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Record W4415021645 · doi:10.1016/j.trd.2025.105001

Energy and environmental benefits of truck platooning for a busy freight corridor

2025· article· en· W4415021645 on OpenAlexafffundabout
Brian McAuliffe, Sean McTavish, Arash Raeesi, S. J. Harrison

Bibliographic record

VenueTransportation Research Part D Transport and Environment · 2025
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsNational Research Council Canada
FundersTransport Canada
KeywordsTruckYield (engineering)Fuel efficiencyEnergy (signal processing)Separation (statistics)Cost–benefit analysisEnergy consumption

Abstract

fetched live from OpenAlex

Cooperative Truck Platooning Systems (CTPS) have been demonstrated, in controlled testing environments, to yield significant energy savings, while real-world trials have shown much lower benefits. A simulation-based study was undertaken for a busy freight corridor in Canada, to examine the potential fuel savings and emissions reductions associated with the introduction of CTPS, while accounting for many of the variables previously neglected in feasibility and economic analyses. Results demonstrate lower benefits than previously predicted, closer to those observed in on-road trials. Upwards of 6.5 % savings are possible for platoons no longer than four trucks with a minimum 15 m separation distance, with only 3.7 % overall savings when a 60 % usage rate is assumed. This provides emissions reductions of about 100 kT of CO 2 per year (about 0.2 % of Canada’s current heavy-freight transportation emissions). Additionally, CTPS benefits are further reduced with precipitation- and location-based usage restrictions for the same simulation conditions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.220
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes3
Has abstractyes

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