Abstract
Roadway assets, such as pavements and bridges, are essential for economic activity and growth, facilitating the movement of goods and people. In Texas, U.S., transportation funding primarily comes from the State Highway Fund, which includes various taxes and fees. However, traditional funding sources are insufficient to meet the state’s growing transportation needs. This study analyzes the impact of commercial and oversize/overweight trucks on roadway assets, assigning monetary values to structural life consumption and comparing them with state highway agency revenues. The study findings reveal significant funding gaps because of higher consumption costs from these vehicles. Four recovery scenarios were proposed: 1) adjusting vehicle registration fees, 2) implementing weight-distance fees, 3) revising truck permit fees, and 4) introducing mileage-based fees. These scenarios resulted in roughly $300 million additional revenue, addressing the deficit identified from the roadway assets’ expenses and promoting a more equitable distribution of costs among users. The study highlights the inadequacies of current overweight fee structure and road users charge mechanisms, and provides a framework for policymakers to develop equitable and efficient alternative funding strategies, ensuring users pay proportionately to their impact on the roadways.
Roadway assets, such as pavements and bridges, are vital elements of a nation’s transportation network, playing a crucial role in economic growth by facilitating the movement of people and goods within the country. High transport costs can hinder a country’s products from competing effectively on internal markets, thereby stalling economic development. Roadway costs encompass not only the expenses of building and maintaining infrastructure assets but also vehicle operating costs directly related to infrastructure condition, road crashes, congestion, and delays ( 1 ). Therefore, an efficient transportation system requires resources for maintaining existing roadway assets and expanding links in the current network to accommodate growth, reduce traffic congestion and all other associated costs.
In Texas, U.S., transportation funds mainly come from the State Highway Fund (SHF), which includes motor fuel taxes, vehicle registration fees, oversize/overweight (OS/OW) permit fees, oil and natural gas severance taxes, and sales taxes from general goods sales and motor vehicle sales and rentals ( 2 ). The SHF is also incremented by Propositions 1 (oil and gas severance tax) and 7 (sales and use tax, vehicle sales and rental tax), the State Infrastructure Bank, regional subaccounts with toll, and concession revenue from comprehensive development agreements (an umbrella term for public-private partnerships) ( 3 ).
Charges tied to vehicle usage, such as motor fuels tax revenue, vehicle registration fees, and truck permit fees, are commonly referred to as “road user charges.” While road user charges are expected to be economically efficient and equitable—each user pays according to their respective usage—most of the funding sources only partially meet these criteria ( 1 ). For example, taxes on vehicle fuel do not accurately reflect the proportional damage caused by heavy vehicles (despite consuming more fuel per mile traveled, they also consume a greater portion of the roadway assets’ service life), vehicle license fees are not directly related to usage since their revenue remains the same regardless of vehicle miles traveled (VMT), and tolls can hinder economic benefits by causing traffic delays at tollbooths or diverting traffic to secondary roads, which in turn increases their maintenance costs and vehicle operation costs.
Over time, traditional funding sources have proven insufficient to address the demands of Texas’ growing population and evolving economy. Studies such as this are commonly conducted by comparing transportation infrastructure cost responsibilities and revenue contributions among road users ( 4 ). When funding shortages are identified, alternative cost recovery scenarios typically consider improving user fees or adding new general revenue sources. However, this approach has been found to have a minor impact on improving equity across different road users, although it generates significant additional revenue ( 5 ).
The proposed study analyzes the impact of commercial and OS/OW trucks on roadway assets by assigning a monetary value to the structural life consumption and comparing it with the resources available for maintenance and rehabilitation, at the Texas Department of Transportation (TxDOT). Once funding gaps were identified, four recovery scenarios were proposed and evaluated, resulting in more than $300 million additional revenue.
Addressing the Impact of Motor Vehicles on Roadway Assets
It is well-established in the literature that motor vehicles produce several types of external cost to society. For example, passenger cars populate and affect congestion on traffic streams, leveraging emissions, while heavy trucks affect the roadway assets structural performance ( 6 – 8 ). Moreover, the varying types of roadway user—passenger cars, lightweight trucks, and heavy trucks—consistently pose a challenge for policymakers in maintaining price equity relative to the revenue and costs incurred.
