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THE $1M
QUESTION

Every year, your agency allocates a fixed budget across hundreds of lane-miles. The method you use to decide determines outcomes far more than the dollar amount itself.

D+
ASCE grade for
U.S. roads, 2025
ASCE 2025 Infrastructure Report Card
39%
of major U.S. roads
in poor/mediocre condition
ASCE / TRIP, 2025
$684B
10-year road funding
gap nationwide
ASCE Bridging the Gap, 2024
75%
of U.S. roads owned
by local governments
FHWA Federal-Aid Essentials
The Problem

How Most Agencies Currently Decide

Four common approaches β€” each with a documented flaw that quietly erodes both network condition and budget efficiency.

🚨
Worst-First
Fix the lowest-PCI roads regardless of cost trajectory. Forces reconstruction when preservation was still possible β€” the most expensive path available.
Research verdict: Formally identified as "the least cost-effective" pavement management strategy by NCHRP Synthesis 223 (Geoffroy, TRB, 1996).
14Γ— more
costly than preventive maintenance
Michigan DOT via FHWA, 2000
πŸ“‹
Ranking / Priority Score
Composite score by PCI, traffic, road class β€” select top-N. Misses budget efficiency: high-ranked roads are often the most expensive per unit of condition gained.
The hidden cost: Worst-first & ranking strategies fail to account for deterioration compounding β€” roads past PCI 55 lose condition 5–10Γ— faster than roads in the preservation window. (Pavement Life-Cycle, Pavement Interactive / FHWA)
~30%
budget allocated to
low-impact treatments
πŸ“Š
Cost-Benefit Analysis
Ranks segments by a single ratio. Can only optimize for one objective at a time β€” no way to simultaneously enforce LOS floors by road class, budget sub-allocations by district, intervention frequency limits, and condition targets. Real capital programs have many simultaneous constraints.
The rigidity problem: When the optimization objective changes (equity vs. condition vs. lifecycle cost), the entire analysis must be re-run manually. No scenario comparison, no constraint flexibility, no what-if testing.
1 objective
real programs have 5–10
simultaneous constraints
πŸ“
Spreadsheet / Manual Selection
Engineer judgment + stakeholder pressure. Months of work, no defensible audit trail, no scenario comparison, impossible to re-run with changed assumptions.
3–6 mo.
to produce a plan that
can't be easily revised
The core gap: None of these methods account for deterioration compounding, multi-year budget constraints, intervention timing optimization, network equity, or the ability to rapidly test alternatives. They answer "which roads?" β€” not "which treatment, on which road, in which year, to maximize network health?"
Network Baseline

Your Network Today

A representative 200 lane-mile network with a $1M annual budget. Average PCI: 58 β€” a classic "Fair" network with a growing deferred backlog.

Avg Network PCI
58
Borderline Fair/Poor. Untreated networks lose 3–5 PCI points/yr and deteriorate 5–10Γ— faster once past the inflection point at PCI 55.
Source: FHWA LTPP; Pavement Interactive lifecycle model
02540557085 100
Annual Budget
$1M
Covers ~5,000 LM crack seal, ~60 LM microsurfacing, ~30 LM thin overlay, or just 4–6 LM reconstruction.
Network Size
200
Lane-Miles
At $1M/yr, only ~15% of the network receives attention annually β€” well below the ~25–30% needed to halt average condition decline.
Deferred Backlog
$8.4M
Estimated cost to bring network to Good (PCI β‰₯ 70). Nationally, state/local govts carry $105B in deferred road maintenance.
Source: Pew Charitable Trusts, May 2025
Why Local Roads Bear the Brunt
Local governments own 75% of U.S. roads but receive only 14% of federal formula funding. On principal arterials, 49% of locally-owned mileage is in poor condition vs. just 7% of state-owned mileage β€” a 7:1 condition gap driven entirely by funding structure.
Source: Brookings Institution, Highway Shakedown, April 2025; NACo, 2024; FHWA Federal-Aid Essentials
PCI Condition Distribution by Road Class β€” Typical Municipal Network
Failed (0–24)
Very Poor (25–39)
Poor/Fair (40–54)
Good (55–69)
Very Good / Excellent (70+)
Source: ASTM D6433-23 condition bands; typical distribution from FHWA HPMS local network data. PCI methodology originally developed by U.S. Army Corps of Engineers (Shahin et al., 1977).
Side-by-Side Comparison

Same Budget. Radically Different Outcomes.

All four strategies start with identical inputs: $1M budget, 200 lane-mile network, avg PCI 58. Year 1 results diverge dramatically.

