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Record W1988219048 · doi:10.1002/atr.5670430105

Platooning on two‐lane two‐way highways: An empirical investigation

2009· article· en· W1988219048 on OpenAlexvenueno aff
Ahmed Al‐Kaisy, Casey Durbin

Bibliographic record

VenueJournal of Advanced Transportation · 2009
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
FundersMontana State University
KeywordsPlatoonHeadwayRange (aeronautics)Transport engineeringTraffic flow (computer networking)Traffic engineeringTraffic speedComputer scienceSpeed limitSimulationEnvironmental scienceEngineeringControl (management)Computer security

Abstract

fetched live from OpenAlex

Abstract This paper presents an empirical investigation into platooning on two‐lane two‐way highways. The main objective is to better understand this phenomenon that has important implications on traffic performance and safety. Field data from three study sites in the state of Montana were used in this study. Separate investigations were performed to examine the relationships among platoon‐related variables, namely; time headway, travel speed, and platoon size. The study confirmed that interaction between successive vehicles in the traffic stream generally diminishes beyond a time headway threshold value that fell in the range of 5–7 seconds. Also, the study revealed that very short headways (less than one second) are more associated with aggressive driving and higher speeds than with slow‐moving platoons due to lack of passing opportunities. Further, the study found that amount of impedance to traffic is proportional to the size of platoon as evidenced by the relative difference between mean speed of various size platoons and the mean speed of unimpeded vehicles. The study provided other valuable insights into the platooning phenomenon on two‐lane highways that are essential in developing a better understanding of traffic operation on two‐lane highways.

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 score0.966
Threshold uncertainty score0.456

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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations25
Published2009
Admission routes1
Has abstractyes

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