A Mixed Breadth-Depth First Search Strategy for Sequenced Group Trip Planning Queries
Bibliographic record
Abstract
We study Sequenced Group Trip Planning Queries (SGTPQs). Consider a road network where some vertices represent Points of interest (POIs) and each POI belongs to exactly one Category of Interest (COI), e.g., A COI can be "Restaurants" and each POI in this COI is a specific instance of a restaurant. Given a group of users, each starting from a (possibly distinct) source location and going ultimately to a (possibly distinct) destination, as well as a ordered sequence of COIs, the SGTPQ finds, for each user, the route from his/her source location to his/her destination such that all users go through the same POIs, each one belonging to the specified sequence of COIs, while minimizing the total distance travelled by all users in the group. Different from previous work which investigated SGTPQs in Euclidean distance, we focus on SGTPQs in the more realistic case of road networks. The only existing algorithm for processing SGTPQs which may be also applicable in road networks, named IA, suffers from two drawbacks: it is not able to produce optimal answers and is computationally expensive. The first contribution of this paper is a small modification to IA, so that it can provide optimal solutions. The second and main contribution is a new approach, called Progressive Group Neighbour Exploration (PGNE) that delivers the optimal solution while being more efficient than IA. Our extensive experiments based on real and synthetic datasets show that PGNE is always faster than the modified IA and, in particular, typically twice as fast with respect to the number of users in the group travelling together, an important parameter for this type of query.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".