# 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 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/docs.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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
## Subway Store Locator Scraper
Extract every **Subway restaurant location URL** in the United States from the official Subway store locator β 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 β city β 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 β the only optional input is a result limit.
### What can Subway Store Locator Scraper do?
- πΊοΈ **Scrape the full US Subway store locator** β every state, every city, every restaurant page URL.
- β‘ **Runs without any input** β click Start and it crawls the entire public directory.
- π’ **Limit your results** β cap the run with a single `Max restaurant links` field for fast, cheap test runs.
- π§Ή **Automatic deduplication** β each restaurant URL appears exactly once in the dataset.
- π€ **Export Subway data as JSON, CSV, Excel, HTML, or XML** straight from the Output tab.
- π **Full REST API access** β start runs and pull results programmatically from **Python**, **JavaScript**, or any HTTP client via the Apify API.
- β° **Schedule recurring runs** to keep your Subway location list fresh as stores open and close.
- π **Monitoring and alerts** built into the Apify platform, so you know immediately if a run fails.
- π **Integrations with Zapier, Make, Google Sheets, Slack, and webhooks** β push new restaurant links straight into your own stack.
- π **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 β 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 β 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`) β 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
- π½οΈ **Restaurant location intelligence** β build a complete map of Subway's US footprint for market and territory analysis.
- π **Competitive analysis** β compare Subway's store density against other QSR chains by state or city.
- π’ **Site selection and real estate research** β identify underserved cities or saturated markets before opening a new location.
- π **Data pipelines** β use these URLs as the input list for a metadata scraper that extracts addresses, hours, and phone numbers.
- π° **Store opening and closing tracking** β schedule recurring runs and diff the results to detect new or removed restaurants.
- π **Academic and journalistic research** on fast-food distribution, food access, and franchise growth.
- π€ **Enriching internal datasets** β 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 β 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 β 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 β 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 β 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 0 for unlimited β the Actor then crawls the entire state β city β store directory and returns every US Subway location (roughly 20,000 links, which takes longer and costs more). A small value like 100 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 '' with your token
const client = new ApifyClient({
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(`πΎ 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);
});
// π Want to learn more π? Go to β 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 = { "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("πΎ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# π Want to learn more π? Go to β 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 "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Subway Store Locator Scraper",
"description": "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.",
"version": "0.0",
"x-build-id": "wJvgnYZ9jRVfWVDrW"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/datacach~subway-store-locator-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-datacach-subway-store-locator-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/datacach~subway-store-locator-scraper/runs": {
"post": {
"operationId": "runs-sync-datacach-subway-store-locator-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/datacach~subway-store-locator-scraper/run-sync": {
"post": {
"operationId": "run-sync-datacach-subway-store-locator-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"properties": {
"maxResults": {
"title": "Max restaurant links",
"minimum": 0,
"type": "integer",
"description": "Maximum number of unique restaurant page links to collect. Set to 0 for unlimited β the Actor then crawls the entire state β city β store directory and returns every US Subway location (roughly 20,000 links, which takes longer and costs more). A small value like 100 is ideal for a quick first run. On the free plan this is capped at 10 links regardless of the value entered.",
"default": 0
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```