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Integrated Utility-Transit Model for a Comprehensive Transition Plan for Battery-Electric Bus Fleets

2022· article· en· W4312256773 on OpenAlexaff
Mostafa F. Shaaban, M.M.A. Salama

Bibliographic record

Venue2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT) · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of Waterloo
FundersQatar National Research Fund
KeywordsElectrificationPurchasingPublic transportSizingPlan (archaeology)Operations researchBattery (electricity)Computer scienceTransit (satellite)Transport engineeringElectricityEngineeringOperations managementElectrical engineering

Abstract

fetched live from OpenAlex

Electrification of public transportation is becoming increasingly popular due to its significant impact on the environment. The new electric fleets have a substantial impact on the electrical distribution system due to their electric nature. A novel fleet replacement or transition problem is proposed in this paper, which incorporates the perspectives of transit agencies and electric utilities in order to determine the optimal purchasing decisions and when to make them. In the proposed problem, a series of sequential problems are posed that aim to minimize the total annualized costs, together resulting in an optimal transition plan. In this study, simulation results were obtained for the transition of a 5.75 km route using overnight charging. The optimal location of the depot is determined, followed by fleet sizing and charger selection. These results are incorporated into the final transition problem which determines when to make purchase decisions. The results demonstrate the effectiveness of the proposed approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2022
Admission routes1
Has abstractyes

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