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Record W4384930992 · doi:10.54327/set2023/v3.i2.78

HetroTraffSim: A Novel Traffic Simulation Software for Heterogeneous Traffic Flow

2023· article· en· W4384930992 on OpenAlexaff
Ali Zeb, Khurram Shehzad Khattak, Muhammad Rehmat Ullah, Zawar Hussain Khan, T. Aaron Gulliver

Bibliographic record

VenueScience Engineering and Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTraffic flow (computer networking)SoftwareTraffic simulationComputer scienceNetwork traffic simulationTraffic generation modelTraffic optimizationTraffic congestion reconstruction with Kerner's three-phase theoryFloating car dataTransport engineeringRoad trafficMicroscopic traffic flow modelSimulation softwareWork (physics)SimulationReal-time computingMicrosimulationTraffic congestionComputer networkEngineeringNetwork traffic control

Abstract

fetched live from OpenAlex

Traffic simulation software (TSS) is employed for planning, designing, and managing road networks. Among existing TSS, only SUMO and HETROSIM can be used for heterogeneous traffic. The objective of this work is to develop new software for heterogeneous traffic simulation which is effective and efficient. This is motivated by the fact that traffic in developing countries is typically heterogeneous. The HetroTraffSim TSS was developed using Unity3D and is based on a recently developed macroscopic traffic flow model. A 360 m section of University Road which is a two-lane arterial road in Peshawar, Pakistan, is used to evaluate the performance using real traffic data. The results obtained show that an increase in density decreases the velocity. Further, HetroTraffSim can be used to characterize and predict heterogeneous traffic behavior.

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 score0.131
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.009
GPT teacher head0.211
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

Citations1
Published2023
Admission routes1
Has abstractyes

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