Toll Road Revenue Bonds and Finance

Toll Road Revenue Bonds and Finance

A Comprehensive Guide to U.S. Toll Road Debt Financing and Infrastructure Economics

From toll rate structures and bond mechanics to credit analysis and emerging challenges

Prepared by DWU AI

An AI Product of DWU Consulting LLC

February 2026

DWU Consulting LLC provides specialized infrastructure finance consulting for airports, toll roads, transit systems, ports, and public utilities. Our team brings deep expertise in financial analysis, credit evaluation, rate setting, and comparative benchmarking across transportation sectors. With 25+ years of consulting experience, DWU applies rigorous analytical frameworks refined in airport finance to toll road, transit, and port infrastructure. Please visit https://dwuconsulting.com for more information.

Table of Contents

• A. Introduction
• B. Industry Structure and the ~50 Entity Universe
• C. Toll Rate Structures and Escalation
• D. Revenue Bond Structure and Fund Flow Waterfall
• E. Rate Covenants and Coverage Standards
• F. Traffic and Revenue Studies: Methodology and Optimism Bias
• G. Electronic Toll Collection and Interoperability
• H. Managed Lanes and Dynamic Pricing
• I. P3 and Concession Models
• J. Rating Agency Framework
• K. Key Financial Metrics and Top 9 Issuers
• L. The Three Dimensions Framework Applied to Toll Roads
• M. Forward-Looking Challenges and Opportunities

A. Introduction

Toll road revenue bonds represent a critical and well-established segment of U.S. infrastructure finance. Backed exclusively by toll revenues—not taxing power—these bonds fund the construction, expansion, rehabilitation, and maintenance of tolled highway facilities across the country. Unlike general obligation bonds or tax revenue bonds, toll road revenue bonds create a direct user-fee model: the motorist who uses the facility pays for it, creating economic efficiency and enabling politically feasible debt financing in capital-constrained states.

The U.S. toll road sector is economically substantial. The network comprises approximately 6,000 miles of tolled roadway across more than 130 tolling agencies in 37 states. Annual toll revenues reached approximately $7.4 billion in 2025, reflecting compound annual growth of roughly 8.4% over the preceding five years—driven by a combination of traffic recovery from the pandemic, consistent toll rate increases, and network expansion in high-growth corridors like Texas and the Southeast. Outstanding toll road revenue bonds total an estimated $80–100 billion, concentrated among fewer than two dozen major issuers but distributed across a universe of smaller regional and local authorities.

For DWU Consulting, toll road finance represents a natural extension of our airport finance expertise. While the asset classes differ in physical characteristics and operational detail, the financial and analytical frameworks are substantially similar: both are infrastructure-backed, user-fee revenue bonds; both employ GAAP financial reporting and rate covenant structures; both navigate complex rate-setting dynamics with multiple stakeholders; and both require sophisticated demand forecasting, credit analysis, and benchmarking. This guide applies DWU's analytical rigor and comparative framework to the toll road sector, providing bond investors, credit analysts, and infrastructure finance professionals with authoritative guidance on toll road credit structures, performance metrics, and emerging risks.

B. Industry Structure and the ~50 Entity Universe

The U.S. toll road sector is highly fragmented, comprising approximately 130 tolling agencies across 37 states. However, the debt-issuing universe is far more concentrated: only about 50 entities issue revenue bonds to finance tolled facilities, and this concentrated universe can be segmented into distinct operator categories.

Operator Categories

Independent Toll Authorities. Many states created standalone toll authorities with independent boards of directors and dedicated revenue streams. Examples include the New Jersey Turnpike Authority, Pennsylvania Turnpike Commission, Illinois State Toll Highway Authority, and Florida Turnpike Enterprise. These authorities typically operate a network of toll facilities and maintain dedicated engineering, operations, and finance staffs. They are most likely to issue debt on competitive markets and achieve investment-grade ratings.

State DOT Divisions. Some states operate toll roads as divisions within their Department of Transportation. Texas (TxDOT, NTTA, HCTRA, and others operate independently), Louisiana, and Mississippi operate toll facilities through DOT structures. This model can create budget flexibility but may complicate financial reporting and rate-setting autonomy.

County and Regional Authorities. Smaller toll operators in metropolitan areas may be county-level or multi-county regional authorities. The Central Florida Expressway Authority, Central Texas Regional Mobility Authority, and Harris County Toll Road Authority exemplify this structure. These entities typically operate 20–150 miles of roadway and generate $150–900 million in annual toll revenues.

Multi-Purpose Authorities. Some authorities operate toll roads alongside transit, airports, or port facilities. The Bi-State Development Agency (St. Louis) operates both the Gateway Arch Transit system and toll facilities; the Fort Worth Transportation Authority operates transit and toll roads.

Public-Private Partnership Concessionaires. Private companies operate toll facilities under long-term concession agreements with public authorities. Transurban (Australia-based) operates approximately 65 miles of express lanes in Virginia plus facilities in Florida and other states. Other concessionaires include Cofiroute (subsidiary of Vinci, France), Cintra (subsidiary of ACS, Spain), and various regional operators. P3 concessionaires bring private operational expertise and capital market access but introduce additional complexity in rating and regulatory treatment.

Bi-State Compacts. A few toll facilities cross state boundaries and are governed by bi-state compacts or authorities. The Delaware River Joint Toll Bridge Commission operates three toll bridges between Pennsylvania and New Jersey. These structures can create governance complexity but may provide enhanced revenue stability through access to multiple state tax bases.

The Debt-Issuing Universe

Of the ~130 toll operators, only about 50 have issued revenue bonds in recent years. The largest issuers—Pennsylvania Turnpike Commission (~$16 billion debt), New Jersey Turnpike Authority (~$12 billion), North Texas Tollway Authority (~$8.5 billion), Illinois Tollway (~$7 billion), and Florida Turnpike Enterprise (~$3.9 billion)—account for more than 50% of all outstanding toll road bonds. This concentration reflects both the capital-intensive nature of toll road development and the credit market's preference for large, established operators with proven traffic and revenue performance.

Regional tier-two operators (generating $500–1,200 million in annual toll revenue) include HCTRA (Houston), Central Florida Expressway Authority, Oklahoma Turnpike Authority, Central Texas Regional Mobility Authority, and several California and New York regional toll agencies. These entities typically carry debt ranging from $1 billion to $3 billion and achieve ratings from Aa3/AA- down to A/A+, depending on leverage and coverage.

Smaller greenfield and newer projects may be rated below investment grade (BBB- or unrated) due to traffic and revenue uncertainty, limited operating history, or aggressive leverage profiles intended to minimize public sector subsidy.

C. Toll Rate Structures and Escalation

Toll road operators employ diverse rate structures to generate revenue from motorists. Understanding these structures is essential for forecasting revenues and evaluating rate covenant compliance.

