Truck Validation

This page compares Medium (Single Unit, “MD”) and Heavy (Combination Unit, “HV”) truck percentages and VMT by functional class from three independent sources, so the model’s truck mix can be checked against observed data:

1. CCS Station Data — truck percentages measured directly at UDOT Continuous/Classification Count Station (CCS) locations, matched to their nearest model segment.
2. HPMS VMT Data — UDOT’s AADT and truck-factor estimates (AADT2023, SUTRUCKS, CUTRUCKS) applied to the full network and rolled up to an effective, distance-weighted truck percentage.
3. Model VMT Data — the model’s own segment volumes by vehicle class, rolled up the same way.

A fourth, external check against FHWA’s statewide VM-4 data is discussed in Regional Context below.

Methodology note: The CCS truck percentage is a volume-weighted average — for each functional class, total medium/heavy truck volume across all matching stations is divided by total volume across those stations (so higher-volume stations count more, rather than every station counting equally). The HPMS and Model truck percentages are VMT-weighted — each segment’s truck/total volume is multiplied by its length (DISTANCE) before summing, so longer segments contribute proportionally more, consistent with how the existing VMT validation figures on the Highway Assignment page are built. A small number of non-highway/connector segments have UDOT truck factors that aren’t considered reliable; those segments are still counted toward total HPMS volume but excluded from the Medium/Heavy truck volume in the HPMS table.

VMT comparison basis: CCS and HPMS stations/segments don’t cover the whole network the way the model does, so the Medium/Heavy Truck VMT tables below express all three sources as an Effective VMT — each source’s own truck percentage for a functional class, applied to the model’s full-network total VMT for that class. This puts CCS, HPMS, and Model on the same complete-network basis so they’re directly comparable; the Model VMT column is simply the model’s own actual VMT (it needs no extrapolation, since it already covers every segment). Note this means “Model VMT” here is the full network total and will differ from the Highway Assignment page’s “2b. Total Daily VMT by Roadway Class” table, whose “Model” column is restricted to only the segments that also have valid HPMS data (via an inner join) — a narrower, HPMS-matched comparison basis.

Downloads

CSV exports of the data behind the tables and charts below:

• 2023 HPMS VMT by Super District and Functional Class — from County-Level HPMS VMT with Proposed Adjustment
• VMT Targets by Super District and Facility Type — from VMT Targets (Adjusted)
• CCS-Derived 2025/2003 Reclassification Ratio — from CCS-Derived 2025/2003 Reclassification Ratio
• 2025 Recommended LT/MD/HV Split Factors — from Recomputed LT/MD/HV Split Factors

Detailed Source Data

The tables below show each source’s own functional-class breakdown in full detail, including sample size (Stations/Segments) and the underlying methodology for each source.

CCS Station Truck Percentages

Functional Class	Stations	Medium Truck %	Heavy Truck %
Freeway	36	12.4%	6.5%
Expressway	12	11.0%	2.7%
Principal Arterial	23	11.5%	2.0%
Minor Arterial	5	10.2%	1.2%
Collector	4	9.5%	0.9%
All Stations	80	12.1%	5.5%

CCS Truck Volume by Vehicle Length Class

The table below shows the average daily (Tue/Wed/Thu) raw CCS count volume, split into Medium and Heavy truck volume using the Wavetronix/UDOT length-bin factors below, summed across all CCS stations. This is a region-wide cross-check on the Medium/Heavy split feeding the functional-class table above — the Total row’s MD/HV volumes should match the “All Stations” MD/HV totals used there.

Wavetronix Class	Length Bin	LT	MD	HV
Class 6: 0-8ft	L0-8	100%	0%	0%
Class 8: 8-20ft	L8-20	100%	0%	0%
Class 9: 20-30ft	L20-30	50%	50%	0%
Class 2: 30-50ft	L30-50	30%	50%	20%
Class 3: 50-79ft	L50-79	0%	10%	90%
Class 5: >79ft	L79-120	0%	0%	100%

Vehicle Length Class	Total Volume	MD Volume	HV Volume
Class 6 (0-8 ft)	139,505	0	0
Class 8 (8-20 ft)	4,349,519	0	0
Class 9 (20-30 ft)	1,190,001	595,001	0
Class 2 (30-50 ft)	272,357	136,178	54,471
Class 3 (50-79 ft)	217,323	21,732	195,591
Class 5 (>79 ft)	99,551	0	99,551
Total	6,268,256	752,911	349,613

