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Record W3209986075 · doi:10.1109/access.2021.3122087

Bus Trajectory Optimization With Holding, Speed and Traffic Signal Actuation in Controlled Transit Systems

2021· article· en· W3209986075 on OpenAlexafffundabout
Lucas Zimmermann, Leandro C. Coelho, Werner Kraus, Rodrigo Castelan Carlson, Luiz Alberto Koehler

Bibliographic record

VenueIEEE Access · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversité Laval
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoNatural Sciences and Engineering Research Council of CanadaMinistério da EducaçãoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsTrajectoryComputer scienceSIGNAL (programming language)Transit (satellite)Control theory (sociology)Trajectory optimizationReal-time computingEngineeringTransport engineeringControl (management)PhysicsPublic transportArtificial intelligence

Abstract

fetched live from OpenAlex

We propose an optimal actuation of operational commands for regularizing bus headways. Assuming that a transit headway control system manages the bus operation by issuing reference arrival times at the next station for a bus, the problem is how to implement the control decisions for the bus in terms of three nonexclusive alternative actions: holding the bus at stops; varying bus speeds; and controlling traffic lights. Mathematical programming provides the basis of the approach. The constraints specify the bus trajectory model and the operator objectives are formulated as a multi-objective cost function solved by a lexicographic method. Results for a bus run on a single segment between two stops highlight the properties of the solution. A simulation study of an entire transit corridor in Quebec City, Canada, shows the advantage of the method over both headway control based on pure holding at stops and on one that combines holding and absolute transit signal priority.

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 score0.093
Threshold uncertainty score0.369

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 communication0.0000.001
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 head0.026
GPT teacher head0.291
Teacher spread0.266 · 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

Citations14
Published2021
Admission routes3
Has abstractyes

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