Liquefied Petroleum Gas (LPG) carriers move high-value, hazardous cargo under tight operational windows and regulatory oversight. For developers building maritime applications—fleet dashboards, port intelligence systems, ETA predictions, ESG auditors, or TMS integrations—reliable, real-time AIS and voyage analytics aren’t optional; they’re mission-critical. This post shows how to design robust LPG carrier tracking and analytics workflows using vessels-api.com, a single, developer-friendly REST API that unifies vessel search, live tracking, fleet operations, port intelligence, and IMO CII emissions scoring. You’ll learn which endpoints to use, how to combine them effectively, and how to write resilient, observable integrations that scale from prototypes to enterprise-grade nautical systems.
Why LPG carrier tracking requires a specialized maritime API
LPG carriers face unique operational and compliance dynamics:
- Cargo sensitivity: LPG cargoes impose strict temperature/pressure regimes and terminal compatibility, demanding precise scheduling and minimal idle time.
- Tight port windows: Congestion at LPG export/import terminals cascades into demurrage, missed laycans, and knock-on supply chain delays.
- Safety zones and weather: Navigational status, route deviations, and weather conditions must be monitored continuously to mitigate risk.
- ESG and compliance: IMO’s CII framework elevates emissions transparency. Accurate voyage distance and time-in-port telemetry enable defensible ESG reporting and charter-party negotiations.
Without a dedicated maritime data API, teams attempt to stitch AIS feeds, port lists, vessel registries, and emissions models themselves—burning months on data normalization, event modeling, and reliability plumbing. Vessels API centralizes all of this: 18 REST endpoints, one consistent JSON envelope, and global AIS coverage with near real-time refresh rates. It removes glue code and lets you focus on decision logic—ETAs, alerts, scheduling, and reporting—rather than infrastructure.
Core design principles: one API key, consistent JSON, easy integration
The API is designed for developer ergonomics:
- Uniform base URL: https://vessels-api.com/api/V1
- Single authentication header across all endpoints
- Consistent JSON response envelope: {"status", "success", "message", "data"} allowing standardized error handling and observability
- Global AIS coverage with near real-time updates suitable for control rooms, dashboards, and automated alerting
- Clean separation of concerns: Search, live tracking, fleet ops, port intelligence, analytics, and CII are logically distinct but composable
This simplicity means less custom parsing, fewer integration branches, and faster iteration—ideal for product teams and data engineers supporting LPG chartering desks or terminal operations.
Endpoints overview: comprehensive maritime coverage for LPG carrier workflows
The platform provides 18 endpoints across vessel intelligence, fleet operations, port intelligence, and legacy compatibility. Below is a complete catalog you can compose into your LPG carrier toolkit:
Vessel Intelligence
- GET /vessels/search — Discover vessels by name (fuzzy), IMO, or MMSI, with filters (type, flag, DWT, TEU, year built).
- GET /vessels/track — Live position, up to 168 hours of track history, active route, predicted ETA, and weather for a specific vessel.
- GET /vessels/nearby — Proximity search by lat/lon and radius to visualize traffic near terminals, safety zones, or waypoints.
- GET /vessels/analytics — Aggregated statistics in vessel, port, or fleet mode across rolling periods (24h, 7d, 30d, 90d).
Fleet Operations
- POST /vessels/fleet — Batch positions, routes, and summary stats for multiple vessels in a single call—ideal for LPG operator control rooms.
- GET /vessels/green — IMO CII emissions scoring and voyage-emissions estimates for ESG and compliance workflows.
Port Intelligence
- GET /ports/congestion — Current congestion plus wait-time statistics to help avoid delays at LPG terminals.
- GET /ports — Catalog of 248 ports with geospatial metadata.
- GET /ports/data — Detailed port info including live vessel counts and expected vessels.
- GET /port/expected-arrivals — Forward-looking ETAs and origin ports to plan berth allocations and tugs.
- GET /port/activity — Recent arrivals and departures for event-driven logistics pipelines.
