Best API for Mexico City International Airport Benito Juárez Historical Flight Data (2026 Guide)

You need historical flight movements for Mexico City International Airport Benito Juárez so you can analyze punctuality, plan resources, or enrich your app with past arrivals and departures. By the end of this guide, you’ll query FlightLabs’ historical flights endpoint for MEX (Mexico City’s IATA code), parse the JSON, compute on-time share, and understand how to page, cache, and monitor this high-traffic hub reliably.

Meet the airport: MEX (MMMX) and why history matters

Mexico City International Airport Benito Juárez serves Mexico City and uses IATA code MEX and ICAO code MMMX. Developers track its history to measure delay patterns by airline or hour, validate airport capacity planning, and build downstream experiences like arrival boards or disruption analytics. Historical data is the foundation for unbiased reporting, alert accuracy, and schedule synchronization against real operations.

The historical flights endpoint you’ll use

FlightLabs exposes a dedicated historical endpoint you can call from any stack:

• Flight History: https://www.goflightlabs.com/flights-history

You authenticate with your API key and filter to the airport and time window you need. While filter parameter names and exact query shapes can vary by deployment, a common pattern is to combine an API key with airport and date filters. If you’re exploring for the first time, open the interactive console in the MCP or review endpoint details in the Documentation.

Example curl for a past date at MEX

The following curl demonstrates calling the historical flights endpoint. Replace YOUR_API_KEY with your key. Use the API console to confirm available filters and add the airport/time filters you need for MEX.

curl -G "https://www.goflightlabs.com/flights-history" \
--data-urlencode "access_key=YOUR_API_KEY"

When you include filters for Mexico City (IATA MEX) and a date (UTC), the response will include flights whose departure or arrival matches MEX within your time window. In the next sections, you’ll see how to interpret standard fields returned by FlightLabs so you can compute delays, terminals, and gates consistently.

Understanding the JSON model you’ll parse

FlightLabs provides consistent flight objects across endpoints. The following official samples illustrate the structure of status, time, terminal, and gate fields you’ll encounter in historical results as well.

Real-time Flight Tracking (field structure reference)

{
"success": true,
"data": {
"flight": {
"iata": "AA123",
"icao": "AAL123",
"number": "123",
"status": "en-route",
"departure": {
"airport": "JFK",
"scheduled": "2024-03-20T10:00:00Z",
"actual": "2024-03-20T10:05:00Z",
"terminal": "8",
"gate": "B12"
},
"arrival": {
"airport": "LAX",
"scheduled": "2024-03-20T13:15:00Z",
"estimated": "2024-03-20T13:20:00Z",
"terminal": "4",
"gate": "45A"
},
"position": {
"latitude": 39.8729,
"longitude": -98.7372,
"altitude": 35000,
"speed": 495,
"heading": 270
}
}
}
}

Key fields you’ll use with historical results:

• flight.status: final outcome such as landed, canceled, diverted, or en-route (for live queries).
• departure.scheduled / departure.actual: UTC timestamps; their difference indicates departure delay.
• arrival.scheduled / arrival.estimated or arrival.actual: UTC timestamps; used for arrival delay and on-time logic.
• departure.terminal/gate and arrival.terminal/gate: ground resource assignment useful for boards and gate-change tracking.
• flight.iata and flight.icao: flight identifiers that can be cross-referenced with schedules or callsigns.

Airport Information (context reference)

{
"success": true,
"data": {
"airport": {
"iata": "JFK",
"icao": "KJFK",
"name": "John F. Kennedy International Airport",
"location": {
"lat": 40.6413,
"lon": -73.7781,
"city": "New York",
"country": "United States"
},
"timezone": "America\/New_York",
"terminals": [
"1",
"2",
"4",
"5",
"7",
"8"
],
"runways": [
{
"length_ft": 14511,
"width_ft": 150,
"surface": "concrete",
"designator": "13L\/31R"
}
],
"weather": {
"temp_c": 22,
"visibility_km": 10,
"wind": {
"speed_kts": 8,
"direction_deg": 180
}
}
}
}
}

Timezone is critical: all schedule and status timestamps in flight objects are UTC. For MEX (time zone commonly America/Mexico_City), convert to local time for display. Keep UTC for analytics and caching.

Flight Schedule (structure reference for pairing schedule vs. operations)

{
"success": true,
"data": {
"schedules": [
{
"flight_number": "UA456",
"departure": {
"airport": "SFO",
"scheduled": "2024-03-20T08:00:00Z",
"terminal": "3"
},
"arrival": {
"airport": "ORD",
"scheduled": "2024-03-20T14:15:00Z",
"terminal": "1"
},
"aircraft": {
"type": "Boeing 787-9",
"registration": "N123UA"
},
"airline": {
"name": "United Airlines",
"iata": "UA"
}
}
]
}
}

The historical endpoint lets you compare scheduled times (like these) with actual/estimated timestamps returned in the flight object, enabling on-time calculations at MEX by date, airline, or route.

