You need a reliable way to quantify congestion at Laem Chabang (THLCH) that your transportation systems and planners can use without scraping dashboards or guessing from AIS maps. By the end of this guide, you’ll call the FreightPulse Port Congestion API for Laem Chabang, understand the IMF PortWatch methodology behind the numbers, parse the fields that matter (including the 7‑day vs 90‑day baseline), and ship a production-safe integration.
What the Laem Chabang congestion API provides
The FreightPulse Port Congestion endpoint for Laem Chabang (THLCH) returns a normalized congestion signal derived from IMF PortWatch. It’s designed for planning and analytics, not live anchorage or queue time. You’ll receive:
- A congestion level label (e.g., low, moderate, high) for Laem Chabang.
- Recent 7-day average port calls versus a 90-day baseline for the same port.
- As-of date, source, and methodology metadata, including the expected 6–7 day lag.
Use this to adjust routing logic, ETD/ETA buffers, terminal appointment expectations, or tender acceptance rules in transportation workflows.
Methodology: IMF PortWatch (7-day vs 90-day, lagged 6–7 days)
FreightPulse congestion uses IMF PortWatch built on UN Global Platform AIS data. The key signal compares the last 7 days’ average port calls to a 90-day baseline at the same port. Because the upstream data aggregates daily AIS, expect a 6–7 day lag; this is not a live wait-time data feed. The output is robust for trend analysis and operational planning but should not be used as a minute-by-minute anchorage queue indicator.
Authentication, envelope, and quotas
Requests accept an API key via the header X-API-Key. Guests without a key are allowed 100 requests per IP per day. All responses are wrapped in an envelope of the form {"success":true,"data":{...}}.
- Auth header: X-API-Key: YOUR_API_KEY
- Guest limit: 100 requests/day per IP
- Envelope: {"success":true,"data":{...}}
Quickstart: cURL for Laem Chabang (THLCH)
Run this request to retrieve Laem Chabang’s congestion signal. The port parameter accepts the UN/LOCODE without the space.
Live FreightPulse congestion for THLCH.
Source: live
cURL:
curl -H "X-API-Key: YOUR_API_KEY" "https://freightpulsehq.com/api/v1/port-congestion?port=THLCH"Response fields you will actually use
JSON:
{
"success": true,
"data": {
"results": [
{
"port": "Laem Chabang",
"locode": "TH LCH",
"country": "Thailand",
"congestion": "moderate",
"as_of": "2026-10-02",
"metrics": {
"recent_avg_portcalls_per_day": 19,
"baseline_avg_portcalls_per_day": 18.9,
"ratio_vs_baseline": 1
}
}
],
"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": "581baf37-a5a7-4a23-8c7a-f6800b4856e4"
}
}Key fields for Laem Chabang integrations:
- data.results[0].congestion: human-readable level (e.g., moderate) for decisioning and alerts.
- data.results[0].as_of: the date the metric refers to; use it to annotate dashboards and manage data freshness. Dates are calendar dates; treat them as local-to-port context or UTC day boundaries in storage.
- data.results[0].metrics.ratio_vs_baseline: normalized ratio of the 7-day average vs the 90-day baseline. Do not substitute values from any other port; use the returned value as-is.
- data.results[0].metrics.recent_avg_portcalls_per_day and baseline_avg_portcalls_per_day: raw counts that underpin the ratio. Useful for threshold tuning and QC.
- data.meta.source and data.meta.data_lag_days: make the lag and source explicit to downstream consumers; prevents confusion with live AIS wait-time feeds.
