# Subway Store Locator Scraper (`datacach/subway-store-locator-scraper`) Actor

Scrape all Subway US restaurant locations from the official store locator. Crawls the restaurants.subway.com state/city directory and outputs every restaurant page URL as structured data.

- **URL**: https://apify.com/datacach/subway-store-locator-scraper.md
- **Developed by:** [DataCach](https://apify.com/datacach) (community)
- **Categories:** Automation, Developer tools, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.99 / 1,000 stores

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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Subway Store Locator Scraper

Extract every **Subway restaurant location URL** in the United States from the official Subway store locator — no coordinates, no API key, no code required.

### What is Subway Store Locator Scraper?

**Subway Store Locator Scraper** is a fast, zero-configuration **web scraper** that collects the page link of every **Subway restaurant in the US** by crawling the public [Subway restaurant directory](https://restaurants.subway.com/united-states). It walks the directory's **state → city → restaurant** hierarchy and returns each location's URL as clean, deduplicated **JSON data** you can **export to CSV, Excel, or JSON**.

This Actor is the **discovery layer** of a two-step Subway data pipeline: it builds the complete list of restaurant pages, which you then feed into a metadata scraper to extract full details for each store. Run it with a single click — the only optional input is a result limit.

### What can Subway Store Locator Scraper do?

- 🗺️ **Scrape the full US Subway store locator** — every state, every city, every restaurant page URL.
- ⚡ **Runs without any input** — click Start and it crawls the entire public directory.
- 🔢 **Limit your results** — cap the run with a single `Max restaurant links` field for fast, cheap test runs.
- 🧹 **Automatic deduplication** — each restaurant URL appears exactly once in the dataset.
- 📤 **Export Subway data as JSON, CSV, Excel, HTML, or XML** straight from the Output tab.
- 🔌 **Full REST API access** — start runs and pull results programmatically from **Python**, **JavaScript**, or any HTTP client via the Apify API.
- ⏰ **Schedule recurring runs** to keep your Subway location list fresh as stores open and close.
- 📊 **Monitoring and alerts** built into the Apify platform, so you know immediately if a run fails.
- 🔗 **Integrations with Zapier, Make, Google Sheets, Slack, and webhooks** — push new restaurant links straight into your own stack.
- 🌐 **Proxy rotation support** through Apify Proxy for reliable, uninterrupted crawling.

### What data does Subway Store Locator Scraper extract?

Each dataset item represents one Subway restaurant page discovered in the store locator:

| Field | Type | Description |
|---|---|---|
| `url` | string | Full URL of the Subway restaurant page on `restaurants.subway.com`, including state, city, and street slug |
| `coordinate` | string | How the link was discovered. This Actor crawls the static directory, so the value is `static source` |

The `url` field encodes useful location data on its own — state code, city, and street address all appear in the slug, so you can parse a rough location breakdown without any extra requests.

### How do I scrape Subway restaurant locations?

1. Open the Actor and go to the **Input** tab.
2. *(Optional)* Set **Max restaurant links** to a small number such as `100` for a quick first run, or leave it at `0` to scrape the **entire US Subway directory**.
3. Click **Start** and watch the log as the scraper walks the state and city directory pages.
4. When the run finishes, open the **Output** tab and **download your Subway restaurant data** as JSON, CSV, or Excel — or fetch it through the Apify API.
5. *(Optional)* Feed the collected URLs into the **Subway Restaurant Metadata** Actor to extract addresses, phone numbers, geolocation, and opening hours for each store.

### Input

The Actor is designed to run with **no configuration at all**. There is a single optional input:

- **Max restaurant links** (`maxResults`) — the maximum number of unique restaurant URLs to collect. Set it to `0` for **unlimited**, which crawls the whole directory and returns roughly 20,000 US locations. A small value keeps test runs quick. Accounts on the Apify free plan are capped at 10 links per run.

