Modeling Transportation Time Series using Bayesian Dynamic Linear Models
Bibliographic record
Abstract
Sudden large-scale changes like the recent COVID-19 pandemic make the management and planning of transport systems difficult, despite the ever-increasing availability of data. The primary goal of this work is to model transportation data time series using a dynamic model that is interpretable and can be used for long-term forecasting. The Bayesian Dynamic Linear Model (BDLM) is chosen because it can account for complex data and can be easily adapted to the data. A component for the BDLM is introduced to recognize the underlying patterns using temporal control points. A moving-event component is also added to take into account events that do not occur on the same date every year such as sports games. The proposed model is parsimonious and can learn from the data. After providing a brief summary of the theory of the model, experimental results are shown for transport demand data obtained from smart card transaction data for the Montreal subway system. The proposed model is compared to different time series models and shows superior accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".