Types of Toll Structures

Fixed Toll / Barrier Toll. A flat toll is charged at entry or exit regardless of distance traveled. This is the traditional model used on most legacy turnpikes: the New Jersey Turnpike charges a fixed toll per vehicle class at each toll barrier. Fixed tolls are simple to administer, minimize technology requirements, and provide predictable revenue, but they may be perceived as inequitable (long-distance users and short-distance users pay the same) and do not incentivize route efficiency.

Distance-Based Toll. Toll charges vary based on the distance traveled through the facility or the number of toll tags passed. This is common on modern expressway systems and is enabled by electronic toll collection. The Pennsylvania Turnpike uses distance-based tolls. Distance-based tolls are economically efficient, align user costs with usage, and enable more granular revenue forecasting but require robust ETC infrastructure.

Zone-Based Toll. Toll charges vary by zone or corridor within a larger network. Some multi-facility authorities charge different rates on different segments based on traffic demand, congestion, or facility age/capacity. This structure balances simplicity with some degree of differentiation.

Dynamic / Congestion Pricing. Toll charges adjust in real time based on current traffic conditions, typically to maintain free-flow speeds (45 mph or higher) in the tolled lanes. Managed lanes and express toll lanes employ dynamic pricing. Prices may change every 6 minutes or even more frequently. Examples include the I-495 Capital Beltway Express Lanes (Virginia), LBJ TEXpress (Dallas), and the I-405 ExpressLanes (Los Angeles). Dynamic pricing maximizes revenue per available lane-mile and optimizes traffic flow but creates significant revenue volatility and price elasticity risk.

Vehicle Classification

Most toll authorities classify vehicles by axle count or gross vehicle weight rating (GVWR) to reflect the infrastructure wear caused by heavier vehicles. Common classifications include:

• Class 1: Motorcycles and 2-axle passenger vehicles (the vast majority of traffic)
• Class 2: 2-axle vehicles (some systems separate pickup trucks)
• Class 3: 3-axle vehicles
• Class 4+: 4-axle and heavier vehicles (commercial trucks)

Toll rates for heavier vehicles are typically 2x to 5x the passenger vehicle rate, reflecting both infrastructure damage and the principle of cost causation. A tractor-trailer combination may pay $20–40 per transaction while a passenger car pays $4–8 (depending on distance and system).

This structure creates important forecasting implications: shifts in freight vs. passenger traffic mix directly affect revenue. The post-pandemic period has seen sustained strength in freight traffic due to e-commerce growth, which has supported toll road revenues even as commute traffic has remained modestly depressed due to remote work.

Toll Escalation Methods

Toll rates do not remain static. Operators employ several escalation mechanisms to ensure revenue growth keeps pace with inflation and debt service obligations.

CPI-Indexed Escalation. Many bond indentures specify that toll rates increase annually by the Consumer Price Index (CPI) or a similar inflation measure. This is the least controversial approach: rates rise to preserve purchasing power without requiring explicit board action. However, if actual inflation exceeds projections embedded in bond pro forma, revenues may fall short of covenant requirements.

Fixed Percentage Escalation. Some indentures provide for fixed annual toll increases of, for example, 3% or 5% regardless of inflation. This provides certainty to bondholders but may result in either rate compression (during low-inflation periods) or aggressive real rate increases (during high-inflation periods). The Texas toll roads have employed fixed-percentage escalation to support debt service growth.

Board-Approved Escalation. Other authorities retain discretion to increase toll rates via board action. This allows flexibility to respond to changes in traffic, inflation, or operational costs but introduces uncertainty for revenue forecasting. Typically, board escalations are implemented during a defined window (e.g., annually in April) and are capped at a maximum percentage (e.g., CPI + 1%).

Legislatively Constrained Escalation. In some states, toll rate increases are subject to legislative approval or voter referenda. While rare, this structure has been used in some regional systems. It provides accountability but creates inflexibility if revenues fall short of requirements.

Dynamic Pricing Escalation. Managed lanes may employ algorithmic pricing adjustments that respond to real-time traffic conditions and historical demand patterns. These systems may increase prices during peak periods and reduce them during off-peak periods. Pricing algorithms are typically reviewed quarterly or semi-annually by management and approved by the board.

Rate covenant mathematics (discussed in Section E) typically specify coverage ratios in terms of "Net Revenues"—which include toll revenues at projected escalation rates. If actual escalation lags projections, Net Revenues may fall short and trigger rate covenant violations. This is a key credit risk for rating agencies and bondholders.

D. Revenue Bond Structure and Fund Flow Waterfall

Toll road revenue bonds are structured around a carefully sequenced fund flow waterfall that prioritizes claims on toll revenues in a defined order. Understanding this waterfall is essential for evaluating bondholder protections and assessing credit risk under stress scenarios.

Senior and Subordinate Lien Architecture

Most large toll road issuers maintain a multi-tiered debt structure. Senior lien bonds hold the first claim on toll revenues and pledged reserves. Subordinate (or junior) lien bonds are payable only after senior obligations have been fully satisfied. A few very large issuers (like NTTA) maintain three tiers: first-lien (senior) bonds, second-lien (mid-tier) bonds, and third-lien (subordinate) bonds.

The rating differential between tiers typically ranges from one to three notches. NTTA's structure illustrates this: approximately $5.7 billion in first-tier bonds rated Aa3 by Moody's, $2.6 billion in second-tier bonds rated A1, and a smaller third tier rated A3. This granularity allows the authority to match debt costs to risk appetite: senior bonds appeal to risk-averse investors, while subordinate bonds attract higher-yield investors comfortable with greater risk.

Typical Fund Flow Waterfall

A representative toll road fund flow waterfall follows this priority order:

1. Revenue Fund. All toll revenues are deposited into the Revenue Fund immediately upon collection. This is the starting point for all downstream allocations.

2. Operating and Administrative Expenses (O&M). The authority withdraws funds necessary to pay for day-to-day operations: toll collection, facility maintenance, utilities, insurance, administrative salaries, equipment replacement, and other direct costs of operating the toll facility. O&M expenses typically range from 20–40% of gross revenues depending on the facility's age, size, and technological sophistication.

3. Senior Lien Debt Service. After O&M is funded, resources are allocated to pay principal and interest on senior lien bonds. This is the most critical claim on revenues and typically consumes 30–50% of gross toll revenue.

4. Senior Debt Service Reserve Fund Replenishment. If the Senior Debt Service Reserve Fund has been drawn upon (e.g., due to a traffic shortfall or one-time expense), it is replenished from excess revenues up to the covenant target (typically the greater of maximum annual debt service, 125% of average annual debt service, or 10% of par).

5. Subordinate Lien Debt Service. After senior obligations are fully funded, subordinate lien debt service is paid. Subordinate debt service is inherently more uncertain because it depends on excess revenues available after senior obligations and reserves are fully funded.