HPMS Truck Percentages (VMT-Weighted)

Functional Class	Segments	Medium Truck %	Heavy Truck %
Freeway	170	8.9%	6.8%
Expressway	93	11.6%	7.8%
Principal Arterial	834	10.3%	4.3%
Minor Arterial	1,092	3.6%	2.2%
Collector	1,681	0.3%	0.2%
Local	1	12.4%	4.2%
All Roadways	3,871	7.7%	5.0%

Model Truck Percentages (VMT-Weighted)

Functional Class	Segments	Medium Truck %	Heavy Truck %
Freeway	175	8.8%	8.4%
Expressway	98	9.5%	7.3%
Principal Arterial	845	10.5%	4.1%
Minor Arterial	1,159	10.2%	3.4%
Collector	2,080	10.0%	3.1%
Local	22	10.9%	6.7%
All Roadways	4,379	9.5%	6.2%

Regional Context: FHWA VM-4 State Comparison

The tables above validate the model against three sources that are all local to the WFRC/MAG region. As an outside sanity check, FHWA’s Highway Statistics 2023 Table VM-4 (“Distribution of Annual Vehicle Distance Traveled”) reports each state’s own truck share of urban VMT, split into single-unit (“SU”, roughly comparable to Medium/“MD”) and combination (“Comb”, roughly comparable to Heavy/“HV”) trucks, for the Interstate System and for Other Arterials. Comparing Utah’s statewide figures to neighboring states shows whether Utah’s reported truck mix – which UDOT’s HPMS submission (and therefore this page’s HPMS table) is itself built from – is broadly in line with the region, or an outlier.

Important caveats: these are statewide Utah figures, not WFRC/MAG-area-specific, so they will not match the HPMS/Model tables above exactly. FHWA’s road categories (Interstate System / Other Arterials) are also coarser than the model’s six-class FTCLASS taxonomy – Interstate roughly maps to Freeway, Other Arterials roughly spans Principal + Minor Arterial. And because VM-4 is itself compiled from each state’s HPMS submission, it is not an independent ground truth – it’s a plausibility check on Utah’s reported truck factors against its peers, not a validation of the model.

State	Interstate SU %	Interstate Comb %	Other Arterial SU %	Other Arterial Comb %
Utah	9.2%	8.3%	11.2%	5.1%
Idaho	3.5%	9.4%	2.4%	1.5%
Wyoming	2.7%	30.2%	3.2%	2.6%
Colorado	3.1%	5.1%	2.0%	1.5%
Arizona	4.5%	8.3%	4.0%	1.7%
Nevada	6.7%	16.6%	1.6%	2.2%

For context, on Freeway segments this page’s HPMS table shows a Heavy Truck % of 6.8% and the Model shows 8.4%, versus Utah’s statewide urban Interstate combination-truck share of 8.3% reported to FHWA – compared to a 5.1% to 30.2% range across Idaho, Wyoming, Colorado, Arizona, and Nevada. Wyoming’s high combination-truck share reflects its heavy long-haul freight volumes on a thin urban VMT base, not a comparable population/land-use mix to the Wasatch Front – it’s included as a regional bookend rather than a close peer.

Summary Comparison

Quick summary: the four tables below place CCS, HPMS, and Model side-by-side by functional class — the first two compare truck percentages, the second two compare truck VMT. Use the “All Roadways” row for the region-wide figure, and scan down each column to see which functional classes the model over- or under-represents relative to the observed sources. A “—” means that source has no data for that functional class (e.g. no CCS stations were matched to Local roads).

Medium Truck %

Functional Class	CCS	HPMS	Model
Freeway	12.4%	8.9%	8.8%
Expressway	11.0%	11.6%	9.5%
Principal Arterial	11.5%	10.3%	10.5%
Minor Arterial	10.2%	3.6%	10.2%
Collector	9.5%	0.3%	10.0%
Local	—	12.4%	10.9%
All Roadways	12.1%	7.7%	9.5%

Heavy Truck %

Functional Class	CCS	HPMS	Model
Freeway	6.5%	6.8%	8.4%
Expressway	2.7%	7.8%	7.3%
Principal Arterial	2.0%	4.3%	4.1%
Minor Arterial	1.2%	2.2%	3.4%
Collector	0.9%	0.2%	3.1%
Local	—	4.2%	6.7%
All Roadways	5.5%	5.0%	6.2%

