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You're trying to understand and monitor congestion at the Port of Shanghai (CNSHA) with metrics you can automate into dashboards and alerts. By the end of this guide, you will be able to query FreightPulse’s congestion endpoint for Shanghai, parse the response, interpret the 7-day vs 90-day congestion signal, and productionize it with sensible caching and error handling.

What this congestion metric is (and is not)

FreightPulse’s port congestion signal for Shanghai is sourced from IMF PortWatch and uses AIS-derived port call counts. It is lagged by approximately 6–7 days and reflects the 7-day average port calls versus a 90-day baseline for the same port.

Illustration: Shanghai Port Congestion

This is not a live AIS wait-time feed and does not estimate anchorage or berth delays in real time. Treat it as a short-term trend indicator of throughput versus a rolling baseline, not a queue-length or delay-duration measurement.

Endpoint, authentication and quotas

Use the FreightPulse congestion endpoint to request the Shanghai metric:

The response includes an array of results for the requested port and a global summary block. Dates are daily ticks (ISO-like date strings) with a lag; expect one refreshed value per day rather than intraday updates.

Reference the official Documentation for any schema clarifications not covered here.

Official sample request and response

Below is the official, copy-pasteable curl for CNSHA and a live sample response. Use these exactly as shown to validate your integration.

cURL:

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

JSON:

{
"success": true,
"data": {
"results": [
{
"port": "Shanghai",
"locode": "CN SGH",
"country": "China",
"congestion": "moderate",
"as_of": "2026-10-02",
"metrics": {
"recent_avg_portcalls_per_day": 69.9,
"baseline_avg_portcalls_per_day": 81.8,
"ratio_vs_baseline": 0.85
}
}
],
"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": "cae242bf-540f-4502-a1de-71cd9627c48b"
}
}

You will primarily use these fields:

Interpreting Shanghai’s congestion values

The metric compares recent activity to a 90-day baseline of port calls. A ratio_vs_baseline near 1.0 implies activity close to baseline; lower values suggest reduced throughput relative to the baseline window, which the service may label as moderate or high congestion depending on methodology thresholds.

Important nuance: fewer port calls can correlate with congestion if vessels bunch at anchorage or if port operations slow. However, the signal itself does not measure anchorage time or berth occupancy. Use ratio_vs_baseline as a port activity trend indicator and corroborate with your own operational data.

Quickstart: production-ready Python example

The following Python script queries CNSHA, checks the envelope, and extracts the fields you will graph or alert on. Replace YOUR_API_KEY with your key if you need higher quota than the guest tier allows.

import os
import sys
import time
import json
import urllib.request

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

def fetch_json(url, api_key, timeout=20):
req = urllib.request.Request(url)
req.add_header("X-API-Key", api_key)
with urllib.request.urlopen(req, timeout=timeout) as resp:
raw = resp.read().decode("utf-8")
return json.loads(raw)

def main():
try:
payload = fetch_json(URL, API_KEY)
except Exception as e:
print(f"Request failed: {e}", file=sys.stderr)
sys.exit(1)

if not isinstance(payload, dict) or not payload.get("success"):
print("Unexpected response format or success=false", file=sys.stderr)
print(json.dumps(payload, indent=2))
sys.exit(2)

data = payload.get("data", {})
results = data.get("results", [])
if not results:
print("No results returned for CNSHA", file=sys.stderr)
sys.exit(3)

r0 = results[0]
port = r0.get("port")
as_of = r0.get("as_of")
congestion = r0.get("congestion")
metrics = r0.get("metrics", {})
ratio = metrics.get("ratio_vs_baseline")
recent = metrics.get("recent_avg_portcalls_per_day")
baseline = metrics.get("baseline_avg_portcalls_per_day")

print(f"Port: {port}")
print(f"As of: {as_of}")
print(f"Congestion label: {congestion}")
print(f"7d vs 90d ratio: {ratio}")
print(f"Recent 7d avg calls/day: {recent}")
print(f"90d baseline calls/day: {baseline}")

if __name__ == "__main__":
main()

What to do with these values:

Implementation patterns that save time

Caching and refresh cadence

Because the source is updated daily and lagged by ~6–7 days, polling more than 1–2 times per day rarely adds value. Cache a successful response for 24 hours, or trigger a refresh once each morning in the port’s local business context. The as_of date is a daily tick; treat it as immutable for a given run.

Idempotent reads and backoff

Use retry with exponential backoff for transient network errors (HTTP 5xx or timeouts). Because the data is not real-time, there is no need to retry aggressively: two retries over 30–60 seconds is typically sufficient. Fail closed to your last known value if you must maintain a dashboard indicator.

Envelope checks and null safety

Always verify the envelope: success must be true, data must exist, and results should be a non-empty array. Guard against missing metrics keys and unexpected nulls, logging the top-level meta.request_id to aid support tickets if needed.

Rate limits and keys

Without an API key you may be capped at 100 requests per day per IP. Production pipelines should set the X-API-Key header. Keep keys out of client-side code; inject via server-side environment variables.

Field-level details for Shanghai (CNSHA)

These are the fields you will likely map into your internal data model:

Units are counts per day; there is no currency component. Dates are calendar dates, not timestamps. If you standardize on UTC in your pipeline, treat as_of as a date with no time zone conversion required for calculations.

Integrating into dashboards and alerts

For dashboards, show:

For alerting, pick thresholds that match your risk tolerance. For example, you might notify your operations channel if ratio_vs_baseline drops below 0.9 for three consecutive days, or if the label shifts into high. Use rolling windows to reduce noise.

Operational caveats and compliance notes

Troubleshooting CNSHA requests

Security and deployment checklist

FAQ

Is this a live wait-time estimate for Shanghai?
No. It is a 7-day average of port calls compared against a 90-day baseline, sourced from IMF PortWatch. It is lagged ~6–7 days and does not measure vessel wait or anchorage time.

How often should I poll the endpoint?
Once per day is sufficient. The data updates daily with a lag, so higher frequency polling will rarely change results and only consumes quota.

What fields should I store for long-term tracking?
At minimum: as_of, congestion, and metrics.ratio_vs_baseline. Keeping recent_avg_portcalls_per_day and baseline_avg_portcalls_per_day helps with deeper analytics and QA.

Do I need an API key?
Guest access allows up to 100 requests/day per IP. For production reliability and higher volume, include X-API-Key: YOUR_API_KEY in your requests.

Can I compare Shanghai’s ratio directly to other ports?
Use caution. While ratio_vs_baseline is normalized to each port’s 90-day baseline, operational realities differ across ports. Treat comparisons as directional unless you have additional context.

Ready to integrate the Shanghai congestion signal into your workflows? Create an account and get an API key in minutes: Register. For schema details and additional guidance, see the Documentation.