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Record W4366366375 · doi:10.1139/cjce-2023-0002

New signal priority strategies to improve public transit operations in an urban corridor

2023· article· en· W4366366375 on OpenAlexaffvenue
Arash Mazaheri, Ciprian Alecsandru

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsConcordia University
Fundersnot available
KeywordsVisSimBus priorityIntersection (aeronautics)Public transportTransport engineeringQueueing theoryScheduleComputer scienceTraffic simulationTransit (satellite)SIGNAL (programming language)Signal timingOperations researchSimulationEngineeringReal-time computingTraffic signalComputer network

Abstract

fetched live from OpenAlex

By facilitating public busesʼ movement through traffic signal-controlled intersections, a transit signal priority (TSP) strategy can contribute to the reduction of queuing time at intersections. However, the traditional TSP also has a significant downside; it is detrimental to non-prioritized movements and other transport modes. This research proposes new TSP strategies that account for optimization for the person delay at an intersection along a corridor. This research focused both on major arterial roads and minor arterial roads, whereas the majority of the publications only addressed the major arterial approach. As part of this research, the bus schedule was also taken into consideration, whereas in other studies, the measure of giving priority was only the approach to the intersection. The proposed method is described in detail and also is implemented on arterial corridor in VISSIM. The study area simulation results indicated that the proposed TSP methods performed better than the conventional TSP.

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: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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