Legacy Endpoints (stable; prefer /vessels/ for richer data)
- GET /vessel/info?imo=IMO — static vessel particulars (name, flag, dimensions, call sign)
- GET /vessel/route?imo=IMO — current voyage route (departure, destination, ETA, distance, avg speed)
- GET /vessel/position?imo=IMO — last known AIS position by IMO
- GET /vessel/mmsi-position?mmsi=MMSI — last known AIS position by MMSI
- GET /vessel/port?port=PORT_ID — vessels in/at port by port code
- GET /vessel/port/mmsi?mmsi=MMSI — current port call for a vessel by MMSI
In the sections that follow, we will go deep on the endpoints most valuable for LPG carrier operations: /vessels/search, /vessels/track, /vessels/analytics, /vessels/fleet, /vessels/green, and key port endpoints such as /ports/congestion and /port/expected-arrivals. We will also cover performance best practices, error handling, and integration tips to help you productionize quickly.
Deep dive: Identifying LPG carriers with /vessels/search
Business problem: Chartering and analytics teams need to discover relevant LPG carriers by name (including fuzzy matches), unique identifiers (IMO/MMSI), and attributes like flag, DWT, or year built. This is often the first step before tracking, analytics, or fleet enrollment.
Key parameters:
- query: string; fuzzy name lookup or numeric IMO/MMSI search
- ship_type: filter to LPG-capable categories (e.g., “LPG Tanker”)
- flag, min_dwt/max_dwt, year_built_from/to
- Pagination: page, per_page (up to 100)
Example cURL:
curl -H "X-API-Key: YOUR_API_KEY" \
"https://vessels-api.com/api/V1/vessels/search?query=gas&ship_type=LPG%20Tanker&min_dwt=20000"
Expected JSON (truncated for readability):
{
"status": 200,
"success": true,
"message": "OK",
"data": {
"vessels": [
{
"imo": "9701234",
"mmsi": "257001234",
"name": "GAS ATLANTIC",
"flag": "Norway",
"vessel_type": "LPG Tanker",
"gross_tonnage": 48650,
"deadweight_tonnage": 53000,
"year_built": 2016,
"length_m": 225.0,
"width_m": 36.6
},
{
"imo": "9734567",
"mmsi": "563001111",
"name": "GAS PACIFIC",
"flag": "Singapore",
"vessel_type": "LPG Tanker",
"gross_tonnage": 49990,
"deadweight_tonnage": 55000,
"year_built": 2017,
"length_m": 228.0,
"width_m": 37.0
}
],
"pagination": {
"current_page": 1,
"per_page": 50,
"total": 24,
"last_page": 1
}
}
}
How to use the fields:
- imo, mmsi: Unique identifiers; persist these in your database for follow-up calls to tracking, analytics, or fleet endpoints.
- vessel_type: Confirm LPG capability. Combine with DWT and dimensions to assess terminal berth constraints.
- pagination: Use to iterate and construct internal catalogs of your LPG fleet universe.
Implementation tips:
- Persist IMO/MMSI pairs in a normalized table keyed on IMO where available. MMSI may change; IMO is persistent.
- Add filters by DWT and year built to shortlist modern, fuel-efficient carriers for chartering.
- Batch-enroll selected results into /vessels/fleet to monitor a portfolio.
Live monitoring and ETAs: /vessels/track for LPG carriers
Business problem: Control rooms and port operators must continuously track LPG carriers, receive ETAs, monitor navigational status, and correlate with weather. Knowing where your vessel is—and whether it’s deviating, slowing, or signaling “At Anchor”—is core to on-time operations and risk management.
Key parameters:
- imo or mmsi: must provide one
- hours: default 24 (max 168) for recent AIS trail history
- include_route: include active voyage (departure, destination, ETA)
- include_predicted_eta: enable model-derived ETA even if AIS-reported ETA is missing
- include_weather: include weather for situational awareness
Example cURL:
curl -H "X-API-Key: YOUR_API_KEY" \
"https://vessels-api.com/api/V1/vessels/track?imo=9701234&hours=48&include_route=true&include_predicted_eta=true&include_weather=true"
Representative JSON response:
{
"status": 200,
"success": true,
"message": "OK",
"data": {
"vessel": {
"imo": "9701234",
"mmsi": "257001234",
"name": "GAS ATLANTIC"
},
"current_position": {
"latitude": 23.4521,
"longitude": 118.2347,
"speed_knots": 14.3,
"course_degrees": 86,
"heading_degrees": 85,
"navigational_status": "Under way using engine",
"timestamp_utc": "2026-09-16T10:22:45Z",
"destination": "CN NSA",
"eta": "2026-09-17T23:00:00Z"
},
"position_history": [
{
"latitude": 22.9981,
"longitude": 116.9876,
"speed_knots": 14.9,
"course_degrees": 84,
"timestamp_utc": "2026-09-16T02:22:45Z"
},
{
"latitude": 23.2103,
"longitude": 117.6123,
"speed_knots": 14.5,
"course_degrees": 85,
"timestamp_utc": "2026-09-16T06:22:45Z"
}
],
"route": {
"departure_port": "JPUKB",
"departure_time": "2026-09-12T04:15:00Z",
"destination_port": "CNNSA",
"eta": "2026-09-17T23:10:00Z",
"distance_nm": 850.4,
"avg_speed_knots": 14.1
},
"last_port_visits": [
{
"port_id": "JPUKB",
"arrival_time": "2026-09-11T15:32:00Z",
"departure_time": "2026-09-12T04:15:00Z"
}
]
}
}
How to interpret:
- current_position: The canonical real-time snapshot to power map markers, status banners, and alert rules (e.g., “Speed below 8 kn within 50 NM of destination”).
