An Efficient Solving Method to Vehicle and Passenger Matching Problem for Sharing Autonomous Vehicle System
Bibliographic record
Abstract
With the potential of increasing mobility and reducing cost, shared mobility of autonomous vehicles (AVs) is going to gain solid growth in the coming decade. The major issue for the shared use of AVs is how to project serving routes in an efficiently way. From another perspective, this issue could be understood as to segment maximum number of passengers into groups. Therefore, this paper intends to investigate passengers’ similarity instead of directly matching AVs and passengers. The goal is to determine the minimum number of groups and assign each group with an AV. To this end, a cluster-based algorithm is proposed to classify passengers. Numerical experiments with both small-size and large-size demands are performed to present the validity of the proposed algorithm. Results indicate that the cluster-based algorithm could bring benefit to minimizing the number of vehicles and total travel distance. At last, sensitivity analysis of key parameters shows that vehicle capacity will have little impact when the number of seats exceeds four, and time windows could make continuous influence on gathering passengers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".