Evaluating Model of Road Traffic in Open Housing Estate Based on Cellular Automaton
Bibliographic record
Abstract
It has been referred in document issued by the State Council recently that China will promote the block system gradually in the future, no more enclosed residential compounds will be built in principle, and existing residential and corporate will open up step by step as well. The proposal of open area quickly aroused a heated discussion in the whole society. In addition to the most basic security issues, it is one of the main topics that whether the open district can really optimize the road network structure and improve the traffic in the end.Based on the cellular automata model and the actual situation, this paper simulates the traffic flow around the residential area, establishes motor vehicle driving model and makes a comprehensive evaluation of the surrounding road traffic after the opening of different types of residential area. According to the result of the index, it shows that three structures of residential area can relieve the burden of urban traffic flow while one structure of residential area that will aggravate the burden of urban traffic flow. Finally, the paper comes to the conclusion that excessive traffic flow of the trunk road which has adverse effect on road traffic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".