters
• Strong coverage for both rate data and lot context
• Clean structure for analytics and reporting
• Works for quick checks and recurring collection

📋 How to Use

1. Sign up: Create a free account w/ $5 credit
2. Open the actor on Apify.
3. Provide city or bbox.
4. Set maxItems, rateRequest, duration, and optional entryTime.
5. Start the run.
6. Download results from the dataset in JSON, CSV, or Excel.

🎯 Business Use Cases

• Parking operators and garage managers use this actor to monitor competitor rates in their neighborhood and adjust their own rates to stay competitive without manual daily checks.
• Mobility app developers pull structured lot data to power in-app parking search, show real-time availability, and let users compare nearby garages before they leave home.
• Commercial real estate analysts use lot occupancy, capacity, and coverage data to evaluate parking supply when assessing sites for retail or mixed-use development.
• Urban planners and city teams track parking inventory and rate trends across districts to inform policy decisions and transportation demand management programs.
• Travelers and frequent commuters automate weekly spot checks on parking options near their destinations to budget trip costs and avoid last-minute surprises.

❓ FAQ

Can I use city and bbox in the same run?

Yes. You can provide both in one run, but bbox is treated as the final search area when present. This is useful when you want precise geographic control and still keep a city value for reference.

Does this actor include lot details or only listing data?

It includes both. The actor first collects listing results and then enriches each lot with additional detail fields so you get a more complete output per item in one dataset.

Why are some fields null or empty for certain lots?

Some lots do not publish every field in the source data. When a value is not provided, the actor returns null or an empty array instead of guessing, so your dataset stays accurate and consistent.

What is a good prefill to start with?

Use this tested starter setup: city = New York, NY, rateRequest = R, duration = 60, entryTime = 2026-03-01T09:00, maxItems = 10. It gives stable results and is a good baseline before trying larger runs.

Do free users have limits?

Yes. Free users can collect up to 100 items per run. If a higher maxItems is provided, the run is automatically capped to the free limit.

Can I schedule recurring runs?

Yes. You can schedule daily, weekly, or custom recurring runs directly in Apify. Scheduled runs are useful for rate tracking and trend monitoring over time.

🔗 Integrate ParkMe Scraper with any app

Connect this actor with your favorite tools:

• Make - Automate workflows
• Zapier - Connect 5000+ apps
• GitHub - Version control integration
• Slack - Get notifications
• Airbyte - Data pipelines
• Google Drive - Export to spreadsheets

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🆘 Need Help?

If you need help using this actor, want a custom data workflow, or want to request a new scraper, contact us through the form below.

📞 Contact

Contact us to request a new scraper, propose a custom data project, or report a technical issue:

https://tally.so/r/BzdKgA

⚠️ Disclaimer

This actor is an independent tool and is not affiliated with, endorsed by, or sponsored by ParkMe.