A popular view among economists about how to address equity among road users is to charge motorists for each trip to directly cover their “marginal external cost,” which depends on the distance traveled, the vehicle characteristics (weight and maintenance condition), and other factors such as travel time ( 9 ). Although this seems a straightforward task, in practice feasible calculations of marginal external costs require some degree of averaging among road users’ costs. As a result, taxes and charges on motorists, particularly passenger cars, often exceed the marginal external costs of their trips, while the revenue collected from heavy trucks (commercial and overweight [OW]) generally falls short of their expected marginal external costs. Therefore, assessing charges on trucks is crucial for achieving economic efficiency and equity, as it helps to balance out the overcharges on other road users. Endorsing this situation, past studies in equity among road users indicated that lightweight vehicles contribute substantially more to revenue than their share of costs imposed to the infrastructure, while single-unit trucks—with four or more axles—and multi-unit trucks underpay their costs responsibilities by between 37% and 92% ( 5 ). Nevertheless, if congestion and pollution were factored in, the appearance of underpricing could well be reduced ( 9 ). Therefore, how can this issue be addressed?
A study conducted by the North Carolina Department of Transportation suggested that a mile-based user fee could be a long-term solution toward equity among light and heavier vehicles ( 5 ). Other study conducted by the Minnesota Department of Transportation showed that load-related expenditures could be brought closer to the estimated revenue, resulting in greater equity ratios, if a weight-milage fee were to be considered ( 10 ). Although policy choices in highway cost allocation are commonly made on the basis of revenue-cost comparisons by vehicle class, forecasting these values is always a complex task. Also, there is a constant need to determine where in the modeling effort—on both the cost and revenue sides—more accuracy would affect the outcomes. That is, from all the possible errors in estimation (e.g., VMT forecasts, pavement and bridge damage, fuel tax revenue per vehicle), which would result in the most significant impact on the revenue and costs forecast such that it would affect policy choices? ( 11 ). Efforts should be made to identify the errors that have a more critical impact on the revenue-cost analysis than on just improving its accuracy. Moreover, budget fluctuations also affect road user charges and should also be addressed for more accurate estimates ( 12 ).
Finally, most state DOTs’ revenues come from federal aid, motor fuel taxes, and vehicle license fees, the so-called “road user fees,” which still provide most highway revenues ( 13 ). Therefore, as an alternative approach after performing a revenue-cost analysis, this study proposes four long-term solutions toward equitable revenues: 1) adjustment on vehicle registration fees, 2) new weight-distance fees, 3) adjustment of OW permit fees, and 4) a new VMT-based fee structure.
Methodology for Revenue-Cost Analysis in Texas
In this study, to calculate costs and revenues on roadway assets, traffic data from 2019 was analyzed. This specific year was selected to avoid the atypical traffic patterns which resulted from the COVID-19 pandemic, as this choice aligns with the context at which the study was conducted, which was updating truck permitting policies and fee adjustment. The road users that affect highway structure integrity—heavy vehicles, commercial trucks, and OW trucks—were assessed separately. Oversize (OS) trucks were categorized among regular commercial trucks since they still comply with the state’s weight restrictions.
The analyses were conducted on a VMT basis so that the proportional usage of the roadway assets could be addressed. VMT was derived from annual average daily traffic data provided by TxDOT and geographic information system (GIS) technology, particularly for vehicles that were otherwise unavailable or untraceable, and may include VMT produced by non-Texas registered vehicles. Information on registered vehicles and OW permits were obtained through the Texas Department of Motor Vehicles (TxDMV). For more detailed information on traffic characterization, VMT calculations, and OS/OW data exploration, please refer to Prozzi et al. ( 2 ). The revenue-cost comparison was done by first calculating the roadway assets’ expenses generated by each truck type (commercial or OW), followed by a revenue analysis and the evaluation of cost-recovery scenarios.
Pavement Consumption Analysis
Pavement evaluation was conducted using equivalent consumption factor (ECF), where equivalent consumption represents a numerical factor that relates the amount of pavement life a given axle configuration consumes relative to the equivalent single-axle load (ESAL). This concept is similar to the load equivalence factor developed during the AASHO Road Test, but it has been improved to assess the effect of different axle groups, pavement types, and failure criteria, as initially proposed by Prozzi and De Beer ( 2 , 14 ).