Python example: fetch, parse, and act on Laem Chabang congestion
This example calls the same endpoint and extracts congestion, ratio_vs_baseline, and as_of for Laem Chabang (THLCH). Replace YOUR_API_KEY with your key, or omit the header for guest access within quota.
import os
import time
import requests
API_URL = "https://freightpulsehq.com/api/v1/port-congestion"
PORT = "THLCH"
API_KEY = os.getenv("FREIGHTPULSE_API_KEY", "YOUR_API_KEY")
def get_thlch_congestion():
headers = {"X-API-Key": API_KEY} if API_KEY else {}
params = {"port": PORT}
resp = requests.get(API_URL, headers=headers, params=params, timeout=15)
resp.raise_for_status()
payload = resp.json()
if not payload.get("success"):
raise RuntimeError("API indicated failure")
results = payload.get("data", {}).get("results", [])
if not results:
raise RuntimeError("No results for THLCH")
row = results[0]
congestion_level = row.get("congestion")
as_of = row.get("as_of")
metrics = row.get("metrics", {})
ratio = metrics.get("ratio_vs_baseline")
recent = metrics.get("recent_avg_portcalls_per_day")
baseline = metrics.get("baseline_avg_portcalls_per_day")
return {
"congestion": congestion_level,
"as_of": as_of,
"ratio_vs_baseline": ratio,
"recent_avg": recent,
"baseline_avg": baseline,
"source": payload.get("data", {}).get("meta", {}).get("source"),
"lag": payload.get("data", {}).get("meta", {}).get("data_lag_days"),
}
def main():
data = get_thlch_congestion()
# Example decision: nudge buffers based on level
buffer_minutes = {
"low": 0,
"moderate": 30, # add 30 minutes to drayage/yard ops
"high": 90 # add 90 minutes
}.get(str(data["congestion"]).lower(), 0)
print(f"Laem Chabang congestion: {data['congestion']} (as_of={data['as_of']})")
print(f"7-day vs 90-day ratio: {data['ratio_vs_baseline']}")
print(f"Recent avg port calls/day: {data['recent_avg']} vs baseline {data['baseline_avg']}")
print(f"Operational buffer recommendation (mins): {buffer_minutes}")
print(f"Source: {data['source']} | Lag: {data['lag']}")
if __name__ == "__main__":
# Simple retry wrapper for transient network issues
for attempt in range(3):
try:
main()
break
except Exception as e:
if attempt == 2:
raise
time.sleep(2 ** attempt)
Design notes for production use at Laem Chabang
Data freshness and lag: The data is typically 6–7 days lagged relative to present. Store as_of and display it alongside any congestion label to avoid confusion with live AIS. If your UI shows “today”, annotate that the metric reflects last week’s aggregated port calls.
Update cadence: The source aggregates daily; polling once daily is usually sufficient. If you cache, set a TTL of 24 hours. If you operate across multiple timezones, standardize date handling to UTC midnight for storage, and present the as_of date plainly without converting its semantics.
Thresholds and alerts: Because congestion is derived from port calls rather than direct wait times, design alerting thresholds around ratio_vs_baseline and the congestion label. For example: fire a “monitor” alert when label is moderate and ratio_vs_baseline is at or above 1.0; escalate to “action” if label becomes high. Avoid tying SLA penalties directly to this signal without corroborating operational data.
Modeling decisions centered on Laem Chabang (THLCH)
- ETA buffers for THLCH: Prepend a modest ETA buffer when congestion is moderate; the Python example shows a simple policy. Tailor by service string (e.g., intra-Asia feeders vs long-haul) if your planning stack supports it.
- Appointment lead times: When congestion increases, pre-book terminal or yard appointments earlier, shifting from just-in-time to a conservative window.
- Carrier and service route evaluation: Use ratio_vs_baseline to tag Laem Chabang calls in historical shipments and assess route reliability impacts attributed to port-side friction.
- Inventory staging: If Laem Chabang serves as a key gateway for your Thailand lanes, consider forward-positioning safety stock when ratios trend consistently above baseline.
Error handling and resilience
Transport-level errors: Use timeouts (10–15 seconds) and exponential backoff for retries on 5xx and transient network failures. Persist last-known-good values per port to avoid blank tiles in dashboards.
Schema expectations: Responses are wrapped in {"success":true,"data":{...}}. Always check success before reading data. Expect arrays in data.results, even if you query a single port.
Missing or delayed data: If Laem Chabang’s result is temporarily absent, fall back to the previously cached record and flag staleness in your UI. Do not substitute values from other ports or from prior regions.
