Airline route intelligence: new routes, frequencies and launch fares

Route, carrier and announcement date

Start of the lifecycle; links to the press release or news page.

First bookable date and first operating date

The on-sale lead time is a strategy signal in itself.

Weekly frequency and days of operation

Per IATA season, so summer and winter are compared like for like.

Aircraft type and cabins offered

Seat capacity and whether premium cabins are sold on the route.

Launch fare by departure month

The opening price level and how it moves as the route sells.

Competitor fares on the city pair

The context a launch fare is set against.

Schedule changes

Dated changes to frequency, timing or aircraft, with the before and after.

Status

Announced, on sale, operating, reduced, suspended.

1. 01

   Watch announcements

   Poll airline and airport news pages and route maps through a rotating proxy pool. A new city pair or a changed route map entry opens a route record.

2. 02

   Detect the on-sale date

   From the announcement on, query the booking calendar for the new route daily until it returns bookable dates. Record the first day it does and the first departure date offered.

3. 03

   Sweep schedules each week

   Read frequencies, times and aircraft per week of the season. Diff against the previous sweep and log every change with its date.

4. 04

   Price the route from launch

   Sample fares for a fixed set of departure dates per month, from the route's selling markets, with one sticky session per search flow. Add the competing routes on the same dates.

   Launch-fare sampling for a new route17 linescopy

   import secretsfrom datetime import date, timedeltaBASE_USER = "USERNAME_pool-arcfast_country-de"def sample_dates(first_departure: date, months: int = 6) -> list[date]:    """Two departures per month: a Tuesday and a Friday in week two."""    out = []    for m in range(months):        start = (first_departure.replace(day=1) + timedelta(days=32 * m)).replace(day=8)        for weekday in (1, 4):            out.append(start + timedelta(days=(weekday - start.weekday()) % 7))    return outdef proxy_for(market: str) -> str:    user = BASE_USER.replace("_country-de", f"_country-{market}")    return f"http://{user}_session-{secrets.token_hex(4)}_sesstime-10:PASSWORD@proxy.arcproxies.io:8000"

5. 05

   Close the loop with operated traffic

   Where published traffic data exists for the route, compare it with your schedule record once it is released. Departures performed against departures scheduled shows how much of the published timetable actually flew.

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