Indian Railways Trains, Schedules & Live Status Scraper

Scrape Indian Railways train data without IRCTC logins or paid APIs. Returns schedules between stations, full train routes with per-stop timings, fares by class, and live running status — pulled from public sources at erail.in and runningstatus.in.

Indian Railways Scraper Features

Searches all trains running between any two Indian Railways stations
Looks up a train by number and returns its full route, fares, and seat availability
Tracks live train running status — current position, delay, and next scheduled stop
Returns fares per class in INR (1A / 2A / 3A / SL / CC / EC / 2S / 3E / FC) where the source publishes them
Decodes the IR weekly bitmask into a clean array of three-letter day labels
Pure HTTP — no browser, no captcha solver, no proxy required
Handles all train types: Rajdhani, Shatabdi, Vande Bharat, Duronto, Garib Rath, Superfast, Mail/Express, Passenger
Coverage spans 13,000+ trains and 7,300+ stations across the Indian Railways network

Who Uses Indian Railways Train Data?

Travel apps — Power booking flows that need IR schedules without paying RailAPI rates per query.
Aggregators — Combine train, bus, and flight inventory into a single multi-modal route engine.
Fintech and SaaS — Embed train running status into expense apps, employee travel tools, or logistics dashboards.
Researchers and analysts — Build mobility datasets, study delay patterns, or model passenger flows on the world's busiest rail network.
Indian developers building side projects — Skip the IRCTC API approval queue and just get the data.

How the Indian Railways Scraper Works

Pick a mode — train_search, train_number, or live_status.
Provide the inputs that mode needs (station codes, or a list of train numbers).
The scraper hits erail.in for schedules and fares, and runningstatus.in for live status.
Each train comes back as one record in the dataset, with consistent field names across modes.

Input

{
  "mode": "train_search",
  "originStation": "NDLS",
  "destinationStation": "BCT",
  "maxItems": 50,
  "sp_intended_usage": "test",
  "sp_improvement_suggestions": "test"
}

Field	Type	Default	Description
`mode`	string	`"train_search"`	One of `train_search`, `train_number`, `live_status`, `pnr_status`.
`originStation`	string	`"NDLS"`	Indian Railways station code for the boarding station (e.g. `NDLS`, `BCT`, `MAS`, `HWH`). Used by `train_search`.
`destinationStation`	string	`"BCT"`	Station code for the alighting station. Used by `train_search`.
`trainNumbers`	array of strings	`[]`	Train numbers to look up (e.g. `["12951", "22439"]`). Used by `train_number` and `live_status`.
`date`	string	`""` (today)	Optional `YYYY-MM-DD` date for `live_status`. Other modes ignore this.
`maxItems`	integer	`50`	Maximum number of records returned.
`proxyConfiguration`	object	`{useApifyProxy:false}`	Leave default. erail.in and runningstatus.in are public and don't need proxies.

Train number lookup

{
  "mode": "train_number",
  "trainNumbers": ["12951", "12302"],
  "maxItems": 5
}

Live train status

{
  "mode": "live_status",
  "trainNumbers": ["12951"],
  "maxItems": 1
}

pnr_status mode is reserved in the schema for a future release. The IRCTC PNR flow needs a CAPTCHA solver and an India residential proxy, and v1 chooses to be honest about that rather than ship something flaky. The actor returns a clear error if you select it.

Indian Railways Scraper Output Fields

Every record uses the same schema regardless of mode. Fields that don't apply to the chosen mode are returned as empty arrays, empty objects, or empty strings.

`train_search` example

{
  "train_number": "12951",
  "train_name": "Mumbai Central New Delhi MUMBAI RAJDHANI",
  "train_type": "RAJDHANI",
  "origin_station": "Mumbai Central",
  "origin_code": "MMCT",
  "destination_station": "New Delhi",
  "destination_code": "NDLS",
  "from_station": "Mumbai Central",
  "from_code": "MMCT",
  "to_station": "New Delhi",
  "to_code": "NDLS",
  "departure_time": "17:00",
  "arrival_time": "08:32",
  "duration_minutes": 932,
  "distance_km": 1384,
  "runs_on": ["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"],
  "classes_available": ["1A", "2A", "3A"],
  "route_stations": [],
  "fare_by_class": {"1A": 5335, "2A": 3235, "3A": 2405},
  "seat_availability": [
    "1A: avail 10",
    "2A: avail 87 / WL 60 / RAC 20",
    "3A: avail 266 / WL 160 / RAC 55"
  ],
  "live_status": {},
  "source_url": "https://erail.in/rail/getTrains.aspx?Station_From=BCT&Station_To=NDLS",
  "scraped_at": "2026-04-25T13:57:07.231Z"
}

`train_number` example

Same schema as above, plus a populated route_stations array — one pre-formatted string per stop.

