Port Congestion API: How to Access Real-Time Data for 114+ Ports

March 16, 2026 · 9 min read · By FreightPulse Engineering

Port congestion costs the global shipping industry an estimated $30 billion annually in delays, demurrage charges, and rerouting expenses. Yet most logistics teams still monitor congestion manually — checking port authority websites, reading industry newsletters, or relying on word-of-mouth from carriers.

A port congestion API changes this entirely. Instead of reactive firefighting, you can build proactive systems that monitor congestion across 114+ ports in real-time, alerting stakeholders before delays impact their shipments.

Why Port Congestion Data Matters

Port congestion doesn't just cause delays — it cascades through the entire supply chain:

• Demurrage and detention fees — Containers stuck at congested ports rack up $150-300/day in charges
• Rate spikes — Congestion in key ports triggers spot rate increases within 48-72 hours
• Vessel schedule reliability — A congested port throws off schedules for weeks downstream
• Inventory planning — Late arrivals mean stockouts or expensive expedited alternatives

Having this data programmatically means your logistics platform can factor congestion into every decision — from route selection to customer ETA promises.

What a Port Congestion API Returns

FreightPulse's /v1/port-congestion endpoint provides a comprehensive view of port conditions. Here's a sample request:

curl https://freightpulsehq.com/api/v1/port-congestion \
  -H "X-API-Key: your_api_key" \
  -G \
  -d "port_code=USLAX"

And the response:

{
  "port": {
    "code": "USLAX",
    "name": "Los Angeles",
    "country": "US",
    "region": "North America"
  },
  "congestion": {
    "level": "moderate",
    "index": 62,
    "vessels_at_anchor": 8,
    "avg_wait_hours": 36,
    "berth_utilization": 0.78,
    "container_dwell_days": 4.2
  },
  "trend": {
    "direction": "improving",
    "change_7d": -5,
    "forecast_7d": "likely_improving"
  },
  "updated_at": "2026-03-16T14:00:00Z"
}

Key Data Points Explained

Congestion Index (0-100)

A normalized score where 0 = no congestion and 100 = severe. This composite metric factors in vessel queues, berth utilization, and container throughput. Most logistics teams set alerts at index > 70 (high) or > 85 (critical).

Vessels at Anchor

The number of ships waiting outside the port for a berth. This is the most visible indicator of congestion and directly correlates with wait times. During the 2021 crisis, Los Angeles peaked at 109 vessels at anchor.

Average Wait Time

Hours from vessel arrival to berthing. This metric directly impacts transit time calculations. If your route has a 14-day ocean transit but the destination port adds 48 hours of wait time, your customer's ETA needs to reflect that.

Container Dwell Time

How long containers sit at the terminal before being picked up. High dwell times (>5 days) often signal inland transportation bottlenecks — trucking shortages or rail capacity issues.

Building a Global Congestion Dashboard

The /v1/port-congestion/global endpoint returns congestion data for all monitored ports in a single call — ideal for building overview dashboards:

curl https://freightpulsehq.com/api/v1/port-congestion/global \
  -H "X-API-Key: your_api_key" \
  -G \
  -d "min_congestion=50"

This returns only ports with congestion index above 50, keeping the response focused on ports that need attention. You can filter by region, country, or congestion level.

Real-World Integration Examples

1. Smart Route Selection

Before booking a shipment through a specific port, check congestion levels. If the primary port shows high congestion, suggest alternative ports to the user. For example, if Long Beach is congested, Oakland or Tacoma might offer faster turnaround.

2. ETA Adjustment

Layer congestion data on top of carrier-provided ETAs. If a vessel is heading to a port with a 48-hour average wait time, adjust the delivery estimate accordingly. Your customers will trust your ETAs because they actually account for reality.

3. Demurrage Risk Scoring

Combine port congestion (dwell times), inland transport capacity, and historical pickup patterns to calculate a demurrage risk score for each shipment. Flag high-risk containers for early pickup coordination.

4. Rate Prediction

Port congestion is a leading indicator for rate increases. When congestion index rises above 75 on a major trade lane, expect spot rates to follow within 2-5 days. Build this correlation into your rate forecasting models.

Webhook Alerts for Congestion Changes

Polling for congestion data works, but webhooks are more efficient for alerting. FreightPulse supports congestion alert webhooks on Pro plans and above:

# Register a webhook for congestion alerts
curl -X POST https://freightpulsehq.com/api/v1/webhooks \
  -H "X-API-Key: your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "event": "port_congestion_change",
    "port_codes": ["USLAX", "CNSHA", "NLRTM"],
    "threshold": 70,
    "url": "https://your-app.com/webhooks/congestion"
  }'

When any monitored port crosses your threshold, you'll receive a POST with the current congestion data — no polling required.

Historical Data for Seasonal Planning

Port congestion follows seasonal patterns. Chinese New Year, US peak season (August-October), and European summer holidays all create predictable congestion spikes. The /v1/port-congestion/history endpoint gives you historical data to build these patterns into your planning tools.

With the Business plan, you get 180 days of history. Enterprise unlocks 365 days — enough to identify year-over-year trends and build reliable seasonal models.

Getting Started

Port congestion monitoring is available on all FreightPulse plans, including the free tier. The free plan gives you 100 API calls/month — enough to prototype your integration and validate the data quality before scaling up.

Check the API documentation for the complete port congestion endpoint reference, including all available filters, response fields, and error codes.