Fast and efficient rendezvous in street networks
Bibliographic record
Abstract
We address the problem of rendezvous between two agents in urban street networks. Specifically, we consider the case where the agents have variable speeds and they need to schedule a rendezvous or a meeting under uncertainty in their travel times. Examples of such a scenario range from everyday life where two people would like to coordinate a meeting while going from office to home; to a futuristic case where automated taxis would like to meet each other for load balancing passengers. The scheduling for such scenarios can easily become challenging with uncertainties such as delayed departures, road blocks due to construction or traffic congestion. Any solution for such a task is required to minimize the waiting time and the planning overhead. In this paper, we propose an algorithm that optimizes the total travel time and the waiting time for two agents to complete their respective paths from start to rendezvous and from rendezvous to goal locations subject to delays along their paths. We validate our approach with a street network database which has a cost associated with every query made to the database server. Thus our algorithm intelligently optimizes for rendezvous trajectories that effectively mitigate the scourge of traffic delays, while simultaneously limiting the number of queries through careful analysis of the informative value of each potential 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".