We parse Seat61's complex HTML into structured JSON, CSV, or Parquet. Extract train schedules, route configurations, station details, and fare structures directly to your S3 bucket or data warehouse.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Routes & Connections objects from seat61.com. All fields typed and schema-versioned.
"origin_city": "London", "destination_city": "Paris", "origin_station": "St Pancras International", "destination_station": "Gare du Nord", "operator": "Eurostar", "estimated_duration": "2h 16m", "transport_type": "High-speed rail"
| # | route_id | origin_city | destination_city | origin_station | destination_station | via_stations |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Schedules & Timetables objects from seat61.com. All fields typed and schema-versioned.
"train_number": "ES 9014", "train_type": "Eurostar e320", "departure_time": "09:31", "arrival_time": "12:47", "days_of_operation": "Daily", "frequency": "Scheduled", "notes": "Mandatory reservation"
| # | schedule_id | route_id | departure_time | arrival_time | train_number | train_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fares & Ticketing objects from seat61.com. All fields typed and schema-versioned.
"ticket_class": "Standard Premier", "fare_type": "Advance", "advance_price_min": 79.0, "currency": "GBP", "booking_horizon_days": 180, "pass_holder_fee": 26.0, "booking_url": "https://www.eurostar.com"
| # | fare_id | route_id | ticket_class | fare_type | advance_price_min | advance_price_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Train & Fleet Details objects from seat61.com. All fields typed and schema-versioned.
"operator": "SNCF", "train_model": "TGV Duplex", "wifi_available": true, "power_sockets": "Standard European", "dining_options": "Café bar", "bike_storage": "Requires reservation", "sleeper_options": "None"
| # | train_id | operator | train_model | class_configurations | dining_options | wifi_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Station Information objects from seat61.com. All fields typed and schema-versioned.
"station_name": "Paris Gare du Nord", "city": "Paris", "country": "France", "left_luggage_facilities": true, "transfer_time_min": 30, "lounge_access": "Eurostar Business Premier", "connecting_transit": "Metro lines 4, 5; RER B, D"
| # | station_id | station_name | city | country | layout_description | left_luggage_facilities |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Seat61 is a masterclass in travel advice, but it is not a database. We apply custom HTML table parsing and NLP to convert paragraphs and merged cells into strict relational datasets.
Parse complex, nested HTML tables with varying rowspans and colspans into normalised timetable rows.
Map origin and destination strings to standardised UN/LOCODE or IATA equivalents for immediate routing use.
Capture advance, walk-up, and pass-holder fares across multiple currencies.
Extract affiliate and direct booking URLs associated with specific routes and operators.
Structure unstructured text about seating, Wi-Fi, and dining configurations per train type.
Extract multimodal transit links that bridge rail gaps across bodies of water or borders.
Parse textual descriptions of station facilities, minimum transfer times, and left-luggage options.
Capture high-resolution URLs for seating plans, train exterior photos, and station maps.
Monitor updates to schedules and fare rules, outputting only diffs when a page is modified.
Apply NLP heuristics to convert narrative paragraphs into categorical data points.
Brief in. Clean data out.
Provide specific countries, routes, or regions. We map the required data fields.
We configure Scrapy extractors with custom HTML table parsers and text-cleaning regex rules.
Verify schedule accuracy, station name normalisation, and currency extraction against the raw text.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Seat61 relies on hand-coded HTML rather than a CMS. Here is how we extract structured entities from decades of narrative travel advice.
Seat61 relies heavily on hand-coded HTML tables with varying rowspan and colspan attributes. We build custom matrix-parsing logic to normalise these into flat relational records.
Much of the site's value lies in paragraph-form advice. We use regex and NLP to extract structured entities like booking horizons and luggage rules from free text.
Affiliate links and redirect chains obscure actual operator booking URLs. Our crawlers resolve these chains to provide the final destination endpoint.
Pages are updated manually rather than via a CMS database. We use hash-based diffing to detect when a specific route's timetable or fare structure changes.
Date and time formats vary across pages (e.g., 24-hour vs 12-hour, varying date strings). We standardise all temporal data to ISO 8601 formats.
Incorporate niche rail routes and multimodal ferry connections into global routing engines.
Map station facilities, transfer times, and accessibility data for comprehensive journey planning apps.
Extract train types and route distances to calculate accurate emissions alternatives to short-haul flights.
Enrich destination guides with up-to-date rail access information, train classes, and onboard amenities.
Integrate detailed luggage policies, Wi-Fi availability, and business class fare rules into corporate booking tools.
Feed complex, edge-case timetable data (like overnight sleepers and rail-ferry links) into pathfinding models.
"Seat61 contains the most comprehensive ground travel knowledge base on the internet, but accessing it programmatically requires untangling decades of hand-coded HTML."
Extracting data from Seat61 is an exercise in complex DOM parsing. The site lacks a standard CMS structure, relying instead on nested tables, inline styles, and narrative text. DataFlirt handles the normalisation, converting Mark Smith's expert travel advice into strict relational schemas ready for your routing engine.
Everything supported by our seat61.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We deploy specialised lxml and BeautifulSoup pipelines to handle Seat61's non-standard, hand-coded HTML tables, resolving complex row and column spans into flat data.
Raw text is piped through regex and NLP models to standardise station names, extract ISO 8601 schedules, and categorise train amenities.
Pipelines run on AWS ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About seat61.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available factual data, such as train timetables and route connections, is generally permissible. DataFlirt extracts only factual, non-copyrightable data points. Clients should review terms of service and consult legal counsel.
We build custom parsers for specific page templates. Our extraction logic accounts for merged cells (rowspan/colspan) and normalises the output into strict relational schemas.
Yes. We can scope the pipeline to target specific regional pages, such as European rail networks, Asian train routes, or specific ferry connections.
Seat61 pages are updated manually by the author. We typically run weekly or monthly change-detection crawls to capture updates without generating redundant data.
No. Seat61 provides static schedule advice and historical fare ranges. Real-time availability requires direct API integration with rail operators (e.g., SNCF, Deutsche Bahn).
Yes. We can implement post-processing steps to map text-based station names to standard UIC, IATA, or UN/LOCODE identifiers using our internal reference tables.
We monitor schema compliance and null-rates on every run. If a page's HTML structure changes significantly, our alerting stack flags it, and our engineers update the selectors within the SLA window.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete global timetable dump or specific multimodal route connections — we scope, build, and operate the pipeline. Tell us what you need.