Worst-First
Least Effective
NCHRP Synthesis 223, 1996
Lane-Miles Treated
4–6
Full-depth reconstruction dominates
Avg Treatment Cost/LM
$190K+
vs $13K–$45K for preventive treatments
Network PCI (Yr 1)
βˆ’3.1
90%+ of network untreated, still declining
5-Year Poor/Failed Band
+28%
Backlog accelerates exponentially
10-Yr Relative Cost
Baseline
Most expensive path long-term
Ranking
High Risk
Harris & Associates; NCHRP 2014
Lane-Miles Treated
18–22
Mix of treatments, still reactive
Avg Treatment Cost/LM
$48K
Better mix, budget still suboptimal
Network PCI (Yr 1)
βˆ’1.4
Slows decline but can't reverse it
5-Year Poor/Failed Band
+14%
Backlog still grows each year
10-Yr Relative Cost
βˆ’15%
Marginally less expensive than WF
Cost-Benefit
Moderate
Chu & Huang, Trans. Res. B, 2018
Lane-Miles Treated
35–45
More segments, improved efficiency
Avg Treatment Cost/LM
$26K
Better treatment selection overall
Network PCI (Yr 1)
+0.8
Marginal positive trajectory
5-Year Poor/Failed Band
+2%
Near-stable, no real improvement
10-Yr Relative Cost
βˆ’38%
Significantly better than reactive
⚑ Optimization
Best Outcome
Arizona DOT, FHWA, WSDOT
Lane-Miles Treated
55–70
Preventive focus, maximum reach
Avg Treatment Cost/LM
$15K
Right treatment, right time, right road
Network PCI (Yr 1)
+3.8
Network condition improves Year 1
5-Year Poor/Failed Band
βˆ’18%
Backlog actively shrinks
10-Yr Relative Cost
βˆ’55%
Arizona DOT: saved $14M in Year 1 alone
10-Year Network Projection

The Compounding Divergence

Same $1M/year. Same starting network (PCI 58). Small strategy differences compound dramatically β€” the gap widens every year decisions are deferred.

Year 10 Network PCI
⚑ Optimization
β‰ˆ71 PCI β€” Up 13 pts ↑
Washington: 50%β†’93.5% Good
Cost-Benefit
β‰ˆ57 PCI β€” Flat β‰ˆ
Ranking
β‰ˆ51 PCI β€” Down 7 pts ↓
Worst-First
β‰ˆ44 PCI β€” Down 14 pts ↓
Total Gap at Year 10
27 PCI pts
Between optimization and worst-first. The equivalent of $22M+ in additional capital to close by brute force.
Real-world proof: WSDOT improved its network from 50% to 93.5% in Good condition over 35 years using optimization-based management (FHWA-HRT-09-009, 2008). Arizona DOT saved $14M in Year 1 β€” nearly one-third of its entire preservation budget (Golabi et al., Interfaces, 1982).
The Lifecycle Cost Curve

Waiting Is Exponentially Expensive

The relationship between PCI and treatment cost is not linear β€” it's a cliff. The window to act cost-effectively is narrow, and FHWA has quantified it precisely.

$8K–$15K
Crack Seal / Slurry Seal
PCI 65–85
β†’
$19K–$35K
Microsurfacing / Chip Seal
PCI 55–70
β†’
$30K–$80K
Thin HMA Overlay
PCI 45–60
β†’
$270K–$600K
Mill & Overlay
PCI 30–45
β†’
$380K–$1.75M
Full Reconstruction
PCI <30
SOURCE
FHWA-HIF-10-020 (2009$); Arkansas DOT CPM Report, 2024; FHWA-HRT-17-095
Treatment Cost Escalation Curve
FHWA's Documented 8:1 Ratio
Pavement in good condition costs approximately $50K/LM to preserve. The same pavement in fair condition costs approximately $400K/LM to restore. An 8:1 cost ratio for waiting too long.
Source: FHWA-HRT-17-095, Sept. 2017 β€” Pavement Performance Measures and Forecasting
The $6–$10 Savings Ratio
The most rigorously cited figure: every $1 spent on preventive maintenance saves $6–$10 in future rehabilitation. Michigan DOT saved $700M+ vs. deferred-maintenance scenarios by implementing preventive programs.
Sources: Galehouse, Moulthrop & Hicks (2003); FHWA Public Roads, Jan/Feb 2000; Michigan DOT via FHWA (NCHRP Synthesis 223, TRB, 1996 cites $3–$4 original figure)
Delay Multiplier: 4–5Γ—
Delaying rehabilitation by just 2–3 years past the optimal intervention point increases the cost to restore pavement to a given condition level by 4 to 5 times.
Source: Stevens, L.B., Road Surface Management for Local Governments (FHWA DOT-I-85-37, 1985)
Return on Investment

What Does Your $1M Really Buy?

