Livestock carriers have almost zero margin for error on ETA and port dwell. Holding thousands of live animals at anchorage is unsafe, expensive, and risks regulatory action. By the end of this guide you’ll be able to wire up AIS tracking for livestock carriers, watch port congestion in real time, and programmatically surface the safest arrival window using vessels-api.com.
Why livestock carrier teams wire to AIS + port intelligence
Compared to bulkers or containers, livestock carriers face added constraints:
- Animal welfare windows: minimize anchorage and idle time.
- Port selection: divert early if congestion spikes.
- ETA precision: coordinate vets, feed, and freshwater on berth.
- Regulatory reporting: prove compliance, including CII tracking.
With vessels-api.com you get one API key and one base URL for 18 REST endpoints spanning live tracking, port congestion, expected arrivals, fleet ops, analytics, and IMO CII scoring. Every response uses the same JSON envelope: {status, success, message, data}. That consistency matters when you’re stitching together alerts, dashboards, and control-room automations.
Endpoints we’ll use for livestock carrier AIS + port planning
- GET /vessels/track — live AIS position, 168h history, route, predicted ETA, and last port visits.
- GET /ports/congestion — snapshot of anchorage/berth counts and wait-time statistics.
- GET /port/expected-arrivals — in-bound traffic with ETA and origin to forecast berthing pressure.
- POST /vessels/fleet — batch positions/routes for multiple carriers in one call.
- GET /vessels/analytics — aggregate voyage stats to benchmark time at sea vs. port dwell.
Base URL for all calls: https://vessels-api.com/api/V1. Authenticate with the X-API-Key header.
Live tracking quickstart for a livestock carrier
GET /vessels/track returns the current position, up to 168 hours of position history, the active route, and (when available) a predicted ETA. Use it to power your map, drive ETA alerts, and document the last port visits.
Official cURL sample
curl -H "X-API-Key: YOUR_API_KEY" "https://vessels-api.com/api/V1/vessels/track?mmsi=258785000&hours=48"Official JSON sample
{
"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": "2017-01-24T04:07:27+00:00",
"age_minutes": 5095940,
"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 read for livestock ops:
- data.vessel: identifiers for your database join keys (IMO/MMSI).
- data.current_position: latitude/longitude, speed_knots, course_degrees, and timestamp_utc (UTC ISO-8601) for map and freshness checks. navigational_status is AIS NAV status code (e.g., 5 = moored/anchored context-dependent).
- data.route: departure_port/destination_port and eta if broadcast or derived, avg_speed_knots for ETA modeling.
- data.last_port_visits: for compliance narratives and port-call history.
- data.predicted_eta: when present, vessels-api.com provides a prediction alongside AIS-broadcast ETA.
Spot congestion before you commit to an approach
Use GET /ports/congestion to determine whether your target port’s anchorage or berth situation risks welfare standards. Query by UNLOCODE supplied in the endpoint parameter. For example, ARBUE is Buenos Aires.
The response includes:
- data.snapshot: vessels_in_anchorage, vessels_at_berth.
- data.statistics: wait-time and berth-time aggregates over the selected period (24h, 3d, or 7d).
Practical usage: trigger an early diversion candidate list when vessels_in_anchorage exceeds your threshold or when avg_wait_time_hours_last_7d is above your welfare SLA.
Deconflict berthing: expected arrivals and ETA
Expected inbound traffic is a leading indicator for berth pressure. GET /port/expected-arrivals returns vessels headed to a port with their ETA and departure_port. Use it to correlate your own ETA with arrival waves from similar ship types or large moves that could affect tug/pilot availability.
Read data.expected_arrivals[].eta (UTC), vessel_type to filter to carriers similar to your operation, and departure_port to understand which weather systems or choke points may cascade delays into your window.
Operate multiple carriers in one call
For operators running multiple livestock carriers, POST /vessels/fleet batches positions and routes in one request, avoiding N calls in your cron and simplifying retries. You can pass IMO or MMSI per vessel record.
