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Record W1803546102 · doi:10.1002/atr.1265

Exploring suitable traffic conditions for intermittent bus lanes

2014· article· en· W1803546102 on OpenAlexvenueno aff
Feng Qiu, Wenquan Li, Jian Zhang, Xuechi Zhang, Qiufeng Xie

Bibliographic record

VenueJournal of Advanced Transportation · 2014
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsTraffic flow (computer networking)Transport engineeringLevel of serviceRange (aeronautics)Computer scienceCellular automatonSimulationEngineeringComputer network

Abstract

fetched live from OpenAlex

Summary As one of the most promising bus priority techniques, the innovative intermittent bus lane (IBL) strategy has drawn more attention in the past few years. In this paper, some improvements on the operation of the IBL strategy are proposed, and two cellular automaton models for a roadway section with two lanes, one with no bus priority and the other with an intermittent bus lane, are built to study the characteristics of urban traffic flow. Computer simulations and analytical models are developed to conduct quantitative research on the influence of IBL on the traffic density distribution, traffic velocity, and traffic capacity of the roadway section. By comparing the average paces in the two cases, this paper proposes a methodology to determine suitable traffic conditions for the IBL strategy implementation. The results indicate that for the designed scenarios, the IBL strategy is effective only when the traffic density is in the range of 25 to 74 pcu/km, which suggests that level of service C is the inflection point for implementing the IBL strategy. Copyright © 2014 John Wiley & Sons, Ltd.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.291

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 head0.020
GPT teacher head0.224
Teacher spread0.204 · 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

Citations38
Published2014
Admission routes1
Has abstractyes

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