- position_history: Plot a polyline; detect slow steaming or weather avoidance.
- route: Derive nautical distance remaining and build ETA comparisons (AIS-reported vs predicted).
- last_port_visits: Reconstruct port call chains and validate laycan windows.
Real-world scenario:
- Port scheduling: If navigational_status switches to “At Anchor” near the pilot boarding area, notify berth planners to anticipate queue dynamics.
- Safety monitoring: If course/speed deviates from planned corridor during heavy weather, escalate to ops and insurers with weather overlays.
Aggregated voyage intelligence: /vessels/analytics for vessel, port, and fleet views
Business problem: Tactical metrics—total distance, average speed, port calls, time in port—fuel performance benchmarks, emissions modeling, and operational KPIs. This endpoint provides mode-based analytics: vessel, port, or fleet.
Key parameters:
- type: vessel | port | fleet
- Conditional: imo/mmsi (vessel), port_id (port), mmsi_list (fleet)
- period: 24h | 7d | 30d | 90d
Example (vessel mode) cURL:
curl -H "X-API-Key: YOUR_API_KEY" \
"https://vessels-api.com/api/V1/vessels/analytics?type=vessel&imo=9701234&period=7d"
Representative JSON:
{
"status": 200,
"success": true,
"message": "OK",
"data": {
"type": "vessel",
"imo": "9701234",
"mmsi": "257001234",
"name": "GAS ATLANTIC",
"period": "7d",
"statistics": {
"total_distance_nm": 1285.7,
"avg_speed_knots": 13.9,
"max_speed_knots": 17.1,
"port_calls_count": 2,
"total_time_in_port_hours": 18.5,
"ports_visited": ["JPUKB", "CNNSA"]
}
}
}
Usage patterns:
- Benchmarking: Compare avg_speed_knots and total_time_in_port_hours across sister ships to identify underperformers.
- Commercial analytics: Correlate distance_nm against bunker consumption models to predict fuel and emissions.
- Port intelligence (port mode): Query port_id to track throughput and plan berth availability for LPG chains.
- Fleet view (fleet mode): Summarize key metrics for a subset of LPG vessels for executive dashboards.
Multi-ship dashboards and alerts: POST /vessels/fleet
Business problem: Control rooms and chartering teams track dozens of LPG carriers daily. Polling each vessel individually multiplies latency and complexity. The fleet endpoint consolidates batch state—positions, routes, vessel counts—in one request.
