Data Quality
Common Traffic Counting Errors
Avoiding preventable specification and observation issues.
By SWAT Alliance Editorial Team · Updated 25 August 2026
Insight at a glance
A short visual checklist for better study decisions.
Use the article as a starting point, then confirm the project-specific scope, method, review and output with the delivery team.
Question
What decision should this evidence help someone make?
Scope
Which locations, movements, classes and periods matter?
Review
How will completeness and definitions be checked?
Output
What format will make the result easiest to use?
Short answer
Common traffic-counting errors include wrong direction, wrong movement, missed or double-counted vehicles, interval shifts, inconsistent classification and unrecorded source limitations.
Prevent errors at setup
Use a current site plan, named approaches, a movement matrix, class dictionary, interval rule and source-acceptance checklist before observation.
Find errors during review
Reconcile totals, inspect unusual jumps, compare direction and movement logic, check interval continuity and sample ambiguous or high-volume periods.
Correct transparently
Record corrections and exceptions in the review trail. Do not silently change a delivered table or hide an unobservable period.
Frequently asked questions
What is the most important first check?
Confirm that the observation area, period and movement/class definitions match the approved brief.
Can QA eliminate every error?
No. It reduces preventable errors and documents limitations against the available evidence.
Go deeper
Traffic Data Validation & Quality Control Methodology
A documented review framework for traffic data before it is delivered or relied upon.
View Methodology →Related services