Analysis, Design and Implementation of an Agent Based System for Simulating Connected Vehicles.
Bibliographic record
Abstract
PARAMICS traffic microsimulator is a popular simulator among universities and government agencies since it is capable of representing many parts of the world's street maps and designed to handle scenarios ranging from a single intersection to a congested freeway, or the modeling of a complete traffic system. However, it lacks the ability of simulating Connected Vehicle (CV) system and its applications of the Intelligent Transportation System (ITS) through designated traffic simulation network. In this study, we utilized the Multi Agent System Engineering (MaSE) methodology, step by step, to model CV as a Multiagent System (MAS). We implemented the MaSE artifacts as extensions for the PARAMICS using two APIs (Application Programming Interface) to add the ability to simulate CV systems. In this paper we provide detailed explanation of the MAS design and at the end introduce two experiments, made based on this research, as the case studies to evaluate the proposed CV system for estimating and improving traffic safety and mobility parameters in the network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".