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Record W2959077396 · doi:10.3390/app9142848

A Macroscopic Traffic Model based on Driver Reaction and Traffic Stimuli

2019· article· en· W2959077396 on OpenAlexaff
Zawar H. Khan, Waheed Imran, Sajid Azeem, Khurram Shehzad Khattak, T. Aaron Gulliver, Muhammad Sagheer Aslam

Bibliographic record

VenueApplied Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsUniversity of Victoria
FundersHigher Education Commision, PakistanUniversity of Engineering and Technology, PeshawarHigher Education Commission, PakistanUniversity of Engineering and Technology, Lahore
KeywordsBottleneckHeadwayMicroscopic traffic flow modelTraffic bottleneckTraffic flow (computer networking)Three-phase traffic theoryTraffic congestion reconstruction with Kerner's three-phase theoryTraffic waveSimulationComputer scienceTraffic modelTraffic generation modelTransport engineeringEngineeringTraffic optimizationTraffic congestionReal-time computingFloating car dataComputer network

Abstract

fetched live from OpenAlex

A new macroscopic traffic flow model is proposed, which considers driver presumption based on driver reaction and traffic stimuli. The Payne–Whitham (PW) model characterizes the traffic flow based on a velocity constant C 0 which results in unrealistic density and velocity behavior. Conversely, the proposed model characterizes traffic behavior with velocities based on the distance headway. The performance of the proposed and PW models is evaluated over a 300 m circular road for an inactive bottleneck. The results obtained show that the traffic behavior with the proposed model is more realistic.

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 score0.040
Threshold uncertainty score0.377

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 head0.010
GPT teacher head0.212
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 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

Citations44
Published2019
Admission routes1
Has abstractyes

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