Home → Blog

You need a reliable way to programmatically check current container-traffic congestion signals for Shanghai (CNSHA) to drive ETAs, routing choices, and exception alerts. By the end of this guide, you will query the FreightPulse Port Congestion API for Shanghai, parse the results in Python, and safely integrate the lagged congestion signal into your own dashboards and services.

What the Shanghai congestion API provides (and what it does not)

The Port Congestion endpoint exposes a lagged traffic signal for Shanghai (CNSHA), derived from IMF PortWatch. It measures the 7-day average of port calls versus a 90-day baseline to classify congestion conditions. This is not a real-time AIS wait-time feed and does not represent queue durations at anchorage.

Illustration: API to Check Congestion at Shanghai

You will read three primary fields to power logic in your app:

Data is updated daily but typically lags the present day by approximately 6–7 days. Treat it as a stable macro signal for port activity rather than a live operations metric.

Endpoint, authentication, and response envelope

Endpoint (HTTP GET): /api/v1/port-congestion?port=CNSHA

Base URL: https://freightpulsehq.com

Authentication: send an API key in the header X-API-Key. Guests without a key are allowed up to 100 requests per day per IP. Always prefer using your key for predictable quotas.

Response envelope: all responses are wrapped as {"success": true, "data": {...}} on success.

Documentation and onboarding:

Quick test with cURL: Shanghai (CNSHA)

Run this request exactly as shown. Replace YOUR_API_KEY with your token if you have one; otherwise, the call may still succeed under guest limits.

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

Official sample JSON response for Shanghai:

{
"success": true,
"data": {
"results": [
{
"port": "Shanghai",
"locode": "CN SGH",
"country": "China",
"congestion": "moderate",
"as_of": "2026-10-16",
"metrics": {
"recent_avg_portcalls_per_day": 70.7,
"baseline_avg_portcalls_per_day": 84,
"ratio_vs_baseline": 0.84
}
}
],
"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": "7cafa1aa-b0e8-4ea1-8ef8-de8cb387b364"
}
}

Field notes that matter when building logic:

Python: production-ready fetch and parse for CNSHA

The snippet below calls the endpoint, validates the envelope, and extracts congestion, ratio_vs_baseline, and as_of for Shanghai. It also demonstrates basic error handling and a small cache layer to avoid refetching within a TTL window.

import os
import time
import json
import requests
from typing import Dict, Any, Optional, Tuple

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

# Simple in-memory cache with TTL (seconds)
_cache: Dict[str, Tuple[float, Dict[str, Any]]] = {}
CACHE_TTL = 6 * 60 * 60 # 6 hours

def get_from_cache(key: str) -> Optional[Dict[str, Any]]:
now = time.time()
hit = _cache.get(key)
if hit is None:
return None
ts, payload = hit
if now - ts < CACHE_TTL:
return payload
return None

def set_cache(key: str, payload: Dict[str, Any]) -> None:
_cache[key] = (time.time(), payload)

def fetch_congestion(port: str = PORT) -> Dict[str, Any]:
cache_key = f"congestion:{port}"
cached = get_from_cache(cache_key)
if cached:
return cached

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()

# Basic envelope checks
if not isinstance(payload, dict) or not payload.get("success", False):
raise ValueError("Unexpected API envelope or success=false")

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

set_cache(cache_key, payload)
return payload

def parse_congestion(payload: Dict[str, Any]) -> Dict[str, Any]:
data = payload["data"]
rec = data["results"][0]
metrics = rec.get("metrics", {})
return {
"port": rec.get("port"),
"locode": rec.get("locode"),
"country": rec.get("country"),
"congestion": rec.get("congestion"),
"as_of": rec.get("as_of"), # ISO date, UTC
"ratio_vs_baseline": metrics.get("ratio_vs_baseline"),
"recent_avg_portcalls_per_day": metrics.get("recent_avg_portcalls_per_day"),
"baseline_avg_portcalls_per_day": metrics.get("baseline_avg_portcalls_per_day"),
"data_lag_note": data.get("meta", {}).get("data_lag_days"),
"methodology": data.get("meta", {}).get("methodology"),
}

if __name__ == "__main__":
payload = fetch_congestion()
summary = parse_congestion(payload)
print(json.dumps(summary, indent=2))

What this prints is suitable to feed into a UI widget or an alerting rule. For example, alert if ratio_vs_baseline drops below a threshold for multiple consecutive API dates.

