Heavy-lift voyages are unforgiving. When a multi-million-dollar transformer or wind turbine blade is aboard, delays ripple through cranes, barges, pilots, and onshore crews. In this guide, you’ll build a data-driven workflow for heavy-lift vessel AIS tracking that produces reliable ETAs and live port congestion context, using a single, consistent REST API. By the end, you’ll query live tracks, compute ETAs, monitor port queues, and surface the exact fields your operations and logistics teams need.
What you’ll build with Vessels API
We’ll use vessels-api.com to stitch together a simple but production-ready flow:
- Resolve a target heavy-lift ship and pull its live AIS track plus up to 168 hours of history (positions, route, ETA).
- Overlay port congestion signals for ETA realism at key breakbulk terminals.
- Batch-check multiple vessels in your project portfolio with a single POST.
- Extract voyage analytics (distance, speeds, port calls) for schedule performance and downstream reporting.
Every endpoint shares the same base URL and authentication model:
- Base URL: https://vessels-api.com/api/V1
- Authentication: X-API-Key request header (no OAuth)
- Response envelope: {"status","success","message","data"}
Endpoints we’ll use for heavy-lift transportation
- GET /vessels/track — Live position, 24–168h history, active route, predicted ETA, weather.
- GET /ports/congestion — Real-time congestion snapshot and wait-time statistics.
- POST /vessels/fleet — Batch positions/routes for a multi-vessel project roster.
- GET /vessels/analytics — Voyage statistics: distance, average speed, port calls.
These four endpoints give you a focused toolkit for ETA reliability and port operations situational awareness in heavy-lift logistics.
Live AIS tracking and ETA: anchor your workflow on /vessels/track
For heavy-lift cargoes, accurate ETAs feed everything: crane booking, barge ops, customs scheduling, drayage windows. Start with the track endpoint and request the last 48 hours to contextualize speed trends and course changes. The official sample below uses MMSI 258785000.
Required cURL (official sample)
curl -H "X-API-Key: YOUR_API_KEY" "https://vessels-api.com/api/V1/vessels/track?mmsi=258785000&hours=48"Official JSON response (verbatim)
{
"status": 200,
"success": true,
"message": "OK",
"data": {
"vessel": {
"imo": "9122556",
"mmsi": "258785000",
"name": null
},
"current_position": {
"latitude": 53.33708,
"longitude": 7.17993,
"speed_knots": 0,
"course_degrees": 212,
"heading_degrees": null,
"navigational_status": 5,
"timestamp_utc": "2017-01-24T04:07:00+00:00",
"age_minutes": 5093061,
"destination": null,
"eta": null
},
"predicted_eta": null,
"position_history": [],
"route": {
"departure_port": "HERACLIO",
"departure_time": "2026-04-27T21:35:36+00:00",
"destination_port": "HERACLIO",
"eta": "2026-04-30T09:00:00+00:00",
"distance_nm": null,
"avg_speed_knots": 17.1
},
"last_port_visits": [
{
"port_id": "156",
"port_name": "EMDEN",
"arrival_time": "2026-07-30T13:00:10+00:00",
"departure_time": null,
"duration_hours": null
},
{
"port_id": "10",
"port_name": "HERACLIO",
"arrival_time": "2026-04-20T12:00:00+00:00",
"departure_time": "2026-04-23T08:00:00+00:00",
"duration_hours": 68
}
],
"weather": null
}
}What to use for heavy-lift ops:
- data.current_position.speed_knots: Zero speed at anchorage/berth or slow-steaming flags schedule risk.
- data.current_position.navigational_status: A numeric status indicator you can map to “at anchor,” “moored,” etc. for internal dashboards.
- data.route.eta: Route-level ETA when the vessel is on a voyage plan; use with caution if a new destination is set en route.
- data.last_port_visits: Fast provenance check (e.g., turbine blades out of EMDEN heading to project port).