The response includes a fleet summary (vessels_at_sea, vessels_in_port) plus an array of vessel objects with position and route if requested. Use this to power your operations dashboard safely within a single API transaction.
Benchmark welfare-friendly operations with analytics and CII
Two endpoints can support both continuous improvement and ESG narratives:
- GET /vessels/analytics: Aggregate voyage stats like total_distance_nm, avg_speed_knots, port_calls_count, and total_time_in_port_hours over a period (24h|7d|30d|90d). This helps verify whether routing policies reduce time-in-port for animal welfare.
- GET /vessels/green: IMO CII metrics for compliance teams. Returns distance_nm, estimated_emissions (co2_tons and co2_per_nm), and cii.score with rating A-E based on MEPC.339(76). Useful for disclosures or for decision support when choosing among ports/approaches that change speed profiles.
Examples:
Python: ETA watch + congestion guardrail
The snippet below polls a livestock carrier’s AIS position and route, then cross-checks target-port congestion to compute a risk score for approaching vs. diverting. It uses the consistent response envelope and UTC timestamps.
import os
import time
import requests
from datetime import datetime, timezone
API_KEY = os.getenv("VESSELS_API_KEY", "YOUR_API_KEY")
BASE = "https://vessels-api.com/api/V1"
HEADERS = {"X-API-Key": API_KEY}
def get_track(mmsi: str, hours: int = 24):
url = f"{BASE}/vessels/track"
params = {"mmsi": mmsi, "hours": hours, "include_route": "true", "include_predicted_eta": "true"}
r = requests.get(url, headers=HEADERS, params=params, timeout=20)
r.raise_for_status()
j = r.json()
if not j.get("success"):
raise RuntimeError(f"Track error: {j.get('message')}")
return j["data"]
def get_congestion(port_id: str, period: str = "7d"):
url = f"{BASE}/ports/congestion"
params = {"port_id": port_id, "period": period}
r = requests.get(url, headers=HEADERS, params=params, timeout=20)
r.raise_for_status()
j = r.json()
if not j.get("success"):
raise RuntimeError(f"Congestion error: {j.get('message')}")
return j["data"]
def get_expected_arrivals(port_id: str):
url = f"{BASE}/port/expected-arrivals"
params = {"port": port_id}
r = requests.get(url, headers=HEADERS, params=params, timeout=20)
r.raise_for_status()
j = r.json()
if not j.get("success"):
raise RuntimeError(f"Arrivals error: {j.get('message')}")
return j["data"]
def utc_iso_to_dt(s):
return datetime.fromisoformat(s.replace("Z", "+00:00")).astimezone(timezone.utc) if s else None
def approach_risk_score(congestion, eta_dt):
snap = congestion.get("snapshot", {})
stats = congestion.get("statistics", {})
anch = snap.get("vessels_in_anchorage") or 0
berth = snap.get("vessels_at_berth") or 0
avg_wait = stats.get("avg_wait_time_hours_last_7d") or 0
# Simple heuristic: higher anchorage + wait time -> higher risk.
score = anch * 2 + berth * 0.5 + (avg_wait / 6.0)
# Tighten risk if ETA aligns with typical morning peaks (06:00–10:00 UTC).
if eta_dt and 6 <= eta_dt.hour <= 10:
score += 2
return score
def main():
mmsi = "258785000" # documented track fixture
target_port = "ARBUE" # Buenos Aires UNLOCODE
track = get_track(mmsi, hours=48)
vessel = track.get("vessel", {})
current = track.get("current_position", {})
route = track.get("route", {})
predicted_eta = track.get("predicted_eta") or route.get("eta")
eta_dt = utc_iso_to_dt(predicted_eta)
congestion = get_congestion(target_port, period="7d")
arrivals = get_expected_arrivals(target_port)
risk = approach_risk_score(congestion, eta_dt)
print(f"Vessel MMSI: {vessel.get('mmsi')}, IMO: {vessel.get('imo')}")
print(f"Current lat/lon: {current.get('latitude')}, {current.get('longitude')}, ts: {current.get('timestamp_utc')}")
print(f"Route dest port: {route.get('destination_port')}, ETA: {predicted_eta}")
print(f"Congestion snapshot: anch={congestion.get('snapshot',{}).get('vessels_in_anchorage')} "
f"berth={congestion.get('snapshot',{}).get('vessels_at_berth')}")
print(f"Expected arrivals count: {len(arrivals.get('expected_arrivals', []))}")
print(f"Approach risk score: {risk:.1f}")
if risk >= 8:
print("Action: Evaluate diversion or adjust speed to shift arrival window.")
