⚠️ Work in progress — data and figures are preliminary.

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Sacramento Area Pavement Analysis

Pavement & Parking Analysis | Last computed 2026-07-12

City Summary

City Area (acres) 185296.4
City Area (sq mi) 289.53
Total Paved (acres) 19974.9
% Paved 10.8%

roads

Features 134675
Area (acres) 16207.3
Area (sq mi) 25.32

parking

Features 5537
Area (acres) 3538.0
Area (sq mi) 5.53

sidewalks

Features 17902
Area (acres) 989.5
Area (sq mi) 1.55
Coverage %
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Interactive Forecast

Tier Min PCI Max PCI $/unit

Methodology

This section describes the data sources, models, and assumptions behind the analysis presented in each dashboard.

Data sources

The exact sources and endpoints used for a given example are listed in that example's Config tab.

Decay model

Each road classification decays independently via

PCI(t) = PCI₀ · exp(−k · t)

where k is an annual decay constant that depends on the road classification. Higher-class roads (motorway, trunk, primary) decay more slowly than lower-class roads (residential, service) because they are built to thicker, more rigorous design standards and typically receive more frequent maintenance. Default values are derived from LTPP data reported in FHWA-RD-01-156, Long-Term Pavement Performance and ship as part of the forecast package; they are continental-US averages and do not account for local climate, traffic, or construction quality. A config may set a per-city decay_rate to tune for local conditions (e.g. freeze/thaw or road salt); that override is applied as the rate for a typical road and scales every road class proportionally, so the per-class ordering (higher classes decay slower) is preserved rather than flattened. Sidewalks decay on a separate, slower track and are not treated as a highway class.

Cost model

Treatment costs are banded by PCI: each band has a representative $/sq m value, and costs between bands are linearly interpolated at the tier midpoints, so the cost-versus-PCI curve is smooth rather than step-shaped. Above the highest anchor (the midpoint of the preventive tier) and below the lowest anchor (the midpoint of the reconstruction tier), the cost is clamped to that anchor's value rather than extrapolated. Default cost tiers are expressed in $/sq m and sourced from FHWA treatment-selection guidance; they are calibration inputs, not measurements, and local bid prices will differ. Roads and sidewalks use independent cost tiers because the treatment economics differ substantially.

Condition spread

A real network is a distribution of conditions — some segments excellent, some failed — not a single average. Because the cost-versus-PCI curve is non-linear, pricing everything at the network-average PCI under-states the true program cost (the failed/poor tail is disproportionately expensive). To correct this without requiring per-segment condition data the model does not have, PVMT spreads the single configured average PCI into a distribution around that average (a Beta distribution on the 0–100 scale, with the average preserved exactly) and prices each slice separately. The result is a more realistic — and modestly higher — cost than pricing the average alone. This is a deliberately conservative approximation of the real spread; it is applied automatically and moves the solvency dollars in the direction validated against published city data. When field-measured per-segment condition becomes available, it will replace this assumed spread.

Treatment cycle

Real pavement is maintained on a multi-year cycle: a city treats roughly one slice of its network each year, not the whole network annually. The model captures this with a treatment cycle of N years (default 12, the midpoint of a typical 10–14 year municipal cycle, configurable via treatment_cycle_years). Each forecast year only 1/N of the network is scheduled for treatment, so the annual treatment need is the full-network retreatment cost divided by N. This is what makes the break-even budget a realistic annual program cost rather than the one-off cost of rebuilding the entire network at once. A cycle of N = 1 reproduces the older behavior (the whole network priced every year), which overstated the hold-steady budget several-fold.

The cost level and the cycle length are not independent — the break-even budget scales as cost ÷ N, so a cheaper cost basis and a shorter cycle trade off exactly. We anchor the cycle to the physical rate at which a city actually repaves (lane-miles treated per year, not dollars): for Berkeley that hold-steady cadence is ≈ 12 years, which is the default. With the cycle fixed there, the default bare-construction cost tiers reproduce the city's cited real hold-steady spend (~$5.6 per m² per year), so the break-even dollars are calibrated to reality rather than chosen freely. See the validation report for the derivation and the limits of this anchor.

Scenario comparisons

PVMT ships with three comparison runs driven by annual funding level, all using the worst-first allocation strategy (budget is spent on the lowest-PCI segments first):

A do-nothing baseline (no spend, uncontrolled decay) is shown alongside the funded runs for comparison.

The forecast library also implements a preventive-first strategy (prioritize highest-PCI segments that are still in the preservation window), but the default UI comparisons do not exercise it. Preventive vs. worst-first allocation is governed by per-strategy efficiency multipliers; those multipliers are illustrative calibration constants chosen to reflect the direction and sign of the effect reported in FHWA-HIF-12-042, Pavement Preservation: Preserving our Investment — that $1 of preventive maintenance is reported to avoid $6–$10 of future reconstruction — not to reproduce that benefit-cost ratio as a single-year spending efficiency.

