Optimization of Transit Route and Frequency for Integrated Urban–Rural Transit Network
Bibliographic record
Abstract
The integration of urban and rural transit networks is a prerequisite for the integration of urban and rural transportation systems. With the promotion of rural revitalization and new urbanization, the existing transit network operated separately in urban and rural areas is insufficient in meeting the travel demands of urban and rural residents. It is necessary to plan the urban and rural transit network rationally and to enhance the overall system performance of the urban and rural transit network. This paper proposes a biobjective model to optimize the integrated urban–rural transit network. The model minimizes both passengers’ and bus operators’ costs by optimizing the bus routes and frequencies simultaneously. Furthermore, we propose a subregional operations model and explore a performance comparison between the integrated and subregional optimization approaches. The genetic algorithm is developed to solve the proposed models. Finally, we conduct numerical experiments to identify the efficacy of the proposed models and algorithms. The results indicate that the integrated operation of the urban–rural transit network has more optimization space than the subregional operation, and can effectively reduce the number of transfers. Furthermore, under integrated operations, changes in operating costs have a more pronounced impact on total passenger travel time. When the demand is within a particular range, the integrated operation generates a shorter total passenger travel time than the subregional operation for the exact operating cost. In addition, the Pareto‐optimal solution generated under varying interregional demands provides a trade‐off between the total passenger travel time and the operating costs of the bus operator.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.003 | 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".