Forecasting of Highway Revenues Under Various Options

Agbelie, Bismark; Bai, Qiang; Labi, Samuel

doi:10.5703/1288284314268

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…Since then, a number of past researchers have used methods that are based mostly on other attributes besides traffic counts. These include the driving-age cohort and demographic characteristics, odometer readings, fuel sales, socioeconomic regression models, and vehicle registrations (Agbelie, Bai, Labi, & Sinha, 2010;Kumapley & Fricker, 1994;Maring, 1974;Schipper & Moorhead, 2000;Vasudevan & Nambisan, 2013). It is worthy to note that data on demographics, household characteristics, economic activity, and fuel efficiencies must be updated because these attributes change with time.…”

Section: Vmt Estimation Using Methods Not Based On Traffic Countsmentioning

confidence: 99%

“…Regression models that use cross-sectional data have been applied to estimate VMT for a specific spatial area. The explanatory variables may include the per capita income, gross state income, gross domestic product, and vehicle registrations (Agbelie et al, 2010;Sinha, Labi, Hodge, Tine, & Shah, 2005;Varma et al, 1992).…”

Section: Methods Of Non-traffic-based Estimationmentioning

confidence: 99%

“…This method uses regression models and crosssectional data on socio-economic characteristics. The regression models developed in a past Indiana study (Agbelie et al, 2010) were used in this study. As shown in Figure 3.2, the outputs of the regression models provide statewide coverage with aggregation by vehicle group.…”

Section: Vmt Estimation Based On Socioeconomic Regressionmentioning

confidence: 99%

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Estimation and Prediction of Statewide Vehicle Miles Traveled (VMT) by Highway Category and Vehicle Classification

Klatko¹,

Agbelie²,

Labi³

et al. 2017

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Vehicle Miles Traveled (VMT) is a critical performance measure that is used extensively in highway transportation management for financial analysis, resource allocation, impact assessments, and reporting to oversight agencies. As highway revenue from fuel taxes continues to plummet and user-based taxes such as VMT fees become increasingly attractive, consistent and reliable VMT estimates have become critical for highway funding evaluation and administration. At the present time, there are several methods for VMT estimation that typically yield estimates that are inconsistent or inaccurate. This study was commissioned by the Indiana Department of Transportation (INDOT) to develop a benchmark method for VMT estimation and provide calibration factors for the VMT estimation methods. The study's core outcome is a segment-level framework for VMT estimation For the state roads, a comprehensive database was developed which facilitates extensive aggregations of VMT by geographical scope, route, functional class, and vehicle class. For the local roads, a sample of counties of different spatial locations and degrees of urbanization were used, and cluster analysis, geographic information systems (GIS), and spatial interpolation techniques were used to expand the VMT estimates from the local road sample to the population of all counties in the state. The results of this study indicate that there is significant variation in the results from the various VMT estimation methods. The technique developed in this study for reconciling these different VMT estimates was validated using the estimate from the benchmark method (segment-level) as a basis. The implementation platform developed in this study was designed to produce outcomes that address the VMT data needs of the intended end users and stakeholders and could be enhanced in the future as and when data become available.

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Section: Vmt Estimation Using Methods Not Based On Traffic Countsmentioning

confidence: 99%

Section: Methods Of Non-traffic-based Estimationmentioning

confidence: 99%

Section: Vmt Estimation Based On Socioeconomic Regressionmentioning

confidence: 99%

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Estimation and Prediction of Statewide Vehicle Miles Traveled (VMT) by Highway Category and Vehicle Classification

Klatko¹,

Agbelie²,

Labi³

et al. 2017

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“…For example, the 4-year average gasoline tax revenue was $539.5 million. Highway revenues from fuel-related sources were attributed to the relevant vehicle classes on the basis of their numbers of VMT, fleet fuel efficiency, and the tax rates by use of the methodology developed in a 2010 study (15). Registration-related user revenues were attributed on the basis of the number of registered vehicles and fees.…”

Section: User Revenue Attributionmentioning

confidence: 99%

Updating State and Local Highway Cost Allocation and Revenue Attribution: A Case Study for Indiana

