Refined Judgment of Urban Traffic State Based on Machine Learning and Edge Computing
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Machine learning is a discipline that covers probability theory, statistics, approximate theoretical knowledge, and complex algorithm knowledge. It is committed to real-time simulation of human learning methods, which can effectively improve learning efficiency. The main function of this calculation method is to use a relatively open platform to integrate the Internet, computers, memory, and other terminal applications through integrated technical means. For providing short-distance services and applications, we should start from the edge of the program to create a faster network. Service response covers the basic needs of real-time processing industry, intelligent applications, and security and privacy protection. This paper aims to study the recognition of urban traffic conditions and refine the recognition through the improvement of edge computing algorithms. This paper proposes a method to calculate traffic flow parameters, preprocess the traffic flow data, delete irrelevant features, use flow theory to delete wrong data, and change the data in time according to actual needs, carrying out refined discrimination and analysis of urban traffic status. The experimental results in this paper show that the use of edge computing to fine-tune the state of urban traffic can divert traffic, greatly reduce traffic congestion, and increase traffic safety by 13%. Among them, the efficiency of road network time and space resources has also increased by 23%.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".