The analysis of ECF required the use of mechanistic concepts validated through empirical equations to predict pavement behavior. To optimize computational time in computing ECFs, the AASHTOWare ME Pavement Design software was utilized. The experimental design incorporated different environments of Texas with more than 30 pavement structures of varying layer materials, soil support, and traffic conditions ( 15 ). A modified proportional method was used to estimate consumption costs. This method is founded on the principle that highway construction costs should be allocated based on the proportional consumption of each truck type; that is, adjust the pavement structure specifically by changing the asphalt layer thickness (or asphalt concrete overlay for rigid pavements) to ensure it has sufficient bearing capacity to accommodate the traffic. It is important to note that commercial trucks are already factored into design considerations. Therefore, any structural changes required by consumption analysis were solely to accommodate OW vehicles. A crucial aspect of this process involves obtaining accurate estimates for construction costs and materials, which was achieved by referencing to TxDOT’s average low bid price portal.
Bridge Consumption Analysis
Bridge consumption was calculated based on the incremental effect of traffic exceeding the bridge’s original design capacity. For example, if a bridge, designed to withstand a specific number of load repetitions (or load-related momentum), fails to meet its estimated service life because of increased traffic levels (as considered in this analysis), the entire structure would need to be redesigned to accommodate the new structural demands to ensure it either fails at the same estimated lifespan or at an equivalent load-related momentum level.
To compile the inventory of bridges in Texas, the National Bridge Inventory was instrumental in determining appropriate load ratings based on design factors such as materials and traffic levels (16). Since bridges are location-specific, a methodology incorporating GIS and informed truck routes (as estimated by TxDOT) helped in determining bridge consumption costs on a dollars per mile basis. The dollar values for the proposed structural adjustments were obtained through TxDOT’s bid price portal, similar to what was done for the pavement costs. To account for bridge density variations across the state, Texas was divided into west and east counties with separate analyses performed to yield a weighted-average statewide estimate. For further details on the bridge consumption methodology, please refer to Weissmann et al. ( 16 ).
Revenue Analysis
The revenue sources from SHF, including motor fuel taxes, vehicle registration fees, OS/OW permit fees, and other non-user sources, were split between commercial and OW vehicles. State and federal motor fuel taxes were assigned to these vehicle categories using the Transportation Revenue Estimator and Needs Determination System (TRENDS) model ( 17 ). Vehicle registration fees provided by TxDMV were distributed based on the type of registration: approximately 10% allocated to commercial vehicles and 1% to OW vehicles. For the truck permit fees collected, the share from the OS vehicles was assigned to the commercial vehicle category, while the remainder was attributed to the OW vehicles.
Other non-user fee revenues included in SHF—such as Proposition 1 and Proposition 7—significantly contribute to funds available for the construction and maintenance of the Texas highway network. These funds were allocated to commercial and OW vehicles based on their VMT: 13.0% and 0.2%, respectively (86.8% corresponded to passenger cars). Lastly, to avoid overestimation, a correction was made to the total revenue, as some funds are used for purposes other than the construction or maintenance of roadway assets and these were then excluded from the analysis.
Additionally, expenses earmarked for debt repayment were omitted from this revenue analysis, along with funds transferred out of SHF for other uses. Any discounts on the overall revenue were proportionally distributed among the vehicle categories based on their revenue share.
Results
Pavement and Bridge Consumption Costs
The pavement consumption unit costs were based on the cost of providing the additional structure to support OW loads. The average statewide consumption costs were calculated to be 5.7 and 4.6 cents per ESAL per mile for flexible and rigid pavements, respectively. The weighted average of the statewide consumption costs between flexible and rigid pavements resulted in 5.6 cents per ESAL per mile. The total pavement consumption cost was calculated by multiplying each vehicle VMT by the statewide consumption cost ( 18 ).
For the bridge consumption analysis, a Monte Carlo simulation was performed to calculate VMT for each representative truck configuration. In this process, GIS segments of approximately 10 mi were randomly sampled. The MOANSTER package was employed to conduct structural analysis and to reach a final bridge consumption per mile. A sensitivity analysis performed to evaluate bridge consumption cost per mile relative to annual mileage found low sensitivity to annual mileage beyond 20,000 mi, which is because, as annual mileage increases, the number of bridges per mile also increases, thereby stabilizing the “bridge consumption per mile” ratio. The cost per mile was higher for counties on the eastern side than for those on the western side, because of the higher bridge density in the former. Results from the west and east were then averaged, yielding a statewide bridge consumption cost per mile. The consumption costs for both pavement and bridge structures are summarized in Table 1.
