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Record W2550148882 · doi:10.1139/cjce-2016-0275

Assessment of level of service measures for two-lane intercity highways under heterogeneous traffic conditions

2016· article· en· W2550148882 on OpenAlexvenueno aff
Amardeep Boora, Indrajit Ghosh, Satish Chandra

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
FundersNational Rice Research Institute, Indian Council of Agricultural Research
KeywordsTransport engineeringLevel of serviceHighway Capacity ManualRange (aeronautics)Service (business)StatisticsValue (mathematics)Traffic volumeCluster (spacecraft)Environmental scienceComputer scienceMathematicsEngineeringBusiness

Abstract

fetched live from OpenAlex

Many researchers have studied the performance of two-lane intercity highways with the help of different measures. In the current study, the performance of such highways under heterogeneous traffic conditions was examined by using several speed and followers related measures. The data were collected from five study sites located in different regions of India. A new methodology was proposed where followers were identified by using a speed difference (between two consecutive vehicles) range of −4 to + 10 km/h and gap threshold value (lower than a particular gap value) of 10 s. By using acceptance curve method, different critical gap values were suggested for each site to identify the followers. Out of all the performance measures, the number of followers as a proportion of capacity (NFPC) and follower density were found to be the best and second best parameters. Finally, different level of service ranges were proposed based on NFPC using cluster analysis.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.899

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 head0.031
GPT teacher head0.230
Teacher spread0.199 · 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 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

Citations9
Published2016
Admission routes1
Has abstractyes

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