You’re trying to quantify how congested Jebel Ali (AEJEA) is right now, but the numbers you’re seeing aren’t “live wait times.” By the end of this post, you’ll be able to fetch the latest available IMF PortWatch-based congestion signal for Jebel Ali via a simple API request, understand its 6–7 day lag, and interpret the 7‑day average vs 90‑day baseline metrics correctly in your code.
What “congestion” means here (and what it doesn’t)
The FreightPulse congestion signal for Jebel Ali comes from IMF PortWatch using UN Global Platform AIS data. It’s updated daily but lags roughly 6–7 days. The core metric is a comparison of average port calls over the last 7 days vs a 90‑day baseline for the same port. This is not a measurement of vessel wait time at anchorage, queue length, or terminal turn-time from live AIS.
In practice, that means:
- The congestion value is an interpretation based on port calls, not a live queue estimate.
- “As of” dates reflect the most recent processed day in the lagged feed, not the moment you query the API.
- Use the 7‑day average vs 90‑day baseline to determine if activity is above or below typical levels, not to infer exact dwell or berth delays.
Key facts you need before coding
- Auth: header X-API-Key: YOUR_API_KEY. Guest without key: 100 requests/day per IP.
- Envelope: {"success":true,"data":{...}}
- Port congestion is IMF PortWatch, lagged ~6-7 days, 7-day avg vs 90-day baseline — not live AIS.
- Do not quote a freight-rates USD fare as today’s price.
- Port: Jebel Ali (AEJEA)
- curl: curl -H "X-API-Key: YOUR_API_KEY" "https://freightpulsehq.com/api/v1/port-congestion?port=AEJEA"
- Show the curl and field names (congestion, ratio_vs_baseline, as_of).
If you need broader API details or edge cases, check the Documentation.
Query Jebel Ali congestion in one request
Use the AEJEA UN/LOCODE to fetch Jebel Ali’s congestion signal. Authentication is via the X-API-Key header. If you omit a key, you’re treated as a guest with a 100 requests/day per IP limit.
cURL (copy/paste)
Live FreightPulse congestion for AEJEA.
Source: live
cURL:
curl -H "X-API-Key: YOUR_API_KEY" "https://freightpulsehq.com/api/v1/port-congestion?port=AEJEA"Official JSON response
{
"success": true,
"data": {
"results": [
{
"port": "Jebel Ali",
"locode": "AE JEA",
"country": "United Arab Emirates",
"congestion": "moderate",
"as_of": "2026-10-30",
"metrics": {
"recent_avg_portcalls_per_day": 4,
"baseline_avg_portcalls_per_day": 4.5,
"ratio_vs_baseline": 0.88
}
}
],
"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": "a5962f91-d4a2-4c98-8a28-ae5868c66088"
}
}Fields you’ll actually use:
- data.results[0].congestion: categorical label summarizing the ratio outcome (e.g., “moderate”).
- data.results[0].as_of: ISO-8601 date for the last processed day in the dataset (lagged ~6–7 days).
- data.results[0].metrics.ratio_vs_baseline: numeric ratio of the 7‑day average vs 90‑day baseline.
- data.results[0].metrics.recent_avg_portcalls_per_day and baseline_avg_portcalls_per_day: supporting numerators/denominators for the ratio.
- data.meta.data_lag_days and data.meta.methodology: machine-readable context you can surface or log.
Minimal Python example (parse the fields you need)
This snippet requests AEJEA, checks the envelope, and extracts congestion, as_of, and ratio_vs_baseline. Replace only the key value with YOUR_API_KEY when you’re ready.
import os
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():
headers = {"X-API-Key": API_KEY}
params = {"port": PORT}
r = requests.get(API_URL, headers=headers, params=params, timeout=15)
r.raise_for_status()
payload = r.json()
# Envelope: {"success": true, "data": {...}}
if not payload.get("success"):
raise RuntimeError("API returned success=false")
data = payload.get("data", {})
results = data.get("results", [])
if not results:
raise RuntimeError("No results returned for AEJEA")
item = results[0]
congestion = item.get("congestion")
as_of = item.get("as_of") # date string, e.g. "2026-10-30" (lagged ~6–7 days)
metrics = item.get("metrics", {})
ratio = metrics.get("ratio_vs_baseline")
recent = metrics.get("recent_avg_portcalls_per_day")
baseline = metrics.get("baseline_avg_portcalls_per_day")
return {
"port": item.get("port"),
"locode": item.get("locode"),
"country": item.get("country"),
"congestion": congestion,
"as_of": as_of,
"ratio_vs_baseline": ratio,
"recent_avg": recent,
"baseline_avg": baseline,
"source": data.get("meta", {}).get("source"),
"lag": data.get("meta", {}).get("data_lag_days"),
}
if __name__ == "__main__":
info = fetch_jebel_ali_congestion()
print(f"Jebel Ali ({info['locode']}): {info['congestion']} as of {info['as_of']}")
print(f"7-day vs 90-day ratio: {info['ratio_vs_baseline']} "
f"({info['recent_avg']} vs {info['baseline_avg']} port calls/day)")
print(f"Source: {info['source']} | Lag: {info['lag']}")Interpreting Jebel Ali’s signal in your app
Because the signal is lagged, cache your result for at least a few hours; polling multiple times per hour won’t change the underlying “as_of” date until the next daily update posts. If you need to show “today” in the UI, label it as “as of YYYY‑MM‑DD” to avoid implying real-time AIS wait times.