Medium Truck VMT

Functional Class	CCS Effective VMT	HPMS Effective VMT	Model VMT
Freeway	3,156,520	2,278,717	2,244,866
Expressway	450,174	476,406	388,359
Principal Arterial	1,297,188	1,159,459	1,188,568
Minor Arterial	784,707	278,587	780,231
Collector	529,064	17,295	559,867
Local	—	2,202	1,937
All Roadways	6,549,925	4,184,175	5,163,828

Heavy Truck VMT

Functional Class	CCS Effective VMT	HPMS Effective VMT	Model VMT
Freeway	1,661,334	1,745,596	2,136,560
Expressway	112,801	318,935	299,085
Principal Arterial	228,351	481,440	468,476
Minor Arterial	92,971	169,830	261,107
Collector	49,734	12,326	172,339
Local	—	745	1,190
All Roadways	2,983,365	2,727,545	3,338,758

Proposed Changes to Truck Classification

The LT/MD/HV length-bin split factors used throughout this page (100%/100%/50%/30%/0%/0% LT for the six bins, etc. — see the classification table in CCS Truck Volume by Vehicle Length Class) came from UDOT Planning. Separately, UDOT’s Statistics Department has published its own FHWA 13-class vehicle-length distributions — a 2003-vintage dataset (roughly contemporaneous with the current factors), a finalized 2024 dataset, and a finalized 2025 dataset.

Rolling the 13 FHWA classes into the same three groups used elsewhere on this page — Classes 1–4 (motorcycles, passenger cars, pickups/vans, buses) → LT, Classes 5–7 (single-unit trucks) → MD, Classes 8–13 (combination trucks) → HV — and summing each vintage’s class shares within each length bin produces the split factors below. This section documents where the UDOT Statistics data diverges from the UDOT Planning factors currently in use, and what adopting the newer data would do to this page’s CCS results. It is a discussion starting point, not an implemented change — nothing elsewhere on this page uses these alternate factors.

Recomputed LT/MD/HV Split Factors

Length Bin	Current LT	2003 LT	2024 LT	2025 LT	Current MD	2003 MD	2024 MD	2025 MD	Current HV	2003 HV	2024 HV	2025 HV
L0-8	100%	100%	100%	100%	0%	0%	0%	0%	0%	0%	0%	0%
L8-20	100%	100%	100%	99%	0%	0%	0%	1%	0%	0%	0%	0%
L20-30	50%	52%	94%	79%	50%	45%	6%	21%	0%	3%	0%	0%
L30-50	30%	67%	78%	67%	50%	17%	19%	25%	20%	16%	3%	8%
L50-79	0%	0%	0%	2%	10%	0%	0%	2%	90%	100%	100%	96%
L79-120	0%	0%	0%	1%	0%	0%	0%	0%	100%	100%	100%	99%

LT / MD / HV composition by length bin and data vintage

The current UDOT Planning factors and the UDOT Statistics data agree closely on the two shortest and the longest bins (L0-8, L8-20, L79-120 — effectively 100% LT, LT, and HV respectively in both sources). They diverge substantially in the middle:

• L20-30: this is the highest-volume length bin by far (see CCS Truck Volume by Vehicle Length Class — 1.19M of ~6.27M total daily volume, roughly 19%), and it shows the largest practical impact. The current factors assume a 50/50 LT/MD split here; the UDOT Statistics data shows LT increasingly dominant over time (52% in 2003, rising to 79–94% by 2024/2025). Because this bin carries so much volume, reclassifying most of it from “half truck” to “almost all light vehicle” drives a large net reduction in total truck percentage below, not just a reshuffling between Medium and Heavy.
• L30-50: the current factors assume only 30% LT (70% MD/HV combined). The UDOT Statistics data — consistently across 2003, 2024, and 2025 — puts this bin at 67–78% LT. Same direction as L20-30, smaller volume.
• L50-79: the current factors assume a 10%/90% MD/HV split. All three UDOT Statistics vintages instead show this bin as ~100% HV, with MD near zero — a shift within the truck categories (Medium → Heavy) rather than a change in total truck volume, but this bin’s volume is small enough that it doesn’t offset the L20-30/L30-50 reduction.

Download 2025 Recommended LT/MD/HV Split Factors (CSV)

Impact on CCS Truck Percentages

The table below reruns the CCS Station Truck Percentages calculation (same spatial matching, same functional-class grouping) substituting the 2003, 2024, and 2025 UDOT Statistics split factors for the current UDOT Planning factors, to show how much the CCS-derived truck percentages would shift.

