Optimization Methods for Customized Bus Routes in Random Environments
Bibliographic record
Abstract
The customized bus in operation faces numerous random factors that affect the service level and attractiveness to passengers. Therefore, this paper investigates the optimization problem of customized bus routes considering random vehicle travel times and the capability to respond to dynamic requests in real time. We developed a stochastic programming model that minimizes total cost and passenger travel time. The innovation lies in the model’s ability to respond to requests made by passengers during service and to model the randomness of vehicle travel times using a known distribution. Furthermore, we propose a heuristic algorithm combining the nondominated sorting genetic algorithm II (NSGA‐II) and a variable neighborhood search operator. This algorithm starts by generating an optimized initial path based on initial reservation demands and then employs a dynamic adjustment mechanism to respond to real‐time requests. The effectiveness and superiority of our algorithm are validated through an illustrative example. Finally, numerical experiments using taxi trajectory data demonstrate that considering both randomness and real‐time aspects can significantly reduce the total cost and penalties for early and late arrivals and improve the bus service level.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".