Example input:

```json
{
  "maxResults": 100
}
```

### Output example

Every item in the dataset is one Subway restaurant link:

```json
{
  "url": "https://restaurants.subway.com/united-states/al/abbeville/644-ozark-road",
  "coordinate": "static source"
}
```

A run returns a list of these records, which you can **download in JSON, CSV, Excel, HTML, or XML format** from the Output tab, or retrieve through the dataset API endpoint.

### Use cases

- 🍽️ **Restaurant location intelligence** — build a complete map of Subway's US footprint for market and territory analysis.
- 📈 **Competitive analysis** — compare Subway's store density against other QSR chains by state or city.
- 🏢 **Site selection and real estate research** — identify underserved cities or saturated markets before opening a new location.
- 🔄 **Data pipelines** — use these URLs as the input list for a metadata scraper that extracts addresses, hours, and phone numbers.
- 📰 **Store opening and closing tracking** — schedule recurring runs and diff the results to detect new or removed restaurants.
- 🎓 **Academic and journalistic research** on fast-food distribution, food access, and franchise growth.
- 🤖 **Enriching internal datasets** — join Subway locations with your own delivery, logistics, or CRM data.

### Subway and restaurant-related Actors

| Actor | What it does |
|---|---|
| **Subway Restaurant Metadata** | Takes the URLs from this Actor and extracts full restaurant details — address, phone, geolocation, and opening hours |
|  | |

### FAQ

#### Is it legal to scrape Subway restaurant data?

This Actor collects only **publicly available information** from the same public directory pages that Subway serves to any visitor. It does not collect personal data and does not access anything behind a login. That said, laws and Terms of Service vary by jurisdiction and use case — if you plan to use the data commercially, review Subway's Terms of Service and consult legal counsel. For general guidance, see Apify's [ethical web scraping resources](https://blog.apify.com/is-web-scraping-legal/).

#### How many Subway locations will this scraper return?

With **Max restaurant links** set to `0`, the Actor crawls the entire US directory, which currently contains roughly **20,000 restaurant pages**. Set a lower limit if you only need a sample.

#### Can I get Subway restaurant addresses and opening hours?

Not from this Actor — it returns **restaurant page URLs only**. Pair it with the **Subway Restaurant Metadata** Actor, which takes these URLs and extracts the address, phone number, geolocation, and hours for each store.

#### Can I use this Subway scraper through an API?

Yes. Every Apify Actor exposes a **REST API**, so you can start runs and download results from **Python**, **JavaScript**, or any HTTP client. Official API clients are available for both languages, and you can also trigger runs on a **schedule** or through **Zapier, Make, and webhook integrations**.

#### Why did my run return no links?

The most common cause is anti-bot blocking on the target site. Blocked requests are logged as warnings, and the run fails only if **no links at all** were collected. If this happens, retry the run, or enable **Apify Proxy** with residential proxies for more reliable access.

#### Can I scrape Subway locations outside the United States?

Not currently. This Actor targets the **US store directory** at `restaurants.subway.com/united-states`. If you need another country, open an issue and it can be considered for a future version.

### Support

Found a bug, or is a field missing from the output? Open a ticket on the **Issues tab** of this Actor — issues are monitored and addressed as quickly as possible.

Need a **custom scraping solution**, a different output format, or coverage for another restaurant chain? Get in touch through the Issues tab and describe what you need.

# Actor input Schema

## `maxResults` (type: `integer`):

Maximum number of unique restaurant page links to collect. Set to <b>0</b> for unlimited — the Actor then crawls the entire state → city → store directory and returns every US Subway location (roughly 20,000 links, which takes longer and costs more). A small value like <b>100</b> is ideal for a quick first run. On the free plan this is capped at 10 links regardless of the value entered.

## Actor input object example

```json
{
  "maxResults": 100
}
```

# Actor output Schema

## `dataset` (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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "maxResults": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("datacach/subway-store-locator-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "maxResults": 100 }

# Run the Actor and wait for it to finish
run = client.actor("datacach/subway-store-locator-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 '{
  "maxResults": 100
}' |
apify call datacach/subway-store-locator-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=datacach/subway-store-locator-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/dFlcrDbxXoLazNLQz/builds/wJvgnYZ9jRVfWVDrW/openapi.json