Traffic Data Validation & Quality Control Methodology

Definition and purpose

Traffic-data QA/QC checks whether the delivered dataset matches the agreed scope, rules and structure. It is planned from the brief rather than applied only at the end.

Use it to make collection and analysis outputs more complete, consistent and reviewable.

Required inputs

• Approved study specification
• Data dictionary and movement matrix
• Source/coverage record
• Delivery template
• Exception and correction process

Video and camera considerations

For source-based QA, retain enough information to review coverage and documented exceptions through approved project workflows.

Observation, classification and intervals

Observation process

Define quality checkpoints at setup, during processing and before final release. Assign a consistent method for recording corrections and exceptions.

Movement and classification process

Validate that category names, rules and totals are consistent with the approved data dictionary; do not silently remap a client class scheme.

Time intervals

Check interval continuity, timestamp logic, partial periods and aggregation formulas before final delivery.

Validation and QA/QC

Reconcile totals, movement logic, classifications, source coverage and output format. Record what was checked and any approved limitation.

Common problems and limitations

• A late change to the brief can invalidate earlier tables.
• Hidden spreadsheet formulas can distort aggregations.
• Missing exception notes make QA outcomes hard to interpret.

QA/QC improves traceability and catches errors; it does not make unsuitable source material reliable or replace a clear brief.

Typical deliverables

• QA checklist or review record
• Validated data files
• Exception/correction log where agreed
• Method and version notes

Illustrative output format

Illustrative sample data only — not a project result or benchmark.

Frequently asked questions