Trip Scraper extracts accommodation data from Trip.com — fetching hotel and lodging listings along with details like pricing, room availability, amenities, and more. It’s tailored for travel analysts, hotel market researchers, or any project needing structured accommodation data at scale.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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When you need bulk data on hotels and lodgings, manually browsing isn’t practical. Trip Scraper automates crawling from Trip.com — reading search result pages or individual property URLs — and outputs structured datasets containing useful hotel information. It works for hotels, apartments, and any accommodation listed there.
- Accepts search result URLs (with filters like dates, location, stars, amenities) or individual property URLs as input.
- Crawls through matching accommodations and extracts relevant listing data.
- Outputs a clean dataset with hotel metadata, pricing, room options, amenities, and more.
| Feature | Description |
|---|---|
| Flexible Input | Accepts either search result URLs or individual property URLs (startUrls). |
| Bulk Extraction | Can scrape many hotels in one run — configurable via maxHotels. |
| Accommodation Details | Extracts hotel name, cover image, address, description, rating, stars, amenities, and available rooms. |
| Room-level Data (Optional) | Optionally include detailed room facilities and availability. |
| Price & Availability | Captures price, room availability, and availability status per listing. |
| Structured Output | Returns data in JSON (or other export formats) for easy use in pipelines or analysis workflows. |
| Field Name | Field Description |
|---|---|
| hotelName | Name of the accommodation / hotel. |
| coverImageUrl | Main image or cover photo URL of the hotel. |
| address | Full address of the hotel. |
| description | Detailed hotel description. |
| starsOrRating | Stars or rating score of the hotel. |
| amenities | List of amenities or popular facilities available. |
| availableRooms | Number and types of rooms currently available (if requested). |
| price | Price for the available rooms (or base price) captured at scrape time. |
| roomFacilities | (Optional) Detailed facilities per room variant. |
| detailPageUrl | Link to the original listing page at Trip.com. |
[
{
"hotelName": "Grand Plaza Hotel",
"coverImageUrl": "https://cdn.trip.com/hotels/12345/image1.jpg",
"address": "123 Main Street, City, Country",
"description": "Elegant city-center hotel with free breakfast and Wi-Fi …",
"starsOrRating": 4.5,
"amenities": ["Free WiFi", "Breakfast included", "Gym", "Pool"],
"availableRooms": 5,
"price": "USD 120",
"roomFacilities": [
{ "roomType": "Double", "beds": 1, "maxAdults": 2 },
{ "roomType": "Suite", "beds": 2, "maxAdults": 4 }
],
"detailPageUrl": "https://www.trip.com/hotels/detail/12345"
}
]
trip-scraper/
├── src/
│ ├── main.js (or main.py)
│ ├── crawler/
│ │ ├── search_parser.js
│ │ └── hotel_parser.js
│ ├── utils/
│ │ ├── request_handler.js
│ │ └── data_cleaner.js
│ └── config/
│ └── input_schema.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── package.json (or requirements.txt)
└── README.md
- Travel agencies compile accommodation databases for price comparison and offerings analysis.
- Market researchers analyze hotel availability, pricing trends, and accommodation supply across regions.
- Data scientists build datasets for modeling travel demand, occupancy rates, or price elasticity.
- Startups creating travel-booking aggregators or recommendation platforms.
- Hospitality analysts monitor competitor hotel offerings and amenities over time.
What input formats are supported?
Accepts an array of startUrls pointing to search result pages or individual listing URLs. You can also specify maxHotels to limit the number of accommodations scraped.
Can I skip room-level data to save space?
Yes — by disabling extractRoomFacilities, you’ll get lighter output focusing on hotel-level data only.
Is it compatible with search filters from Trip.com?
Yes — search URLs generated with filters (date, stars, amenities, etc.) will be respected by the scraper.
Will it handle large-scale scraping?
Yes — for large jobs, increase memory limit (e.g., to 8192 MB). Otherwise, you may hit performance bottlenecks depending on dataset size and complexity.
Primary Metric:
A typical run scraping 350–600 hotel listings consumes approximately the same credit amount as stated under pricing ($5 of Free-plan allocation). :contentReference[oaicite:0]{index=0}
Reliability Metric:
Most runs complete successfully when the site structure remains stable; 97% success rate noted in historical run data. :contentReference[oaicite:1]{index=1}
Efficiency Metric:
Scraper throughput depends on memory allocation — higher memory gives faster runs by enabling parallel page processing. :contentReference[oaicite:2]{index=2}
Quality Metric:
Extracted data includes sufficient fields (name, price, address, amenities, room details) giving a comprehensive snapshot of each accommodation listing. :contentReference[oaicite:3]{index=3}
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