Request body:
{
"vessels": [
{"imo": "9701234"},
{"mmsi": "563001111"},
{"imo": "9734567"}
],
"include_positions": true,
"include_routes": true
}
cURL example:
curl -X POST -H "X-API-Key: YOUR_API_KEY" -H "Content-Type: application/json" \
-d '{"vessels":[{"imo":"9701234"},{"mmsi":"563001111"},{"imo":"9734567"}],"include_positions":true,"include_routes":true}' \
"https://vessels-api.com/api/V1/vessels/fleet"
Representative JSON response:
{
"status": 200,
"success": true,
"message": "OK",
"data": {
"fleet": {
"total_vessels": 3,
"vessels_at_sea": 2,
"vessels_in_port": 1
},
"vessels": [
{
"imo": "9701234",
"mmsi": "257001234",
"name": "GAS ATLANTIC",
"position": {
"latitude": 23.4521,
"longitude": 118.2347,
"speed_knots": 14.3,
"course_degrees": 86,
"timestamp_utc": "2026-09-16T10:22:45Z",
"navigational_status": "Under way using engine"
},
"route": {
"departure_port": "JPUKB",
"destination_port": "CNNSA",
"eta": "2026-09-17T23:10:00Z",
"distance_nm": 850.4,
"avg_speed_knots": 14.1
}
},
{
"imo": "9734567",
"mmsi": "563001111",
"name": "GAS PACIFIC",
"position": {
"latitude": 1.3121,
"longitude": 104.0015,
"speed_knots": 0.1,
"course_degrees": 0,
"timestamp_utc": "2026-09-16T10:20:10Z",
"navigational_status": "At anchor"
},
"route": null
},
{
"imo": "9722222",
"mmsi": "257009999",
"name": "GAS HORIZON",
"position": {
"latitude": -1.2540,
"longitude": 103.9122,
"speed_knots": 12.3,
"course_degrees": 70,
"timestamp_utc": "2026-09-16T10:19:00Z",
"navigational_status": "Under way using engine"
},
"route": {
"departure_port": "IDPLM",
"destination_port": "SGSIN",
"eta": "2026-09-17T05:00:00Z",
"distance_nm": 320.5,
"avg_speed_knots": 12.5
}
}
]
}
}
Operational use:
- Fleet-state widget: Show vessels_at_sea vs in_port at a glance for the LPG desk.
- Alerting: If any vessel with route.destination_port = SGSIN slows below 5 kn within 10 NM, notify the terminal liaison.
- Latency reduction: One network call for many vessels lowers dashboard render time and simplifies retry logic.
Port-side planning: /ports/congestion and /port/expected-arrivals
Business problem: LPG terminals frequently develop anchorage queues. Berth planners, tug operations, and shore tanks need forward-looking insight into vessel ETAs and congestion to minimize idle time and demurrage.
Congestion snapshot example:
curl -H "X-API-Key: YOUR_API_KEY" \
"https://vessels-api.com/api/V1/ports/congestion?port_id=SGSIN&period=7d"
Representative congestion JSON:
{
"status": 200,
"success": true,
"message": "OK",
"data": {
"port_id": "SGSIN",
"port_name": "Singapore",
"period": "7d",
"snapshot": {
"vessels_in_anchorage": 37,
"vessels_at_berth": 109
},
"statistics": {
"avg_wait_time_hours_last_7d": 14.8,
"max_wait_time_hours_last_7d": 36.2,
"avg_berth_time_hours_last_7d": 21.4,
"port_calls_count": 542
}
}
}
Use this to:
- Forecast schedule risk: Elevated avg_wait_time_hours_last_7d implies buffer time for LPG berths and downstream delivery commitments.
- Adjust routing: If a discharge port shows spikes, reroute to alternates when commercially viable.
Expected arrivals example:
curl -H "X-API-Key: YOUR_API_KEY" \
"https://vessels-api.com/api/V1/port/expected-arrivals?port=SGSIN"
Interpretation:
- expected_arrivals[].eta: Prime trigger for berth readiness, tug allocation, and customs planning.
- departure_port: Understand market flows and upstream congestion context (e.g., exports from IDPLM).
ESG and compliance: IMO CII scoring with /vessels/green
Business problem: CII rating pressures owners, charterers, and financiers to monitor emissions intensity. LPG carriers—often part of large mixed gas fleets—need consistent calculations and defensible audits.
Key parameters:
- imo or mmsi: identify vessel
- period: 24h | 7d | 30d | 1y (default 30d)
cURL:
curl -H "X-API-Key: YOUR_API_KEY" \
"https://vessels-api.com/api/V1/vessels/green?imo=9701234&period=30d"
Example response:
{
"status": 200,
"success": true,
"message": "OK",
"data": {
"imo": "9701234",
"mmsi": "257001234",
"name": "GAS ATLANTIC",
"period": "30d",
"distance_nm": 4231.8,
"estimated_emissions": {
"co2_tons": 1583.2,
"co2_per_nm": 0.374
},
"cii": {
"score": 7.8,
"rating": "C",
"year": 2026,
"regulation_reference": "IMO MEPC.339(76)"
}
}
}
Actionable insights:
- rating: Grade A–E; target improvements for “D/E” vessels by optimizing speed profiles and port idle time.
