Indiana State Highway Cost Allocation and Revenue Attribution Study and Estimation of Travel by Out-of-State Vehicles on Indiana Highways

Volovski, Matthew; Bardaka, Eleni; Zhang, Zhibo; Agbelie, Bismark; Labi, Samuel; Sinha, Kumares C.

doi:10.5703/1288284315709

Cited by 4 publications

(24 citation statements)

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“…The population of traffic counts and continous segment-by-segment data are available for state routes, and a sample of counts is used as a basis for computing the local route VMT. Time-series traffic data were available for 2009 to 2012 (Volovski et al, 2015); this allowed for VMT estimation at the link level. These four years of traffic data were used to populate the comprehensive spreadsheet-based database developed in this study for estimating and predicting the future traffic volumes and consequently VMT.…”

Section: Development Of Methodology Frameworkmentioning

confidence: 99%

“…Some of these are also capable of reporting separate VMTs by vehicle class, road class, or jurisdiction. Furthermore, the VMT estimation process is enriched where there is a capability to estimate the in-state and out-of-state split of the statewide VMT by vehicle class or functional class (see Volovski et al, 2015). Figure 2.3 summarizes the differences between the methods for VMT estimation based on the data type used.…”

Section: Characteristics Of Vmt Estimation Approachesmentioning

confidence: 99%

“…The original values were given in barrels and converted to gallons for consistency with Indiana DOR estimates. Data on the traffic distribution streams by vehicle class for the weighted fleet fuel efficiencies, were taken from Indiana's SPR 3704 report (Volovski et al, 2015) 2014d) was used to adjust the original data provided by rural and urban designation.…”

Section: Data For Fuel Revenue and Fleet Efficiencymentioning

confidence: 99%

“…This allows for a continuous AADT/VMT coverage for all state routes. To represent vehicle class distributions for all segments, sampling procedures from INDOT-sponsored research study SPR 3704 (Volovski et al, 2015) were used as a building block to develop a database representing vehicle class percentages for all state route segments. The data collection and compilation for state routes is discussed in Section 3.5.2.…”

Section: State Routes Frameworkmentioning

confidence: 99%

“…The observed 2009-2012 trends did not indicate significant variation in the relative distribution of vehicles at the statewide level. The vehicle classifications were determined using methods developed in the recently completed INDOT-sponsored SPR-3704 study (Volovski et al, 2015) which utilized weighted-distance methods with Kriging spatial interpolation to estimate vehicle class distributions across the state.…”

Section: Vehicle Class Distributionsmentioning

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: Development Of Methodology Frameworkmentioning

confidence: 99%

Section: Characteristics Of Vmt Estimation Approachesmentioning

confidence: 99%

Section: Data For Fuel Revenue and Fleet Efficiencymentioning

confidence: 99%

Section: State Routes Frameworkmentioning

confidence: 99%

Section: Vehicle Class Distributionsmentioning

confidence: 99%

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