Global Port Congestion and Container Dwell Monitor

Monitor port, terminal, and container snapshots to detect congestion, dwell-time escalation, throughput pressure, and logistics risk across trade lanes. Compare current and previous observations to surface operational changes in structured output for alerts, automation, and AI workflows.

Why use this Actor

Logistics teams can collect vessel, terminal and shipment data, but they need normalized change signals showing where congestion and container dwell are worsening enough to affect routing, inventory or supplier decisions.

Key features

• Buyer-native inputs aligned to container dwell time monitor.
• Predictable structured output in the default Apify Dataset.
• Pay Per Event pricing tied to measurable result rows or reports.
• Optional MCP connector delivery for downstream automation and AI agents.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "id": "port-la",
      "port": "Los Angeles",
      "region": "US West",
      "dwellHours": 86,
      "vesselsWaiting": 14,
      "throughputIndex": 71,
      "containersDelayed": 4200,
      "date": "2026-09-23"
    },
    {
      "id": "port-sg",
      "port": "Singapore",
      "region": "SEA",
      "dwellHours": 42,
      "vesselsWaiting": 8,
      "throughputIndex": 84,
      "containersDelayed": 1600,
      "date": "2026-09-23"
    },
    {
      "id": "port-rtm",
      "port": "Rotterdam",
      "region": "EU",
      "dwellHours": 31,
      "vesselsWaiting": 5,
      "throughputIndex": 91,
      "containersDelayed": 900,
      "date": "2026-09-23"
    }
  ],
  "previousItems": [
    {
      "id": "port-la",
      "port": "Los Angeles",
      "region": "US West",
      "dwellHours": 54,
      "vesselsWaiting": 7,
      "throughputIndex": 83,
      "containersDelayed": 2100,
      "date": "2026-09-16"
    },
    {
      "id": "port-sg",
      "port": "Singapore",
      "region": "SEA",
      "dwellHours": 39,
      "vesselsWaiting": 7,
      "throughputIndex": 85,
      "containersDelayed": 1450,
      "date": "2026-09-16"
    }
  ],
  "maxItems": 20000
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current records: Current source or normalized rows to analyze.
• currentDatasetId — Current Apify Dataset ID: Optional Apify Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional previous Apify Dataset ID used instead of previousItems.
• maxItems — Maximum records: Maximum records loaded from a Dataset input.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.008000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Quick start: port congestion change analysis

Provide currentItems with port or terminal observations, and previousItems for comparison. Alternatively use the corresponding dataset IDs. The Actor analyzes your supplied records to flag changes in dwell time, vessel queues and throughput. It does not automatically collect live shipping data. Use the included example input as a starting point; replace sample measurements with real, legally obtained observations. Output is a structured decision-support report suitable for recurring monitoring and alerts.

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Global Port Congestion and Container Dwell Monitor

Monitor port, terminal, and container snapshots to detect congestion, dwell-time escalation, throughput pressure, and logistics risk across trade lanes. Compare current and previous observations to surface operational changes in structured output for alerts, automation, and AI workflows.

Detailed documentation

Detailed documentation

Detailed documentation

Convert port, terminal and container snapshots into congestion, dwell-time escalation, throughput pressure and logistics-risk intelligence across monitored trade lanes.

Why use this Actor

Logistics teams can collect vessel, terminal and shipment data, but they need normalized change signals showing where congestion and container dwell are worsening enough to affect routing, inventory or supplier decisions. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Current-versus-previous snapshot comparison with deterministic deltas.

• Confidence-scored GO/WARN/BLOCK decision contract.

• Agent-ready recommended actions instead of raw rows.

• Dataset-ID inputs for recurring Apify workflows.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• Port congestion and terminal-risk monitoring.
• Container dwell escalation alerts.
• Supply-chain routing and inventory planning.
• Trade-lane disruption intelligence.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.008 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Output

The Actor writes one decision-ready record to the default Apify Dataset. The record includes marketSignalScore, executiveDecision, decisionConfidenceScore, recommendedAction, baseline availability, change counts, ranked topSignals, and optional economic-impact fields when the user supplies valuation inputs. This stable Gen2/Gen4 contract is designed for dashboards, automations and AI-agent workflows.