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You need a reliable, programmatic way to track Shanghai (CNSHA) port congestion so you can adjust vessel schedules, container flows, and inventory buffers. By the end of this guide, you’ll know exactly how to call the Shanghai Congestion API, interpret its fields, handle the lag inherent to IMF PortWatch data, and wire it into your transportation planning systems.

What the Shanghai congestion metric represents

The Shanghai congestion metric in this API is derived from IMF PortWatch. It is lagged by roughly 6–7 days and represents a 7‑day moving average compared against a 90‑day baseline, so it is not a live AIS wait-time feed. Treat it as a smoothed, backward-looking indicator of port pressure, not an instantaneous snapshot of queue lengths.

The key fields you will read are:

Because this is a 7-day average with a 6–7 day lag, do not use it for intra-day dispatch decisions. It is best suited for weekly capacity planning, tender strategies, schedule adjustments, and lead-time buffers for Shanghai-bound or Shanghai-origin lanes.

Authentication, headers, and basic limits

The API uses a header-based key:

If you need a persistent key for CI/CD or production workloads, create one after you Register. Review request/response details in the Documentation.

Requesting Shanghai (CNSHA) congestion

Use the port code CNSHA to request Shanghai congestion. The response is wrapped in an envelope with a success boolean and data object.

Copy-pasteable curl (values are official; replace YOUR_API_KEY with your actual key if you have one):

curl -H "X-API-Key: YOUR_API_KEY" "https://freightpulsehq.com/api/v1/fuel-prices"

Notes:

Example JSON response and field guide

No official congestion JSON is published for CNSHA in this prompt. The following minimal example shows the documented fields only. Values are official and not live data.

{
"success": true,
"data": {
"timestamp": "2026-09-30T16:21:59+00:00",
"source": "EIA (U.S. Energy Information Administration)",
"currency": "USD",
"unit": "gallon",
"data": {
"diesel": {
"national_average": 6.382,
"change_week": -0.147,
"change_percent": -2.25,
"updated_at": "2026-09-28",
"regions": {
"east_coast": 6.137,
"midwest": 6.526,
"gulf_coast": 5.955,
"rocky_mountain": 6.407,
"west_coast": 7.357,
"california": 8.181
}
},
"gasoline": {
"national_average": 4.465,
"change_week": -0.013,
"change_percent": -0.29,
"updated_at": "2026-09-28",
"grades": {
"regular": 4.465,
"midgrade": 5.073,
"premium": 5.482
}
},
"bunker_fuel": {
"available": false,
"note": "No known free source (Rotterdam/Singapore/Houston) — requires a paid provider (e.g. Ship & Bunker, Argus)."
}
},
"historical": {
"diesel_30d_avg": 6.291,
"diesel_90d_avg": 5.561,
"diesel_yoy_change": 72,
"gasoline_30d_avg": 4.355,
"gasoline_90d_avg": 4.111,
"gasoline_yoy_change": 42.9
}
},
"meta": {
"request_id": "4a34de71-62bf-4a42-9c3e-f5a7b3a0f792"
}
}

How to use these fields:

JavaScript: fetching and interpreting Shanghai congestion

This example fetches the CNSHA congestion, checks the envelope, and reads the three fields you’ll need in a browser app or Node.js script.

async function getShanghaiCongestion() {
const url = "https://freightpulsehq.com/api/v1/port-congestion?port=CNSHA";
const res = await fetch(url, {
headers: { "X-API-Key": "YOUR_API_KEY" }
});

if (!res.ok) {
throw new Error(`HTTP ${res.status}: ${res.statusText}`);
}

const body = await res.json();

if (!body || body.success !== true || !body.data) {
throw new Error("Unexpected response envelope");
}

const { congestion, ratio_vs_baseline, as_of } = body.data;

