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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".