Flight Busiest Traveling Period

API version: 1.0.2

Auth

No authentication required.

Base URL

https://test.api.amadeus.com/v1

Setup

1. No auth setup needed
2. GET /travel/analytics/air-traffic/busiest-period -- verify access

Endpoints

1 endpoints across 1 groups. See references/api-spec.lap for full details.

travel

Method	Path	Description
GET	/travel/analytics/air-traffic/busiest-period	Returns a list of air traffic reports.

Enhanced Skill Content

Question Mapping

• "When is the busiest travel period for Paris?" -> GET /travel/analytics/air-traffic/busiest-period
• "What are the peak travel months for NYC in 2024?" -> GET /travel/analytics/air-traffic/busiest-period
• "Which month has the most arrivals to London?" -> GET /travel/analytics/air-traffic/busiest-period (direction=ARRIVING)
• "When do the most people depart from Tokyo?" -> GET /travel/analytics/air-traffic/busiest-period (direction=DEPARTING)
• "What is the busiest travel season for Madrid?" -> GET /travel/analytics/air-traffic/busiest-period
• "Show me air traffic peaks for LAX last year" -> GET /travel/analytics/air-traffic/busiest-period
• "Which quarter has the highest flight volume for Dubai?" -> GET /travel/analytics/air-traffic/busiest-period
• "Compare inbound vs outbound traffic peaks for Chicago" -> GET /travel/analytics/air-traffic/busiest-period (call twice: direction=ARRIVING then direction=DEPARTING)
• "Is summer or winter busier for flights to Rome?" -> GET /travel/analytics/air-traffic/busiest-period
• "What are the off-peak months for Berlin air traffic?" -> GET /travel/analytics/air-traffic/busiest-period (interpret lowest-ranked periods)
• "Find the least busy travel period for Bangkok" -> GET /travel/analytics/air-traffic/busiest-period (read results in reverse)
• "When should I avoid flying to Sydney due to crowds?" -> GET /travel/analytics/air-traffic/busiest-period

Response Tips

• busiest-period: Results are ranked periods (months) with traveler scores. Look for data[] array sorted by traffic volume. The analytics object contains travelers.score as a relative ranking. cityCode uses IATA codes. period is a year (e.g., 2024). direction defaults to both arriving and departing combined if omitted. A 400 error typically means invalid IATA code or malformed period year.

Anomaly Flags

• Invalid IATA code: Surface immediately if 400 response mentions cityCode -- suggest valid IATA codes for the intended city
• No data for period: If response returns empty data[], flag that the requested year may not have analytics available yet or the city may have insufficient traffic data
• Direction mismatch: If user asks about arrivals but direction param was omitted, note that results reflect combined traffic and offer to re-query with explicit direction
• Rate limiting: Amadeus test API has strict rate limits -- surface 429 responses and suggest waiting or switching to production credentials
• Stale period data: If user requests current year and results seem incomplete, flag that analytics data may lag by several months

Playbook

1. Find Peak Travel Season for a City

1. Identify the IATA city code (e.g., PAR for Paris, LON for London, NYC for New York)
2. Call GET /travel/analytics/air-traffic/busiest-period?cityCode=PAR&period=2024
3. Review the ranked months in data[] -- the first entries are the busiest
4. Present the top 3 months as the peak season

2. Compare Arrival vs Departure Peaks

1. Call GET /travel/analytics/air-traffic/busiest-period?cityCode=LON&period=2024&direction=ARRIVING
2. Call GET /travel/analytics/air-traffic/busiest-period?cityCode=LON&period=2024&direction=DEPARTING
3. Compare the top-ranked months from each response
4. Highlight any months that appear in both top lists (high overall traffic) vs months that differ (seasonal asymmetry)

3. Identify Off-Peak Travel Windows

1. Call GET /travel/analytics/air-traffic/busiest-period?cityCode=BKK&period=2024
2. Read the results from the bottom of the ranked list (lowest scores)
3. Cross-reference the 2-3 lowest-traffic months
4. Present these as recommended off-peak travel windows with a note that prices and crowds are typically lower

4. Year-Over-Year Trend Analysis

1. Call the endpoint for consecutive years: period=2023 then period=2024
2. Compare the ranking order of months across both responses
3. Flag any months that shifted significantly in rank (e.g., moved from #8 to #2)
4. Summarize whether the city's travel pattern is stable or shifting

Response Tips

• Check response schemas in references/api-spec.lap for field details

References

• Full spec: See references/api-spec.lap for complete endpoint details, parameter tables, and response schemas

Generated from the official API spec by LAP