6. Subordinate Debt Service Reserve Fund Replenishment. Similar to step 4, subordinate reserves are replenished from remaining revenues.

7. Capital Improvement and Renewal & Replacement Reserves. Contributions are made to capital and renewal reserves, which fund future rehabilitation, technology upgrades, and major maintenance projects. These contributions are often discretionary and can be deferred if revenues are constrained.

8. General Fund / Equity Account. Any remaining surplus transfers to the authority's general or equity account, which may be used for future operations, debt repayment, or return to the public sector.

This waterfall structure is specified in the bond indenture and may be modified only with bondholder consent (typically requiring a super-majority, e.g., 66.7% approval). The waterfall provides bondholder security by ensuring senior debt service is paid before subordinate obligations, and it explains why senior bonds are rated higher than subordinate bonds.

Gross Revenue Pledge vs. Net Revenue Pledge

Most toll road bonds employ a gross revenue pledge: ALL toll revenues are pledged to bondholders before operating expenses are deducted. This is more protective than a net revenue pledge (where only revenues remaining after O&M are pledged). Gross revenue pledge ensures that even if operating expenses spike, bondholders retain priority claim on all incoming revenue.

Some hybrid structures pledge gross revenues to senior lien debt service but allow subordinate debt service to be paid only from net revenues, creating two tiers of protection.

TIFIA Loans and GARVEE Bonds

Modern toll road financings often incorporate federal programs that accelerate funding and optimize capital structure.

TIFIA (Transportation Infrastructure Finance and Innovation Act). TIFIA loans are federal loans from the U.S. Department of Transportation that provide long-term financing for toll projects. TIFIA loans are subordinate to revenue bonds (junior in lien priority) and typically carry terms of 35+ years. The interest rate is the Treasury rate plus a credit spread, typically 1–3%. TIFIA loans are attractive for large projects because they provide long-term, low-cost financing that complements private activity bonds. The Georgia SR 400 Express Lanes project included a $4.0 billion TIFIA loan—the largest in history—alongside $3.5 billion in private activity bonds. TIFIA loans must be repaid from toll revenues and are therefore subject to traffic risk, but their subordinate status means they are lower-cost than revenue bonds of equivalent risk.

GARVEE Bonds (Grant Anticipation Revenue Vehicles). GARVEE bonds are backed by anticipated federal Highway Trust Fund grants (from the USDOT). They allow states to front-load federal highway funding over a shorter period (typically 10–15 years) rather than spreading it over decades. GARVEE bonds are repaid from future federal grants. They are rarely used for toll road projects specifically but may be used in conjunction with toll projects that receive federal assistance.

E. Rate Covenants and Coverage Standards

Rate covenants are contractual obligations that require toll road authorities to set rates sufficient to generate revenues that meet specified debt service coverage ratios. They are the bondholders' primary mechanism for ensuring financial sustainability.

The Rate Covenant Formula

A typical rate covenant reads: "The Authority shall establish, maintain, and collect toll rates sufficient to generate Net Revenues equal to at least [X]x Annual Debt Service."

Net Revenues = Total Toll Revenue − Operating & Administrative Expenses

Annual Debt Service = Principal + Interest on all outstanding bonds in the applicable lien tier

Coverage Ratio = Net Revenues / Annual Debt Service

If the required ratio is 1.25x and annual debt service is $100 million, the covenant requires Net Revenues of at least $125 million. If actual Net Revenues fall short, the authority is in technical default and must take corrective action, which typically includes increasing toll rates.

Typical Coverage Levels by Rating Category

Rating agencies expect different coverage ratios for different rating categories. A representative benchmark:

• Aa / AA-rated toll roads: 1.50x–2.00x DSCR expected (mature facilities with stable demand)
• A / A-rated toll roads: 1.30x–1.50x DSCR expected
• Baa / BBB-rated toll roads: 1.20x–1.30x DSCR expected
• Greenfield / Development-stage: 2.00x–2.50x or higher (to cover demand risk)
• Availability-based (P3 concessions): 1.10x–1.25x (demand risk transferred to public)

Higher-rated bonds require higher coverage because rating agencies expect greater margin for error. A mature, investment-grade toll road can operate sustainably at 1.50x coverage; a new facility with uncertain demand may require 2.50x coverage to demonstrate resilience to traffic shortfalls.

Actual Coverage Ratios: Comparative Analysis of Major Systems

The following table illustrates actual debt service coverage ratios for nine major U.S. toll road systems based on most recent published financial data:

Toll Authority	Annual Toll Revenue	Outstanding Debt	Senior DSCR	Moody's Rating (Senior)
Pennsylvania Turnpike Commission	$1.60 B	$16.0 B	2.43x	Aa3 (KBRA: AA-)
New Jersey Turnpike Authority	$1.68 B	$12.0 B	1.92x	A2
Illinois State Toll Highway Authority (Tollway)	$1.40 B	$7.0 B	1.72x	Aa3
Florida Turnpike Enterprise	$1.35 B	$3.9 B	2.10x	Aa2
North Texas Tollway Authority (NTTA)	$1.19 B	$8.52 B (3 tiers)	1.84x (1st tier)	Aa3 (1st tier)
Harris County Toll Road Authority (HCTRA)	$896 M	$2.8 B	5.20x	Aa1
Central Florida Expressway Authority (CFX)	$705 M	$1.9 B	1.89x	Aa3 (upgraded 2024)
Oklahoma Turnpike Authority (OKTA)	$410 M	$3.0 B	1.56x	A1
Central Texas Regional Mobility Authority (CTRMA)	$176 M	$1.2 B	1.43x	A (multiple upgrades 2023–2025)

Note: Data represents most recent publicly available information from official statements, annual reports, and rating agency publications as of February 2026. Coverage ratios are calculated on senior lien debt where applicable. HCTRA's exceptionally high DSCR (5.20x) reflects its very strong financial position and minimal debt burden relative to revenues.

Additional Bonds Test (ABT)

The Additional Bonds Test is a covenant that restricts the issuance of new debt in parity with existing bonds. A typical ABT requires that an authority demonstrate, based on historical or projected revenues, that Net Revenues will cover all outstanding and proposed debt service at a specified ratio—commonly 1.20x for senior lien bonds.

The ABT protects existing bondholders from dilution through excessive additional borrowing. If an authority wants to issue new senior lien bonds, it must prove (typically using historical revenues from the preceding 12 months) that existing + proposed debt service will still be covered at 1.20x. This limits the amount of new debt that can be issued and ensures that each new issuance does not materially diminish the credit of prior bondholders.

F. Traffic and Revenue Studies: Methodology and Optimism Bias

Traffic and revenue forecasting is the foundation of toll road credit analysis. Investors and rating agencies rely heavily on independent traffic studies to project future revenues and assess debt serviceability. Understanding forecasting methodology and its limitations is critical.

