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Record W644991872

Charging for Road Use When Road Systems Have Multiple Independent Road Owners

2013· article· en· W644991872 on OpenAlexaboutno aff
Bern Grush, Gabriel Roth

Bibliographic record

VenueTransportation Research Board 92nd Annual MeetingTransportation Research Board · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentTransport engineeringRevenueIncentiveBusinessFinanceEngineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

The dedicated, unindexed fuel taxes commonly used to charge for road use in the United States and Canada are generally considered to be unsatisfactory because, with falling fuel consumption, they do not raise sufficient revenues. They are also unsatisfactory because they cannot function as effective prices for road use, which prevents roads from becoming part of the market economy. This paper reviews the ways in which flexible, autonomous charging methods and technologies can operate within a single, interoperating charging system, permitting road-use charging on any configuration of private, state, regional or municipal road, with minimal roadside infrastructure. Payments by road users would be made to “Payment Operators” who would aggregate the amounts payable for the use of specific road segments and credit the individual road owners with the amounts due to them. After setting out some essential criteria for national (even international) mileage-based user fee (MBUF) systems, the paper describes “thick” and “thin” “autonomous” road use metering. It concludes that only thick, autonomous systems can meet these criteria, and that, to reduce their costs, MBUF systems should be offered in conjunction with benefits desired by road users, such as insurance premiums based on distance travelled. The paper also suggests that such new charging methods should be introduced on a voluntary basis, giving road users the choice of using one of them before the existing one (fuel taxes) is replaced, and that private firms should be invited to offer these new payment systems in conjunction with appropriate incentives.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0020.004
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.093
GPT teacher head0.382
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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