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Record W1521964278 · doi:10.1002/atr.1268

Investigations of interactions between bus rapid transit and general traffic flows

2014· article· en· W1521964278 on OpenAlexvenueno aff
Xiqun Chen, Zhiheng Li, Han Jiang, Meng Li

Bibliographic record

VenueJournal of Advanced Transportation · 2014
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
Fundersnot available
KeywordsBottleneckBus rapid transitTransport engineeringTraffic bottleneckComputer scienceQueueQueueing theoryLevel of serviceIntersection (aeronautics)Traffic congestionComputer networkTraffic optimizationEngineeringPublic transportFloating car data

Abstract

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Summary Bus rapid transit (BRT) is a popular strategy to increase transit attraction because of its high‐capacity, comfortable service, and fast travel speed with the exclusive right‐of‐way. Various engineering designs of right‐of‐way and the violation enforcement influence interactions between BRT and general traffic flows. An empirical assessment framework is proposed to investigate traffic congestion and lane‐changing patterns at one typical bottleneck along a BRT corridor. The BRT bottleneck consists of bus lane, BRT station, video enforcement zone, and transit signal priority intersection. We analyze oblique cumulative vehicle counts and oblique cumulative lane‐changing maneuvers extracted from videos. The cumulative vehicle counts method widely applied in revealing queueing dynamics at freeway bottlenecks is extended to an urban BRT corridor. In the study site, we assume four lane‐changing patterns, three of which are verified by the empirical measurements. Investigations of interactions between buses and general traffic show that abnormal behaviors (such as lane violations and slow moving of the general traffic) induce 16% reduction in the saturation rate of general traffic and 17% increase in bus travel time. Further observations show that the BRT station and its induced increasing lane‐changing maneuvers increase the downstream queue discharge flows of general traffic. The empirical results also contribute to more efficient strategies of BRT planning and operations, such as alternative enforcement methods, various lane separation types, and optimized traffic operations. 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.319

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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