Matrix Factorization for Globally Consistent Periodic Flow Prediction in Taxi Systems
Bibliographic record
Abstract
Predicting flow in taxi systems can improve taxi operations and reduce passengers’ waiting time. With the increasing availability of taxi mobility data sets, more studies have tried to analyze and model taxi demand. However, most existing works merely attempt to predict the number of pick-ups and drop-offs by using local observations. Nevertheless, predicting the passengers’ pair-wise flow specifically through simultaneous consideration of local variations and network communities in the taxi systems has been neglected. In this paper, we introduce a globally consistent periodic flow prediction for taxi systems that integrates communities in the matrix factorization model. We apply non-negative tensor factorization to capture the periodic variations in the passenger flow in different areas and predict the flow (and pick-ups and drop-offs) in the next periods by integrating the most recent observation with detected patterns from historical data. The results show an improvement in the prediction accuracy from some baselines and a state-of-the-art method. We also propose a method for visualizing flow that potentially improves taxi operations by assisting drivers with selecting passengers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".