Area growth

Optional compound annual growth applies to pavement area each year:

Area(y) = Area₀ · (1 + g)^y

where g is configured per city (default zero). This lets an example model a city that is still expanding its street network; it does not model demolition or removal.

Solvency metrics (streets/roads only)

The dashboard's Financials headline and the cross-city leaderboard report three solvency figures. They are computed on the roads/streets cohort only — the aggregate scenarios blend roads, parking, and sidewalks but cost the blend at road tiers, which would mis-price sidewalks, so an absolute dollar claim must be roads-only. They are derived from a worst-first run at the city's configured annual budget.

Three caveats apply to these figures specifically:

Compare-tab scores

The multi-city Compare view presents two distinct per-city scores:

Assumptions and limitations

References

Project Configuration

Input Configuration

# Sacramento area, CA — fast-growing capital region, Overpass-only
#
# A regional analysis across eight Sacramento-area jurisdictions, spanning
# the City of Sacramento and its rapidly expanding suburbs (Elk Grove,
# Roseville, Folsom). The Compare tab ranks these neighbors against one
# another.
#
# Techniques demonstrated:
#   - growth_rate (0.01) models the region's expanding road network in the
#     forecast horizon.
#   - Per-city hex_edge_m overrides for the two largest cities.
#
# Minimal config: every city runs on the top-level [forecast] defaults (no
# per-city PCI/budget). Values below are illustrative regional tuning, not
# cited measurements. Overpass-only. Expect ~8 Nominatim + Overpass pulls
# for `pvmt all ingest`.

config_id = "sacramento-ca"

[grid]
hex_edge_m = 125

[forecast]
years = 20
growth_rate = 0.01

[export]
title = "Sacramento Area Pavement Analysis"

[[cities]]
name = "Sacramento, CA"
overpass = true
hex_edge_m = 200  # largest jurisdiction (~256 km²)

[[cities]]
name = "Elk Grove, CA"
overpass = true
hex_edge_m = 150  # sprawling suburb (~108 km²)

[[cities]]
name = "Roseville, CA"
overpass = true

[[cities]]
name = "Folsom, CA"
overpass = true

[[cities]]
name = "Citrus Heights, CA"
overpass = true

[[cities]]
name = "Rancho Cordova, CA"
overpass = true

[[cities]]
name = "Davis, CA"
overpass = true

[[cities]]
name = "West Sacramento, CA"
overpass = true

Effective configuration (with defaults)

[grid]
  hex_edge_m = 125.0

[display]
  units = "imperial"
  min_hex_area = 100.0

[export]
  title = "Sacramento Area Pavement Analysis"
  coordinate_decimals = 0

[forecast]
  initial_pci = 85.0
  decay_rate = 0.035
  growth_rate = 0.01
  years = 20
  treatment_cycle_years = 0.0

  [[forecast.cost_tiers]]
    min_pci = 70.0
    max_pci = 101.0
    cost_per_sqm = 5.0
    label = "preventive"

  [[forecast.cost_tiers]]
    min_pci = 40.0
    max_pci = 70.0
    cost_per_sqm = 50.0
    label = "rehab"

  [[forecast.cost_tiers]]
    min_pci = 0.0
    max_pci = 40.0
    cost_per_sqm = 150.0
    label = "reconstruction"

[[cities]]
  name = "Sacramento, CA"
  overpass = true
  arcgis_url = ""
  hex_edge_m = 200.0
  boundary_relation_id = 0
  allow_private_arcgis = false

[[cities]]
  name = "Elk Grove, CA"
  overpass = true
  arcgis_url = ""
  hex_edge_m = 150.0
  boundary_relation_id = 0
  allow_private_arcgis = false

[[cities]]
  name = "Roseville, CA"
  overpass = true
  arcgis_url = ""
  hex_edge_m = 0.0
  boundary_relation_id = 0
  allow_private_arcgis = false

[[cities]]
  name = "Folsom, CA"
  overpass = true
  arcgis_url = ""
  hex_edge_m = 0.0
  boundary_relation_id = 0
  allow_private_arcgis = false

[[cities]]
  name = "Citrus Heights, CA"
  overpass = true
  arcgis_url = ""
  hex_edge_m = 0.0
  boundary_relation_id = 0
  allow_private_arcgis = false

[[cities]]
  name = "Rancho Cordova, CA"
  overpass = true
  arcgis_url = ""
  hex_edge_m = 0.0
  boundary_relation_id = 0
  allow_private_arcgis = false

[[cities]]
  name = "Davis, CA"
  overpass = true
  arcgis_url = ""
  hex_edge_m = 0.0
  boundary_relation_id = 0
  allow_private_arcgis = false

[[cities]]
  name = "West Sacramento, CA"
  overpass = true
  arcgis_url = ""
  hex_edge_m = 0.0
  boundary_relation_id = 0
  allow_private_arcgis = false

City Showdown

How does your city's pavement stack up?
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Region Rollup

The whole network as one fiscal entity — what does it cost to keep solvent?
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