Research on Real Time Traffic Information Data Model and Its Data Transmit
Bibliographic record
Abstract
Real-time of geographic information system for transportation (GIS-T) is one of the essential conditions to alleviate the traffic jam and guide the traffic flow rationally. In order to make it convenient for sharing and maintaining data, this paper structures the independent real-time traffic information database, seamless merging real-time traffic information and GIS data through data fusion method. In order to realize this purpose, the paper research on baseline network data model, baseline network is composed of base points and baselines. Base points are exclusive locating on the road network, which can be determined in field, and also can be resumed. Baseline is line element, which replaces traffic event, the baseline locate road network by the point, and therefore, it is easy to realize data share for various linear reference system. According to the data model, designing structure and introducing data transmit flow of the Geographic Information System for Transportation. Key words: Data Model; Data Fusion; GIS; Traffic Information This paper is supported by the Department of Instrument Science and Engineering, Southeast University, and professor DE-JUN WAN and professor QING WANG
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".