Subway Store Locations Scraper

Scrape Subway restaurant locations in the US and export address, phone number, geolocation, opening hours, and services as structured JSON, CSV, or Excel — no coding required.

What is Subway Store Locations Scraper?

Subway Store Locations Scraper is a tool that extracts complete store data from Subway US restaurant pages on restaurants.subway.com. Give it a list of store page URLs and it returns one clean, structured record per restaurant: street address, city, state, ZIP code, phone number, latitude/longitude, opening hours for every day of the week, and the services each store offers (breakfast, catering, mobile ordering, delivery).

It reads the store data embedded in each public page — the same data the Subway website itself displays — so results are accurate and fast, with no browser rendering involved.

What can Subway Store Locations Scraper do?

• 🥪 Extract full location details for any Subway restaurant in the United States
• 📍 Geolocation included — routable latitude/longitude for mapping and routing
• 🕐 Opening hours per day — structured intervals, ready to parse
• 🛎️ Services per store — breakfast, catering, mobile ordering, and Subway delivery availability
• 📤 Export data as JSON, CSV, Excel, or HTML from the dataset
• ⚡ Run at scale with configurable concurrency and automatic deduplication of input URLs
• 🔁 Automate on Apify: schedule recurring runs, monitor them, trigger via API, and connect to Zapier, Make, and other integrations

What data does Subway Store Locations Scraper extract?

Field	Description
name	Restaurant brand name
street, city, state, zipcode	Full store address
phone	Store phone number (E.164 format)
latitude, longitude	Routable geolocation coordinates
hours_raw	Opening hours for every day of the week, with open/close intervals
services	Services shown on the store page (Breakfast, Catering, Mobile Ordering, Subway Delivery)
pickup_and_delivery	Pickup and delivery options (In-Store Pickup, Delivery)
url	The scraped Subway restaurant page URL

How do I scrape Subway restaurant locations?

1. Get Subway store page URLs — typically by running the Subway Store Locator Scraper Actor, or from your own list.
2. Open this Actor, paste the URLs into the Restaurant URLs field on the Input tab.
3. Optionally adjust Max concurrency (default 5 is a safe balance of speed vs. reliability).
4. Click Start. Each restaurant becomes one dataset record.
5. Download your data from the Output tab in JSON, CSV, Excel, or HTML.

Input

The Actor needs just one thing: a list of Subway restaurant page URLs (each starting with https://restaurants.subway.com/). You can also tune Max concurrency — how many store pages are fetched in parallel. Duplicate URLs are removed automatically.

Output example

{
  "url": "https://restaurants.subway.com/united-states/al/abbeville/644-ozark-road",
  "name": "Subway",
  "street": "644 Ozark Road",
  "city": "Abbeville",
  "state": "AL",
  "zipcode": "36310",
  "phone": "+13345851021",
  "latitude": 31.55100650846701,
  "longitude": -85.2832063982217,
  "hours_raw": {
    "hours": [
      { "day": "MONDAY", "intervals": [{ "start": 800, "end": 2100 }], "isClosed": false },
      { "day": "TUESDAY", "intervals": [{ "start": 800, "end": 2100 }], "isClosed": false },
      { "day": "WEDNESDAY", "intervals": [{ "start": 800, "end": 2100 }], "isClosed": false },
      { "day": "THURSDAY", "intervals": [{ "start": 800, "end": 2100 }], "isClosed": false },
      { "day": "FRIDAY", "intervals": [{ "start": 800, "end": 2100 }], "isClosed": false },
      { "day": "SATURDAY", "intervals": [{ "start": 800, "end": 2100 }], "isClosed": false },
      { "day": "SUNDAY", "intervals": [{ "start": 800, "end": 2100 }], "isClosed": false }
    ]
  },
  "services": ["Breakfast", "Catering", "Mobile Ordering", "Subway Delivery"],
  "pickup_and_delivery": ["In-Store Pickup", "Delivery"]
}

You can download the dataset in various formats such as JSON, CSV, Excel, or HTML.

Use cases for Subway location data

• 🗺️ Location intelligence & mapping — plot every Subway store with accurate coordinates
• 🏪 Retail site selection — analyze competitor density before opening a new food location
• 📊 Market research — study coverage, hours, and service availability (delivery, catering) by region
• 🚚 Food delivery platforms — keep restaurant address, phone, and hours data fresh
• 📇 Lead generation & enrichment — verified phone numbers and addresses for franchise outreach
• 🔄 Data pipelines — schedule recurring runs on Apify and push updates to your systems via API

Subway-related Actors

Actor	What it does
Subway Store Locator Scraper	Discovers all Subway US store page URLs — the natural first step before this Actor

FAQ

Is it legal to scrape Subway store locations?

This Actor collects only publicly available data — the same store information anyone can see on the Subway website. It does not collect personal or gated data. Still, you should review Subway's Terms of Service and consult a legal professional for your specific use case.

Can I get Subway location data via API?

Yes. Every Apify Actor is automatically an API: start runs, monitor progress, and fetch dataset items programmatically via the Apify API or the JavaScript/Python clients.

How do I get the list of Subway restaurant URLs?

Run the Subway Store Locator Scraper Actor first — it discovers store page URLs across the US. Then feed its output into this Actor.

Why did some URLs fail or get blocked?

Subway's site uses anti-bot protection. The Actor detects blocks and reports them in the log instead of returning bad data. Lowering Max concurrency reduces the chance of blocks; failed URLs can simply be re-run.

Does it work for Subway restaurants outside the US?

The Actor currently supports US store pages on restaurants.subway.com. URLs from other domains are rejected during validation.

Support and feedback

Found a bug or need a new field? Open an issue on the Actor's Issues tab and we'll get back to you. If you need a custom scraping solution (other restaurant chains, custom outputs, delivery integrations), feel free to reach out through the Actor page.