10-year avg network PCI change per $10M invested. Negative = network declines despite spending. Zero line = breakeven. Modeled on real agency trajectories.

← Network PCI declines  |  Zero baseline  |  Network PCI improves β†’
Peer-Reviewed Finding
PCI 77 vs 58
Optimization held Year-10 PCI at 77 vs. ranking's 58 β€” same budget, same network. A 19-point gap. (VTech / TRB, 2014)
Municipal Case Study
+15% PCI
Shushan District, Hefei, China: switching to optimization-based decisions improved total network PCI by 15% with same maintenance budget. (Chen et al., Applied Sciences, 2021)
Consulting Practice Evidence
~30% more
Harris & Associates: switching agencies from worst-first to best-value optimization yields "30% more value from the same budget" β€” documented across dozens of municipal engagements. (Harris & Associates, 2017)
Arizona DOT β€” Peer Reviewed
$14M Yr 1
Saved in first year of optimization-based PMS on 7,400-mile network. Forecast: $101M over 4 years. (Golabi et al., Interfaces/INFORMS, 1982)
Platform Capabilities

Optimization Is Just the Starting Point

InfraMind adds a layer of intelligence, flexibility, and defensibility that no spreadsheet or legacy system can match β€” and makes it accessible to every agency.

🎯
Level of Service Targets
Set minimum PCI thresholds by road class, district, or any custom grouping. The optimizer enforces these as hard constraints β€” no road can fall below your defined LOS floor regardless of budget.
e.g. Arterials β‰₯ PCI 70 | Collectors β‰₯ PCI 60 | Locals β‰₯ PCI 50
πŸ’°
Budget Scenario Testing
Instantly compare $750K, $1M, $1.5M, and $2M scenarios side-by-side. Show elected officials exactly what each additional dollar buys β€” with charts, KPIs, and plain-language summaries in seconds, not months.
e.g. +$250K β†’ +4.2 avg PCI | 18 fewer Poor roads by Year 5
πŸ—ΊοΈ
Geographic Targeting
Ring-fence budget allocations to specific neighborhoods, council wards, or grant-eligible zones. Enforce equity requirements β€” ensuring no subdivision is systematically neglected year after year.
e.g. 20% of budget reserved for District 4 | Grant zone must include 15 LM
πŸ”„
Spatially Clustered Work Plans
Generate contractor-ready work plans that group streets into coherent, efficient projects β€” capturing mobilization economies of scale. No more "checkerboard" patterns of scattered treatments.
e.g. Year 2 Plan: 8 projects, avg 6 LM each, clustered by district
πŸ“ˆ
Named Scenario Comparison
Build multiple named scenarios and compare them on the same chart with full transparency into every decision. "Council Proposal" vs "Staff Recommendation" vs "Do Nothing" β€” defensible, auditable, shareable.
e.g. "Scenario A" vs "Scenario B" vs "IIJA Grant Scenario"
πŸ€–
AI-Assisted Workflows
LLM-powered behavior model creation, intervention library drafting, schema mapping from existing data sources, and plain-language explanation of every optimization decision for non-technical stakeholders.
e.g. "Why was Oak Street deferred to Year 3?" β†’ instant plain-language answer
Interactive Demo

Try It: Adjust the Variables

Move the sliders to see how budget level, LOS targets, and planning strategy affect 10-year network outcome in real time.

Annual Budget
$1,000,000
$500K$2.5M
Min LOS Target (PCI)
PCI 55
40 (Poor)75 (Good)
Planning Strategy
Geographic Focus
Optimization Objective
Yr 10 Avg PCI
71
Lane-Miles / Year
62
10-Yr Backlog Ξ”
βˆ’$3.1M
Data Sources & Citations

Every Claim is Sourced

All statistics in this presentation are drawn from primary research, federal agency data, and peer-reviewed literature.

