Home → Blog

You need a reliable way to gauge current congestion at Ningbo (CNNBO) to plan sailings, allocate equipment, and set customer expectations. By the end of this guide, you will be able to query the FreightPulse Port Congestion endpoint for Ningbo, parse the response, understand what the metrics mean (and what they do not), and integrate the data safely into your transportation workflow.

What the Ningbo congestion data measures (and what it does not)

The FreightPulse endpoint for port congestion returns IMF PortWatch metrics for Ningbo (CNNBO). The series uses AIS-derived port calls, lagged about 6–7 days, and reports a 7-day average compared to a 90-day baseline. This is a statistical proxy for congestion; it is not a live measurement of vessel wait or anchorage times.

Illustration: Ningbo Port Congestion API

In practice, you will consume three core fields for decisioning:

Because the data is lagged and smoothed, it is best suited to trend-aware planning (e.g., weekly schedule resilience) rather than same-day reroutes.

Authentication, quota, and envelope

Requests are authenticated by an X-API-Key header. Guests without a key are limited to 100 requests per day per IP. Every successful response uses an envelope of {"success":true,"data":{...}} that contains the payload you will parse. When integrating, always check the top-level success flag before using fields deeper in the object.

Auth header:

Quickstart: curl for Ningbo (CNNBO)

Use the following curl command to fetch the latest available congestion metrics for Ningbo:

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

This GET endpoint takes a single required query parameter port with value CNNBO for Ningbo. It returns a JSON envelope containing the congestion classification and underlying baseline comparison metrics.

Official JSON example and the fields you will use

Below is the official FreightPulse JSON example for Ningbo congestion. Copy and run the curl above to retrieve current values; do not hard-code values in production.

{
"success": true,
"data": {
"results": [
{
"port": "Ningbo",
"locode": "CN NBO",
"country": "China",
"congestion": "moderate",
"as_of": "2026-09-25",
"metrics": {
"recent_avg_portcalls_per_day": 44.9,
"baseline_avg_portcalls_per_day": 45.8,
"ratio_vs_baseline": 0.98
}
}
],
"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": "668b81ce-daae-40da-a940-8cd2cf29ea78"
}
}

Fields you will operationalize:

Remember: this is IMF PortWatch methodology, lagged ~6–7 days, and not a live queue length or anchorage wait time feed.

End-to-end example in Python

The snippet below fetches the Ningbo congestion payload, validates the envelope, and extracts the fields your scheduler or TMS would consume. It also shows a simple cache key structure you can reuse if you run this as a scheduled job.

import os
import requests
from datetime import datetime

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

def fetch_cnnbo_congestion():
headers = {"X-API-Key": API_KEY}
params = {"port": PORT}
resp = requests.get(API_URL, headers=headers, params=params, timeout=20)
resp.raise_for_status()
data = resp.json()

if not data.get("success"):
raise RuntimeError("FreightPulse request failed: success=false")

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

row = results[0]
metrics = row.get("metrics", {})

# Extract the fields you will use downstream
congestion_label = row.get("congestion")
as_of = row.get("as_of") # YYYY-MM-DD
ratio = metrics.get("ratio_vs_baseline")
recent = metrics.get("recent_avg_portcalls_per_day")
baseline = metrics.get("baseline_avg_portcalls_per_day")

return {
"port": row.get("port"),
"locode": row.get("locode"),
"country": row.get("country"),
"congestion": congestion_label,
"as_of": as_of,
"ratio_vs_baseline": ratio,
"recent_avg_portcalls_per_day": recent,
"baseline_avg_portcalls_per_day": baseline,
}

if __name__ == "__main__":
payload = fetch_cnnbo_congestion()
# Example decision use: guardrail dispatch logic
print(f"Ningbo congestion: {payload['congestion']} (ratio={payload['ratio_vs_baseline']}) as_of {payload['as_of']}")
# Suggest a cache key you can store for 24h:
cache_key = f"freightpulse:cnnbo:{payload['as_of']}"
print("cache_key:", cache_key)

Units, timestamps, and update cadence

Robust request patterns and error handling

As a practical integration pattern, wrap calls with these safeguards:

How to use congestion and ratio safely in planning

For Ningbo, you will typically wire three checks into your planning app:

Do not treat this as a live wait-time or berthing delay feed. It is best used to shape weekly allocations, feeder scheduling, and contingency buffers rather than same-day linehaul changes.

JavaScript fetch example (browser or Node)

This example shows how to call the same endpoint and extract the operational fields. In browsers, do not expose real keys; proxy through your backend if needed.

const API_URL = "https://freightpulsehq.com/api/v1/port-congestion?port=CNNBO";

async function getCnnboCongestion() {
const resp = await fetch(API_URL, {
headers: { "X-API-Key": "YOUR_API_KEY" }
});
if (!resp.ok) {
throw new Error("HTTP error " + resp.status);
}
const json = await resp.json();
if (!json.success) {
throw new Error("FreightPulse response success=false");
}
const results = json.data?.results || [];
if (results.length === 0) {
throw new Error("No results for CNNBO");
}
const row = results[0];
const metrics = row.metrics || {};
return {
congestion: row.congestion,
as_of: row.as_of,
ratio_vs_baseline: metrics.ratio_vs_baseline
};
}

getCnnboCongestion()
.then(d => {
console.log(`Ningbo congestion: ${d.congestion} (ratio=${d.ratio_vs_baseline}) as_of ${d.as_of}`);
})
.catch(err => {
console.error("Failed to load CNNBO congestion:", err);
});

Validation checklist for your first deployment

Origin catalog facts you must keep in your implementation

Common pitfalls to avoid

FAQ

Is the Ningbo congestion feed real-time?

No. It is based on IMF PortWatch, derived from AIS data but lagged about 6–7 days and aggregated as a 7-day average vs a 90-day baseline. It is not a live queue or anchorage time feed.

Which fields should my integration depend on?

Use congestion, as_of, and metrics.ratio_vs_baseline at a minimum. Keep recent_avg_portcalls_per_day and baseline_avg_portcalls_per_day if you want transparent diagnostics.

How often should I refresh?

Once per day is sufficient. Cache the response for 12–24 hours since updates are daily and lagged.

Can I call without an API key?

Yes, but guests are limited to 100 requests per day per IP. For production workloads, add your X-API-Key header.

Does the endpoint provide live wait times or anchorage delays?

No. It provides a congestion approximation from port-call activity relative to a 90-day baseline. It does not measure vessel wait or anchorage time.

Ready to wire Ningbo (CNNBO) congestion into your TMS or planning scripts? Create an account to get an API key and start building: Register. For schema reference and additional implementation notes, see the Documentation.