# Subway Store Locations Scraper β Hours, Phone & Services (`datacach/subway-locations-scraper`) Actor
Scrape Subway restaurant locations in the US: address, phone, geolocation, opening hours, and services (breakfast, catering, delivery) from store page URLs. Export as JSON, CSV, or Excel.
- **URL**: https://apify.com/datacach/subway-locations-scraper.md
- **Developed by:** [DataCach](https://apify.com/datacach) (community)
- **Categories:** Automation, Developer tools, Lead generation
- **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 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** β 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 β the same data the Subway website itself displays β so results are accurate and fast, with no browser rendering involved.
### What can Subway Store Locations Scraper do?
- π₯ͺ **Extract full location details** for any Subway restaurant in the United States
- π **Geolocation included** β routable latitude/longitude for mapping and routing
- π **Opening hours per day** β structured intervals, ready to parse
- ποΈ **Services per store** β breakfast, catering, mobile ordering, and Subway delivery availability
- π€ **Export data** as JSON, CSV, Excel, or HTML from the dataset
- β‘ **Run at scale** with configurable concurrency and automatic deduplication of input URLs
- π **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 β 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** β how many store pages are fetched in parallel. Duplicate URLs are removed automatically.
### Output example
```json
{
"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
- πΊοΈ **Location intelligence & mapping** β plot every Subway store with accurate coordinates
- πͺ **Retail site selection** β analyze competitor density before opening a new food location
- π **Market research** β study coverage, hours, and service availability (delivery, catering) by region
- π **Food delivery platforms** β keep restaurant address, phone, and hours data fresh
- π **Lead generation & enrichment** β verified phone numbers and addresses for franchise outreach
- π **Data pipelines** β 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 β the natural first step before this Actor |
### FAQ
#### Is it legal to scrape Subway store locations?
This Actor collects only **publicly available data** β 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 β 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.
# Actor input Schema
## `links` (type: `array`):
Subway restaurant page URLs to extract metadata from, one per line. Each URL must start with https://restaurants.subway.com/, e.g. https://restaurants.subway.com/united-states/al/abbeville/644-ozark-road. Typically this is the output of the Subway Store Locator Scraper Actor. Duplicate URLs are removed automatically. The free plan processes only the first 5 links.
## `maxConcurrency` (type: `integer`):
Maximum number of restaurant pages fetched in parallel. Higher values finish large runs faster but increase the chance of being blocked by Subway's anti-bot protection; lower values are gentler and more reliable. The default of 5 is a safe balance.
## Actor input object example
```json
{
"links": [
"https://restaurants.subway.com/united-states/al/abbeville/644-ozark-road"
],
"maxConcurrency": 5
}
```
# 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 = {
"links": [
"https://restaurants.subway.com/united-states/al/abbeville/644-ozark-road"
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("datacach/subway-locations-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 = { "links": ["https://restaurants.subway.com/united-states/al/abbeville/644-ozark-road"] }
# Run the Actor and wait for it to finish
run = client.actor("datacach/subway-locations-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 '{
"links": [
"https://restaurants.subway.com/united-states/al/abbeville/644-ozark-road"
]
}' |
apify call datacach/subway-locations-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=datacach/subway-locations-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Subway Store Locations Scraper β Hours, Phone & Services",
"description": "Scrape Subway restaurant locations in the US: address, phone, geolocation, opening hours, and services (breakfast, catering, delivery) from store page URLs. Export as JSON, CSV, or Excel.",
"version": "0.0",
"x-build-id": "DM0O8f3MMHQE1yGpk"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/datacach~subway-locations-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-datacach-subway-locations-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/datacach~subway-locations-scraper/runs": {
"post": {
"operationId": "runs-sync-datacach-subway-locations-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/datacach~subway-locations-scraper/run-sync": {
"post": {
"operationId": "run-sync-datacach-subway-locations-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"required": [
"links"
],
"properties": {
"links": {
"title": "Restaurant URLs",
"type": "array",
"description": "Subway restaurant page URLs to extract metadata from, one per line. Each URL must start with https://restaurants.subway.com/, e.g. https://restaurants.subway.com/united-states/al/abbeville/644-ozark-road. Typically this is the output of the Subway Store Locator Scraper Actor. Duplicate URLs are removed automatically. The free plan processes only the first 5 links.",
"items": {
"type": "string"
}
},
"maxConcurrency": {
"title": "Max concurrency",
"minimum": 1,
"maximum": 20,
"type": "integer",
"description": "Maximum number of restaurant pages fetched in parallel. Higher values finish large runs faster but increase the chance of being blocked by Subway's anti-bot protection; lower values are gentler and more reliable. The default of 5 is a safe balance.",
"default": 5
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```