- Timestamps: timestamp_utc and route/departure_time are UTC ISO-8601. Store as UTC and convert in the UI.
Tip: Include the hours param up to 168 to get a complete last-7-days track when diagnosing weather slowdowns or waiting patterns outside breakbulk terminals. Optional parameters include include_route, include_predicted_eta, and include_weather for richer context where available.
Python example: track + ETA fields for a heavy-lift dashboard
import requests
import datetime
API_KEY = "YOUR_API_KEY"
BASE_URL = "https://vessels-api.com/api/V1"
def track(mmsi: str, hours: int = 48):
url = f"{BASE_URL}/vessels/track"
params = {"mmsi": mmsi, "hours": hours}
r = requests.get(url, headers={"X-API-Key": API_KEY}, params=params, timeout=20)
r.raise_for_status()
payload = r.json()
if not payload.get("success"):
raise RuntimeError(payload.get("message", "Unknown error"))
data = payload["data"]
cp = data.get("current_position", {}) or {}
route = data.get("route", {}) or {}
# Normalize useful fields
return {
"imo": data.get("vessel", {}).get("imo"),
"mmsi": data.get("vessel", {}).get("mmsi"),
"lat": cp.get("latitude"),
"lon": cp.get("longitude"),
"speed_knots": cp.get("speed_knots"),
"navigational_status": cp.get("navigational_status"),
"timestamp_utc": cp.get("timestamp_utc"),
"route_departure_port": route.get("departure_port"),
"route_destination_port": route.get("destination_port"),
"route_eta": route.get("eta"),
"avg_speed_knots": route.get("avg_speed_knots"),
}
if __name__ == "__main__":
row = track("258785000", hours=48)
print("Track summary:", row)
Port congestion: align crane windows with realistic arrival readiness
Heavy-lift calls hinge on berth readiness, heavy-duty crane availability, and weather windows that often shrink to a day or two. Layering port congestion metrics onto vessel ETA prevents you from booking cranes against unrealistic berthing times.
Use GET /ports/congestion to snapshot queue and wait-time stats. The request requires a UNLOCODE-style port identifier in the port_id parameter and supports period=24h|3d|7d windows. Example call:
Key fields to consume:
- data.snapshot.vessels_in_anchorage and data.snapshot.vessels_at_berth: Live directional indicators for securing pilots and tugs.
- data.statistics.avg_wait_time_hours_last_7d and max_wait_time_hours_last_7d: Use to pad ETA with realistic queue buffers for project cargo berths.
- All timestamps and calculation periods are in UTC; treat them as inputs for ETA confidence scores in your workflow.
Practical tip: Cache congestion responses for 5–10 minutes in your service layer to avoid unnecessary calls when multiple users view the same port dashboard simultaneously. Respect 429 rate-limit responses by backing off and retrying with jitter.
Batch visibility for multiple heavy-lift units: /vessels/fleet
Project logistics often track several heavy-lift and multipurpose vessels concurrently (e.g., hull modules, nacelles, and blades on different ships). Instead of calling /vessels/track for each vessel, hit POST /vessels/fleet once with the list of MMSI/IMO identifiers.
What to render:
- data.fleet.vessels_at_sea and vessels_in_port for a portfolio summary tile.
- Per-vessel position.speed_knots and route.eta for a rolling ETA board across your heavy-lift projects.
Engineering note: /vessels/fleet is the only POST in this workflow; everything else is GET. Maintain a unified API client that sets the X-API-Key header automatically and serializes JSON bodies for POST only.
Port-side planning: expected arrivals for crane and barge scheduling
When a port’s heavy-lift berth is shared across multiple projects, it’s useful to see competitors or partner ships inbound with ETAs and last ports. Use GET /port/expected-arrivals to list planned arrivals that could drive berth conflicts or tug scarcity.
Fields to extract:
- data.expected_arrivals[].eta and departure_port to trace flows that align with project cargo corridors.