Summary of Pavement and Bridge Consumption Costs per Vehicle Category
Note: MU = multi trailer; ST = single trailer; SU = single unit.
It can be noted from Table 1 that the pavement consumption costs for commercial vehicles emerge as the primary contributors, accounting for 88% of the overall costs, because of their extensive mileage. The average cost per mile for commercial vehicles was $ 0.043 (median of $ 0.068/mi), suggesting that lighter vehicles incurred lower costs. In contrast, heavier configurations, such as six-axle multi-trailer vehicles, incurred costs 10% above the average. OW vehicles, such as the general, hubometer, and envelope permits, had an average cost of $ 0.70/mi (median of $ 0.63/mi). Hubometer-permitted vehicles were shown to be the most cost-efficient with regard to pavement consumption, whereas the general permitted vehicles were the least cost-efficient. Notably, five-axle trailer trucks under hubometer permits showed significantly higher consumption costs because of less effective load distribution, emphasizing the pivotal role of axle load distribution in reducing pavement wear and maintenance needs.
For the bridge consumption analysis, over 80% of estimated costs were below $ 0.25/mi, with commercial vehicles bearing the majority of the statewide VMT (roughly 86% of the total bridge consumption costs). Among OW vehicles, general permits incurred the highest cost per mile at $ 0.24/mi, followed by hubometer permits at $ 0.16/mi and envelope permits at $ 0.13/mi. Despite their lower proportional cost per mile, commercial vehicles’ sheer volume underscores their significant contribution to bridge consumption costs, particularly Class 9 which is commonly represented by the so-called “18-wheeler.”
The consumption cost results highlight the distinct policy challenges posed by regular commercial versus OW vehicles. While the costs associated with standard commercial trucks could feasibly be addressed through existing mechanisms such as flat registration fees or fuel taxes, OW vehicles demand a more sophisticated fee structure. Such a system should explicitly account for the nonlinear relationship between weight and pavement or bridge deterioration, ensuring that charges more accurately reflect the true infrastructure impact (accounting for the distance traveled) of these heavier configurations.
Revenue in Texas
To evaluate whether the revenue in Texas is sufficient to cover the estimated consumption costs, an annual revenue estimation for highway maintenance and rehabilitation was conducted. This estimation was derived by combining the SHF and federal fuel tax revenues, then allocating them to each vehicle category based on their respective VMT. The same allocation approach was applied to vehicle registration and permit fees. Additional revenue sources were incorporated proportionally to each category’s share of fuel tax revenue. To maintain the focus on highway maintenance, state expenditures unrelated to roadway construction, maintenance, or rehabilitation were excluded from the analysis. A summary of the annual and net revenues is presented in Table 2.
Annual State Highway Fund Revenue in Texas
Note: na = not applicable.
Oversize permit fee allocated withing commercial trucks.
The negative sign concerns only the expenses excluded from the net revenue.
The expenses not related to roadways discounted from the revenue analysis totaled $1,031 million, remaining a net revenue of $2,130.4 million. The total net revenue was then compared with the consumption costs calculated in Table 1, resulting in the revenue-cost summary presented in Table 3.
Annual Revenue Gap on Roadway Assets in Texas
The results in Table 3 indicate a funding gap for commercial and OW trucks of more than $300 million dollars. Although OW trucks had a higher consumption rate than commercial trucks, 89% of the expenses were attributed to commercial trucks because of their higher VMT. Congestion costs, which were not factored into the revenue-cost analysis, are important considerations but face challenges related to data availability and economic effects that complicate determining the true congestion cost attributable to road users. Therefore, no estimates on congestion costs were attempted.
