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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".