Field-by-field integration checklist for THLCH
- Read data.results[0].congestion for the label used in user-facing elements and simple rules engines.
- Store data.results[0].as_of as a date string; treat it as the effective date of the metric, not an ingestion timestamp.
- Use data.results[0].metrics.ratio_vs_baseline to drive quantitative policies; it normalizes short-term traffic vs a 90-day history.
- Retain data.meta.source and data.meta.data_lag_days for compliance, attribution, and UX disclosure.
- Log meta.request_id for traceability when users report discrepancies.
Caching, pagination, and rate considerations
Caching: A 24-hour cache per port is typical and lines up with daily upstream updates. On cache misses, fetch on demand and store both the JSON and a parsed record for rapid UI rendering.
Pagination: The single-port query returns a small payload; pagination is generally not applicable. If you later expand to multi-port queries, handle potential pagination per the product’s Documentation.
Rate limits: Guests (no key) are capped at 100 requests/day per IP. For unattended production services, add the X-API-Key header and implement application-level throttling to avoid burst errors.
Validation and QA specific to transportation workflows
- Sanity bounds: ratio_vs_baseline should be a reasonable scalar relative to recent and baseline averages. Validate consistency between ratio and its component averages.
- Temporal continuity: Track changes over time for THLCH; sharp one-day swings are unusual with a 7-day average. Flag anomalies for operator review.
- Cross-signal checks: If you pair this with internal dwell or milestone data, expect directionally aligned trends but not perfect correlation (this is not measuring anchorage wait time).
- UI clarity: Label the metric “7-day vs 90-day port calls (lagged ~1 week)” to prevent planners from over-interpreting it as live quay or yard status.
Security and operations
Key handling: Store YOUR_API_KEY as a secret in your CI/CD and runtime (e.g., environment variables). Do not hard-code it in client-side apps. For public dashboards, proxy requests server-side and cache responses to avoid exposing your key.
Monitoring: Record response status codes, latency, and last as_of date to alert if data stops advancing for THLCH. Include meta.request_id in logs to speed support inquiries.
Implementation walkthrough: from request to decision
1) Fetch
Call GET https://freightpulsehq.com/api/v1/port-congestion?port=THLCH with the X-API-Key header. For build verification, start without a key under the guest quota, then add your key before production cutover.
2) Parse
Read congestion, as_of, and metrics.ratio_vs_baseline. Also capture recent_avg_portcalls_per_day and baseline_avg_portcalls_per_day for diagnostics.
3) Normalize
Store as_of as an ISO date string. If your system uses time-buckets, map this to the UTC date partition equal to as_of.
4) Decide
Implement policy-driven buffers and alerts. For example, for Laem Chabang, set a baseline ETA buffer that increments when the label is moderate and escalates if it becomes high. Keep the logic observable: log the ratio used and the buffer applied.
5) Display
Show the label and the as_of date on your transportation dashboard. Consider a small trendline using historical daily pulls to contextualize today’s reading for planners.
FAQ
Is this live AIS wait-time data for Laem Chabang?
No. The signal is derived from IMF PortWatch using aggregated AIS. It compares a 7-day average of port calls to a 90-day baseline and is typically 6–7 days lagged. It does not measure anchorage or berth wait times.
How often should I poll the THLCH endpoint?
Daily. The upstream updates are daily, and the signal is lagged. A 24-hour cache aligns well with the data’s cadence.
Which fields should I store to drive operations?
Store congestion, as_of, and metrics.ratio_vs_baseline. Keep recent_avg_portcalls_per_day and baseline_avg_portcalls_per_day for validation and threshold tuning. Persist data.meta.source and data.meta.data_lag_days for transparency.
Do I need an API key?
You can test as a guest with 100 requests/day per IP. For production reliability and higher throughput, include X-API-Key: YOUR_API_KEY in requests.
What about pagination or multiple ports?
Single-port calls like THLCH typically don’t require pagination. If you expand, review pagination guidance in the product Documentation and architect your fetcher accordingly.
Ready to integrate Laem Chabang congestion into your transportation stack? Create an account and get your API key here: Register. For endpoint details and field references, see the Documentation.