Identifying Regions of High Demand for Transportation Services based on Cluster Evolution and Graph Analysis
Bibliographic record
Abstract
Identifying regions of high demand for transportation services can help drivers maximize their profits, assist companies in dynamic pricing or resource allocation, and reduce passenger’s wait times. However, their identification is not trivial because of the many factors that impact the demand, such as the weather or time of the day, and the possible lack of communication between drivers. In this paper, we present a framework to identify regions of high demand for transportation services based on cluster evolution and graph analysis. The framework identifies how clusters of moving objects evolve, creates a graph to represent the evolution, and use cluster relationships to calculate a rate of change based on the objects that enter of leave the cluster. Results can be described based on the day or the evolution of a cluster. A use case with taxis in Rome is performed and two main regions of high demand for taxis are identified, one near hotels and another near a bus, taxi, and train terminal.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".