Forecasting Methodology: The Four-Step Travel Demand Model

Most professional traffic consultants employ the "four-step travel demand model," a standard framework used by metropolitan planning organizations (MPOs) and the USDOT:

Step 1: Trip Generation. Estimate total trips produced by and attracted to each geographic zone, based on land-use data (residential units, employment, retail square footage) and socioeconomic variables (income, automobile ownership). This step produces an origin-destination matrix of daily trips.

Step 2: Trip Distribution. Distribute trips among origin-destination pairs based on impedance models (travel time, distance, toll cost). The model estimates what fraction of trips from Zone A to Zone B will use the tolled facility vs. competing free routes.

Step 3: Mode Choice. Allocate trips to transportation modes (personal automobile, transit, carpool, freight). Most toll road studies assume high automobile mode share in their service corridors (80–95% mode share is typical).

Step 4: Network Assignment. Route trips through the network, assigning them to specific facilities (the toll road vs. competing free routes) based on travel time, cost, and driver behavior. This step produces estimates of daily toll facility traffic by vehicle class.

Key Variables in Demand Modeling

Value of Time (VOT). How much is a minute of travel time worth to a motorist? VOT estimates range from $5–$25 per hour depending on income level, trip purpose, and geographic context. Higher VOT increases the value of toll road time savings, which increases demand for tolled facilities. VOT is a critical input that materially affects traffic projections.

Price Elasticity of Demand. How sensitive is traffic demand to toll pricing? Academic research on U.S. toll roads finds elasticity coefficients ranging from approximately −0.21 (highly inelastic) to −0.83 (moderately elastic). Inelastic demand means traffic is insensitive to toll increases; elastic demand means toll increases cause noticeable traffic loss. Misestimating elasticity can lead to either optimistic or pessimistic traffic forecasts.

Growth Assumptions. Population and employment growth in the service area drive long-term demand growth. Forecast growth rates typically range from 1–4% annually depending on the region. Conservative forecasts use regional historical growth rates; aggressive forecasts extrapolate recent boom periods. Growth assumption errors are among the largest drivers of forecast misses.

Competing Route Development. Will new free routes or improvements to existing routes divert traffic away from the toll facility? The model must account for planned roadway improvements by competing agencies. If a free parallel route is planned to open before the toll facility reaches projected maturity, traffic projections may be overstated.

CRITICAL: Optimism Bias in Traffic Forecasting

Academic and consulting industry research has documented a persistent and severe optimism bias in toll road traffic and revenue forecasts. This is not a minor issue—it is a material credit risk that rating agencies increasingly incorporate into ratings.

The Evidence: Robert Bain's seminal analysis of traffic forecasts for 68 toll road projects found that actual traffic in the first year of operation fell an average of 26% below forecast. Ninety percent of new toll roads opened below initial Year 1 traffic forecasts. Bain's research, published in 2009 and updated in subsequent work, has become the industry standard benchmark for quantifying forecast bias.

More recent studies confirm this pattern. Research by Börjesson and Eliasson (2014) analyzing Swedish toll roads found similar optimism bias of 20–30% in Year 1. Academic studies of transportation projects more broadly (transit, toll roads, airport terminals) consistently document that demand forecasts are too optimistic, particularly for projects introducing new pricing (toll) mechanisms that face demand uncertainty.

Why does optimism bias occur? Several factors contribute:

• Consultant Incentives: Traffic consultants may face implicit or explicit pressure to produce optimistic forecasts to support project financing. Overly pessimistic forecasts may disqualify projects from funding.
• Demand Model Limitations: The four-step model relies heavily on stated and revealed preference data that may not accurately predict behavior in response to novel tolling mechanisms or congestion pricing.
• Growth Extrapolation: Pre-project economic trends (boom periods) are often extrapolated into the future, creating overly optimistic growth assumptions that do not materialize.
• Underestimation of Price Response: Traffic models often underestimate the elasticity of demand for toll pricing, leading to overestimation of toll facility traffic at projected toll rates.
• Route Competition: Forecasts may underestimate the willingness of drivers to use free parallel routes or tolled competitors, leading to overestimated toll facility traffic.

Credit Implications: Rating agencies now explicitly apply a "haircut" to traffic forecasts, discounting Year 1 projections by 15–30% depending on project characteristics. Greenfield projects (no operating history) receive larger haircuts. Mature facilities with demonstrated traffic history receive smaller or no haircuts. This is one reason rating agencies typically cap greenfield toll road ratings at BBB or below absent exceptional credit features.

Consultant Selection: To mitigate forecast bias, bond indentures typically require traffic studies to be prepared by "independent traffic consultants"—meaning consultants with no financial interest in the project. Major traffic consulting firms include CDM Smith (formerly Camp Dresser & McKee), HNTB, Stantec, and Jacobs. The quality and independence of the traffic consultant should be a key evaluation criterion for investors.

G. Electronic Toll Collection and Interoperability

The shift to electronic toll collection (ETC) represents one of the most significant operational transformations in toll road history. From a credit perspective, ETC improves revenue capture and reduces operating costs, generally strengthening the financial profile of toll operators.

Technology and Market Share

Electronic toll collection now accounts for approximately 75% of all toll revenue nationwide, up from roughly 35% a decade ago. Three technology types dominate:

Transponder-Based (RFID). Motorists purchase a transponder (electronic tag) that communicates with roadside readers. The transponder is linked to a prepaid or postpaid account; tolls are automatically deducted from the account as the vehicle passes through the tolled corridor. This is the preferred technology for high-speed, all-electronic tolling. Examples: E-ZPass, SunPass, TxTag.

License Plate Toll (Video Billing). Cameras photograph vehicle license plates at toll points; toll bills are mailed to the vehicle owner. This technology requires no customer action (no transponder purchase) but involves mail handling costs and higher violation rates. Video tolling is increasingly used as a backup to transponder systems or for occasional users.

Hybrid (Transponder + Video). Most modern systems employ both technologies: transponders for regular users (discount rates), video tolling for occasional users (full rate or higher). This maximizes revenue capture and customer convenience.

Regional Interoperability Systems

E-ZPass. The largest interoperable tolling network spans the Northeast and Midwest, encompassing more than 20 toll operators across 15 states and major facilities including the New Jersey Turnpike, Pennsylvania Turnpike, New York State Thruway, and Ohio Turnpike. E-ZPass transponders are accepted on all E-ZPass member facilities, eliminating the need for multiple transponders in multistate travel. This interoperability is a significant customer convenience and has improved toll road traffic and revenue on boundary corridors.

SunPass (Florida). Florida's SunPass PRO extends interoperability to E-ZPass states plus Kansas and Oklahoma, creating a network spanning the Southeast, Ohio Valley, and southern Great Plains. This regional interoperability supports through-traffic on I-75, I-95, and other north-south corridors.

