Traffic count reports land in network planning inboxes every quarter. Most teams extract the AADT number, compare it against a threshold, and move on. That workflow misses two-thirds of the signal in the data.
This guide walks through a traffic count report field by field and shows you which numbers actually predict cell tower demand — and which ones are noise.
What Is a Traffic Count Report?
Traffic count reports are published by state Departments of Transportation (DOT) and the Federal Highway Administration (FHWA). They document how many vehicles pass a given point on a roadway per day, broken down by direction, vehicle class, and time of day.
The primary metric is Annual Average Daily Traffic (AADT) — a 24-hour vehicle count averaged across the full year. Most planners treat AADT as the only number that matters. It is not.
Step 1: Find the Station Record, Not Just the Route Average
Traffic count reports are organized by traffic counting stations — fixed measurement points on a route. A single highway segment (say, 40 miles of I-40 through western Texas) may have a dozen stations.
Route-level averages smooth over variation. A 25,000 AADT route average hides individual station readings of 8,000 and 44,000. The 44,000-AADT station adjacent to an uncovered corridor is an opportunity. The 8,000-AADT station is not.
What to do: Work at the station level, not the route level. Request or download station-level data from your state DOT. If your state only publishes route summaries, file a data request — station-level data is public record.
Step 2: Read the Growth Trend, Not Just the Current Count
AADT is a snapshot. Growth rate is the forecast.
Most count reports include traffic history going back 5–10 years. The year-over-year growth rate tells you:
- Whether demand is expanding at this station (and therefore whether a tower built today will still be justified in 5 years)
- Whether growth is accelerating — a station growing at 7% year-over-year for three consecutive years is signaling a structural traffic shift, not a measurement blip
- Whether a recent spike is durable — one anomalous year means nothing; three consecutive years of growth means something
A station with 30,000 AADT and 6% annual growth for three years is a better siting candidate than a station with 45,000 AADT and flat traffic. The lower-volume station will have more unmet demand in 36 months.
For more on how traffic data interacts with tower siting decisions, see How Traffic Count Data Reveals Your Next Tower Site.
Step 3: Break Down Vehicle Classification
Not all vehicles create equal demand for cell coverage.
Traffic count reports include vehicle classification data — the breakdown of traffic by vehicle type:
- Class 1–3 (motorcycles, passenger cars, light trucks): Highest per-device data demand; commuters and consumer traffic
- Class 4–7 (light commercial vehicles, single-unit trucks): Fleet management demand; IoT and logistics connectivity
- Class 8–13 (heavy trucks, semis, multi-trailer combinations): Trucking corridor demand; highest priority for commercial carriers deploying private LTE
A station dominated by Class 8–13 vehicles is a trucking corridor — different deployment economics than a commuter highway. Trucking corridors have sustained demand for voice and IoT connectivity across long stretches, which favors macro tower spacing. Commuter corridors with high Class 1–3 counts and short trip distances favor denser small cell deployment.
If your count report does not include vehicle classification, request it separately. FHWA requires classification counts at a subset of stations in every state.
Step 4: Check the Temporal Distribution
AADT is an average across all 168 hours per week. The distribution matters.
Peak hour data tells you the ratio of maximum hourly traffic to daily average. A highway with 30,000 AADT but a peak hour ratio of 15% (4,500 vehicles in the busiest hour) creates a demand spike that a steady-volume highway does not.
For network planning purposes:
- High peak-to-average ratio = congestion-driven coverage demand during commute windows; small cell density helps
- Flat 24-hour distribution = freight corridor; consistent load, simpler macro tower deployment
- Weekend peak = recreation corridor; may justify seasonal capacity that is uneconomical year-round
Temporal distribution data is not always in the published report. FHWA Continuous Count Stations (CCS) publish hourly data. Short-count stations may only publish AADT with a seasonal adjustment factor.
Step 5: Cross-Reference Against Coverage Data
Traffic count data tells you where demand exists. Coverage data tells you whether that demand is currently served.
The FCC Form 477 dataset provides carrier-reported coverage by census block. Overlaying 477 coverage on traffic station locations reveals:
- Unserved stations — high AADT, zero or one carrier, weakest signal grades
- Underserved stations — moderate-to-high AADT, 2+ carriers but poor signal grade
- Saturated stations — multiple carriers, strong signal, no coverage gap opportunity
The most actionable signals in TowerScope's scoring model are stations where AADT exceeds 15,000, year-over-year growth is above 3%, and carrier coverage is absent or weak. That combination produces the highest-confidence build recommendations in our dataset.
For a breakdown of how coverage gaps interact with traffic data, see How to Find Coverage Gaps Before Your Competitors Do.
Step 6: Validate Against Population Growth Data
Highway traffic patterns shift as population centers grow. A corridor that passes through a low-density county today may run through a fast-growing exurb in five years.
Population growth data (US Census Bureau estimates, state DOT projections) helps validate whether a traffic station's growth trend is structural. Stations in corridors adjacent to counties growing at 2%+ annually tend to sustain their traffic growth trajectories.
This validation step filters out anomalous traffic spikes driven by construction detours, temporary event venues, or measurement error.
Common Mistakes When Reading Count Reports
Using route-level averages. Always work at the station level.
Treating AADT as the only metric. Growth rate and vehicle classification provide different signal than volume alone.
Ignoring temporal distribution. A flat 24-hour distribution and a 15% peak-hour ratio have different implications for capacity planning.
Failing to cross-reference coverage. A high-AADT station in a well-served corridor is not an opportunity — it is an argument for better equipment at an existing site.
Treating every gap as equal. Coverage gaps on high-growth corridors near population centers are structurally different from gaps on stable-volume rural routes. The former will close faster and carry more demand.
How TowerScope Automates This Process
Manually pulling station-level data, overlaying FCC 477, and calculating growth rates across hundreds of corridor segments takes weeks. TowerScope does this automatically across the full highway dataset.
Each opportunity in the platform shows AADT, year-over-year growth, vehicle classification where available, carrier coverage grade, and a composite signal score. The analysis you would spend a week building manually is available in the first search.
If your team is making tower siting decisions based on route-level AADT and gut instinct, you are working with a fraction of the available signal. The full picture is in the data — it just needs to be assembled correctly.