Analyzing Taiwanese Traffic Patterns on Consecutive Holidays through Forecast Reconciliation and Prediction-based Anomaly Detection Techniques
Bibliographic record
Abstract
This dataset supports research on Taiwanese highway traffic behavior during consecutive holidays, focusing on traffic anomaly detection and the evaluation of traffic management strategies. The data is sourced from the Taiwan Freeway Bureau Traffic Database and was originally collected at 5-minute intervals by the Electronic Toll Collection (ETC) system on Taiwan’s three main highways. For analysis, the data was aggregated to an hourly level and pre-processed. hour2021-final.csv: Contains hourly traffic data from January 1, 2021, to April 30, 2021. hour.all.csv: Includes hourly traffic data from January 1, 2019, to April 30, 2021, covering 73 days of consecutive holidays across three years. This dataset enables analysis of traffic anomalies, seasonal patterns, and spatial variations in traffic flow, providing valuable insights for improving traffic control and management strategies in Taiwan. Data Source Acknowledgment:Any use, redistribution, or publication of this dataset or any derivative works must explicitly acknowledge that the data originates from the "交通部高速公路局『交通資料庫』" (Taiwan Freeway Bureau Traffic Database). Permitted Uses: The data may be used for research, analysis, application development, and public sharing.Value-added services or research results based on this dataset must include proper attribution as specified above.Reference links: https://tisvcloud.freeway.gov.tw/ (Since 2025, available on only local Taiwan IPs) - https://tdx.transportdata.tw/data-service/historical
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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 | 0.003 | 0.004 |
| 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) | 0.003 | 0.002 |
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".