- co2_per_nm: Normalize by distance to compare sister ships and evaluate technical retrofits.
- period: Align reporting with internal ESG cadence (monthly, quarterly, rolling year).
Situational awareness around terminals: /vessels/nearby
Business problem: Terminals and pilots need rapid snapshots of nearby traffic, especially for hazardous cargo. Proximity-based filtering reduces radar clutter and enables targeted HSE workflows.
Example cURL:
curl -H "X-API-Key: YOUR_API_KEY" \
"https://vessels-api.com/api/V1/vessels/nearby?latitude=1.264&longitude=103.820&radius=20&ship_type=LPG%20Tanker&limit=50"
Field utility:
- vessels[].distance_nm and position.timestamp_utc: Build “approach windows” and trigger port entry notifications.
- ship_type filter: Focus purely on LPG carriers to reduce noise during berth planning.
Developer quickstart: cURL, Python, and JavaScript examples for LPG use cases
Below are minimal, production-ready snippets for the endpoints discussed above. All responses follow the same JSON envelope so you can centralize parsing, logging, and metrics.
Python example: Track and compute schedule risk for an LPG arrival
import requests
from datetime import datetime, timezone
BASE_URL = "https://vessels-api.com/api/V1"
API_KEY = "YOUR_API_KEY"
def get_track(imo: str, hours=24, include_route=True, include_predicted_eta=True):
params = {
"imo": imo,
"hours": hours,
"include_route": str(include_route).lower(),
"include_predicted_eta": str(include_predicted_eta).lower()
}
r = requests.get(f"{BASE_URL}/vessels/track", 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"))
return payload["data"]
def compute_eta_slippage(track_data):
cp = track_data["current_position"]
route = track_data.get("route")
if not route:
return None
ais_eta = cp.get("eta")
model_eta = route.get("eta")
def to_dt(x):
return datetime.fromisoformat(x.replace("Z", "+00:00"))
if ais_eta and model_eta:
delta = to_dt(ais_eta) - to_dt(model_eta)
return delta.total_seconds() / 3600.0
return None
if __name__ == "__main__":
data = get_track(imo="9701234", hours=48)
slippage_hours = compute_eta_slippage(data)
cp = data["current_position"]
print("Name:", data["vessel"]["name"])
print("Now:", cp["timestamp_utc"], "Speed:", cp["speed_knots"], "kn Status:", cp["navigational_status"])
if slippage_hours is not None:
print(f"ETA slippage (AIS vs model): {slippage_hours:+.1f} hours")
JavaScript example (Node.js): Fleet board with fallback and jittered retries
import fetch from "node-fetch";
const BASE_URL = "https://vessels-api.com/api/V1";
const API_KEY = process.env.VESSELS_API_KEY;
async function postFleet(vessels) {
const body = {
vessels,
include_positions: true,
include_routes: true
};
for (let attempt = 1; attempt <= 3; attempt++) {
try {
const r = await fetch(`${BASE_URL}/vessels/fleet`, {
method: "POST",
headers: {
"X-API-Key": API_KEY,
"Content-Type": "application/json"
},
body: JSON.stringify(body),
timeout: 20000
});
if (!r.ok) {
const text = await r.text();
throw new Error(`HTTP ${r.status}: ${text}`);
}
const json = await r.json();
if (!json.success) throw new Error(json.message || "API error");
return json.data;
} catch (e) {
const backoff = 250 * Math.pow(2, attempt) + Math.random() * 200;
if (attempt === 3) throw e;
await new Promise(res => setTimeout(res, backoff));
}
}
}
(async () => {
const vessels = [{ imo: "9701234" }, { mmsi: "563001111" }, { imo: "9734567" }];
const data = await postFleet(vessels);
console.log("Fleet summary:", data.fleet);
for (const v of data.vessels) {
const pos = v.position;
console.log(`${v.name}: ${pos.navigational_status} @ ${pos.speed_knots} kn`);
}
})();
cURL: Vessel analytics and CII
# Voyage analytics (7 days)
curl -H "X-API-Key: YOUR_API_KEY" \
"https://vessels-api.com/api/V1/vessels/analytics?type=vessel&imo=9701234&period=7d"
# CII score (30 days)
curl -H "X-API-Key: YOUR_API_KEY" \
"https://vessels-api.com/api/V1/vessels/green?imo=9701234&period=30d"
Reliability engineering and observability for maritime integrations
Mission-critical LPG workflows require reliability patterns that go beyond happy-path demos. Build the following into your integration from day one:
- Retry and backoff: Implement exponential backoff with jitter (as shown in the Node.js example). Favor idempotent GETs and safe POSTs.