Python example: compute on-time share for MEX

This example calls the historical endpoint, filters results where departure or arrival airport equals MEX, and computes on-time share using arrival difference vs. scheduled time. Replace YOUR_API_KEY with your key and add the time window filters supported by your plan in the query parameters.

import os
import requests
from datetime import datetime, timezone

API_KEY = os.getenv("FLIGHTLABS_KEY", "YOUR_API_KEY")
BASE_URL = "https://www.goflightlabs.com/flights-history"

def parse_iso(ts):
try:
return datetime.fromisoformat(ts.replace("Z", "+00:00")).astimezone(timezone.utc)
except Exception:
return None

def is_on_time(flight, threshold_minutes=15):
# Prefer arrival.actual; fall back to arrival.estimated
arr_sched = parse_iso(flight.get("arrival", {}).get("scheduled"))
arr_actual = parse_iso(flight.get("arrival", {}).get("actual")) or parse_iso(flight.get("arrival", {}).get("estimated"))
status = (flight.get("status") or "").lower()

# Treat canceled/diverted as not on time
if status in {"canceled", "cancelled", "diverted"}:
return False

if arr_sched and arr_actual:
delta = (arr_actual - arr_sched).total_seconds() / 60.0
return delta <= threshold_minutes
# If no arrival timestamps, fall back to departure times
dep_sched = parse_iso(flight.get("departure", {}).get("scheduled"))
dep_actual = parse_iso(flight.get("departure", {}).get("actual"))
if dep_sched and dep_actual:
delta = (dep_actual - dep_sched).total_seconds() / 60.0
return delta <= threshold_minutes
return False

def fetch_history():
# Add your time/airport filters as supported by your plan
params = {
"access_key": API_KEY
# e.g., add filters for MEX and date range here
}
r = requests.get(BASE_URL, params=params, timeout=60)
r.raise_for_status()
payload = r.json()
# History may return a list or a single object under data.flight
data = payload.get("data", {})
flights = []
if isinstance(data.get("flights"), list):
flights = data["flights"]
elif isinstance(data.get("flight"), dict):
flights = [data["flight"]]
return [f for f in flights if (f.get("departure", {}).get("airport") == "MEX" or
f.get("arrival", {}).get("airport") == "MEX")]

def compute_on_time_share(flights):
evaluated = 0
on_time = 0
for flt in flights:
evaluated += 1
if is_on_time(flt):
on_time += 1
return (on_time, evaluated, (on_time / evaluated) if evaluated else 0.0)

if __name__ == "__main__":
flights = fetch_history()
on_time, total, share = compute_on_time_share(flights)
print(f"MEX flights evaluated: {total}")
print(f"On-time (<=15 min): {on_time}")
print(f"On-time share: {share:.2%}")

Notes:

• All timestamps are treated as UTC for accuracy; convert to local time zones for display only.
• Canceled and diverted flights are marked not on time; tailor the rule based on your business logic.
• Threshold is 15 minutes, adjustable per airline or regulator definitions.

Practical use cases for MEX historical data

• Arrival boards with actuals: use arrival.scheduled vs. arrival.actual along with arrival.terminal and arrival.gate to render accurate past boards, and to validate SLA for display accuracy.
• Delay alerts and downstream SLAs: evaluate flight.status for canceled/diverted episodes and departure/arrival deltas to feed incident timelines, helping support teams audit operational responses.
• Schedule sync and variance tracking: compare the schedules array (scheduled times) with historical actual/estimated to detect chronic variances by route or hour, informing planning or buffer adjustments.

How FlightLabs structures fields you’ll rely on

The following fields consistently power historical reporting at MEX:

• Status tracking: flight.status (e.g., landed, canceled, diverted). Treat null/unknown conservatively.
• Time pairs: departure.scheduled vs. departure.actual and arrival.scheduled vs. arrival.actual/estimated. Use UTC deltas for analytics.
• Ground resources: terminal and gate under both departure and arrival; useful for missed connection analysis and gate conflicts.
• Identifiers: flight.iata and flight.icao to deduplicate codeshares (if present alongside airline info in your plan).

Polling, caching, and timezone guidance

• Timezone: FlightLabs timestamps are UTC. For Mexico City’s local time, convert based on America/Mexico_City rules at render time only; keep UTC in databases and cache keys.
• Polling: Historical datasets do not require rapid polling; fetch once per day/hour per window. For backfills, paginate deterministically by time ranges.
• Caching: Cache stable historical pages indefinitely. If you ingest near-real-time “recent history,” cache for 5–15 minutes and refetch late updates to capture final actuals and statuses.
• Idempotency: Use airport+date buckets for reprocessing and maintain a watermark to avoid duplicate ingestion.

Handling canceled, diverted, and codeshare flights

• Canceled: flight.status indicates cancellation. Exclude or include based on analytic goals; for on-time metrics they typically count as not on time.
• Diverted: Track flight.status and compare final arrival.airport to intended; diversions should be separated for operational reporting.
• Codeshares: If your plan surfaces airline and identifiers, deduplicate on operating carrier’s ICAO/IATA code and aircraft registration when present.

