A Recommendation System based on Clustering and Classification for Optimal Trajectory Analysis
Bibliographic record
Abstract
Moving objects such as people, animals, and vehicles have generated a huge amount of spatiotemporal data by using location-capture technologies and mobile devices. There is a high demand to analyze this collected data and extract the desired knowledge. In this study, we built a recommendation system based on four data mining techniques which are clustering, classification, sequential pattern mining, and time series analysis. We have focused on predicting traffic status in an effective way by considering the trip destination which can be useful for passengers. We applied clustering and sequential pattern mining to detect taxi trips movement in different areas, then we applied the Nave Bayes classifier to predict the traffic status of each trip. With the real taxi trips data of 441 taxis, we performed qualitative and quantitative analysis for our clustering method, then we evaluated the accuracy of the classification models. The results show that our recommendation system can achieve 70% accuracy in predicting traffic status.
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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 | 0.000 |
| 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".