As previously discussed, any effective cost recovery strategy must align with the rate at which the highway network deteriorates, ensuring that the user-cost balance is maintained. In other words, those who contribute more to pavement and bridge deterioration (particularly OW vehicles) should bear a proportionally higher share of the costs, thereby preventing an undue financial burden on the broader user base. This consideration is critical because the excessive loading from permitted OW trucks accelerates pavement deterioration, which ultimately increases longitudinal roughness. Prior studies have shown that an increase in the International Roughness Index from 63 to 250 in./mi results in an average rise in user costs of approximately $ 0.053/mi, only slightly lower than the average pavement consumption cost across all commercial vehicles ($ 0.058/mi) ( 19 ).
In light of the identified revenue gap and drawing on the experience of other state DOTs and highway agencies, the following sections present the results of the proposed cost recovery scenarios aimed at addressing the $300.2 million shortfall ( 5 , 10 , 11 , 13 ). The first and second strategies target commercial trucks, and involve adjusting the vehicle registration (flat) fees and employing a weight-distance fee, respectively. The third strategy focuses on OW vehicles through a permit fee adjustment. Finally, the fourth strategy addresses the revenue gap across all motor carriers (both commercial and OW) applying differentiated rates based on each vehicle category’s relative infrastructure consumption.
Vehicle Registration Fees
The state portion of the annual vehicle registration fee is based on the vehicle weight. Vehicles weighing up to 10,000 lb represented more than 94% of the vehicles registered in Texas and 87% of the total VMT ( 2 ). Two different approaches are suggested for the registration fee. The first approach involves an increment from current fees, starting at $85 for vehicles weighing between 10,001 and 18,000 lb, and increasing in $5 increments up to $110 for vehicles weighing between 70,001 and 80,000 lb. The second approach consists of a flat fee of $92 regardless of the gross vehicle weight (GVW). Both estimated revenue approaches offset the $132.2 million deficit caused by commercial vehicles, as summarized in Table 4.
Vehicle Registration Fees in 2019
Weight-Distance Fees for Commercial Vehicles
In the U.S., only five states currently charge a weight-distance tax: Connecticut, New Mexico, Kentucky, New York, and Oregon. Data from the last four of these states were considered as references in this study ( 20 – 24 ). Since Connecticut started charging only from 26,000 lb, a combined version of the last four states weight-distance fee was proposed for Texas, as shown in Table 5. An average annual VMT of 19,000 was estimated by dividing the truck’s VMT (commercial and OW) by the number of vehicle registrations, excluding passenger cars and motorcycles.
Weight-Distance Fee Revenue in 2019
The results presented in Table 5 were set to breakeven only the expenses related to commercial vehicles. It can be noted that almost half of the necessary revenue lies in weight ranges between 55,000 and 80,000 lb (federal GVW limit), although most of the VMT estimates were from lighter vehicles. This indicates how equitable this cost recovery scenario is, as emphasized in previous studies ( 5 ). This scenario would also incur administrative costs associated with establishing a new fee and determining the interstate implications.
Overweight (OW) Permit Fee Adjustment
TxDMV issues 35 permit types each year, of which 26 have fees established by statute, while the remaining nine are set in consultation with TxDOT. Typically, 90% of each permit fee is allocated to SHF, with the remaining portion retained by the agency to cover administrative costs associated with permit issuance. The permit fee adjustment was made proportionally to the consumption cost responsibility, affecting only OW permits since OS permits were included in the commercial vehicles category. The proposed adjustments for OW permits are summarized in Table 6.
Summary of Overweight Permit Fee Additional Fees
The revenues presented in Table 6 were calculated to complement Table 5, closing the total revenue deficit of $167.9 million. The results presented were disaggregated for the three main permit types (that represented more than 90% of the total VMT) but included the other OW permits by summing the marginal additional fee of each permit type, weighted by their VMT.
It is important to note that single-trip permits follow a different fee structure than annual permits, with the latter typically consisting of a flat fee. For example, the general permit includes a fixed fee of $40, plus an additional variable fee based on the vehicle’s GVW range (between 80,001 and 200,000 lb). Additional charges may apply for vehicles requiring supervision, such as general permits for GVWs between 200,000 and 254,300 lb, or for permits with basic mileage-based fees, as in the case of the quarterly hubometer permit ( 25 ).
Mileage-Based Fee
Unlike the traditional fee structure, this scenario is based solely on assigning infrastructure consumption costs to commercial and OW vehicles on a mileage basis, in an effort to support the long-term policy changes suggested by Hasnat and Bardaka ( 5 ). The results for are summarized in Table 7.
