A Public Vehicle System with Multiple Origin-Destination Pairs on Traffic Networks
Bibliographic record
Abstract
Substantial technology advances have been made in areas of autonomous and connected vehicles, which opens a wide landscape for future transportation systems. We propose a new type of transportation system, Public Vehicle (PV) system, to provide effective, comfortable, and convenient service. The PV system is to improve the efficiency of current transportation systems, \eg, taxi system. Meanwhile, the design of such a system targets on significant reduction in energy consumption, traffic congestion, and provides solutions with affordable cost. The key issue of implementing an effective PV system is to design efficient scheduling algorithms. We formulate it as the PV Path (PVP) problem, and prove it is NP-Complete. Then we introduce a real time approach, which is based on solutions of the Traveling Salesman Problem (TSP) and it can serve people efficiently with lower costs. Our results show that to achieve the same performance (e.g., the total time: waiting and travel time), the number of vehicles can be reduced by 47%-69%, compared with taxis. The number of vehicles on roads is reduced, thus traffic congestion is relieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".