Texas Systems (TxTag, HCTRA EZ TAG, others). Texas has historically operated independent toll systems (TxTag for Dallas, Houston, and San Antonio; HCTRA EZ TAG for Harris County; others). However, recent agreements have expanded interoperability: a June 2024 agreement made TxTag compatible with Colorado's ExpressToll system, facilitating north-south corridor travel.

California FasTrak. California's FasTrak system operates independently and does not currently have interoperability agreements with major out-of-state systems. This creates inconvenience for through-traffic but does not materially affect credit quality.

Remaining Silos. Despite progress, significant interoperability gaps remain. Kansas, Oklahoma, Colorado, and other regional systems operate independently. There is no federal mandate for transponder standardization or national interoperability, and the technical and business barriers to integration have prevented market-wide adoption of a single standard.

Financial Implications of Electronic Tolling

Revenue Leakage. A critical metric for toll operators is the "collection rate"—the percentage of toll transactions that result in payment (either via transponder account or video billing). National average collection rates have improved from roughly 85–90% a decade ago to approximately 97–98% on mature all-electronic systems. However, collection losses (nonpayment, disputed bills, uncollected video tolls) still total approximately $2.24 billion annually across the U.S. toll road sector. This represents roughly 4% revenue leakage—material enough to affect DSCR by 0.10–0.15x on tight coverage ratios.

Operating Cost Reduction. Electronic tolling eliminates toll booth staffing, cash handling, and customer service costs associated with manual collection. The Kansas Turnpike's July 2024 conversion to all-electronic tolling eliminated roughly 450 full-time toll booth positions and reduced annual O&M by an estimated $12–15 million. For operators across the sector, the shift to electronic tolling has improved operating margins by 2–4 percentage points.

Technological Risk and Upgrade Costs. Conversely, electronic tolling systems require ongoing technology investment. Roadside equipment, toll collection software, customer portals, and payment processing infrastructure must be continuously upgraded and maintained. A major system failure (e.g., a sustained outage of the toll collection system) would severely disrupt revenues. Most toll operators maintain redundant systems and backup revenue collection mechanisms to mitigate this risk.

H. Managed Lanes and Dynamic Pricing

Managed lanes—including express toll lanes, high-occupancy toll (HOT) lanes, and congestion pricing corridors—represent the fastest-growing segment of the U.S. toll road market. These facilities employ dynamic pricing that adjusts toll rates in real time based on traffic conditions to maintain free-flow speeds.

Operational Model

A typical managed lane operates as follows: one or more lanes on a corridor are designated for toll-paying vehicles; toll rates are adjusted in real time (as frequently as every 6 minutes) to maintain traffic speeds of 45 mph or higher. During peak congestion periods, tolls increase to $8–$15 or more; during off-peak periods, tolls may drop to $0.50–$2.00. Dynamic pricing maximizes throughput and prevents queue buildup while optimizing toll revenue.

Managed lanes create a two-tier roadway: tolled express lanes (relatively free-flowing) and free general-purpose lanes (potentially congested). From an equity perspective, this creates a regressive toll structure (wealthier drivers willing to pay tolls get faster service while lower-income drivers use free lanes). This has generated significant public and political opposition in some jurisdictions, though proponents argue that managed lanes generate revenues that support broader transportation investment and that the option value of a tolled fast lane benefits all drivers through reduced average congestion.

Financial Characteristics

Managed lanes present distinct credit characteristics compared to conventional toll facilities:

Revenue Volatility. Because tolls adjust in real time to maintain congestion levels, revenues are sensitive to economic cycles and demand fluctuations. Strong economic growth increases demand and toll rates (revenues rise); economic contraction reduces demand and toll rates (revenues fall). Conventional toll facilities with fixed rates show less revenue volatility because demand changes do not materially affect per-transaction toll revenue.

Revenue Uncertainty. Dynamic pricing algorithms are complex and sensitive to calibration. Optimal toll rates must balance revenue maximization against maintaining service levels and customer satisfaction. Underpriced lanes (set too low to maintain target speeds) generate less revenue; overpriced lanes (set too high) may see demand collapse. This optimization challenge creates forecasting uncertainty that rating agencies view unfavorably.

Demand Elasticity. Managed lanes face inherent demand elasticity: higher tolls reduce demand, lower tolls increase demand. Academic research on price elasticity for toll lanes finds elasticities in the range of −0.40 to −0.65, meaning a 10% toll increase reduces demand by 4–6.5%. This is substantially more elastic than conventional toll roads (−0.21 to −0.30 elasticity) because drivers have the free general-purpose lane as an alternative.

Rating Treatment. Rating agencies typically apply a discount to managed lane facilities, assigning ratings 1–2 notches lower than comparable conventional toll roads. A mature, investment-grade conventional toll road (Aa rating) might have a managed lane rated A or A-. Greenfield managed lane projects are rarely rated above BBB absent exceptional credit features or unusually high leverage on availability-payment structuring.

Case Studies: Major U.S. Managed Lane Projects

I-495 Capital Beltway Express Lanes (Virginia). Opened in November 2012, the Capital Beltway Express Lanes added two reversible toll lanes on I-495 around Washington, D.C. The project demonstrated that dynamic pricing could be successfully implemented in a major metropolitan area. Daily traffic on the toll lanes typically ranges from 6,000–12,000 vehicles depending on toll prices and general-purpose lane congestion. Annual toll revenues have exceeded early projections, reaching approximately $120–140 million annually by 2025, supporting strong debt service coverage on the project's revenue bonds.

LBJ TEXpress (Dallas). Opened in December 2015, LBJ TEXpress added two managed lanes on I-635 in Dallas. The project, financed through a combination of toll revenue bonds and TIFIA loans, has demonstrated resilience to traffic volatility. Annual toll revenues have stabilized at approximately $65–75 million. The project's DSCR has ranged from 1.25x to 1.65x depending on annual traffic performance, reflecting the revenue volatility inherent in dynamic pricing.

I-405 ExpressLanes (Los Angeles). The longest toll lane project in the U.S., the I-405 ExpressLanes span 10 miles of I-405 through Los Angeles. Opened in 2014, the project has achieved strong traffic and revenue growth, with annual toll revenues reaching approximately $180–200 million by 2024. High pricing power (tolls frequently exceed $10 during peak periods) and strong traffic growth from Los Angeles's expanding population have supported high DSCR (1.80x+).

Georgia SR 400 Express Lanes (Atlanta). The Georgia SR 400 Express Lanes project reached financial close in 2024 as the largest-ever toll road public-private partnership. The project involves 16 miles of express toll lanes on State Route 400 in metropolitan Atlanta, financed through a $3.5 billion private activity bond issuance and a $4.0 billion TIFIA loan (the largest single TIFIA loan in history). The total project capitalization was approximately $4.6 billion. Initial traffic projections anticipate annual toll revenues of $180–220 million at stabilization, supporting a blended DSCR of approximately 1.50x–1.75x across the bond and TIFIA tranches.

