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You need a reliable way to programmatically check recent port congestion at Jebel Ali so you can plan sailings, manage buffers, and communicate expectations to operations and customers. By the end of this guide you will query the Jebel Ali congestion endpoint, parse the response in Python, understand the lagged IMF PortWatch methodology, and ship a small utility that your transportation stack can run on a schedule.

What this API provides (and what it does not)

The endpoint returns congestion indicators for Jebel Ali (AEJEA) based on IMF PortWatch data. The signal is lagged by about 6 to 7 days and reflects a 7-day moving average benchmarked against a 90-day baseline. It is not a live AIS wait-time feed, and it does not directly measure anchorage or berth waiting hours.

Illustration: API to Check Congestion at Jebel Ali

Because the metric is a relative change in port calls, treat it as an operational risk indicator rather than a real-time queue length. Use it to adjust safety stock, routing choices, or ETD/ETA buffers, but do not represent it as today’s live wait time. Do not quote any freight-rates USD fare as a current price based on this dataset.

Endpoint, parameters, and authentication

Endpoint:

Authentication:

Response envelope:

Jebel Ali identifiers:

Documentation and account setup:

Quickstart: cURL request for Jebel Ali (AEJEA)

Copy and run this request. It targets the Jebel Ali port explicitly via its locode and uses the documented header for authentication.

curl -H "X-API-Key: YOUR_API_KEY" "https://freightpulsehq.com/api/v1/port-congestion?port=AEJEA"

Official sample response and field map

The following is an official sample response. Use it to wire your parser and tests. Note the envelope and where each field lives. Values reflect IMF PortWatch-derived congestion, lagged and averaged, not live AIS wait times.

{
"success": true,
"data": {
"results": [
{
"port": "Jebel Ali",
"locode": "AE JEA",
"country": "United Arab Emirates",
"congestion": "moderate",
"as_of": "2026-10-09",
"metrics": {
"recent_avg_portcalls_per_day": 4.1,
"baseline_avg_portcalls_per_day": 4.7,
"ratio_vs_baseline": 0.87
}
}
],
"global_summary": {
"ports_high_congestion": 0,
"ports_moderate": 1,
"ports_low": 0
},
"meta": {
"source": "IMF PortWatch (portwatch.imf.org) - UN Global Platform AIS data",
"data_lag_days": "~6-7 (daily updates, not real-time)",
"methodology": "congestion level is an approximation: last 7 days' avg port calls vs 90-day baseline for the same port. Does not measure vessel wait/anchorage time."
}
},
"meta": {
"request_id": "b21bae2d-d4b2-46a6-ad86-11428a805221"
}
}

Fields to consume in operations workflows:

Python guide: request, parse, and normalize Jebel Ali congestion

The snippet below authenticates with the X-API-Key header, calls the endpoint for AEJEA, and extracts congestion, ratio_vs_baseline, and as_of. It includes minimal validation and a stable tuple you can cache or log.

Script: fetch and interpret congestion for Jebel Ali

import os
import sys
import time
import json
from typing import Tuple, Optional

import requests

API_URL = "https://freightpulsehq.com/api/v1/port-congestion?port=AEJEA"
API_KEY = os.getenv("FREIGHTPULSE_API_KEY", "YOUR_API_KEY")

def fetch_jebel_ali_congestion(session: Optional[requests.Session] = None) -> Tuple[str, float, str]:
s = session or requests.Session()
headers = {"X-API-Key": API_KEY}
resp = s.get(API_URL, headers=headers, timeout=20)
resp.raise_for_status()

payload = resp.json()

# Envelope: {"success": true, "data": {...}}
if not isinstance(payload, dict) or not payload.get("success"):
raise RuntimeError("API indicated failure or unexpected envelope")

data = payload.get("data", {})
results = data.get("results", [])
if not results:
raise RuntimeError("No results returned for AEJEA")

first = results[0]
congestion = first.get("congestion")
as_of = first.get("as_of")
metrics = first.get("metrics", {})
ratio = metrics.get("ratio_vs_baseline")

if congestion is None or ratio is None or as_of is None:
raise RuntimeError("Missing required fields: congestion, ratio_vs_baseline, or as_of")

# Basic normalization: clamp ratio to a sensible range if needed
try:
ratio = float(ratio)
except Exception as exc:
raise RuntimeError(f"Invalid ratio_vs_baseline: {ratio}") from exc

return congestion, ratio, as_of

def main() -> int:
try:
congestion, ratio, as_of = fetch_jebel_ali_congestion()
except Exception as e:
print(f"Error fetching congestion: {e}", file=sys.stderr)
return 1

# Example downstream usage: simple flags
status_flag = "OK"
if congestion == "high" or ratio < 0.8:
status_flag = "RISK"
elif congestion == "moderate":
status_flag = "WATCH"

print(json.dumps({
"port": "Jebel Ali",
"locode": "AE JEA",
"as_of": as_of,
"congestion": congestion,
"ratio_vs_baseline": ratio,
"status_flag": status_flag
}, ensure_ascii=False))

return 0

if __name__ == "__main__":
raise SystemExit(main())

Notes:

Operational semantics: units, timing, caching, and scheduling

Units and definitions:

Timing and updates:

Caching recommendations:

Scheduling patterns:

Error handling, quotas, and resilience

Quotas and authentication:

HTTP and payload handling:

Idempotency and logging:

Applying the signal in transportation planning at Jebel Ali

Use the combination of congestion and ratio_vs_baseline to drive conditional logic in your sailing plans to and from Jebel Ali. For example, when congestion is moderate and ratio_vs_baseline drops below a chosen threshold, you can extend port buffer time or prioritize sailings with more schedule slack.

Because the metric is lagged and averaged, combine it with your carrier ETAs and internal dwell data to decide whether to re-route through alternative hubs. Always surface the as_of date to planners so they see how fresh the signal is relative to booking cutoffs and vessel ETDs.

Validation checklist before shipping to production

FAQ

Is this a live AIS wait-time feed?
No. It is IMF PortWatch-based and lagged about 6 to 7 days. It compares a recent 7-day average of port calls to a 90-day baseline and does not directly measure anchorage or berth waits.

What is the correct way to authenticate?
Send your key via the X-API-Key header. Guests without a key can make up to 100 requests per day per IP.

Which fields should I store for Jebel Ali?
congestion, metrics.ratio_vs_baseline, as_of, and optionally recent and baseline port calls for transparency.

How often should I poll the endpoint?
Daily is sufficient given the lag and cadence. Cache responses for 12 to 24 hours and check the as_of date.

Can I use these values to quote freight rates?
No. Do not quote a freight-rates USD fare as today’s price based on this congestion dataset.

Get an API key and wire this into your transportation planning today. Start with the cURL above, then drop the Python snippet into your scheduler. When you are ready, create your key via the Register page and review the Documentation for additional integration details.