SFL: A High-precision Traffic Flow Predictor for Supporting Intelligent Transportation Systems
Bibliographic record
Abstract
As a potential solution to the growing conflict between the increasing demand for transportation and the limited capacity of transportation infrastructure, Intelligent Transportation Systems have gained considerable attention for their effectiveness in improving the efficiency of existing transportation infrastructure and enhancing traffic safety. Among various research areas, traffic flow prediction is a vital application, and researchers have devoted a lot of effort to designing accurate and fast algorithms. Currently, to satisfy various performance requirements, hybrid prediction methods that can take advantage of different sub-modules are beginning to emerge and show advantages in prediction accuracy and timeliness over other prediction algorithms that rely solely on machine learning. In this paper, we introduce a novel high precise traffic flow prediction method by utilizing the Fourier analysis (FA)-assisted denoising. Briefly, three sub-modules are introduced. Singular Spectrum Analysis (SSA) module is able to filter the noise of the original data, FA module is applied to extract periodic features of the traffic flow, and Long Short-Term Neural Networks (LSTM) is utilized to predict the future trend of time series residuals. We conducted simulation experiments. The corresponding test results demonstrate a substantial improvement in the accuracy compared to pure sub-models and other machine learning methods.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".