Agbelie

Volovski

Zhang

et al. 2016

Transportation Research Record

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This study investigated the responsibility for the cost of upkeep of the highway infrastructure and the contribution of revenue from highway users on that upkeep. The costs consisted of actual expenditures on the construction, preservation, maintenance, and operation of the infrastructure at both the state and the local levels. The revenues from the federal, state, and local levels were assessed and considered. The types of assets included were pavement, bridge, safety, and mobility assets. The highway users were represented by the 13 FHWA vehicle classes, and the study was based on data on the expenditures made and the revenues collected from 2009 to 2012. The study framework duly recognized the dichotomy between attributable and common costs: for allocation of the attributable costs to the vehicle classes, equivalent single-axle loads, AASHTO load equivalency factors, and passenger car equivalents were used, and for allocation of common costs, the number of vehicle miles traveled adjusted for vehicle width was used. Of the 13 vehicle classes, Vehicle Classes 1 to 4 (passenger vehicles) were found to be overpaying their cost responsibilities, whereas Vehicle Classes 5 to 13 (trucks) were found to be underpaying. In particular, Vehicle Class 2 (automobiles) was found to overpay its cost responsibility by 10%, whereas Vehicle Class 9 (five-axle trucks) was found to underpay by 19%. The product of this study facilitates assessment of the appropriateness of the types and rates of current taxes and fees and provides a data-based and objective platform for devising future funding mechanisms and establishing appropriate user rates to meet Indiana’s future financing needs.

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“…For combination trucks, single unit trucks and buses, published values from the Transportation Energy Data Book of the Oak Ridge National Laboratory were used (6). A detailed discussion of this approach is given in earlier reports (2,7).…”

Section: Estimation Of Vehicle Fleet Fuel Efficiencymentioning

confidence: 99%

Updates to Indiana Fuel Tax and Registration Revenue Projections

Agbelie

Labi

Mannering

et al. 2012

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ACKNOWLEDGMENTSThe guidance provided by John W. Weinmann, Dan Brassard, Mark Ratliff, and Samy Noureldin of INDOT is gratefully acknowledged. JOINT TRANSPORTATION RESEARCH PROGRAMThe Joint Transportation Research Program serves as a vehicle for INDOT collaboration with higher education institutions and industry in Indiana to facilitate innovation that results in continuous improvement in the planning, design, construction, operation, management and economic efficiency of the Indiana transportation infrastructure. https://engineering.purdue.edu/JTRP/index_html AbstractHighway revenues both at the federal and state levels have failed to keep up with expected investments required for infrastructure preservation and improvement. The reasons for this trend include the increasing fuel efficiency of vehicles, slowing of the growth in vehicle-miles of travel, and the erosion of the purchasing power of the dollar due to inflation.Past studies on the issue of highway revenue forecasting for Indiana highways were conducted under different economic conditions than what exists today. The present study updates the revenue projections of particularly with the recognition of new CAFE Standards. The present study also updates the equations for estimating vehicle miles of travel. Impacts of alternative options for changing the fuel tax rate structure are also investigated.The present study predicted fuel tax revenues from 2012 to 2025 under the existing fuel tax rate structure and also considered possible options for changes in fuel tax rates. Fuel tax revenue from existing rate structure indicated a continuous annual decrease from 2012 to 2025 by 2.96% to 3.49% in real terms. Adopting one of the four fuel tax rate modifications would provide additional short-term revenue for a variable number of years. A 1-cent increase would offset the decline in the total fuel tax revenue only for a year after which it will continually decline every year. A 3-cent increase would provide a substantial increase in revenue in the short term but will continually decline, however, the 2025 revenue from 3-cent increase would be a little higher than the 2012 revenue level. Both inflation indexing and an ad valorem tax would also provide substantial increase in fuel tax revenue.17.

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Forecasting of Highway Revenues Under Various Options

Cited by 5 publications

References 23 publications

Estimation and Prediction of Statewide Vehicle Miles Traveled (VMT) by Highway Category and Vehicle Classification

Estimation and Prediction of Statewide Vehicle Miles Traveled (VMT) by Highway Category and Vehicle Classification

Updating State and Local Highway Cost Allocation and Revenue Attribution: A Case Study for Indiana

Updates to Indiana Fuel Tax and Registration Revenue Projections

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