Crowd-sourced carpool recommendation based on simple and efficient trajectory grouping
Bibliographic record
Abstract
We propose a novel carpool recommendation method that is based on simplifying a user's movement traces. An effective carpool recommendation system requires that users following the most similar driving routes be identified and that these routes then be consolidated into one or more 'recommended' optimal carpool driving route options that users' can choose from. Currently mobile users generate a high volume of detailed trajectory data, making it difficult to efficiently derive optimal recommendations. We devise a simple method for building a user's trajectory profile, which is then used in deriving the recommendation(s). Unlike an origin-destination based analysis, which matches up riders with drivers, our method creates feature points along a simplified path that has been derived from the mobile user's moving trace. This maintains the sequence of movements and preserves feature points, including intersections and common places. Feature points are mapped using quad-keys as part of a tile map system that enables a membership of feature points within the range of a given area. Using this membership, recommendations for optimal carpool routes are made by measuring how users share common quad-keys along their trajectories. We tested our proposed method using historical traces of two crowd-sourced projects: TrafficPulse and GeoLife. The results show the advantage of the proposed method for dealing with a high volume of detailed mobile trajectory data, both in terms of requiring reduced data storage space and requiring reduced computational cost.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".