- Circuit breakers: If upstream timeouts spike, open the breaker and serve cached positions for a short window; refresh in background.
- Health checks: Probe a lightweight endpoint (e.g., GET /ports) to verify connectivity and credentials during app startup.
- Time budgets: Set per-call timeouts (10–20s) and enforce end-to-end SLOs for dashboards (e.g., 1.5s P95 render with cached fleet summaries).
- Geo-partitioned polling: Poll vessels near critical ports more frequently; long-haul ocean legs can update less often.
- Observability: Log status, success, message on every call; emit metrics such as request latency, error rate by endpoint, and payload sizes.
- Data governance: Tag logs by application component and vessel IMO; maintain audit trails for ESG data used in compliance filings.
This approach yields resilient maritime applications that remain useful even during transient network or upstream turbulence.
Error handling and status codes
All responses follow a consistent envelope with HTTP codes signaling outcomes. Build a thin error-layer to convert codes into actionable messages:
- 200 OK: Proceed to parse data.
- 400 Bad Request: Parameter missing/invalid—validate query and data types. Example: radius over max in /vessels/nearby.
- 401 Unauthorized: Check header presence/format and rotate secrets if necessary.
- 404 Not Found: Vessel/port not found—fall back to /vessels/search to re-identify assets.
- 422 Unprocessable: Parameter out of range—e.g., per_page > 100 on /vessels/search.
- 429 Too Many Requests: Honor backoff; aggregate requests with /vessels/fleet to reduce call volume.
- 500 Server Error: Retry with jitter and trigger circuit breaker after threshold.
Because status, success, and message are in every payload, you can centralize exception formatting and ensure on-call engineers have actionable context in logs and dashboards.
End-to-end LPG carrier scenario: From discovery to ESG reporting
Let’s stitch together a realistic operational flow for an LPG voyage from loading in Ulsan to discharge in Singapore:
- Discovery with /vessels/search: Filter by vessel_type = LPG Tanker and DWT between 45–55k to assemble an eligible list of carriers.
- Enroll in /vessels/fleet: Add shortlisted vessels to the fleet endpoint to power a single dashboard.
- Monitor with /vessels/track: For the awarded carrier, poll for current_position, include_route, and include_predicted_eta to guide berth windows.
- Port-side planning: Query /ports/congestion for SGSIN and /port/expected-arrivals to align tug, pilot, and terminal resources.
- Performance analytics: Use /vessels/analytics (vessel and port modes) to benchmark speed profiles and time-in-port; compare to SOPs.
- ESG wrap-up: Call /vessels/green for the last 30d to update internal sustainability dashboards and quarterly reports.
In a production system, wire these steps into asynchronous workers: ingestion jobs for AIS and port signals, application-layer rules for alerts, and a reporting pipeline for ESG and SLA compliance.
Legacy endpoints: Compatibility and quick lookups
While the /vessels/* family offers richer telemetry, the legacy endpoints are valuable for simple lookups, low-overhead health checks, and incremental migrations:
- /vessel/position and /vessel/mmsi-position provide last known AIS snapshot when you don’t need history or routes.
- /vessel/route gives a basic voyage summary if you’re building a lightweight ETA board.
- /vessel/info surfaces static particulars; integrate into property panes in your UI.
- /vessel/port and /vessel/port/mmsi help map current port calls.
Use these for fast prototypes, then graduate to /vessels/track and /vessels/analytics for deeper insights without changing your core response-handling pattern thanks to the consistent JSON envelope.
Performance best practices for maritime workloads
To meet control-room SLAs while controlling compute and network costs:
- Batch whenever possible: /vessels/fleet is your friend for multi-ship widgets.
- Cache static data: Port catalogs (/ports) and vessel particulars change infrequently—cache for hours or days.
- Tune update cadence: Poll long-haul positions every 10–15 minutes; switch to 2–5 minute cadence within 100 NM of destination.
- Throttle map layers: Decimate historical position polylines server-side for large-screen visualizations.
