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Componentry Analysis of Intelligent Transportation Systems in Smart Cities towards a Connected Future

2020· article· en· W3157624383 on OpenAlexaff
Priyanka Trivedi, Farhana Zulkernine

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsSAFERSoftware deploymentIntelligent transportation systemPerspective (graphical)ArchitectureConfluenceComputer scienceComputer securityTransport engineeringBusinessEngineeringArtificial intelligenceSoftware engineering

Abstract

fetched live from OpenAlex

Intelligent Transportation System (ITS) is positioned at the confluence of the two most powerful currents of our time, information technology empowered by artificial intelligence and the ever-improving transportation technologies. This confluence is ushering in an era of transportation services that are inclusive, safer, greener, and more efficient at the same time. Like any other big change, this is strongly disruptive as it upends the concept of ownership and availability while challenging authorities all over the world to reimagine logistics, people movement, and environment protection. This paper examines the ITS through its components, architecture, and related applications. It dives deeper into the hood to examine how these components are, both individually and collectively, taking us towards the connected future and smart cities. Finally, it attempts to put a perspective by evaluating the challenges and opportunities inherent in the deployment and propagation of the ITS.

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 score0.418
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.204
Teacher spread0.191 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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