Use ratio_vs_baseline for thresholding. For example:
- ratio_vs_baseline near 1.0 indicates activity aligned with the 90‑day norm.
- Lower ratios can still map to “moderate” depending on categorization thresholds; do not equate this directly to berth delay.
- Consider suppressing alerts when as_of is older than your operational tolerance (e.g., >9 days old), since the underlying PortWatch feed is lagged.
A lightweight data model for downstream systems
If you’re storing this in a database, you likely only need:
- locode (string): “AE JEA”
- as_of (date): ISO-8601 without time; treat as UTC-aligned daily grain
- congestion (enum/string): low|moderate|high
- ratio_vs_baseline (float): 7‑day average divided by 90‑day baseline
- recent_avg_portcalls_per_day, baseline_avg_portcalls_per_day (floats): optional if you only display the ratio
- source and data_lag_days (strings): for provenance in logs/dashboards
Because the grain is daily, there’s no “intra-day” pagination to consider for this endpoint. The envelope returns a results array, so be prepared for future multi-row responses (e.g., if you later query multiple ports). Timestamps in this dataset are dates; no timezone conversion is required for the “as_of” value.
Operational guidance and safeguards
- Rate limits: Guests are capped at 100 requests/day per IP. Add an API key via X-API-Key to lift that cap according to your plan.
- Caching: Cache by {port, as_of}; invalidate when as_of advances. Daily refresh is sufficient for most dashboards.
- UI copy: Prefer “Congestion (IMF PortWatch, lag ~6–7 days), as of YYYY‑MM‑DD”. Avoid wording that implies live anchorage or dwell time.
- Alerting: Combine ratio_vs_baseline with minimum recency checks on as_of. Suppress alerts if as_of is stale versus your SLA.
- Attribution: Surface data.meta.source in a tooltip or “About this data” panel to preempt misunderstandings with operations teams.
Putting it together: small integration checklist
- Step 1: Call /api/v1/port-congestion?port=AEJEA with X-API-Key: YOUR_API_KEY.
- Step 2: Validate success == true and that results has at least one item.
- Step 3: Extract congestion, as_of, and metrics.ratio_vs_baseline. Optionally store recent_avg_portcalls_per_day and baseline_avg_portcalls_per_day for diagnostics.
- Step 4: Label visuals with “as of” and “IMF PortWatch (lag ~6–7 days).”
- Step 5: Cache and only re-poll daily unless you’re monitoring for the next data drop.
Troubleshooting common pitfalls
- “Why doesn’t the number change throughout the day?” The dataset is updated daily and lags ~6–7 days; multiple intra-day polls won’t reflect operational changes.
- “Where are wait times or anchorage durations?” Not included. The method uses port call counts to estimate congestion against a baseline.
- “What if results is empty?” Check the port code (AEJEA), retry later, and inspect meta/source fields. Implement a fallback message in the UI.
- “My team quoted today’s freight rate from this feed.” This endpoint is not a freight rate source; do not treat it as a current USD price.
- “Do I need multiple endpoints for summary?” The single response already contains data.results and a global_summary block for context.
FAQ
Is the Jebel Ali congestion figure real-time?
No. It’s derived from IMF PortWatch and typically lags by ~6–7 days. The as_of date shows the last processed day.
Which fields should I display to users?
Show congestion, as_of, and ratio_vs_baseline. Optionally include recent_avg_portcalls_per_day and baseline_avg_portcalls_per_day for transparency.
What’s the correct port code for Jebel Ali?
Use AEJEA as the query parameter. The response will also include locode “AE JEA”.
How should I handle rate limits?
Without a key, you have 100 requests/day per IP. Add X-API-Key: YOUR_API_KEY to increase headroom. Cache results by day to minimize calls.
Can I treat “moderate” as a delay estimate?
No. The label summarizes relative activity levels based on port calls vs baseline, not queue length or dwell time.
Ready to integrate this into your stack? Create an API key and start pulling AEJEA congestion in minutes: Register. For deeper endpoint details and examples, see the Documentation.