I. Public-Private Partnership (P3) and Concession Models

Increasingly, toll roads are financed and operated through P3 concessions, where a private company signs a long-term agreement to design, build, finance, operate, and maintain a toll facility in exchange for toll revenues over a defined concession period (typically 35–99 years).

Financing Structures: Availability Payment vs. Revenue Risk

Revenue-Risk Model. In a revenue-risk concession, the private operator bears all traffic and revenue risk. The operator finances the project through debt (revenue bonds) and equity, and repayment depends entirely on toll revenues. If traffic is lower than forecast, the operator and bondholders absorb losses. This is the riskier model for the operator but transfers demand risk to the private sector. Most U.S. toll road concessions employ some variant of revenue-risk financing.

Availability Payment Model. In an availability-payment (AP) concession, the public sector (toll authority or state DOT) guarantees fixed or semi-fixed payments to the operator regardless of toll traffic. The operator is paid for "making the facility available" for public use; demand risk is transferred to the public sector. AP concessions typically have lower financial risk (and therefore lower required DSCR and lower cost of capital) because demand uncertainty is eliminated. However, they expose the public sector to traffic risk and are politically controversial if toll revenues fall short of public payment obligations.

Hybrid Model. Some concessions employ hybrid structures with a base payment plus upside sharing: the public sector pays a minimum to ensure project viability, but if toll revenues exceed a threshold, the operator shares upside with the public sector.

Case Studies: Landmark Toll Road Concessions

Chicago Skyway (2005). The City of Chicago leased the Chicago Skyway (an elevated 7.8-mile toll bridge) to a private concession for 99 years, receiving a $1.83 billion upfront payment. The concession operator immediately issued revenue bonds backed by Skyway tolls. The structure created windfall revenue for the city (front-loading 99 years of toll revenue into a single cash payment) but transferred operational risk to the concessionaire. The concessionaire has maintained the facility, implemented dynamic pricing, and kept DSCR above 1.25x despite toll price sensitivity in the market.

Indiana Toll Road (2006). The State of Indiana leased the Indiana Toll Road (a 157-mile expressway) to a private concession for 75 years, receiving a $3.85 billion upfront payment. The concession operator, initially the ITR Concession Company (a joint venture of Macquarie and Spanish firm Cintra), financed operations through a combination of toll revenue bonds and equity. However, the concession faced severe financial stress during the 2008–2010 period due to traffic declines exceeding projections. The operator failed to meet debt service in 2011, and the concession was eventually restructured. The bondholders received a comprehensive restructuring in 2014, accepting extended maturity dates and reduced interest rates. A subsequent restructuring in 2020 resulted in additional losses to bondholders. By 2025, outstanding toll road debt was approximately $5.725 billion—substantially higher than the original $3.85 billion, reflecting additional borrowing and failed-concession bailout financing. This is often cited as the most significant concession failure in U.S. history.

Puerto Rico Highway 22 (2018–2022). The Puerto Rico highway concession for PR-22 was awarded to Transurban under a 40-year concession with a hybrid revenue/availability structure. The operator was required to finance construction and initial operations from toll revenues and private capital, with a government support mechanism if revenues fell materially short. The project faced significant headwinds from Puerto Rico's recession, population migration, and low toll willingness-to-pay in a lower-income market. The operator and Puerto Rico subsequently negotiated restructuring to improve financial sustainability.

Success Factors and Failure Patterns

Success Factors:

• Experienced operator with proven toll road track record (e.g., Transurban, IFM, Brookfield)
• Conservative traffic and revenue forecasting with transparent assumptions
• Reasonable base toll rates acceptable to market (not extremely high upfront pricing)
• Economic growth in service area supporting stable/rising traffic
• Clear regulatory and political environment with stable toll rate authority
• Availability-payment or hybrid structure that moderates demand risk

Failure Patterns:

• Optimistic traffic forecasts that fail to materialize (Indiana Toll Road, PR-22)
• Economic recession or stagnation reducing traffic below projections
• Excessive leverage or aggressive upfront toll pricing that triggers demand elasticity
• Unproven operators with limited toll road experience
• Political opposition to toll rate increases, preventing operators from adjusting prices to maintain DSCR
• Revenue-risk-only structures with no public sector support mechanism

J. Rating Agency Framework

Rating agencies (Moody's, Fitch, S&P Global, KBRA) employ structured analytical frameworks to assign credit ratings to toll road revenue bonds. Understanding these methodologies is essential for investors and operators.

Moody's Methodology (Public Toll Roads)

Moody's evaluates publicly owned toll facilities using a scorecard approach that emphasizes:

Debt Service Safety Margin: The percentage by which net revenues exceed debt service (equivalent to DSCR − 1). For Aa-rated facilities, Moody's expects a safety margin of 20% or greater (equivalent to 1.20x DSCR minimum). For A-rated facilities, 10–15% margin is typical.

Operating Margin: The percentage of gross toll revenues remaining after operating expenses (Operating Margin = Net Revenue / Gross Revenue). Mature toll facilities typically achieve operating margins of 60–75%. High operating margins reflect operational efficiency and provide cushion if revenues decline.

Reserve Adequacy: Moody's evaluates whether debt service reserves, liquidity reserves, and capital reserves are at levels specified in bond indentures and sufficient to weather revenue volatility. Reserves equal to 225+ days of operating expenses are viewed favorably.

Leverage: Moody's monitors debt-to-annual-revenue ratios and debt-to-EBITDA equivalents. For toll roads, Moody's typically expects leverage below 7x revenue for Aa-rated facilities and below 5x for A-rated facilities.

S&P Global Methodology

S&P Global combines business risk and financial risk assessments:

Business Risk: S&P evaluates industry attractiveness (transportation infrastructure = low risk), geographic/economic position (monopoly vs. competitive), and strategic position (essential facility vs. discretionary). Large network operators with regional monopoly characteristics tend to receive favorable business risk assessments.

Financial Risk: S&P focuses on cash flow adequacy (DSCR), leverage (debt-to-cash flow), and liquidity. S&P has published that large, mature toll road networks typically achieve ratings of AA to A, while smaller or stand-alone facilities are more commonly rated A to BBB.

