On Predicting Taxi Movements Modes in Porto City Using Classification and Periodic Pattern Mining
Bibliographic record
Abstract
Trajectory data is produced on a large scale by various types of technologies like the internet of things (IoT) and global positioning system (GPS). When collected data is processed, analyzed, and visualized, it can be transformed into recommendations. This study builds a recommender system by applying the density clustering degree of membership (DOM) measurement. This system contains three phases, in the first phase, we clean, preprocess, and divide the data into samples. The second phase uses the hierarchical density-based clustering (HDBSCAN) to calculate DOM measurement and extracts the class attribute (traffic status). The last phase uses the extracted class attribute and applies the Naïve Bayes and Random Forest classifiers to predict the traffic flow (status) of any given trip. We compare the two classifiers in terms of accuracy and F1-score. For most datasets, the Random Forest outperforms Naïve Bayes in terms of accuracy. Moreover, the Random Forest F1-score was higher than Naïve Bayes for all datasets. This recommender system can be embedded with the recent traffic monitoring systems to provide helpful guidelines for predicting traffic status in origin, destination, and trips routes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".