- data.expected_arrivals[].vessel_type to filter for heavy-lift/multipurpose profiles in your UI.
Combine this with /ports/congestion to differentiate “paper ETAs” from feasible berth windows.
Voyage analytics for post-operation review and planning: /vessels/analytics
Heavy-lift voyages are atypical: off-route standby, weather avoidance at sea, and prolonged port stays for lifting windows. GET /vessels/analytics surfaces the signal you need over a 24h|7d|30d|90d period.
How to use it:
- statistics.total_distance_nm and avg_speed_knots validate whether the planned passage profile was met.
- statistics.port_calls_count and total_time_in_port_hours quantify terminal-induced schedule drag.
- statistics.ports_visited supports segment-by-segment performance reporting for stakeholders.
Design notes for a reliable heavy-lift ETA stack
- Use UTC everywhere. All timestamps returned by the API are UTC ISO-8601; convert to local timezones at the UI boundary.
- Fields may be null. For example, vessel.name or route.distance_nm can be null. Always null-check before arithmetic or formatting.
- Position history windows. hours defaults to 24 and maxes at 168; if your ETA model needs more history, persist points server-side.
- Pagination. /vessels/search supports pagination; keep per_page ≤ 100 and use pagination.last_page for UI nav.
- Error handling. 401 means missing/invalid X-API-Key; 422 signals parameter out of range (e.g., hours > 168); 429 ask your client to back off.
Putting it together: minimal server workflow
- Track target heavy-lift vessel with GET /vessels/track (hours=48) and read current_position + route.eta.
- Fetch GET /ports/congestion for the destination port (e.g., ARBUE). Derive a congestion-adjusted ETA buffer from avg_wait_time_hours_last_7d.
- Query GET /port/expected-arrivals for inbound conflicts within ±24h of your route.eta; adjust pilot/crane bookings accordingly.
- For multiple project ships, POST /vessels/fleet with include_positions and include_routes to render a unified board.
- After berthing, run GET /vessels/analytics (period=7d or 30d) to document voyage performance and update playbooks.
Security, performance, and data hygiene
- Do not embed your X-API-Key in client-side JavaScript. Proxy requests through your server.
- Implement a simple 2–5 minute cache for congestion and expected-arrivals endpoints. For /vessels/track keep cache short (30–60 seconds) to preserve live AIS feel.
- Normalize units: speed in knots, distance in nautical miles, durations in hours. Persist as numeric types.
- Create a small mapping for navigational_status integers to human labels for craneside UI clarity.
Extended integrations
- Fleet overview widgets: Use /vessels/fleet’s data.fleet rollups to display “vessels at sea / in port” at a glance.
- Ops alerts: Trigger notifications when speed_knots drops to 0 within X nautical miles of the pilot station and congestion exceeds a threshold.
- Reporting: Capture /vessels/analytics weekly for management scorecards on route discipline and port dwell times.
FAQ
Q: What header is required for authentication?
A: Send X-API-Key: YOUR_API_KEY on every request. There is no per-endpoint auth variation.
Q: What timezone are timestamps in?
A: All timestamps are UTC ISO-8601 (e.g., 2026-04-27T21:35:36+00:00). Convert at the presentation layer.
Q: How far back can I request position history?
A: Use the hours parameter on GET /vessels/track up to 168 (7 days). Default is 24.
Q: Can I query multiple vessels at once?
A: Yes. Use POST /vessels/fleet with a JSON body containing an array of IMO/MMSI and set include_positions/include_routes as needed.
Q: How do I handle rate limits?
A: If you receive a 429 status, back off and retry with jitter. Cache read-heavy views (like congestion) for several minutes.
Next steps
Spin up your heavy-lift ETA and port congestion workflow in under an hour using the endpoints above. Start with the official /vessels/track sample, then layer in congestion and expected arrivals to de-risk cranes, pilots, and barge moves. Explore the full reference and test live queries below.