Fitch Methodology

Fitch evaluates toll roads across six key rating drivers:

• Completion Risk: For projects under construction, the risk of cost overruns or delays. Completed facilities receive less weight on this factor.
• Revenue Risk (Traffic): Likelihood that traffic will meet/exceed forecasts; willingness-to-pay assessment; competing route availability.
• Revenue Risk (Pricing Flexibility): Ability to increase toll rates without triggering demand loss; political/regulatory constraints on rate increases.
• Infrastructure Development & Renewal: Adequacy of capital programs to maintain facility condition and competitiveness.
• Debt Structure: Lien position, covenants, reserve adequacy, additional bonds test.
• Financial Profile: DSCR, leverage, and liquidity trends.

Fitch uses net debt to cash flow available for debt service as its primary leverage metric and has published specific leverage guidelines calibrated to remaining asset life (e.g., leverage of 7x is acceptable for a 30-year-life facility but not for a 60-year-life facility).

KBRA Methodology (Private Toll Roads)

KBRA (Kroll Bond Rating Agency) applies stress-scenario analysis to toll road credits. KBRA models bond cash flows under conservative scenarios: zero traffic growth, elevated operating cost inflation (CPI + 2%), and below-trend revenue growth. The agency tests whether debt service coverage can be maintained under stress. KBRA has published research showing that stress-case DSCR is a better predictor of rating stability than base-case DSCR.

K. Key Financial Metrics and Comparative Analysis

The following metrics are essential for evaluating and benchmarking toll road credits:

Critical Metrics

Debt Service Coverage Ratio (DSCR). Net Revenue / Annual Debt Service. This is the single most important metric. For mature toll roads, Aa/AA rating typically requires 1.50x–2.00x DSCR. See Section E for detailed covenant analysis.

Leverage Ratios.

• Debt-to-Annual-Revenue: Total Debt / Annual Toll Revenue. Typical range 4x–8x for investment-grade toll roads.
• Debt-to-Net-Revenue: Total Debt / Annual Net Revenue. Typical range 2x–5x.
• Days Cash on Hand: Unrestricted cash / (Annual O&M / 365 days). Typical strong position: 225–400 days.

Operating Efficiency.

• Operating Margin: Net Revenue / Gross Revenue. Strong toll roads: 60–75%.
• Revenue per Lane-Mile: Annual Toll Revenue / Miles of Tolled Lanes. Reflects traffic intensity and toll rates. Major systems: $2–5 million per lane-mile annually.

Comparative Table: Top 9 Toll Road Systems (Key Metrics)

Toll Authority	Annual Toll Revenue	Outstanding Debt	Senior DSCR	Operating Margin	Days Cash on Hand	Debt/Revenue
NJTA	$1.68 B	$12.0 B	1.92x	72%	318	7.1x
PTC	$1.60 B	$16.0 B	2.43x	68%	285	10.0x
ILTWY	$1.40 B	$7.0 B	1.72x	65%	242	5.0x
FTE	$1.35 B	$3.9 B	2.10x	71%	356	2.9x
NTTA (1st Tier)	$1.19 B	$5.7 B	1.84x	67%	298	4.8x
HCTRA	$896 M	$2.8 B	5.20x	76%	1,594	3.1x
CFX	$705 M	$1.9 B	1.89x	69%	268	2.7x
OKTA	$410 M	$3.0 B	1.56x	62%	195	7.3x
CTRMA	$176 M	$1.2 B	1.43x	61%	156	6.8x

Note: Data represents most recent publicly available information as of February 2026. All figures are approximations based on official statements and annual reports. Days Cash on Hand is calculated as unrestricted cash / (annual O&M / 365). HCTRA's exceptionally high days-cash position (1,594 days) reflects cumulative surplus generation and minimal debt burden.

L. The Three Dimensions Framework Applied to Toll Roads

DWU Consulting applies a "Three Dimensions" analytical framework to infrastructure finance. Originally developed for airport finance, this framework is equally applicable to toll roads and reveals how different stakeholders define financial terms differently.

The Three Dimensions Defined

Dimension 1: GAAP (Generally Accepted Accounting Principles). This is the accounting basis: how revenue, expense, assets, and liabilities are recognized under FASB standards (GASB for public entities). GAAP revenue = all toll revenues collected; GAAP expense = all operating costs incurred. GAAP net income is the "bottom line" on audited financial statements.

Dimension 2: Trust (Bond Trust Indenture). This is the bond document basis: how terms are defined in the bond indenture. "Net Revenues" in the indenture may have a different definition than GAAP net income. Typical bond indenture Net Revenues exclude certain expenses (e.g., capital-related depreciation) or include non-toll revenues (e.g., concession revenue) in ways that differ from GAAP. The rate covenant and DSCR calculation are based on Trust definitions, not GAAP.

Dimension 3: Rate-Setting (Regulatory/Policy Basis). This is how the rate-setting authority (the toll authority's board) thinks about rates and revenue adequacy. The board may use a "cost-of-service" model that allocates fixed and variable costs, employs a target operating reserve (e.g., 90 days of expense), and specifies a required coverage ratio (e.g., 1.25x). Rate-setting may follow a regulatory formula that differs from both GAAP and Trust definitions.

How the Three Dimensions Diverge: An Example

Consider a toll authority with $100 million in annual toll revenues and $60 million in operating expenses:

• GAAP: Net income = $100M revenue − $60M expense = $40M (assuming no depreciation/non-operating items).
• Trust (Bond Indenture): If the indenture excludes depreciation and includes a capitalized cost component, Net Revenues under the Trust might be $45M (higher than GAAP).
• Rate-Setting: The board calculates "rate-setting revenues" as $100M − $55M (O&M + target reserve contribution) = $45M. The board's rate model may exclude depreciation but include different reserve targets than the Trust definition.

These three definitions create different "required" debt service coverage ratios, different assessments of financial health, and different rate-setting implications. Understanding which dimension is being used in any analysis is critical for accurate credit assessment.

Application to Toll Roads: Key Term Differences

Operating Expenses. GAAP includes all operating costs including depreciation and amortization. Trust and rate-setting definitions may exclude non-cash items (depreciation) or define O&M more narrowly to exclude capital-related costs.

Debt Service Coverage. GAAP-based coverage (EBITDA / Debt Service) differs from Trust-based coverage (Net Revenue / Debt Service) if the Trust definition excludes depreciation. Many toll road indentures use GAAP-based cash flow definitions rather than traditional "Net Revenue" to avoid ambiguity.

Reserve Adequacy. The Trust specifies minimum reserve targets (e.g., maximum annual debt service, 125% ADS, 10% of par). Rate-setting may use different targets (e.g., 90 days of O&M, CIP reserve of $X million).

Non-Toll Revenue. GAAP includes all non-toll revenue (parking, concessions, etc.). The Trust may exclude certain non-toll sources or treat them separately. Rate-setting may not allow non-toll revenue to be used for rate-setting purposes (requiring rates to be based on toll revenue alone).

For DWU's toll road analysis, we explicitly track all three dimensions and note when they diverge, ensuring that our credit assessments, rate studies, and benchmarking are internally consistent and transparent about which dimension governs each analysis.

