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AI-Powered Container Volume Forecasting for Port of Vancouver

Logistics · Planning & Decision-making

What it collects

Operational data
Anonymized data
  • Canada Border Services Agency (CBSA) import declarations, used as the training and input data source for predicting container volume arrivals. The register states no personal information is involved.
Run by
Transport Canada (TC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Not stated by the Helpful Places.

What it is for

LogisticsPlanning & Decision-making

Transport Canada, in collaboration with Statistics Canada, developed a machine learning model to forecast the volume of shipping containers arriving at the Port of Vancouver. The model uses Canada Border Services Agency import declaration data and was intended for use by Government of Canada employees. The system was never put into production and has since been retired.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Canada Border Services Agency (CBSA) import declarations, used as the training and input data source for predicting container volume arrivals. The register states no personal information is involved.

Processing

Classification & Prediction
  • A machine learning algorithm trained to forecast (predict) container volume arrival counts at the Port of Vancouver from structured CBSA import declaration data.

What it does

Deciding (Analytical AI)
Human decides
  • The model predicts and scores container volume arrivals from structured import declaration data, producing forecasts for review by GC employees who make operational decisions.

Outputs

A recommendation or prediction
Anonymized data
  • Forecasted container volume arrival figures intended as advisory outputs for GC employees. The outputs are predictions, not binding decisions, and do not pertain to any individual person.

Run by

Transport Canada (TC)
  • Transport Canada is the federal department accountable for the development and retirement of this container volume forecasting model, developed in collaboration with Statistics Canada.

Transport Canada AI Register — Container Volume Forecasting (2526-TC-011)

Built by

Statistics Canada (StatCan)
  • Statistics Canada collaborated with Transport Canada on the development of the machine learning algorithm underpinning this forecasting model.

Transport Canada AI Register — Container Volume Forecasting (2526-TC-011)

Kept for

Not stated by the Helpful Places.

Shared with

Not stated by the Helpful Places.

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

The Helpful Places has not listed specific rights for this system.

Risks and safeguards

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