{
  "route_stations": [
    "01. MMCT Mumbai Central | arr First dep 17:00 | day 1 | no halt | 0 km",
    "02. BVI Borivali | arr 17:20 dep 17:22 | day 1 | halt 2 min | 30 km",
    "03. ST Surat | arr 19:32 dep 19:35 | day 1 | halt 3 min | 263 km",
    "04. BRC Vadodara Jn | arr 21:11 dep 21:16 | day 1 | halt 5 min | 392 km",
    "05. KOTA Kota Jn | arr 03:35 dep 03:40 | day 2 | halt 5 min | 925 km",
    "06. NDLS New Delhi | arr 08:32 dep Last | day 2 | no halt | 1384 km"
  ]
}

`live_status` example

{
  "train_number": "12951",
  "train_name": "Ndls Tejas Raj",
  "live_status": {
    "current_station": "GHOLVAD",
    "last_event": "Approaching",
    "delay_minutes": 8,
    "next_station": "Surat",
    "source_station": "MUMBAI CENTRAL",
    "destination_station": "NEW DELHI",
    "journey_date": "25-Apr-2026",
    "summary": "Departed from Gholvad at 25-Apr-2026 18:45. Upcoming station is Bordi Road (non-stopping). Next stoppage station is Surat.",
    "updated_at": "2026-04-25T13:57:07.232Z"
  },
  "source_url": "https://www.runningstatus.in/status/12951-today",
  "scraped_at": "2026-04-25T13:57:07.232Z"
}

Output field reference

Field	Type	Description
`train_number`	string	IR train number (e.g. `12951`).
`train_name`	string	Full train name.
`train_type`	string	Service category: `RAJDHANI`, `SHATABDI`, `VANDE_BHARAT`, `DURONTO`, `GARIB_RATH`, `SUPERFAST`, `MAIL_EXPRESS`, `PASSENGER`, etc.
`origin_station` / `origin_code`	string	Station name and code where the train begins its run.
`destination_station` / `destination_code`	string	Station name and code where the train ends its run.
`from_station` / `from_code`	string	User-effective boarding station for `train_search`. Equals origin for `train_number` mode.
`to_station` / `to_code`	string	User-effective alighting station. Equals destination for `train_number` mode.
`departure_time`	string	Scheduled departure from the boarding station, `HH:MM` 24h.
`arrival_time`	string	Scheduled arrival at the alighting station.
`duration_minutes`	integer	Total journey duration in minutes.
`distance_km`	number	Distance between boarding and alighting stations in kilometres.
`runs_on`	array of strings	Days the train runs (e.g. `["Mon","Wed","Fri"]`).
`classes_available`	array of strings	Seat classes the train carries (`1A`, `2A`, `3A`, `SL`, `CC`, `EC`, `2S`, `FC`, `GN`, `GC`).
`route_stations`	array of strings	Per-stop schedule lines (`train_number` mode only).
`fare_by_class`	object	INR fares keyed by class. Empty for trains where erail.in doesn't publish fares.
`seat_availability`	array of strings	Per-class snapshot of available, waitlist, RAC, and tatkal counts (where the source publishes them).
`live_status`	object	Real-time position object (`live_status` mode only).
`source_url`	string	URL the record was scraped from.
`scraped_at`	string	ISO 8601 timestamp when the record was captured.

FAQ

How do I scrape Indian Railways data?

The Indian Railways Scraper covers three modes. Pick train_search to list every train between two stations. Pick train_number to get a single train's full route and fares. Pick live_status to track a running train in real time. Inputs and outputs are all in the tables above.

How much does the Indian Railways Scraper cost to run?

The Indian Railways Scraper bills $0.10 per actor start plus $0.001 per record returned — the standard Apify pay-per-event pricing. A typical train_search between two metros returns 30-50 trains for about $0.13. A train_number lookup returns one record for $0.101.

What stations are supported by the Indian Railways Scraper?

The Indian Railways Scraper accepts any of the ~7,300 IR station codes — NDLS (New Delhi), BCT (Mumbai Central), MAS (Chennai Central), HWH (Howrah), SBC (Bengaluru), and the rest of the network. Use the official IR station code (3-5 letters), not the city name.

Does the Indian Railways Scraper need a proxy or India IP?

The Indian Railways Scraper does not need a proxy. The two source sites (erail.in and runningstatus.in) serve traffic globally and don't geofence. The default proxy configuration is useApifyProxy: false, which keeps the run free of proxy bandwidth charges.

Why is `pnr_status` mode marked "coming soon"?

PNR lookups need to go through IRCTC or indianrail.gov.in, both of which sit behind CAPTCHAs, F5 bot protection, and (in IRCTC's case) a login. v1 of the Indian Railways Scraper handles the 90% of use cases that don't need PNR. v2 will add it once the captcha-solving plumbing is reliable enough to ship.

Can I get fares for every train?

The Indian Railways Scraper returns whatever fares erail.in publishes. Most major Mail/Express, Superfast, Rajdhani, Shatabdi, and Vande Bharat trains have fare data. Suburban locals and unreserved passengers usually don't — the source just doesn't carry it. Empty fare_by_class is honest about that.

Need More Features?

Need PNR status, station departures, or coverage of another rail network? File an issue or get in touch.

Why Use the Indian Railways Scraper?

Affordable — $0.001 per record, undercutting paid alternatives like RailAPI by an order of magnitude.
Honest about limits — v1 ships the modes that work reliably and labels the one that doesn't, instead of pretending IRCTC's captcha layer is a solved problem.
Clean output — flat records with consistent field names across all three modes. Empty fields stay empty; you don't get strings where numbers belong.

Indian Railways Scraper - Schedules, Fares & Live Status