Summary of Mileage-Based Fee per Vehicle Category in 2019
The differences in the rate per mile presented in Table 7 are justified by the imbalanced consumption rates found during the pavement and bridge analysis. However, implementing such policy based on mileage presents significant challenges, primarily because of privacy concerns and the technical difficulties associated with tracking these vehicles. Although motor carriers have biennial updates with the Federal Motor Carrier Safety Administration, an accurate VMT would only be possible through GPS tracking. Many vehicles may not be equipped with GPS devices and retrofitting them would entail substantial costs and logistical hurdles. Furthermore, privacy implications are a significant concern. The use of GPS tracking to monitor vehicle movements raises ethical issues about the surveillance of individuals and organizations without their consent. A past study has highlighted that GPS tracking can lead to the collection of sensitive data, potentially infringing on privacy rights ( 26 ). Moreover, besides the complex legal framework surrounding GPS tracking, implementing such strategy would require significant administrative resources to manage the data, ensure compliance, and address disputes. The combination of technical, legal, and administrative challenges makes the widespread adoption of GPS-based mileage charges for commercial and OW trucks complex and challenging, but not impossible, especially for OW vehicles. One attempt for this strategy would be to require carriers to obtain a single-trip (routed) permit each time they travel under the terms of a unrouted permit, which would result in a “route record” for future assessment.
Conclusions
This study analyzed pavement and bridge consumption costs, compared those with revenues, and proposed four different funding alternatives to recover the costs imposed by commercial and OS/OW trucks on Texas’s roadway assets. The expenses were obtained for pavement and bridge consumption through a modified incremental approach, which assign a dollar value to the life reduction (or consumption) imposed by traffic to pavements and bridges in the highway network. The pavement and bridge analyses demonstrated that commercial vehicles, because of their significant VMT, are the primary contributors to roadway consumption costs. Conversely, OS/OW vehicles, although fewer in number, incur higher per-mile costs because of their axle load configurations.
The revenue analysis showed that traditional funding sources are insufficient to recover the maintenance and rehabilitation needs of Texas’ growing transportation network. The four proposed scenarios cover one fee adjustment to all user costs, based on the vehicle registration fee; a weight-distance to recover the commercial associated costs; an adjustment on the OW permit fees, to recover only the costs associated to vehicles beyond the state’s weight limit; and a mileage-based fee for both commercial and OW vehicles.
The proposed cost recovery scenarios offer potential solutions for more equitably distributing roadway costs according to vehicle impacts. However, some methodological limitations should be noted and addressed in future research. First, congestion costs were not included because of data limitations, potentially underestimating total user costs. This omission is particularly important, as the accelerated deterioration caused by heavier trucks can necessitate earlier maintenance or rehabilitation activities, often requiring partial or full roadway closures that further exacerbate user delays and costs. Second, the mileage-based fee scenario relies on accurate tracking of VMT, which is challenging in practice because of privacy concerns and the necessary vehicle adaptations, making implementation difficult and often relying on educated estimations. Finally, this study primarily focused on the state-maintained system (“on-system”) but, in reality, motor carriers may detour onto county roads (“off-system”), generating additional costs for local jurisdictions and complicating the equitable allocation of user costs. Future research should explore the integration of real-time vehicle tracking technologies while addressing privacy and legal concerns, assess regional variations in infrastructure costs and revenues, and evaluate the economic and behavioral impacts of different cost recovery mechanisms on motor carriers, thereby strengthening the feasibility and equity of infrastructure funding policies.
Footnotes
Acknowledgements
This project was conducted with the cooperation and support of Texas Department of Transportation (TxDOT) and the Texas Department of Motor Vehicles (TxDMV).
Author Contributions
The authors confirm contribution to the paper as follows: study conception and design: D. Inoue, C. Sabillon-Orellana, J. Prozzi, B. Glover; data collection: J. Prozzi, B. Glover; analysis and interpretation of results: D. Inoue, C. Sabillon-Orellana, J. Prozzi, B Glover; draft manuscript preparation: D. Inoue, C. Sabillon-Orellana. All authors reviewed the results and approved the final version of the manuscript.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by The University of Texas at Austin.