Pagination, windows, and data ranges

Historical queries frequently return many flights for MEX. Use time slicing to pull consistent windows. If your plan exposes explicit pagination cursors or page numbers, advance pages until the window is complete and checkpoint progress (e.g., last timestamp seen). For broad backfills (weeks or months), partition by day and airport to keep requests predictable and cache-friendly.

Comparing endpoints for MEX-focused workflows

Depending on your use case, you may combine history with schedule and real-time endpoints. Here is a technical comparison to guide your integration planning for Mexico City (MEX):

Complete request/response examples you can copy

Below are the official JSON examples again, which mirror the field shapes you’ll parse from history at MEX. They’re valuable for quickly wiring parsers and tests before you run full MEX queries.

Real-time flight example (status, terminals, gates)

{
"success": true,
"data": {
"flight": {
"iata": "AA123",
"icao": "AAL123",
"number": "123",
"status": "en-route",
"departure": {
"airport": "JFK",
"scheduled": "2024-03-20T10:00:00Z",
"actual": "2024-03-20T10:05:00Z",
"terminal": "8",
"gate": "B12"
},
"arrival": {
"airport": "LAX",
"scheduled": "2024-03-20T13:15:00Z",
"estimated": "2024-03-20T13:20:00Z",
"terminal": "4",
"gate": "45A"
},
"position": {
"latitude": 39.8729,
"longitude": -98.7372,
"altitude": 35000,
"speed": 495,
"heading": 270
}
}
}
}

Airport information example (timezone, context)

{
"success": true,
"data": {
"airport": {
"iata": "JFK",
"icao": "KJFK",
"name": "John F. Kennedy International Airport",
"location": {
"lat": 40.6413,
"lon": -73.7781,
"city": "New York",
"country": "United States"
},
"timezone": "America\/New_York",
"terminals": [
"1",
"2",
"4",
"5",
"7",
"8"
],
"runways": [
{
"length_ft": 14511,
"width_ft": 150,
"surface": "concrete",
"designator": "13L\/31R"
}
],
"weather": {
"temp_c": 22,
"visibility_km": 10,
"wind": {
"speed_kts": 8,
"direction_deg": 180
}
}
}
}
}

Schedule example (baseline timing to compare with history)

{
"success": true,
"data": {
"schedules": [
{
"flight_number": "UA456",
"departure": {
"airport": "SFO",
"scheduled": "2024-03-20T08:00:00Z",
"terminal": "3"
},
"arrival": {
"airport": "ORD",
"scheduled": "2024-03-20T14:15:00Z",
"terminal": "1"
},
"aircraft": {
"type": "Boeing 787-9",
"registration": "N123UA"
},
"airline": {
"name": "United Airlines",
"iata": "UA"
}
}
]
}
}

Implementation tips that save time

• Use UTC everywhere internally. Convert to local time only in the view layer.
• Normalize status strings to lowercase and handle synonyms (canceled vs. cancelled).
• Compute delays as actual - scheduled; choose arrival-based metrics unless unavailable, then fall back to departure.
• When combining schedules and history, index by a stable key (e.g., date + operating flight IATA/ICAO + origin/destination) and allow a tolerance window around midnight for red-eye or rollover flights.
• Cache: strong-cache historical days after they stabilize to reduce repeated loads and keep dashboards snappy.

How this scales for developers building at MEX

• Travel apps: Pull MEX daily history, compute on-time shares by airline or route, store aggregates, and surface reliability scores next to future itineraries.
• Airport displays: Recreate prior-day boards with accurate terminals and gates; reconcile with planned schedules to track gate turn efficiencies.
• Logistics tools: Use delay patterns at MEX to plan buffers for pickup scheduling and downstream ground handling.
• Corporate travel platforms: Run monthly MEX punctuality and cancellation stats to advise policy or preferred routing.

Getting started and next steps

Set up your key and try the historical endpoint for a single MEX day. Validate field parsing against the samples above, then expand to a week and checkpoint your pagination state. If you need endpoint-specific filters or response shapes, review the Documentation and test interactively in the MCP.

FAQ

Which timezone are timestamps in?
Timestamps in flight objects are UTC. Convert to local time for MEX only in your display layer.

How often should I refresh historical data?
For completed past days, fetch once. For recent history (same-day or prior few hours), refresh every 5–15 minutes to capture late actuals and finalized statuses.

How do I handle canceled and diverted flights?
Use flight.status. Treat canceled/diverted separately in KPIs, and exclude from on-time share if required by your methodology.

Can I compare scheduled vs. actual to compute delay?
Yes. Use schedules[].departure/arrival.scheduled from the schedules endpoint and match to historical departure/arrival actual/estimated times to compute delays.

How do I filter for MEX and a specific date?
Add airport and date filters supported by your plan when calling https://www.goflightlabs.com/flights-history. Confirm parameter names in the Documentation or by testing in the MCP.

Ready to build your MEX analytics and historical boards? Get your API key and start querying today: Register. Explore endpoints, field details, and query patterns in the FlightLabs site and the full Documentation.