Medium Truck %

Functional Class	Current	2003 Data	2024 Data	2025 Data
Freeway	12.4%	9.5%	2.1%	6.1%
Expressway	11.0%	8.6%	1.9%	5.6%
Principal Arterial	11.5%	9.2%	1.9%	5.8%
Minor Arterial	10.2%	8.3%	1.7%	5.3%
Collector	9.5%	7.9%	1.4%	5.0%
All Stations	12.1%	9.3%	2.0%	6.0%

Medium Truck % by functional class and data vintage

Heavy Truck %

Functional Class	Current	2003 Data	2024 Data	2025 Data
Freeway	6.5%	7.4%	6.2%	6.2%
Expressway	2.7%	3.4%	2.3%	2.4%
Principal Arterial	2.0%	2.6%	1.6%	1.7%
Minor Arterial	1.2%	1.7%	0.8%	0.9%
Collector	0.9%	1.4%	0.6%	0.7%
All Stations	5.5%	6.3%	5.1%	5.2%

Heavy Truck % by functional class and data vintage

The result is not a simple Medium-to-Heavy reshuffle. Region-wide (All Stations), Medium Truck % runs 12.1% (Current) → 9.3% (2003) → 2.0% (2024) → 6.0% (2025), while Heavy Truck % runs 5.5% (Current) → 6.3% (2003) → 5.1% (2024) → 5.2% (2025). The 2003 column — contemporaneous with the current factors — lands close to Current on both, as expected. The bigger break shows up in 2024: Medium drops sharply while Heavy holds roughly steady, meaning total truck share (Medium + Heavy combined) falls overall rather than shifting between the two categories. That’s because L20-30, the single highest-volume length bin, swings from “half truck” (50% MD) under the current factors to “almost entirely light vehicle” (94–79% LT, only 6–21% MD, near-zero HV) under the newer data — so a large share of volume that used to count toward Medium Truck % now doesn’t count as a truck at all. The much smaller L50-79 bin’s shift toward Heavy (10%→0% MD, 90%→100% HV) works in the other direction but isn’t large enough in volume to offset it. 2025 partially reverses the 2024 Medium drop (2.0% → 6.0%) while Heavy stays flat. The overall direction (lower combined truck share than Current/2003) is consistent across both 2024 and 2025 and is worth validating against the raw UDOT Statistics source data before treating it as a basis for changing the page’s methodology.

CCS-Derived 2025/2003 Reclassification Ratio

The table below expresses the CCS reclassification impact (from Impact on CCS Truck Percentages) as a ratio rather than side-by-side percentages: for each functional class, the CCS-derived Medium (or Heavy) Truck % under the 2025 UDOT Statistics split factors divided by the same under the 2003 split factors (the vintage roughly contemporaneous with HPMS’s current truck factors).

Functional Class	MD Ratio (2025/2003)	HV Ratio (2025/2003)
Freeway	0.64x	0.84x
Expressway	0.65x	0.71x
Principal Arterial	0.63x	0.63x
Minor Arterial	0.64x	0.51x
Collector	0.62x	0.49x
Local	—	—
All Stations	0.64x	0.82x

A “—” means CCS had no matched stations for that functional class (e.g. Local), so no ratio could be computed.

CCS-derived 2025/2003 reclassification ratio by functional class

County-Level HPMS VMT with Proposed Adjustment

As a further cross-check, the same 2025/2003 ratio can be applied to actual 2023 HPMS VMT, broken out by county and facility type rather than region-wide. This uses a different, statewide HPMS source than the rest of this page: FHWA’s full HPMS extract for Utah (data/HPMS_FULL_UT_2023.gpkg, fetched by scripts/fetch_hpms_full_ut_2023.py), spatially joined to the model’s super-districts (data/WFv1000_TAZ__DSUP.geojson – whole counties for Davis/Salt Lake/Utah, the model-area slice of the county for Box Elder/Weber) by scripts/build_hpms_vmt_by_county_facility.py. Those two scripts are run manually (not on every quarto render, since the fetch hits an external API); the cell below just reads their cached output and applies the ratio already computed above.

The map below shows exactly which areas each county’s VMT figures cover – note Box Elder and Weber are only their model-area slice, not the full county (see the note above).