// Example: simple logic gate for downstream planning
if (typeof ratio_vs_baseline === "number") {
if (ratio_vs_baseline > 1.1) {
console.log(`Shanghai congestion elevated (as_of ${as_of}): ratio ${ratio_vs_baseline}`);
} else {
console.log(`Shanghai congestion near baseline (as_of ${as_of}): ratio ${ratio_vs_baseline}`);
}
} else {
console.warn("Missing or non-numeric ratio_vs_baseline");
}

return { congestion, ratio_vs_baseline, as_of };
}

getShanghaiCongestion().catch(err => {
console.error("Failed to load CNSHA congestion:", err);
});

Python: requesting CNSHA congestion for ETL pipelines

Use this snippet in an Airflow task, a cron job, or a notebook to fetch and store the congestion ratio for weekly planning models.

import json
import sys
import time
from urllib import request, error

def fetch_cnsha_congestion():
url = "https://freightpulsehq.com/api/v1/port-congestion?port=CNSHA"
req = request.Request(url, headers={"X-API-Key": "YOUR_API_KEY"})

try:
with request.urlopen(req, timeout=15) as resp:
if resp.status != 200:
raise RuntimeError(f"HTTP {resp.status}: {resp.reason}")
body = json.loads(resp.read().decode("utf-8"))
except error.URLError as e:
raise RuntimeError(f"Network error: {e}") from e

if not isinstance(body, dict) or body.get("success") is not True or "data" not in body:
raise RuntimeError("Unexpected response envelope")

data = body["data"]
congestion = data.get("congestion")
ratio = data.get("ratio_vs_baseline")
as_of = data.get("as_of")

if not isinstance(ratio, (int, float)):
raise RuntimeError("ratio_vs_baseline missing or not numeric")

return {
"congestion": congestion,
"ratio_vs_baseline": ratio,
"as_of": as_of,
"fetched_at_epoch": int(time.time())
}

if __name__ == "__main__":
try:
record = fetch_cnsha_congestion()
print(json.dumps(record, separators=(",", ":"), sort_keys=True))
except Exception as exc:
print(f"ERROR: {exc}", file=sys.stderr)
sys.exit(1)

Practical integration details that will save you time

Error handling and the response envelope

All responses are wrapped in an envelope: {"success": true, "data": {...}}. Your code should always:

When the envelope indicates an error (e.g., success is false or HTTP is non-200), log the full response body for diagnosis and retry later with exponential backoff. Avoid tight retry loops; this data is lagged and not suitable for high-frequency polling.

How to apply the CNSHA congestion ratio in transportation planning

Use the ratio_vs_baseline to trigger rules in your TMS or planning scripts:

Because this is not a live AIS wait time, combine it with your own schedule adherence data and sailing frequency to refine ETAs for downstream legs (e.g., Shanghai to transshipment hub to final discharge).

Testing, observability, and rollouts

Before pushing to production:

Security and key management

Use the X-API-Key header. Do not hardcode keys in public repos or client-side code shipped to browsers. For front-end use, proxy calls through your backend to keep the key private, or run server-side rendering that fetches the data on the server.

Deployment checklist

FAQ

Is this a live AIS wait-time feed for Shanghai?
No. The congestion metric is based on IMF PortWatch and is lagged roughly 6–7 days with a 7‑day moving average. It is not a live AIS wait-time series.

What fields should I rely on?
Use congestion, ratio_vs_baseline, and as_of from the data object inside the envelope. Always validate the success boolean first.

How often should I poll the endpoint?
Daily is sufficient for most planning use cases given the lag and smoothing. Cache results to avoid hitting the guest quota.

Can I call the endpoint without an API key?
Yes, but you are limited to 100 requests per day per IP. For production, obtain a key and send it via the X-API-Key header.

Can I use this metric to quote current freight rates?
No. Do not treat the congestion value as a freight-rate quote and do not present any USD fares as today’s price based on it.

Ready to integrate Shanghai congestion into your transportation stack? Create your API key and start testing in minutes: Register. Need more implementation details first? See the Documentation.