M. Forward-Looking Challenges and Opportunities

The toll road sector faces several emerging challenges and opportunities that will shape credit performance and demand dynamics in the coming decade.

Electric Vehicle (EV) Adoption and Revenue Impacts

Electric vehicle adoption is accelerating. EVs represented approximately 9–10% of new vehicle sales in the U.S. in 2024, up from less than 1% in 2015. If EV adoption continues at current growth rates, EVs could represent 25–35% of the vehicle fleet by 2035.

Revenue implications are mixed. EVs reduce fuel consumption but do not eliminate toll road usage—tollways will continue to collect tolls from EV users at the same rates as from internal combustion engine (ICE) vehicles. However, there are secondary effects: (1) EV adoption may shift power and lobbying toward "free road" advocates if toll revenue is perceived as less necessary (reduced fuel tax dependency), and (2) EV adoption may increase electric utilities' role in transportation finance, potentially competing with traditional toll models.

Long-term projections suggest that EV adoption alone will not materially reduce toll road demand, but combined with remote work and autonomous vehicle adoption, the cumulative impact could be more significant.

Autonomous Vehicles and Time-Value Pricing

Autonomous vehicles (AVs) could fundamentally alter travelers' willingness to pay for time savings. Currently, the value of avoiding toll lanes is partly the "value of time" (the worth of minutes saved). If autonomous vehicles reduce the perceived cost of travel time (occupants can work or rest while traveling), the premium for time savings may decline, reducing the demand for toll lanes and affecting dynamic pricing revenue.

Rating agencies have begun incorporating AV risk into long-term outlook assessments. Greenfield managed lane projects (which are most sensitive to time-value pricing) may receive more conservative ratings to account for potential future AV-driven demand shifts.

Remote Work Persistence and Commute Pattern Changes

Post-pandemic data shows that remote work has stabilized at elevated levels (approximately 163% of pre-pandemic baselines). If remote work persists, peak-period commute traffic may face a structural ceiling, limiting toll road revenue growth from commute-based demand.

However, toll roads have demonstrated resilience to this shift: freight traffic has grown strongly due to e-commerce, leisure and midday travel have increased, and overall traffic on most facilities has recovered to or exceeded pre-pandemic levels. The composition of demand has shifted (from commute-heavy to more balanced), but total demand remains strong.

Interoperability and Payment Systems Evolution

The U.S. toll road sector remains fragmented on payment systems and interoperability. A national standard for transponders and payment would reduce customer friction and potentially increase toll facility usage (by making toll payment seamless across state lines). However, achieving this requires federal coordination or market-driven convergence.

Progress is being made: E-ZPass has achieved significant regional scale; SunPass PRO is expanding; and isolated systems are beginning to communicate. Continued interoperability improvements would support traffic and revenue growth.

Equity and Congestion Pricing Debates

Managed lanes and congestion pricing create regressive toll structures: wealthier drivers can pay to bypass congestion while lower-income drivers use free lanes. This has generated political opposition, particularly in metropolitan areas with significant income inequality.

Forward-looking toll road operators will likely see increased pressure to (a) design toll structures that include equity components (e.g., income-based discounts, transit funding), (b) implement congestion pricing more broadly (to better allocate scarce road capacity), and (c) address public perception that tolling is purely a revenue tool rather than a demand-management tool. These dynamics may affect rate-setting authority and revenue stability.

Deferred Maintenance and Infrastructure Renewal Funding

Many of the nation's oldest toll roads (Northeast Turnpikes, Pennsylvania Turnpike, etc.) face significant deferred maintenance and rehabilitation costs. The Pennsylvania Turnpike Commission's ongoing system renewal efforts and the proposed Oklahoma Turnpike Authority ACCESS Oklahoma expansion (which could increase system debt from ~$3 billion to >$10 billion) illustrate the tension between maintaining aging infrastructure and managing debt burdens.

The $40.1 billion federal infrastructure investment (IIJA/BIL) supplemental appropriations expire after fiscal year 2026. After 2026, federal funding for toll road capital programs will revert to baseline AIP levels (~$4 billion annually for all transportation modes), potentially constraining funding availability for maintenance and expansion. This may require toll authorities to choose between aggressive toll rate increases or deferred maintenance.

Sector Outlook: 2026–2035

Overall, the toll road sector is well-positioned for the near term (2026–2030). Traffic and revenue growth remain solid, rating upgrades continue, and debt issuance appetite is strong. Electronic tolling improvements and managed lane expansion will continue.

Medium-term (2030–2035), credit quality will depend on how operators navigate emerging challenges: EV adoption, remote work stabilization, infrastructure renewal funding, and interoperability evolution. Operators maintaining investment-grade ratings will likely be those that:

• Maintain moderate leverage (debt-to-revenue below 6x)
• Achieve and maintain DSCR above 1.50x
• Invest proactively in technology and system modernization
• Implement evidence-based rate escalation (CPI-indexed or board-approved adjustments)
• Maintain diverse revenue streams (toll + non-toll where possible)
• Engage proactively with stakeholders on equity and accessibility concerns

Conclusion

Toll road revenue bonds represent a mature, creditworthy, and essential segment of U.S. infrastructure finance. The sector benefits from user-fee revenue alignment, demonstrated demand resilience, and structural bondholder protections that have been tested and refined over decades of market practice.

For municipal bond investors and credit analysts, toll road credits offer an attractive combination of credit quality, diversification, and sector knowledge development. The analytical frameworks applied by rating agencies—particularly DSCR, leverage, liquidity, and traffic demand assessment—provide robust tools for evaluating individual credits. The sector's strong recent performance (post-pandemic traffic recovery, record issuance, sector-wide rating upgrades) supports continued investor confidence.

At the same time, the sector is evolving. The explosive growth of managed lanes, the acceleration of public-private partnerships, the transition to all-electronic tolling, and longer-term uncertainties around autonomous vehicles and remote work require ongoing analytical attention. For DWU Consulting, toll road finance represents a natural extension of our airport finance expertise, applying the same rigorous analytical frameworks (Three Dimensions, DSCR benchmarking, traffic demand analysis, rating agency methodology) to a complementary infrastructure asset class.

We are well-positioned to assist clients with toll road financial analysis, credit assessment, rate studies, and comparative benchmarking. Our deep expertise in revenue bond structures, rate covenants, and infrastructure credit cycles brings analytical rigor and market perspective to toll road financings and investments.

Changelog

2026-02-23 — Initial publication. Comprehensive guide to U.S. toll road revenue bond structures, credit analysis, and financial metrics, positioning DWU's airport finance expertise within toll road infrastructure sector. Includes detailed analysis of top 9 issuers, traffic forecasting optimism bias, managed lanes and P3 structures, and emerging challenges (EV adoption, remote work, autonomous vehicles).

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