Model super-districts used for the county-level VMT tables below

Toggle super districts on/off below; the table sums whichever ones are checked by facility type, with a Total row across all facility types. All are checked by default, giving the region-wide sum.

counties = [...new Set(transpose(county_adj_ojs).map((d) => d.County))]

classOrder = ["Freeway", "Expressway", "Principal Arterial", "Minor Arterial", "Collector"]

filtered = transpose(county_adj_ojs).filter((d) => selectedCounties.includes(d.County))

byClass = classOrder.map((cls) => {
  const rows = filtered.filter((d) => d["Functional Class"] === cls);
  return {
    "Functional Class": cls,
    total: d3.sum(rows, (d) => d["Total"]),
    hpmsMD: d3.sum(rows, (d) => d["HPMS MD VMT"]),
    adjMD: d3.sum(rows, (d) => d["Adjusted MD VMT"]),
    hpmsHV: d3.sum(rows, (d) => d["HPMS HV VMT"]),
    adjHV: d3.sum(rows, (d) => d["Adjusted HV VMT"]),
  };
})

totalRow = ({
  "Functional Class": "Total",
  total: d3.sum(byClass, (d) => d.total),
  hpmsMD: d3.sum(byClass, (d) => d.hpmsMD),
  adjMD: d3.sum(byClass, (d) => d.adjMD),
  hpmsHV: d3.sum(byClass, (d) => d.hpmsHV),
  adjHV: d3.sum(byClass, (d) => d.adjHV),
})

fmt = d3.format(",.0f")
ratioFmt = (num, den) => (den > 0 ? `${(num / den).toFixed(2)}x` : "—")

viewof selectedCounties = Inputs.checkbox(counties, { value: counties, label: "Super District:" })

htl.html`<table class="table table-sm table-striped small caption-top">
  <thead><tr>
    <th>Facility Type</th>
    <th style="text-align:right">HPMS MD VMT</th>
    <th style="text-align:right">MD Ratio (2025/2003)</th>
    <th style="text-align:right">Adjusted MD VMT</th>
    <th style="text-align:right">HPMS HV VMT</th>
    <th style="text-align:right">HV Ratio (2025/2003)</th>
    <th style="text-align:right">Adjusted HV VMT</th>
  </tr></thead>
  <tbody>
    ${[...byClass, totalRow].map(
      (d) => htl.html`<tr style=${d["Functional Class"] === "Total" ? "font-weight:bold;border-top:2px solid #52514e;" : ""}>
        <td>${d["Functional Class"]}</td>
        <td style="text-align:right">${fmt(d.hpmsMD)}</td>
        <td style="text-align:right">${ratioFmt(d.adjMD, d.hpmsMD)}</td>
        <td style="text-align:right">${fmt(d.adjMD)}</td>
        <td style="text-align:right">${fmt(d.hpmsHV)}</td>
        <td style="text-align:right">${ratioFmt(d.adjHV, d.hpmsHV)}</td>
        <td style="text-align:right">${fmt(d.adjHV)}</td>
      </tr>`
    )}
  </tbody>
</table>`

VMT Targets (Adjusted)

Combines the above into a single set of VMT targets by facility type – Total VMT, PC/LT VMT (the non-truck remainder, Total − Adjusted MD − Adjusted HV), and the two adjusted truck VMTs – for whichever counties are toggled above.

htl.html`<table class="table table-sm table-striped small caption-top">
  <thead><tr>
    <th>Facility Type</th>
    <th style="text-align:right">Total VMT</th>
    <th style="text-align:right">PC/LT VMT</th>
    <th style="text-align:right">MD VMT (Adjusted)</th>
    <th style="text-align:right">HV VMT (Adjusted)</th>
  </tr></thead>
  <tbody>
    ${[...byClass, totalRow].map(
      (d) => htl.html`<tr style=${d["Functional Class"] === "Total" ? "font-weight:bold;border-top:2px solid #52514e;" : ""}>
        <td>${d["Functional Class"]}</td>
        <td style="text-align:right">${fmt(d.total)}</td>
        <td style="text-align:right">${fmt(d.total - d.adjMD - d.adjHV)}</td>
        <td style="text-align:right">${fmt(d.adjMD)}</td>
        <td style="text-align:right">${fmt(d.adjHV)}</td>
      </tr>`
    )}
  </tbody>
</table>`

Download

A full, unfiltered long-format version of the targets above – every Super District × Facility Type combination (Local excluded, same as the tables above, since CCS has no ratio for it) – with three VMT columns: PC/LT, MD (Adjusted), and HV (Adjusted).

Download VMT Targets by Super District and Facility Type (CSV)