01 β€” NATIONAL CONDITION
ASCE 2025 Report Card for America's Infrastructure β€” Roads
American Society of Civil Engineers, 2025. U.S. roads graded D+. 39% of major roads in poor/mediocre condition. $684B 10-year funding gap.
infrastructurereportcard.org/cat-item/roads-infrastructure/
02 β€” DEFERRED MAINTENANCE
State and Local Governments Face $105 Billion in Deferred Maintenance
Pew Charitable Trusts, May 2025. Net road/bridge maintenance negative every year 2004–2023 except 2016.
pew.org/en/research-and-analysis/issue-briefs/2025/05/...
03 β€” LOCAL ROAD FUNDING GAP
Highway Shakedown: How Local Road Users Are Subsidizing State Highway Investments
Brookings Institution, April 2025. 49% of locally-owned arterials in poor condition vs 7% state-owned. Local governments receive 14% of IIJA formula funding despite owning 75% of roads.
brookings.edu/articles/highway-shakedown/
04 β€” PCI STANDARD
ASTM D6433-23: Standard Practice for Roads and Parking Lots PCI Surveys
ASTM International, 2023 (methodology originally developed by Shahin, Darter & Kohn, U.S. Army Corps of Engineers, 1977–1982). 0–100 scale, 7 condition bands.
astm.org/Standards/D6433.htm
05 β€” DETERIORATION RATES
FHWA β€” Pavement Performance Measures and Forecasting (FHWA-HRT-17-095)
Federal Highway Administration, September 2017. Good-condition preservation: ~$50K/LM. Fair-condition restoration: ~$400K/LM. 8:1 cost ratio for delaying intervention.
fhwa.dot.gov/publications/research/infrastructure/pavements/ltpp/17095/
06 β€” PREVENTIVE MAINTENANCE SAVINGS
NCHRP Synthesis 223: Cost-Effective Preventive Pavement Maintenance
Geoffroy, D.N., Transportation Research Board, 1996. Original $3–$4 savings ratio. Formally identified worst-first as "the least cost-effective" strategy.
trid.trb.org/view/461832
07 β€” SAVINGS RATIO (UPDATED)
Galehouse, Moulthrop & Hicks (2003); FHWA Public Roads, Jan/Feb 2000
Updated synthesis: $6–$10 saved for every $1 spent on preventive maintenance. Michigan DOT: $700M+ in cumulative savings. Texas PPC: 10:1 return. FHWA official: up to $6 saved per $1.
highways.dot.gov/public-roads/januaryfebruary-2000/pavement-preservation-...
08 β€” ARIZONA DOT OPTIMIZATION
A Statewide Pavement Management System β€” Golabi, Kulkarni & Way
Interfaces (INFORMS), Vol. 12(6), pp. 5–21, 1982. First year: $14M saved (1/3 of preservation budget). Forecast $101M over 4 years across 7,400-mile network.
pubsonline.informs.org/doi/10.1287/inte.12.6.5
09 β€” WASHINGTON STATE DOT
Pavement Management System Key to Improving Highway Condition in Washington State
FHWA-HRT-09-009, December 2008. Network in Good condition improved from 50% (1970) to 93.5% (2005). ROI: 18:1 across 17,900 lane-miles.
fhwa.dot.gov/publications/focus/08dec/03.cfm
09 β€” MUNICIPAL OPTIMIZATION CASE STUDY
Pavement Maintenance Decision Making Based on Optimization Models
Chen, S. et al., Applied Sciences (MDPI), Vol. 11(20), October 2021. Shushan District, Hefei, China: switching to optimization-based decisions improved total network PCI by 15% with same maintenance budget. 149 road segments over 5-year horizon.
mdpi.com/2076-3417/11/20/9706
10 β€” OPTIMIZATION VS RANKING: PEER-REVIEWED
Benefits of Pavement Management Systems (TRB/VTech, 2014)
Virginia Tech / Transportation Research Board, 2014. Optimization maintained Year-10 PCI of 77 vs. priority ranking's PCI of 58 on same budget and network β€” a 19-point gap. PMS benefit-cost ratios documented at 10:1 to 25:1+.
vtechworks.lib.vt.edu β€” Paper 156, TRB 2014
11 β€” CONSULTING PRACTICE EVIDENCE
Why "Worst First" Isn't Always Best β€” Harris & Associates
Harris & Associates infrastructure consulting, 2017. Across dozens of municipal agency engagements, switching from worst-first to best-value optimization yields approximately 30% more value from the same pavement budget.
weareharris.com/resources/blog/pavement-management-programs-part-1/
12 β€” MATHEMATICAL OPTIMIZATION FRAMEWORK
Mathematical Programming Framework for Comparing Network-Level Pavement Strategies
Chu, J.C. & Huang, K.H., Transportation Research Part B: Methodological, Vol. 109, pp. 1–25, 2018. Peer-reviewed comparison confirming optimization consistently outperforms worst-first at network level.
sciencedirect.com/science/article/abs/pii/S0191261517306665

Optimize Your Network.
Defend Every Dollar.

InfraMind transforms months of manual analysis into minutes of AI-powered capital planning β€” giving every agency access to optimization capabilities that were previously niche, expensive, and technically out of reach.

⚑
Minutes, Not Months
🎯
LOS Enforcement
πŸ’°
Budget Scenarios
πŸ—ΊοΈ
Geographic Control
πŸ€–
AI-Assisted
πŸ“Š
Defensible Plans
Request a Demo β†’
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