Determining Delivery Demand Area Distribution Using Effective Regions of Movement Clustering
Bibliographic record
Abstract
As more transport service providers traverse public roads to provide food and parcel delivery and ridesharing services, there is a need to analyze these delivery/service points to maximize provider profitability while minimizing harmful environmental effects. In this study, we utilize an urban empirical mobility dataset to extract important Global Positioning Systems (GPS) information where most transactions of delivery and services happened. In particular, we only utilized two-wheeled vehicular positions in the study since they offer more services as compared to four-wheeled vehicles. The urban map is uniformly partitioned into grids categorized by its vehicular capacity to locate highly demanded points. We then combine closely related grid positions into its corresponding effective regions of movement (ERMs) according to a preset vehicular capacity threshold. We also compute for the closeness centrality measure of these highly demanded locations and found that points within each ERM have short distances between them and a relatively larger distance among points from other ERMs. Given these findings, ERMs are spatially separated thereby locating the demand area distribution easily.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".