else:
print("Action: Proceed, monitor for spikes hourly.")
if __name__ == "__main__":
main()Production notes that save you time
- Authentication: send X-API-Key on every request; no OAuth or per-endpoint auth differences.
- Consistency: every response uses {status, success, message, data}. Validate success and handle message for clear error reporting.
- Timestamps: timestamps are UTC ISO-8601 (e.g., 2026-04-30T09:00:00+00:00). Normalize to UTC in your app.
- Units: distances in nautical miles (nm), speeds in knots, wait/berth times in hours.
- Pagination: /vessels/search supports page and per_page (max 100). For rolling dashboards, store pagination cursors.
- Polling cadence: for approach decisions, 2–5 minute polling is typical; cache stable fields like vessel particulars and only refresh moving targets.
- Rate limiting: on HTTP 429, implement exponential backoff and jitter. Log the envelope’s message for diagnostics.
- Port identifiers: for /ports/congestion and related endpoints, use the documented identifiers (e.g., ARBUE). You can discover ports via GET /ports.
- Fleet throughput: use POST /vessels/fleet to minimize N parallel calls and to centralize error handling.
End-to-end workflow for a livestock carrier approach window
- Detect approach: call GET /vessels/track with include_route=true. If route.destination_port matches your target port, capture route.eta or predicted_eta.
- Check port load: call GET /ports/congestion for the same port_id with period=7d and compute a congestion score from snapshot and statistics.
- Forecast overlap: call GET /port/expected-arrivals and filter arrivals within +/- 6 hours of your ETA; count large inbound vessels or specific ship types if you need proxy berth load.
- Decide: if the congestion score or arrivals overlap exceed your threshold, notify the master and shore-based ops to slow-steam or consider a diversion candidate.
- Document: store last_port_visits and analytics for ESG and welfare audits. Use GET /vessels/analytics and GET /vessels/green for reporting intervals.
Optional: add a fleet view
To get a dashboard for several carriers:
- POST /vessels/fleet with IMO/MMSI pairs for all ships on your board.
- Merge route.destination_port and any predicted ETA fields into a calendar timeline.
- Side-panel: run GET /ports/congestion for the top 3 destination ports represented in the next 24–72 hours.
cURL cheatsheet (copy/paste)
- Track a livestock carrier (48h history):
- Check Buenos Aires congestion (7d window):
- See expected arrivals for Buenos Aires:
- Batch positions and routes for your fleet:
FAQ
Q: Do I need different auth for each endpoint?
A: No. Use the same X-API-Key header for every endpoint. One key, one base URL.
Q: Are timestamps in local time or UTC?
A: All timestamps are UTC in ISO-8601 format. Normalize to UTC in your pipeline before any comparisons.
Q: How do I avoid calling the API too often?
A: Cache stable data (vessel particulars, last_port_visits older than a day) and poll moving targets (position, congestion) more frequently. Use POST /vessels/fleet to consolidate multiple ships into one request.
Q: How do I interpret navigational_status values?
A: It’s the AIS NAV status code from the transponder. Pair it with speed_knots and position to infer anchored vs. underway conditions.
Q: What happens if predicted_eta is null?
A: Fallback to current_position.eta (if AIS broadcasts it) or route.eta. Build your logic to handle nulls gracefully and recalculate at each poll.
Build your livestock carrier approach dashboard today with live AIS, congestion intel, and programmatic ETAs, all from one consistent API. Get started with your key, explore the endpoints, and ship your first integration in an afternoon: Register, read the Documentation, and explore real-time feeds via the MCP.
