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You're building transportation dashboards or automations and need a reliable, developer-friendly way to monitor port congestion for Shekou (Shenzhen). By the end of this guide, you will know how to call the FreightPulse Port Congestion API for Shekou, parse its fields, apply caching and alerting, and ship production-grade integrations that reflect IMF PortWatch data (7-day average vs 90-day baseline, lagged about 6–7 days).

What the Shekou congestion endpoint returns and what it is (and is not)

The FreightPulse congestion endpoint for Shekou (Shenzhen) provides IMF PortWatch-derived congestion signals updated daily with an approximate 6–7 day lag. The core comparison is a 7‑day average of port calls versus a 90‑day baseline for the same port. This is not a live AIS wait-time feed, and it does not track anchorage or berth-level queueing in real time.

Illustration: Shekou (Shenzhen) Port Congestion API

The result includes a human-readable congestion label (for example, “low”, “moderate”, or “high”), an as_of date for when the data snapshot applies, and metrics that include the ratio_vs_baseline used to assess the congestion level. Your integration should treat these as lagged indicators for operational situational awareness, not as minute-by-minute traffic control.

Authentication, request pattern, and envelope

Authentication is via an API key header. Use the following header in every request:

Guests without a key are allowed up to 100 requests per day per IP.

Responses are wrapped in an envelope of the form {"success": true, "data": {...}}. Always check the success boolean before reading data.

Endpoint and parameter for Shekou (Shenzhen):

Important data characteristics (copying the origin facts):

Quickstart: cURL and official sample response

Use this exact curl to query Shekou. Replace YOUR_API_KEY with your key.

cURL:

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

JSON:

{
"success": true,
"data": {
"results": [
{
"port": "Shekou (Shenzhen)",
"locode": null,
"country": "China",
"congestion": "moderate",
"as_of": "2026-09-25",
"metrics": {
"recent_avg_portcalls_per_day": 7.4,
"baseline_avg_portcalls_per_day": 8.3,
"ratio_vs_baseline": 0.9
}
}
],
"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": "57923611-f1b0-4d81-9d65-99d994844914"
}
}

What you will typically use from results[0]:

Reading the payload safely

In production, always check success is true. Then confirm data.results is a non-empty array before dereferencing the first element. Use the string in congestion directly for user-facing status labels. The ratio_vs_baseline is unitless; values below 1.0 indicate the recent 7‑day average of port calls is below the 90‑day baseline, while values above 1.0 indicate above-baseline activity.

The as_of field is an ISO-formatted date (YYYY-MM-DD). Since updates are daily with a ~6–7 day lag, expect as_of to move forward once per day when new data is ingested. Because it is not real-time, avoid making minute-by-minute operational decisions on this signal alone.

Python example: fetch and parse congestion for Shekou

This sample calls the same endpoint, parses the envelope, and extracts congestion, ratio_vs_baseline, and as_of. Replace YOUR_API_KEY with your key. If you do not have a key, you can still make up to 100 requests/day per IP as a guest; however, including the header is recommended to avoid hitting guest limits.

import os
import requests

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

headers = {"X-API-Key": API_KEY}
params = {"port": PORT}

resp = requests.get(API_URL, headers=headers, params=params, timeout=20)
resp.raise_for_status()
payload = resp.json()

if not payload.get("success"):
raise RuntimeError("API reported success=false")

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

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

print(f"Port: {record.get('port')}")
print(f"Country: {record.get('country')}")
print(f"Congestion: {congestion}")
print(f"As of: {as_of}")
print(f"7-day vs 90-day ratio: {ratio}")

# Example operational check:
if congestion and congestion.lower() != "low":
print("Action: escalate or flag in dashboard")

# Caching hint: cache by (port, as_of) since updates are daily and lagged.

Operational integration patterns for transportation teams

Dashboards: Present the congestion label alongside the as_of date. Since this data is daily and lagged, include the as_of in any UI badge or tooltip to prevent confusion with real-time AIS-derived signals.

Alerting: Trigger notifications when congestion is not “low”. Optionally, use ratio_vs_baseline thresholds that make sense for your workflow. Treat the ratio as a comparative signal of port call volume rather than a direct measure of berth wait times.

Planning: Use the 7‑day vs 90‑day comparison to contextualize scheduling buffers. If ratio dips below 1.0 and the label is “moderate” or “high”, consider factoring in extra slack in truck appointments, yard capacity planning, or feeder coordination. Remember, this does not measure anchorage or queue lengths directly.

Practical details that save time

How to validate your pipeline

Step 1: Verify authentication and envelope

Issue a GET with the header X-API-Key: YOUR_API_KEY. Confirm HTTP 200 and success=true. Log request_id in the top-level meta for traceability.

Step 2: Schema checks

Assert that data.results[0] contains congestion (string), as_of (string date), and metrics.ratio_vs_baseline (number). If any are missing, treat the response as incomplete and retry later.

Step 3: Idempotent storage

Use a composite key of (port, as_of). Overwrite safely if the same as_of is re-ingested; data is daily and stable once published.

Step 4: UI and alert QA

Render the congestion label next to as_of. Trigger a non-intrusive alert if congestion != "low". Avoid combining this lagged series with live AIS metrics in the same badge; if you must, clearly label each metric’s data source and lag.

Field reference you will actually use

Security, resiliency, and deployment tips

FAQ

Is this live AIS wait-time data for Shekou?
No. It is an IMF PortWatch-derived metric that compares 7‑day average port calls to a 90‑day baseline, updated daily with a ~6–7 day lag. It does not measure anchorage queues or berth-level wait times in real time.

What does ratio_vs_baseline mean operationally?
It is a unitless ratio. Values below 1.0 mean the recent 7‑day average port calls are below the 90‑day baseline; values above 1.0 are above baseline. Use the congestion label in tandem with the ratio for alerts.

How often should I poll the API?
Once or twice per day is typically sufficient. Cache by (port, as_of) since the dataset updates daily and is lagged.

Do I need an API key?
You can call as a guest up to 100 requests/day per IP, but using X-API-Key: YOUR_API_KEY is recommended for reliability and to avoid hitting guest caps.

Can I quote this data as today’s freight rate or transit-time prediction?
No. This endpoint reflects congestion indicators, not freight prices or guaranteed transit times.

Ready to integrate Shekou congestion into your transportation systems? Start with a key and the reference guides: Register for API access, then read the Documentation to finalize your deployment.