Detecting taxi movements using Random Swap clustering and sequential pattern mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Moving objects such as people, animals, and vehicles have generated a large amount of spatiotemporal data by using location-capture technologies and mobile devices. This collected data needs to be processed, visualized and analyzed to transform raw trajectory data into useful knowledge. In this study, we build a system to deliver a set of traffic insights and recommendations by applying two techniques, clustering, and sequential pattern mining. This system has three stages, the first stage preprocesses and samples the dataset into 168 subsets, the second stage applies two clustering techniques, the hierarchical density-based spatial clustering (HDBSCAN) and the Random Swap clustering (RS). We compare these two clustering algorithms in terms of processing time and quality of clusters. In the comparative analysis, the Silhouette coefficient shows that RS clustering outperforms HDBSCAN in terms of clusters quality. Moreover, the analysis shows that RS outperforms K-means in terms of the mean of square error (MSE) reduction. After that, we use a Google Maps approach to label the traffic districts and apply sequential pattern mining to extract taxi trips flow. The system can detect 146 sequential patterns in different areas of the city. In the last stage, we visualize traffic clusters generated from the RS algorithm. Furthermore, we visualize the taxi trips heatmap per weekday and hour of the day in Porto city. This system can be integrated with the current traffic control applications to provide useful guidelines for taxi drivers, passengers, and transportation authorities.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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 it