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AI-Powered Cargo Vessel Arrival Forecasting for Ports

Logistics · Planning & Decision-making

What it collects

Operational data
Anonymized data
  • Aggregate bulk vessel registry data from the Port of Vancouver, covering vessel schedules and arrivals. No personal information is involved.
Run by
Transport Canada (TC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization

What it is for

LogisticsPlanning & Decision-making

This system uses machine learning to forecast when cargo vessels will arrive at the Port of Vancouver. It was developed as a pilot project by Transport Canada in 2024 to improve operational planning visibility. The system processed aggregate vessel registry data and its outputs were used by Government of Canada employees — not the general public. The project has since been retired.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Aggregate bulk vessel registry data from the Port of Vancouver, covering vessel schedules and arrivals. No personal information is involved.

Processing

Classification & Prediction
  • A machine learning model trained on historical vessel registry data to forecast future cargo vessel arrival times or patterns at the Port of Vancouver.

What it does

Deciding (Analytical AI)
Human decides
  • The model predicts and forecasts cargo vessel arrival times from structured registry data, producing scores or rankings that GC employees use to inform operational decisions — no automated decision-making about individual vessels or persons.

Outputs

A recommendation or prediction
Anonymized data
  • Forecasts of cargo vessel arrival times or volumes, provided as advisory outputs to GC employees to support operational planning — not binding decisions.

Run by

Transport Canada (TC)
  • Transport Canada is the federal department that led this machine learning pilot project, collaborating with the Port of Vancouver to forecast cargo vessel arrivals for operational planning purposes.

Transport Canada AI Register — Cargo Vessel Forecasting (2526-TC-012)

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs were available to GC employees (Transport Canada and collaborating Port of Vancouver staff) for operational planning purposes only.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Algorithmic TransparencyThis system is listed in the Government of Canada's public AI register. Members of the public may consult the register entry for general information about the system's purpose and data sources. The project has been retired as of the register publication date.

Risks and safeguards

No risks or safeguards have been published for this system yet.