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České dráhy connection scraper

Search train connections on cd.cz and get back a structured dataset of routes, times, train legs, and prices.

What it does

Given a departure station, a destination, and an optional date/time, the actor finds the next train connections offered by České dráhy and stores them in the dataset. For each connection you get:

• Departure and arrival as ISO timestamps
• Total travel time in minutes
• Number of transfers
• Per-train breakdown: name, carrier, end station, departure / arrival, distance in kilometres, expected occupancy
• Intermediary stations for each train leg — the stops between the leg's origin and destination, with arrival / departure timestamps and the cd.cz station id
• Price in CZK (when ticketing is available)

The actor automatically paginates over the cd.cz timetable and skips gaps in service so you reliably get the requested number of connections — even for routes where direct service runs only on selected days.

Inputs

Example input

{
"from":"Praha hl.n.",
"to":"Copenhagen",
"date":"2026-05-14",
"time":"00:00",
"onlyDirectConnections":true,
"maxResults":20,
"fetchPrices":true
}

Output

Each item in the dataset describes one connection.

Example item

{
"id":80002583,
"handle":27910947,
"from":"Praha hl.n.",
"to":"Koebenhavn H",
"departure":"2026-05-14T04:31:00.000Z",
"arrival":"2026-05-14T17:38:00.000Z",
"timeLengthMinutes":787,
"changesCount":0,
"price":4280,
"trains":[
{
"name":"rj 384 Berliner",
"number":"384",
"carrier":"ČD",
"endStation":"Hamburg-Altona",
"from":"Praha hl.n.",
"to":"Koebenhavn H",
"departure":"2026-05-14T04:31:00.000Z",
"arrival":"2026-05-14T17:38:00.000Z",
"timeLengthMinutes":787,
"distanceKilometres":1124,
"delay":-1,
"waitTime":0,
"occupancyLevel":0,
"description":"railjet (Praha hl.n. – Hamburg-Altona)",
"intermediaryStations":[
{
"name":"Praha-Holešovice",
"stationId":5457041,
"arrival":"2026-05-14T04:39:00.000Z",
"departure":"2026-05-14T04:41:00.000Z"
},
{
"name":"Ústí nad Labem hl.n.",
"stationId":5455003,
"arrival":"2026-05-14T05:31:00.000Z",
"departure":"2026-05-14T05:33:00.000Z"
}
]
}
]
}

price is in CZK; null means the price could not be retrieved (sold out, no ticketing available, or fetchPrices: false). changesCount is 0 for direct connections.

intermediaryStations lists each leg's stops between its origin and destination (the leg's own from / to are excluded). arrival / departure are ISO 8601 UTC timestamps, occasionally null when cd.cz reports only one of the two for a stop. An empty array means the leg has no intermediary stops. The field is omitted when includeIntermediaryStations is false. GPS coordinates are not provided — cd.cz does not expose them in the connection data source.

Pricing

The actor uses Apify pay-per-event pricing — one search-result event is charged for every connection pushed to the dataset.

Agentic use

The actor is built for use by AI agents:

• Structured input schema. Every input has a title, type, description, and validation rules so an agent can fill it in correctly without extra prompting.
• Structured output schema. Each dataset item has a stable, typed shape — agents can read fields like departure, price, or changesCount without parsing prose.
• ISO timestamps. Departures and arrivals are emitted as ISO 8601 UTC strings (2026-05-14T04:31:00.000Z), ready to be diffed, sorted, or compared.
• Numeric quantities. timeLengthMinutes, distanceKilometres, changesCount, price are numbers, not formatted strings — no parsing of "2 hod 48 min" or "213 km" required.
• Predictable output. Connections are returned in chronological order; duplicates are removed; gaps in the cd.cz timetable are skipped automatically up to maxResults.
• Event-level cost control. Pay-per-event billing means an agent that sets a small maxResults pays only for what it consumes.

Typical agent prompts that map cleanly onto this actor:

• "Find the cheapest train from Praha to Brno tomorrow morning."
• "How long does the direct train from Praha to Berlin take, and when does the next one leave?"
• "List all direct overnight trains from Praha to Copenhagen in the next two weeks."

The agent populates the input, runs the actor, then reads the resulting dataset items directly — no scraping, no HTML, no string parsing.