Interpreting congestion for Shanghai traffic decisions

Use congestion as a qualitative label in your UI and ratio_vs_baseline to drive thresholds. For instance, you might define:

Always pair the label with the as_of date to avoid confusion about timeliness. The signal reflects average port calls, not queue times or berth occupancy, and is updated daily with a ~6–7 day lag.

Field-by-field reference for Shanghai

Error handling, caching, and rate limits for CNSHA integrations

Rate limits: guests without a key are limited to 100 requests/day per IP. Use an API key in X-API-Key to avoid surprises. Given the data is updated daily and lags by ~6–7 days, aggressive polling is not useful; cache for hours.

Recommended production practices:

End-to-end example: fetch, parse, and annotate

This extension to the earlier Python code adds a computed label that you can place into a status banner for Shanghai:

def annotate_status(congestion: str, ratio: float, as_of: str) -> str:
if ratio is None:
return f"Shanghai congestion: {congestion} (as of {as_of})"
if ratio < 0.8:
tier = "significantly below baseline activity"
elif ratio <= 1.1:
tier = "near baseline activity"
else:
tier = "above baseline activity"
return f"Shanghai congestion: {congestion} ({tier}), as of {as_of}"

payload = fetch_congestion()
rec = parse_congestion(payload)
banner = annotate_status(rec["congestion"], rec["ratio_vs_baseline"], rec["as_of"])
print(banner)

Since this is a lagged macro traffic signal, couple it with your own operational thresholds so stakeholders immediately see what the value implies for your network.

Official JSON sample repeated for testing fixtures

Use the same official response as a deterministic fixture in unit tests. Keeping the test data identical to production schema prevents fragile tests when envelope fields are added in the future.

{
"success": true,
"data": {
"results": [
{
"port": "Shanghai",
"locode": "CN SGH",
"country": "China",
"congestion": "moderate",
"as_of": "2026-10-16",
"metrics": {
"recent_avg_portcalls_per_day": 70.7,
"baseline_avg_portcalls_per_day": 84,
"ratio_vs_baseline": 0.84
}
}
],
"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": "7cafa1aa-b0e8-4ea1-8ef8-de8cb387b364"
}
}

Another complete sample JSON for cache validation

You can also reuse the same canonical payload when validating your caching and replay logic in development environments.

{
"success": true,
"data": {
"results": [
{
"port": "Shanghai",
"locode": "CN SGH",
"country": "China",
"congestion": "moderate",
"as_of": "2026-10-16",
"metrics": {
"recent_avg_portcalls_per_day": 70.7,
"baseline_avg_portcalls_per_day": 84,
"ratio_vs_baseline": 0.84
}
}
],
"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": "7cafa1aa-b0e8-4ea1-8ef8-de8cb387b364"
}
}

Embedding the Shanghai signal into a dashboard

To integrate into a frontend or internal tool, map the categorical congestion level to colors and display the as_of date prominently. A simple pattern:

Under the badge, include “as of YYYY-MM-DD (UTC)” and optionally a small note like “IMF PortWatch; ~6–7 day lag.” Pair with a basic tooltip that shows ratio_vs_baseline and recent_avg_portcalls_per_day/baseline_avg_portcalls_per_day for more technical users.

Server-side safeguards for Shanghai-only deployments

If your service only needs Shanghai, lock the port parameter to CNSHA in server code instead of accepting arbitrary input. This removes a whole class of user-driven errors and keeps your caching model simple.

Also consider a minimal schema guard that fails closed if the envelope changes in critical ways, while allowing non-breaking additions. For instance, assert that data.results is a list with at least one object and that congestion and metrics.ratio_vs_baseline exist before computing derived KPIs.

FAQ

Is the Shanghai congestion value real-time?
No. The source is IMF PortWatch with daily updates that typically lag the current date by approximately 6–7 days. It is not a live AIS wait-time indicator.

What units are used for the averages?
recent_avg_portcalls_per_day and baseline_avg_portcalls_per_day are ships per day. ratio_vs_baseline is a dimensionless ratio.

How often should I poll for CNSHA?
Once per day is sufficient for most uses. The data does not change intra-day, and over-polling only consumes quota. Cache for hours.

What happens if I call without an API key?
Requests may work under the guest policy with 100 requests/day per IP. For reliability, register for an API key and send it via the X-API-Key header.

Does the congestion level indicate queue length or anchorage wait times?
No. The methodology compares recent port call averages against a 90-day baseline. It does not measure berthing delays or anchorage queues.

Ready to integrate the Shanghai congestion signal into your systems? Get an API key and read the docs to start shipping your implementation today: Register and explore the Documentation.