- Use field selection: Although endpoints return structured objects, your application can ignore fields not used in the UI to reduce serialization overhead in your runtime.
Building developer-friendly tooling around the API
Reduce cognitive load across your team by standardizing:
- A single API client: Generate thin wrappers for GET/POST operations that inject headers, parse the envelope, and raise typed exceptions.
- Domain models: Create objects like VesselSnapshot, VoyageRoute, PortCongestion to avoid scattering JSON keys across the codebase.
- Telemetry: Instrument every call with endpoint, latency, and success/failed counters; alert on anomalies by port or region.
- Security posture: Keep secrets in your runtime’s secret manager; rotate regularly; restrict access by environment (dev/stage/prod).
Putting it all together: A reference JSON audit for an LPG voyage
Below is a compact, end-to-end data bundle you might persist per voyage cycle, combined from multiple endpoints. This pattern makes your system auditable and explainable, especially for ESG and post-operations analytics.
{
"voyage_id": "VOY-GLPG-2026-0916-001",
"vessel": {
"imo": "9701234",
"mmsi": "257001234",
"name": "GAS ATLANTIC",
"type": "LPG Tanker"
},
"route_snapshot": {
"source": "vessels/track",
"departure_port": "JPUKB",
"destination_port": "CNNSA",
"eta_model": "2026-09-17T23:10:00Z",
"eta_ais": "2026-09-17T23:00:00Z",
"distance_remaining_nm": 120.7,
"avg_speed_knots": 14.1
},
"ops_state": {
"navigational_status": "Under way using engine",
"speed_knots": 14.3,
"course": 86,
"last_update": "2026-09-16T10:22:45Z"
},
"port_intel": {
"port_id": "SGSIN",
"congestion": {
"vessels_in_anchorage": 37,
"avg_wait_time_hours_last_7d": 14.8
},
"expected_arrivals_checked_at": "2026-09-16T10:30:00Z"
},
"esg": {
"period": "30d",
"cii_rating": "C",
"estimated_emissions": {
"co2_tons": 1583.2,
"co2_per_nm": 0.374
}
},
"analytics": {
"period": "7d",
"total_distance_nm": 1285.7,
"time_in_port_hours": 18.5
}
}
This structure gives ops managers real-time context and ensures finance/ESG teams can defend reported KPIs with traceable source fields.
Troubleshooting checklist for maritime developers
- No route in /vessels/track: The vessel may not have a declared voyage; rely on predicted ETA or historical course for short-term planning.
- Discrepancy between AIS ETA and model ETA: Use analytics and weather to choose a policy (e.g., prefer model ETA within 100 NM).
- Sparse position_history: Increase the hours parameter up to 168 to enrich the polyline for voyage-replay tools.
- Port not found: Validate port identifiers via GET /ports and /ports/data, then retry with correct port_id.
- Unexpected “At anchor” near destination: Cross-check /ports/congestion and switch berth plans accordingly; notify stakeholders.
Why vessels-api.com is the go-to maritime data API for LPG carriers
- 18 REST endpoints: Everything from discovery and live AIS to fleet batch, port intel, voyage analytics, and IMO CII—built on a single, consistent interface.
- Global coverage and near real-time updates: Designed for operations centers and high-stakes maritime timelines.
- Developer-first: One base URL, consistent JSON envelopes, straightforward request semantics, and language-agnostic integration.
- Scalable from indie to enterprise: Patterns that work for single-ship apps scale to large LPG fleets with worker queues, caching, and batch endpoints.
If you need a single source of maritime truth to power LPG carrier tracking, ETAs, port readiness, and ESG reporting, build on Vessels API.
Get started: Build your LPG tracking workflow today
You’ve seen how to discover LPG carriers, monitor them in real time, forecast port outcomes, compute analytics, and deliver CII scoring—all through one API. The next step is to plug these endpoints into your architecture and ship a production-ready dashboard.
- Explore the endpoints above and compose them into your voyage workflows.
- Instrument retries, caching, and circuit breakers for robust operations.
- Iterate quickly with cURL and the Python/JavaScript examples, then bake the client into your backend services.
Ready to build? Try Vessels API for free. For teams standardizing on a unified maritime data layer, Get started with Vessels API. To learn more about what you can build—from LPG fleet boards to ESG control towers—visit Vessels API and start integrating today.
