Multiperspective Bus Route Planning in a Stackelberg Game Framework
Bibliographic record
Abstract
Bus route planning is a challenging task due to multiple perspective interactions among passengers, service providers, and government agencies. This paper presents a multidimensional Stackelberg-game-based framework and mathematical model to best trade off the decisions of multiple stakeholders that previous literature rarely captures, i.e., governments, service providers, and passengers, in planning a new bus route or adjusting an existing one. The proposed model features a bilevel structure with the upper level reflecting the perspective of government agencies in subsidy allocation and the lower level representing the decisions of service providers in dispatching frequency and bus fleet size design. The bilevel model is framed as a Stackelberg game where government agencies take the role of “leader” and service providers take the role of “follower” with social costs and profits set as payoffs, respectively. This Stackelberg-game-based framework can reflect the decision sequence of both participants as well as their competition or collaboration relationship in planning a bus route. The impact of such decisions on the mode and route choices of passengers is captured by a Nested Logit model. A partition-based bisection algorithm is developed to solve the proposed model. Results from a case study in Shanghai validate the effectiveness and performance of the proposed model and algorithm.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".